Datasets:

Kushala

/

singlerowdataset

like 0

import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__)))) from .bin.aion_pipeline import aion_train_model import argparse import sys import os import subprocess INSTALL = 'install' LINUXINSTALL = 'linuxinstall' FE_MIGRATE = 'migrateappfe' LAUNCH_KAFKA = 'launchkafkaconsumer' RUN_LOCAL_MLAC_PIPELINE = 'runpipelinelocal' BUILD_MLAC_CONTAINER = 'buildmlaccontainerlocal' CONVERT_MODEL = 'convertmodel' START_MLFLOW = 'mlflow' COMMON_SERVICE = 'service' TRAINING = 'training' TRAINING_AWS = 'trainingonaws' TRAINING_DISTRIBUTED = 'distributedtraining' START_APPF = 'appfe' ONLINE_TRAINING = 'onlinetraining' TEXT_SUMMARIZATION = 'textsummarization' GENERATE_MLAC = 'generatemlac' AWS_TRAINING = 'awstraining' LLAMA_7B_TUNING = 'llama7btuning' LLM_PROMPT = 'llmprompt' LLM_TUNING = 'llmtuning' LLM_PUBLISH = 'llmpublish' LLM_BENCHMARKING = 'llmbenchmarking' TELEMETRY_PUSH = 'pushtelemetry' def aion_aws_training(confFile): from hyperscalers.aion_aws_training import awsTraining status = awsTraining(confFile) print(status) def aion_training(confFile): from bin.aion_pipeline import aion_train_model status = aion_train_model(confFile) print(status) def aion_awstraining(config_file): from hyperscalers import aws_instance print(config_file) aws_instance.training(config_file) def aion_generatemlac(ConfFile): from bin.aion_mlac import generate_mlac_code status = generate_mlac_code(ConfFile) print(status) def aion_textsummarization(confFile): from bin.aion_text_summarizer import aion_textsummary status = aion_textsummary(confFile) def aion_oltraining(confFile): from bin.aion_online_pipeline import aion_ot_train_model status = aion_ot_train_model(confFile) print(status) def do_telemetry_sync(): from appbe.telemetry import SyncTelemetry SyncTelemetry() def aion_llm_publish(cloudconfig,instanceid,hypervisor,model,usecaseid,region,image): from llm.llm_inference import LLM_publish LLM_publish(cloudconfig,instanceid,hypervisor,model,usecaseid,region,image) def aion_migratefe(operation): import os import sys os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'appfe.ux.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc argi=[] argi.append(os.path.abspath(__file__)) argi.append(operation) execute_from_command_line(argi) def aion_appfe(url,port): #manage_location = os.path.join(os.path.dirname(os.path.abspath(__file__)),'manage.py') #subprocess.check_call([sys.executable,manage_location, "runserver","%s:%s"%(url,port)]) import os import sys os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'appfe.ux.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc argi=[] argi.append(os.path.abspath(__file__)) argi.append('runaion') argi.append("%s:%s"%(url,port)) execute_from_command_line(argi) def aion_linux_install(version): from install import linux_dependencies linux_dependencies.process(version) def aion_install(version): from install import dependencies dependencies.process(version) def aion_service(ip,port,username,password): from bin.aion_service import start_server start_server(ip,port,username,password) def aion_distributedLearning(confFile): from distributed_learning import learning learning.training(confFile) def aion_launchkafkaconsumer(): from mlops import kafka_consumer kafka_consumer.launch_kafka_consumer() def aion_start_mlflow(): from appbe.dataPath import DEPLOY_LOCATION import platform import shutil from os.path import expanduser mlflowpath = os.path.normpath(os.path.join(os.path.dirname(__file__),'..','..','..','Scripts','mlflow.exe')) print(mlflowpath) home = expanduser("~") if platform.system() == 'Windows': DEPLOY_LOCATION = os.path.join(DEPLOY_LOCATION,'mlruns') outputStr = subprocess.Popen([sys.executable, mlflowpath,"ui", "--backend-store-uri","file:///"+DEPLOY_LOCATION]) else: DEPLOY_LOCATION = os.path.join(DEPLOY_LOCATION,'mlruns') subprocess.check_call(['mlflow',"ui","-h","0.0.0.0","--backend-store-uri","file:///"+DEPLOY_LOCATION]) def aion_model_conversion(config_file): from conversions import model_convertions model_convertions.convert(config_file) def aion_model_buildMLaCContainer(config): from mlops import build_container build_container.local_docker_build(config) def aion_model_runpipelinelocal(config): from mlops import local_pipeline local_pipeline.run_pipeline(config) def aion_llm_tuning(config): from llm.llm_tuning import run run(config) def aion_llm_prompt(cloudconfig,instanceid,prompt): from llm.aws_instance_api import LLM_predict LLM_predict(cloudconfig,instanceid,prompt) def llm_bench_marking(hypervisor,instanceid,model,usecaseid,eval): print(eval) from llm.bench_marking import bench_mark bench_mark(hypervisor,instanceid,model,usecaseid,eval) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('-c', '--configPath', help='Config File Path') parser.add_argument('-i', '--instanceid', help='instanceid') parser.add_argument('-hv', '--hypervisor', help='hypervisor') parser.add_argument('-md', '--model', help='model') parser.add_argument('-uc', '--usecase', help='usecase') parser.add_argument('-cc', '--cloudConfigPath', help='Cloud Config File Path') parser.add_argument('-m', '--module', help='MODULE=TRAINING, APPFE, ONLINETRAINING,DISTRIBUTEDTRAINING') parser.add_argument('-ip', '--ipaddress', help='URL applicable only for APPFE method ') parser.add_argument('-p', '--port', help='APP Front End Port applicable only for APPFE method ') parser.add_argument('-ac', '--appfecommand', help='APP Front End Command ') parser.add_argument('-un','--username', help="USERNAME") parser.add_argument('-passw','--password', help="PASSWORD") parser.add_argument('-j', '--jsoninput', help='JSON Input') parser.add_argument('-v', '--version', help='Installer Version') parser.add_argument('-pf', '--prompt', help='Prompt File') parser.add_argument('-r', '--region', help='REGION NAME') parser.add_argument('-im', '--image', help='IMAGE NAME') parser.add_argument('-e', '--eval', help='evaluation for code or doc', default='doc') args = parser.parse_args() if args.module.lower() == TRAINING: aion_training(args.configPath) elif args.module.lower() == TRAINING_AWS: aion_awstraining(args.configPath) elif args.module.lower() == TRAINING_DISTRIBUTED: aion_distributedLearning(args.configPath) elif args.module.lower() == START_APPF: aion_appfe(args.ipaddress,args.port) elif args.module.lower() == ONLINE_TRAINING: aion_oltraining(args.configPath) elif args.module.lower() == TEXT_SUMMARIZATION: aion_textsummarization(args.configPath) elif args.module.lower() == GENERATE_MLAC: aion_generatemlac(args.configPath) elif args.module.lower() == COMMON_SERVICE: aion_service(args.ipaddress,args.port,args.username,args.password) elif args.module.lower() == START_MLFLOW: aion_mlflow() elif args.module.lower() == CONVERT_MODEL: aion_model_conversion(args.configPath) elif args.module.lower() == BUILD_MLAC_CONTAINER: aion_model_buildMLaCContainer(args.jsoninput) elif args.module.lower() == RUN_LOCAL_MLAC_PIPELINE: aion_model_runpipelinelocal(args.jsoninput) elif args.module.lower() == LAUNCH_KAFKA: aion_launchkafkaconsumer() elif args.module.lower() == INSTALL: aion_install(args.version) elif args.module.lower() == LINUXINSTALL: aion_linux_install(args.version) elif args.module.lower() == FE_MIGRATE: aion_migratefe('makemigrations') aion_migratefe('migrate') elif args.module.lower() == AWS_TRAINING: aion_aws_training(args.configPath) elif args.module.lower() == LLAMA_7B_TUNING: aion_llm_tuning(args.configPath) elif args.module.lower() == LLM_TUNING: aion_llm_tuning(args.configPath) elif args.module.lower() == LLM_PROMPT: aion_llm_prompt(args.cloudConfigPath,args.instanceid,args.prompt) elif args.module.lower() == LLM_PUBLISH: aion_llm_publish(args.cloudConfigPath,args.instanceid,args.hypervisor,args.model,args.usecase,args.region,args.image) elif args.module.lower() == LLM_BENCHMARKING: llm_bench_marking(args.hypervisor,args.instanceid,args.model,args.usecase, args.eval) elif args.module.lower() == TELEMETRY_PUSH: do_telemetry_sync() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' # -*- coding: utf-8 -*- # -*- coding: utf-8 -*- import logging logging.getLogger('tensorflow').disabled = True import json import mlflow import mlflow.sklearn import mlflow.sagemaker as mfs # from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split # from sklearn import datasets import time import numpy as np # Load dataset # from sklearn.datasets import load_iris import pickle # Load the pickled model # from matplotlib import pyplot import sys import os import boto3 import subprocess import os.path from os.path import expanduser import platform from pathlib import Path class aionMlopsService: def __init__(self,model,mlflowtosagemakerDeploy,mlflowtosagemakerPushOnly,mlflowtosagemakerPushImageName,mlflowtosagemakerdeployModeluri,experiment_name,mlflow_modelname,awsaccesskey_id,awssecretaccess_key,aws_session_token,mlflow_container_name,aws_region,aws_id,iam_sagemakerfullaccess_arn,sm_app_name,sm_deploy_option,delete_ecr_repository,ecrRepositoryName): try: self.model=model self.mlflowtosagemakerDeploy=mlflowtosagemakerDeploy self.mlflowtosagemakerPushOnly=str(mlflowtosagemakerPushOnly) self.mlflowtosagemakerPushImageName=str(mlflowtosagemakerPushImageName) self.mlflowtosagemakerdeployModeluri=str(mlflowtosagemakerdeployModeluri) self.experiment_name=experiment_name self.mlflow_modelname=mlflow_modelname self.awsaccesskey_id=awsaccesskey_id self.awssecretaccess_key=awssecretaccess_key self.aws_session_token=aws_session_token self.mlflow_container_name=mlflow_container_name self.aws_region=aws_region self.aws_id=aws_id self.iam_sagemakerfullaccess_arn=iam_sagemakerfullaccess_arn self.sm_app_name=sm_app_name self.sm_deploy_option=sm_deploy_option self.delete_ecr_repository=delete_ecr_repository self.ecrRepositoryName=ecrRepositoryName from appbe.dataPath import LOG_LOCATION sagemakerLogLocation = LOG_LOCATION try: os.makedirs(sagemakerLogLocation) except OSError as e: if (os.path.exists(sagemakerLogLocation)): pass else: raise OSError('sagemakerLogLocation error.') self.sagemakerLogLocation=str(sagemakerLogLocation) filename_mlops = 'mlopslog_'+str(int(time.time())) filename_mlops=filename_mlops+'.log' # filename = 'mlopsLog_'+Time() filepath = os.path.join(self.sagemakerLogLocation, filename_mlops) logging.basicConfig(filename=filepath, format='%(message)s',filemode='w') # logging.basicConfig(filename="uq_logging.log", format='%(asctime)s %(message)s',filemode='w') # logging.basicConfig(filename="uq_logging.log", format=' %(message)s',filemode='w') # logging.basicConfig(filename='uq_logging.log', encoding='utf-8', level=logging.INFO) self.log = logging.getLogger('aionMLOps') self.log.setLevel(logging.DEBUG) # mlflow.set_experiment(self.experiment_name) except Exception as e: self.log.info('<!------------- mlflow model INIT Error ---------------> '+str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def mlflowSetPath(self,path): track_dir=os.path.join(path,'mlruns') uri="file:"+str(Path(track_dir)) return uri #Currently not used this delete ecr repository option def ecr_repository_delete(self,rep_name): # import subprocess client = boto3.client('ecr') repositories = client.describe_repositories() ecr_delete_rep=client.delete_repository(registryId=self.aws_id,repositoryName=self.ecrRepositoryName,force=True) mlflow_ecr_delete=subprocess.run(['aws', 'ecr', 'delete-repository','--repository-name',rep_name,'||','true']) self.log.info('Success: deleted aws ecr repository which contains mlops image.') def check_sm_deploy_status(self,app_name): sage_client = boto3.client('sagemaker', region_name=self.aws_region) endpoint_description = sage_client.describe_endpoint(EndpointName=app_name) endpoint_status = endpoint_description["EndpointStatus"] try: failure_reason=endpoint_description["FailureReason"] self.log.info("sagemaker end point creation failure reason is: "+str(failure_reason)) except: pass endpoint_status=str(endpoint_status) return endpoint_status def invoke_sm_endpoint(self,app_name, input_json): client = boto3.session.Session().client("sagemaker-runtime", self.aws_region) response = client.invoke_endpoint( EndpointName=app_name, Body=input_json, ContentType='application/json; format=pandas-split', ) # preds = response['Body'].read().decode("ascii") preds = response['Body'].read().decode("ascii") preds = json.loads(preds) # print("preds: {}".format(preds)) return preds def predict_sm_app_endpoint(self,X_test): #print(X_test) import pandas as pd prediction=None AWS_ACCESS_KEY_ID=str(self.awsaccesskey_id) AWS_SECRET_ACCESS_KEY=str(self.awssecretaccess_key) AWS_SESSION_TOKEN=str(self.aws_session_token) region = str(self.aws_region) #Existing model deploy options # mlflowtosagemakerPushImageName=str(self.mlflowtosagemakerPushImageName) # mlflowtosagemakerdeployModeluri=str(self.mlflowtosagemakerdeployModeluri) try: import subprocess cmd = 'aws configure set region_name '+region os.system(cmd) cmd = 'aws configure set aws_access_key_id '+AWS_ACCESS_KEY_ID os.system(cmd) cmd = 'aws configure set aws_secret_access_key '+AWS_SECRET_ACCESS_KEY os.system(cmd) ''' aws_region=subprocess.run(['aws', 'configure', 'set','region_name',region]) aws_accesskeyid=subprocess.run(['aws', 'configure', 'set','aws_access_key_id',AWS_ACCESS_KEY_ID]) aws_secretaccesskey=subprocess.run(['aws', 'configure', 'set','aws_secret_access_key',AWS_SECRET_ACCESS_KEY]) ''' except: pass #Create a session for aws communication using aws boto3 lib # s3_client = boto3.client('ecr',aws_access_key_id=AWS_ACCESS_KEY_ID,aws_secret_access_key=AWS_SECRET_ACCESS_KEY,aws_session_token=AWS_SESSION_TOKEN,region_name=region) # s3 = boto3.resource('ecr', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key= AWS_SECRET_ACCESS_KEY) session = boto3.Session(aws_access_key_id=AWS_ACCESS_KEY_ID,aws_secret_access_key=AWS_SECRET_ACCESS_KEY,aws_session_token=AWS_SESSION_TOKEN,region_name=region) #X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=2) # query_input = pd.DataFrame(X_test).iloc[[1,5]].to_json(orient="split") try: query_input = pd.DataFrame(X_test).to_json(orient="split") #print(query_input) prediction = self.invoke_sm_endpoint(app_name=self.sm_app_name, input_json=query_input) # self.log.info("sagemaker end point Prediction: \n"+str(prediction)) except Exception as e: print(e) return prediction def deleteSagemakerApp(self,app_name,region): # import mlflow.sagemaker as mfs # region = 'ap-south-1' # app_name = 'aion-demo-app' mfs.delete(app_name=app_name,region_name=region, archive=False,synchronous=True, timeout_seconds=300) # print("AION mlops sagemaker application endpoint is deleted....\n") self.log.info('AION mlops sagemaker application endpoint is deleted, application name is: '+str(app_name)) def deployModel2sagemaker(self,mlflow_container_name,tag_id,model_path): region = str(self.aws_region) aws_id = str(self.aws_id) iam_sagemakerfullaccess_arn = str(self.iam_sagemakerfullaccess_arn) app_name = str(self.sm_app_name) model_uri = str(model_path) app_status=False mlflow_root_dir = None try: os.chdir(str(self.sagemakerLogLocation)) mlflow_root_dir = os.getcwd() self.log.info('mlflow root dir: '+str(mlflow_root_dir)) except: self.log.info("path issue.") try: c_status=self.check_sm_deploy_status(app_name) #if ((c_status == "Failed") or (c_status == "OutOfService")): if ((c_status == "Failed") or (c_status.lower() == "failed")): app_status=False self.log.info("Sagemaker endpoint status: Failed.\n") mfs.delete(app_name=app_name,region_name=region, archive=False,synchronous=True, timeout_seconds=300) elif ((c_status.lower() == "inservice") or (c_status == "InService")): app_status=True self.log.info("Sagemaker endpoint status: InService. Running sagemaker endpoint name: \n"+str(app_name)) else: app_status=False pass except: # print("deploy status error.\n") pass #aws ecr model app_name should contain only [[a-zA-Z0-9-]] import re if app_name: pattern = re.compile("[A-Za-z0-9-]+") # if found match (entire string matches pattern) if pattern.fullmatch(app_name) is not None: #print("Found match: ") pass else: app_name = 'aion-demo-app' else: app_name = 'aion-demo-app' mlflow_image=mlflow_container_name+':'+tag_id image_url = aws_id + '.dkr.ecr.' + region + '.amazonaws.com/' + mlflow_image deploy_option="create" self.log.info('deploy_option: \n'+str(deploy_option)) if (deploy_option.lower() == "create"): # Other deploy modes: mlflow.sagemaker.DEPLOYMENT_MODE_ADD,mlflow.sagemaker.DEPLOYMENT_MODE_REPLACE if not (app_status): try: mfs.deploy(app_name=app_name,model_uri=model_uri,region_name=region,mode="create",execution_role_arn=iam_sagemakerfullaccess_arn,image_url=image_url) self.log.info('sagemaker endpoint created and model deployed. Application name is: \n'+str(app_name)) except: self.log.info('Creating end point application issue.Please check the connection and aws credentials \n') else: self.log.info('Sagemaker application with user endpoint name already running.Please check. Please delete the old endpoint with same name.\n') elif (deploy_option.lower() == "delete"): # import mlflow.sagemaker as mfs # # region = 'ap-south-1' # # app_name = 'aion-demo-app' # mfs.delete(app_name=app_name,region_name=region, archive=False,synchronous=True, timeout_seconds=300) # print("Mlflow sagemaker application endpoint is deleted....\n") # self.log.info('Mlflow sagemaker application endpoint is deleted, application name is: '+str(app_name)) pass elif (deploy_option.lower() == "add"): pass elif (deploy_option.lower() == "replace"): pass else: pass return app_status def mlflow2sagemaker_deploy(self): self.log.info('<!------------- Inside AION mlops to sagemaker communication and deploy process. ---------------> ') deploy_status=False app_name = str(self.sm_app_name) self.log.info('Sagemaker Application Name: '+str(app_name)) uri_mlflow=self.mlflowSetPath(self.sagemakerLogLocation) mlflow.set_tracking_uri(uri_mlflow) mlops_trackuri=mlflow.get_tracking_uri() mlops_trackuri=str(mlops_trackuri) self.log.info('mlops tracking uri: '+str(mlops_trackuri)) localhost_deploy=False try: #Loading aion model to deploy in sagemaker mlflow.set_experiment(self.experiment_name) self.log.info('Endpoint Name: '+str(self.experiment_name)) # Assume, the model already loaded from joblib in aionmlflow2smInterface.py file. aionmodel2deploy=self.model # run_id = None # experiment_id=None # Use the loaded pickled model to make predictions # pred = knn_from_pickle.predict(X_test) with mlflow.start_run(run_name='AIONMLOps') as run: # aionmodel2deploy.fit(X_train, y_train) # predictions = aionmodel2deploy.predict(X_test) mlflow.sklearn.log_model(aionmodel2deploy, self.mlflow_modelname) run_id = run.info.run_uuid experiment_id = run.info.experiment_id self.log.info('AION mlops experiment run_id: '+str(run_id)) self.log.info('AION mlops experiment experiment_id: '+str(experiment_id)) self.log.info('AION mlops experiment model_name: '+str(self.mlflow_modelname)) artifact_uri = {mlflow.get_artifact_uri()} # print("1.artifact_uri: \n",artifact_uri) mlflow.end_run() #If we need, we can check the mlflow experiments. # try: # mlflow_client = mlflow.tracking.MlflowClient('./mlruns') # exp_list = mlflow_client.list_experiments() # except: # pass #print("mlflow exp_list: \n",exp_list) mlflow_modelname=str(self.mlflow_modelname) mlops_trackuri=mlops_trackuri.replace('file:','') mlops_trackuri=str(mlops_trackuri) # mlflow_root_dir = os.getcwd() mlflow_root_dir = None try: os.chdir(str(self.sagemakerLogLocation)) mlflow_root_dir = os.getcwd() self.log.info('mlflow root dir: '+str(mlflow_root_dir)) except: self.log.info("path issue.") model_path = 'mlruns/%s/%s/artifacts/%s' % (experiment_id, run_id,self.mlflow_modelname) # model_path=mlops_trackuri+'\\%s\\%s\\artifacts\\%s' % (experiment_id, run_id,mlflow_modelname) self.log.info("local host aion mlops model_path is: "+str(model_path)) time.sleep(2) #print("Environment variable setup in the current working dir for aws sagemaker cli connection... \n") self.log.info('Environment variable setup in the current working dir for aws sagemaker cli connection... \n ') AWS_ACCESS_KEY_ID=str(self.awsaccesskey_id) AWS_SECRET_ACCESS_KEY=str(self.awssecretaccess_key) AWS_SESSION_TOKEN=str(self.aws_session_token) region = str(self.aws_region) #Existing model deploy options mlflowtosagemakerPushImageName=str(self.mlflowtosagemakerPushImageName) mlflowtosagemakerdeployModeluri=str(self.mlflowtosagemakerdeployModeluri) import subprocess cmd = 'aws configure set region_name '+region os.system(cmd) cmd = 'aws configure set aws_access_key_id '+AWS_ACCESS_KEY_ID os.system(cmd) cmd = 'aws configure set aws_secret_access_key '+AWS_SECRET_ACCESS_KEY os.system(cmd) #Create a session for aws communication using aws boto3 lib # s3_client = boto3.client('ecr',aws_access_key_id=AWS_ACCESS_KEY_ID,aws_secret_access_key=AWS_SECRET_ACCESS_KEY,aws_session_token=AWS_SESSION_TOKEN,region_name=region) # s3 = boto3.resource('ecr', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key= AWS_SECRET_ACCESS_KEY) session = boto3.Session(aws_access_key_id=AWS_ACCESS_KEY_ID,aws_secret_access_key=AWS_SECRET_ACCESS_KEY,aws_session_token=AWS_SESSION_TOKEN,region_name=region) # session = boto3.session.Session( # aws_access_key_id=AWS_ACCESS_KEY_ID, # aws_secret_access_key=AWS_SECRET_ACCESS_KEY, # aws_session_token=AWS_SESSION_TOKEN # ) # awsclient = session.resource('ecr') # s3 = session.resource('s3') self.log.info('aws environment variable setup done... \n') try: os.chdir(mlflow_root_dir) except FileNotFoundError: self.log.info('Directory does not exist. '+str(mlflow_root_dir)) except NotADirectoryError: self.log.info('model_path is not a directory. '+str(mlflow_root_dir)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(mlflow_root_dir)) mlflow_container_name=str(self.mlflow_container_name) mlflow_version=mlflow.__version__ tag_id=mlflow_version if (self.mlflowtosagemakerPushOnly.lower() == "true"): self.log.info('Selected option is <Deploy existing model to sagemaker> \n') aws_id=str(self.aws_id) arn=str(self.iam_sagemakerfullaccess_arn) mlflow_image=mlflow_container_name+':'+tag_id image_url = aws_id+'.dkr.ecr.'+region+'.amazonaws.com/'+mlflow_image # print("image_url:========= \n",image_url) deploy_status=True try: model_path=mlflowtosagemakerdeployModeluri # ##We need to run mlflow docker container command in the artifacts->model directory inside mlruns. self.log.info('Deploy existing model container-Model path given by user: '+str(model_path)) try: os.chdir(model_path) except FileNotFoundError: self.log.info('Directory does not exist. '+str(model_path)) except NotADirectoryError: self.log.info('model_path is not a directory. '+str(model_path)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(model_path)) try: mfs.push_image_to_ecr(image=mlflowtosagemakerPushImageName) deploy_status=True self.log.info('AION mlops pushed the docker container to aws ecr. \n ') except: self.log.info("error in pushing existing container to ecr.\n") deploy_status=False time.sleep(2) #Now,change the working dir to root dir,because now deploy needs full mlruns to model name dir. try: # print(" Changing directory to mlflow root dir....\n") os.chdir(mlflow_root_dir) except FileNotFoundError: self.log.info('model path is not a directory. '+str(mlflow_root_dir)) except NotADirectoryError: self.log.info('model path is not a directory. '+str(mlflow_root_dir)) # print("{0} is not a directory".format(mlflow_root_dir)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(mlflow_root_dir)) # self.deployModel2sagemaker(mlflowtosagemakerPushImageName,tag_id,mlflowtosagemakerdeployModeluri) try: if (deploy_status): self.deployModel2sagemaker(mlflowtosagemakerPushImageName,tag_id,mlflowtosagemakerdeployModeluri) self.log.info('AION creates docker container and push the container into aws ecr.. ') time.sleep(2) except: self.log.info('AION deploy error.check connection and aws config parameters. ') deploy_status=False # self.log.info('model deployed in sagemaker. ') except Exception as e: self.log.info('AION mlops failed to push docker container in aws ecr, check configuration parameters. \n'+str(e)) elif (self.mlflowtosagemakerPushOnly.lower() == "false"): if (self.mlflowtosagemakerDeploy.lower() == "true"): self.log.info('Selected option is <Create and Deploy model> \n') deploy_status=True try: # ##We need to run mlflow docker container command in the artifacts->model directory inside mlruns. try: os.chdir(model_path) except FileNotFoundError: self.log.info('Directory does not exist. '+str(model_path)) except NotADirectoryError: self.log.info('model_path is not a directory. '+str(model_path)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(model_path)) try: mlflow_container_push=subprocess.run(['mlflow', 'sagemaker', 'build-and-push-container','--build','--push','--container',mlflow_container_name]) self.log.info('AION mlops creates docker container and push the container into aws ecr.. ') deploy_status=True time.sleep(2) except: self.log.info('error in pushing aion model container to sagemaker, please check the connection between local host to aws server.') deploy_status=False self.log.info('Now deploying the model container to sagemaker starts....\n ') # Once docker push completes, again going back to mlflow parent dir for deployment #Now,change the working dir to root dir,because now deploy needs full mlruns to model name dir. try: os.chdir(mlflow_root_dir) except FileNotFoundError: self.log.info('model_path does not exist. '+str(mlflow_root_dir)) except NotADirectoryError: self.log.info('model_path is not a directory. '+str(mlflow_root_dir)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(mlflow_root_dir)) # app_name = str(self.sm_app_name) try: if (deploy_status): self.deployModel2sagemaker(mlflow_container_name,tag_id,model_path) except: self.log.info('mlops deploy error.check connection') deploy_status=False except Exception as e: exc = {"status":"FAIL","message":str(e).strip('"')} out_exc = json.dumps(exc) self.log.info('mlflow failed to creates docker container please check the aws iam,ecr permission setup, aws id access_key,secret key values for aion.\n') elif(self.mlflowtosagemakerDeploy.lower() == "false"): deploy_status=False localhost_deploy=True self.log.info('Selected option is <Create AION mlops container in local host .> \n') self.log.info("User selected create-Deploy sagemaker option as False,") self.log.info("Creates the AION mlops-sagemaker container locally starting,but doesn't push into aws ecr and deploy in sagemaker. Check the container in docker repository. ") try: # ##We need to run AION mlops docker container command in the artifacts->model directory inside mlruns. try: os.chdir(model_path) self.log.info('After change to AION mlops model dir, cwd: '+str(model_path)) except FileNotFoundError: self.log.info('Directory does not exist. '+str(model_path)) except NotADirectoryError: self.log.info('model_path is not a directory. '+str(model_path)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(model_path)) # mlflow_container_local=subprocess.run(['AION mlops', 'sagemaker', 'build-and-push-container','--build','--no-push','--container',mlflow_container_name]) try: if not (deploy_status): mlflow_container_local=subprocess.run(['mlflow', 'sagemaker', 'build-and-push-container','--build','--no-push','--container',mlflow_container_name]) self.log.info('AION creates local host bsed docker container and push the container local docker repository. Check with <docker images> command.\n ') localhost_deploy=True time.sleep(2) except: self.log.info('error in pushing aion model container to sagemaker, please check the connection between local host to aws server.') deploy_status=False localhost_deploy=False # print("AION mlops creates docker container and push the container into aws ecr.\n") self.log.info('AION mlops creates docker container and stored locally... ') time.sleep(2) except Exception as e: localhost_deploy=False # print("mlflow failed to creates docker container please check the aws iam,ecr permission setup, aws id access_key,secret key values for aion.\n") self.log.info('AION mlops failed to creates docker container in local machine.\n'+str(e)) else: self.log.info('Deploy option not selected, Please check. ') localhost_deploy=False deploy_status=False else: pass localhost_container_status="Notdeployed" mlflow2sm_deploy_status="Notdeployed" if localhost_deploy: localhost_container_status="success" mlflow2sm_deploy_status="Notdeployed" # print("AION creates local docker container successfully.Please check in docker repository.") self.log.info("AION creates local docker container successfully.Please check in docker repository.") # else: # localhost_container_status="failed" # # print("AION failed to create local docker container successfully.Please check in docker repository.") # self.log.info("AION failed to create local docker container successfully.Please check in docker repository.") if (deploy_status): # Finally checking whether mlops model is deployed to sagemaker or not. app_name = str(self.sm_app_name) deploy_s = self.check_sm_deploy_status(app_name) if (deploy_s == "InService"): # print("AION mlops model is deployed at aws sagemaker, use application name(app_name) and region to access.\n") self.log.info('AION mlops model is deployed at aws sagemaker, use application name(app_name) and region to access.\n'+str(app_name)) mlflow2sm_deploy_status="success" localhost_container_status="Notdeployed" else: # print("AION Mlflow model not able to deploy at aws sagemaker\n") self.log.info('AION mlops model not able to deploy at aws sagemaker.\n') mlflow2sm_deploy_status="failed" localhost_container_status="Notdeployed" # else: # mlflow2sm_deploy_status="None" return mlflow2sm_deploy_status,localhost_container_status except Exception as inst: exc = {"status":"FAIL","message":str(inst).strip('"')} out_exc = json.dumps(exc) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import sys import json import datetime, time, timeit import argparse import logging logging.getLogger('tensorflow').disabled = True import math import shutil import re from datetime import datetime as dt import warnings from config_manager.pipeline_config import AionConfigManager import pandas as pd import numpy as np import sklearn import string from records import pushrecords import logging from pathlib import Path from pytz import timezone from config_manager.config_gen import code_configure import joblib from sklearn.model_selection import train_test_split from config_manager.check_config import config_validate from utils.file_ops import save_csv_compressed,save_csv,save_chromadb LOG_FILE_NAME = 'model_training_logs.log' if 'AION' in sys.modules: try: from appbe.app_config import DEBUG_ENABLED except: DEBUG_ENABLED = False else: DEBUG_ENABLED = True def getversion(): configFolder = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','config') version = 'NA' for file in os.listdir(configFolder): if file.endswith(".var"): version = file.rsplit('.', 1) version = version[0] break return version AION_VERSION = getversion() def pushRecordForTraining(): try: status,msg = pushrecords.enterRecord(AION_VERSION) except Exception as e: print("Exception", e) status = False msg = str(e) return status,msg def mlflowSetPath(path,experimentname): import mlflow url = "file:" + str(Path(path).parent.parent) + "/mlruns" mlflow.set_tracking_uri(url) mlflow.set_experiment(str(experimentname)) def set_log_handler( basic, mode='w'): deploy_loc = Path(basic.get('deployLocation')) log_file_parent = deploy_loc/basic['modelName']/basic['modelVersion']/'log' log_file_parent.mkdir(parents=True, exist_ok=True) log_file = log_file_parent/LOG_FILE_NAME filehandler = logging.FileHandler(log_file, mode,'utf-8') formatter = logging.Formatter('%(message)s') filehandler.setFormatter(formatter) log = logging.getLogger('eion') log.propagate = False for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): log.removeHandler(hdlr) log.addHandler(filehandler) log.setLevel(logging.INFO) return log class server(): def __init__(self): self.response = None self.features=[] self.mFeatures=[] self.emptyFeatures=[] self.textFeatures=[] self.vectorizerFeatures=[] self.wordToNumericFeatures=[] self.profilerAction = [] self.targetType = '' self.matrix1='{' self.matrix2='{' self.matrix='{' self.trainmatrix='{' self.numericalFeatures=[] self.nonNumericFeatures=[] self.similarGroups=[] self.dfcols=0 self.dfrows=0 self.method = 'NA' self.pandasNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] self.modelSelTopFeatures=[] self.topFeatures=[] self.allFeatures=[] def startScriptExecution(self, config_obj, codeConfigure, log): oldStdout = sys.stdout model_training_details = '' model_tried='' learner_type = '' topics = {} pred_filename = '' numericContinuousFeatures='' discreteFeatures='' sessonal_freq = '' additional_regressors = '' threshold=-1 targetColumn = '' numericalFeatures ='' nonNumericFeatures='' categoricalFeatures='' dataFolderLocation = '' featureReduction = 'False' original_data_file = '' normalizer_pickle_file = '' pcaModel_pickle_file = '' bpca_features= [] apca_features = [] lag_order = 1 profiled_data_file = '' trained_data_file = '' predicted_data_file='' dictDiffCount={} cleaning_kwargs = {} grouperbyjson = '' rowfilterexpression='' featureEngineeringSelector = 'false' conversion_method = '' params={} loss_matrix='binary_crossentropy' optimizer='Nadam' numericToLabel_json='[]' preprocessing_pipe='' firstDocFeature = '' secondDocFeature = '' padding_length = 30 pipe = None scalertransformationFile=None column_merge_flag = False merge_columns = [] score = 0 profilerObj = None imageconfig='' labelMaps={} featureDataShape=[] normFeatures = [] preprocess_out_columns = [] preprocess_pipe = None label_encoder = None unpreprocessed_columns = [] import pickle iterName,iterVersion,dataLocation,deployLocation,delimiter,textqualifier = config_obj.getAIONLocationSettings() inlierLabels=config_obj.getEionInliers() scoreParam = config_obj.getScoringCreteria() noofforecasts = config_obj.getNumberofForecasts() datetimeFeature,indexFeature,modelFeatures=config_obj.getFeatures() filter_expression = config_obj.getFilterExpression() refined_filter_expression = "" sa_images = [] model_tried = '' deploy_config = {} iterName = iterName.replace(" ", "_") deployFolder = deployLocation usecaseLocation,deployLocation,dataFolderLocation,imageFolderLocation,original_data_file,profiled_data_file,trained_data_file,predicted_data_file,logFileName,outputjsonFile,reduction_data_file = config_obj.createDeploymentFolders(deployFolder,iterName,iterVersion) outputLocation=deployLocation mlflowSetPath(deployLocation,iterName+'_'+iterVersion) # mlflowSetPath shut down the logger, so set again set_log_handler( config_obj.basic, mode='a') xtrain=pd.DataFrame() xtest=pd.DataFrame() log.info('Status:-|... AION Training Configuration started') startTime = timeit.default_timer() try: output = {'bestModel': '', 'bestScore': 0, 'bestParams': {}} problem_type,targetFeature,profiler_status,selector_status,learner_status,deeplearner_status,timeseriesStatus,textsummarizationStatus,survival_analysis_status,textSimilarityStatus,inputDriftStatus,outputDriftStatus,recommenderStatus,visualizationstatus,deploy_status,associationRuleStatus,imageClassificationStatus,forecastingStatus, objectDetectionStatus,stateTransitionStatus, similarityIdentificationStatus,contextualSearchStatus,anomalyDetectionStatus = config_obj.getModulesDetails() status, error_id, msg = config_obj.validate_config() if not status: if error_id == 'fasttext': raise ValueError(msg) VideoProcessing = False if(problem_type.lower() in ['classification','regression']): if(targetFeature == ''): output = {"status":"FAIL","message":"Target Feature is Must for Classification and Regression Problem Type"} return output from transformations.dataReader import dataReader objData = dataReader() DataIsFolder = False folderdetails = config_obj.getFolderSettings() if os.path.isfile(dataLocation): log.info('Status:-|... AION Loading Data') dataFrame = objData.csvTodf(dataLocation,delimiter,textqualifier) status,msg = save_csv_compressed(dataFrame,original_data_file) if not status: log.info('CSV File Error: '+str(msg)) elif os.path.isdir(dataLocation): if problem_type.lower() == 'summarization': from document_summarizer import summarize keywords, pretrained_type, embedding_sz = summarize.get_params() dataFrame = summarize.to_dataframe(dataLocation,keywords, deploy_loc, pretrained_type, embedding_sz) problem_type = 'classification' targetFeature = 'label' scoreParam = 'Accuracy' elif folderdetails['fileType'].lower() == 'document': dataFrame, error = objData.documentsTodf(dataLocation, folderdetails['labelDataFile']) if error: log.info(error) elif folderdetails['fileType'].lower() == 'object': testPercentage = config_obj.getAIONTestTrainPercentage() #Unnati intermediateLocation = os.path.join(deployLocation,'intermediate') os.mkdir(intermediateLocation) AugEnabled,keepAugImages,operations,augConf = config_obj.getEionImageAugmentationConfiguration() dataFrame, n_class = objData.createTFRecord(dataLocation, intermediateLocation, folderdetails['labelDataFile'], testPercentage,AugEnabled,keepAugImages,operations, "objectdetection",augConf) #Unnati DataIsFolder = True else: datafilelocation = os.path.join(dataLocation,folderdetails['labelDataFile']) dataFrame = objData.csvTodf(datafilelocation,delimiter,textqualifier) DataIsFolder = True if textSimilarityStatus or similarityIdentificationStatus or contextualSearchStatus: similaritydf = dataFrame filter = config_obj.getfilter() if filter != 'NA': dataFrame,rowfilterexpression = objData.rowsfilter(filter,dataFrame) timegrouper = config_obj.gettimegrouper() grouping = config_obj.getgrouper() if grouping != 'NA': dataFrame,grouperbyjson = objData.grouping(grouping,dataFrame) elif timegrouper != 'NA': dataFrame,grouperbyjson = objData.timeGrouping(timegrouper,dataFrame) if timeseriesStatus or anomalyDetectionStatus: from utils.validate_inputs import dataGarbageValue status,msg = dataGarbageValue(dataFrame,datetimeFeature) if status.lower() == 'error': raise ValueError(msg) if not DataIsFolder: if timeseriesStatus: if(modelFeatures != 'NA' and datetimeFeature != ''): if datetimeFeature: if isinstance(datetimeFeature, list): #to handle if time series having multiple time column unpreprocessed_columns = unpreprocessed_columns + datetimeFeature else: unpreprocessed_columns = unpreprocessed_columns + datetimeFeature.split(',') if datetimeFeature not in modelFeatures: modelFeatures = modelFeatures+','+datetimeFeature dataFrame = objData.removeFeatures(dataFrame,'NA',indexFeature,modelFeatures,targetFeature) elif survival_analysis_status or anomalyDetectionStatus: if(modelFeatures != 'NA'): if datetimeFeature != 'NA' and datetimeFeature != '': unpreprocessed_columns = unpreprocessed_columns + datetimeFeature.split(',') if datetimeFeature not in modelFeatures: modelFeatures = modelFeatures+','+datetimeFeature dataFrame = objData.removeFeatures(dataFrame,'NA',indexFeature,modelFeatures,targetFeature) else: dataFrame = objData.removeFeatures(dataFrame,datetimeFeature,indexFeature,modelFeatures,targetFeature) log.info('\n-------> First Ten Rows of Input Data: ') log.info(dataFrame.head(10)) self.dfrows=dataFrame.shape[0] self.dfcols=dataFrame.shape[1] log.info('\n-------> Rows: '+str(self.dfrows)) log.info('\n-------> Columns: '+str(self.dfcols)) topFeatures=[] profilerObj = None normalizer=None dataLoadTime = timeit.default_timer() - startTime log.info('-------> COMPUTING: Total dataLoadTime time(sec) :'+str(dataLoadTime)) if timeseriesStatus: if datetimeFeature != 'NA' and datetimeFeature != '': preproces_config = config_obj.basic.get('preprocessing',{}).get('timeSeriesForecasting',{}) if preproces_config: from transformations.preprocess import timeSeries as ts_preprocess preprocess_obj = ts_preprocess( preproces_config,datetimeFeature, log) dataFrame = preprocess_obj.run( dataFrame) log.info('-------> Input dataFrame(5 Rows) after preprocessing: ') log.info(dataFrame.head(5)) deploy_config['preprocess'] = {} deploy_config['preprocess']['code'] = preprocess_obj.get_code() if profiler_status: log.info('\n================== Data Profiler has started ==================') log.info('Status:-|... AION feature transformation started') from transformations.dataProfiler import profiler as dataProfiler dp_mlstart = time.time() profilerJson = config_obj.getEionProfilerConfigurarion() log.info('-------> Input dataFrame(5 Rows): ') log.info(dataFrame.head(5)) log.info('-------> DataFrame Shape (Row,Columns): '+str(dataFrame.shape)) testPercentage = config_obj.getAIONTestTrainPercentage() #Unnati if DataIsFolder: if folderdetails['type'].lower() != 'objectdetection': profilerObj = dataProfiler(dataFrame) topFeatures,VideoProcessing,tfrecord_directory = profilerObj.folderPreprocessing(dataLocation,folderdetails,deployLocation) elif textSimilarityStatus: firstDocFeature = config_obj.getFirstDocumentFeature() secondDocFeature = config_obj.getSecondDocumentFeature() profilerObj = dataProfiler(dataFrame,targetFeature, data_path=dataFolderLocation) dataFrame,pipe,targetColumn,topFeatures = profilerObj.textSimilarityStartProfiler(firstDocFeature,secondDocFeature) elif recommenderStatus: profilerObj = dataProfiler(dataFrame) dataFrame = profilerObj.recommenderStartProfiler(modelFeatures) else: if deeplearner_status or learner_status: if (problem_type.lower() != 'clustering') and (problem_type.lower() != 'topicmodelling'): if targetFeature != '': try: biasingDetail = config_obj.getDebiasingDetail() if len(biasingDetail) > 0: if biasingDetail['FeatureName'] != 'None': protected_feature = biasingDetail['FeatureName'] privileged_className = biasingDetail['ClassName'] target_feature = biasingDetail['TargetFeature'] algorithm = biasingDetail['Algorithm'] from debiasing.DebiasingManager import DebiasingManager mgrObj = DebiasingManager() log.info('Status:-|... Debiasing transformation started') transf_dataFrame = mgrObj.Bias_Mitigate(dataFrame, protected_feature, privileged_className, target_feature, algorithm) log.info('Status:-|... Debiasing transformation completed') dataFrame = transf_dataFrame except Exception as e: print(e) pass # ---------------------------------------------- ---------------------------------------------- targetData = dataFrame[targetFeature] featureData = dataFrame[dataFrame.columns.difference([targetFeature])] testPercentage = config_obj.getAIONTestTrainPercentage() #Unnati xtrain,ytrain,xtest,ytest = self.split_into_train_test_data(featureData,targetData,testPercentage,log,problem_type.lower()) xtrain.reset_index(drop=True,inplace=True) ytrain.reset_index(drop=True,inplace=True) xtest.reset_index(drop=True,inplace=True) ytest.reset_index(drop=True,inplace=True) dataFrame = xtrain dataFrame[targetFeature] = ytrain encode_target_problems = ['classification','anomalyDetection', 'timeSeriesAnomalyDetection'] #task 11997 if problem_type == 'survivalAnalysis' and dataFrame[targetFeature].nunique() > 1: encode_target_problems.append('survivalAnalysis') if timeseriesStatus: #task 12627 calling data profiler without target feature specified separately (i.e) profiling is done for model features along with target features profilerObj = dataProfiler(dataFrame, config=profilerJson, keep_unprocessed = unpreprocessed_columns.copy(), data_path=dataFolderLocation) else: profilerObj = dataProfiler(dataFrame, target=targetFeature, encode_target= problem_type in encode_target_problems, config=profilerJson, keep_unprocessed = unpreprocessed_columns.copy(), data_path=dataFolderLocation) #task 12627 dataFrame, preprocess_pipe, label_encoder = profilerObj.transform() preprocess_out_columns = dataFrame.columns.tolist() if not timeseriesStatus: #task 12627 preprocess_out_columns goes as output_columns in target folder script/input_profiler.py, It should contain the target feature also as it is what is used for forecasting if targetFeature in preprocess_out_columns: preprocess_out_columns.remove(targetFeature) for x in unpreprocessed_columns: preprocess_out_columns.remove(x) if label_encoder: joblib.dump(label_encoder, Path(deployLocation)/'model'/'label_encoder.pkl') labelMaps = dict(zip(label_encoder.classes_, label_encoder.transform(label_encoder.classes_))) codeConfigure.update_config('train_features',list(profilerObj.train_features_type.keys())) codeConfigure.update_config('text_features',profilerObj.text_feature) self.textFeatures = profilerObj.text_feature deploy_config['profiler'] = {} deploy_config['profiler']['input_features'] = list(profilerObj.train_features_type.keys()) deploy_config['profiler']['output_features'] = preprocess_out_columns deploy_config['profiler']['input_features_type'] = profilerObj.train_features_type deploy_config['profiler']['word2num_features'] = profilerObj.wordToNumericFeatures deploy_config['profiler']['unpreprocessed_columns'] = unpreprocessed_columns deploy_config['profiler']['force_numeric_conv'] = profilerObj.force_numeric_conv if self.textFeatures: deploy_config['profiler']['conversion_method'] = config_obj.get_conversion_method() if anomalyDetectionStatus and datetimeFeature != 'NA' and datetimeFeature != '': if unpreprocessed_columns: dataFrame.set_index( unpreprocessed_columns[0], inplace=True) log.info('-------> Data Frame Post Data Profiling(5 Rows): ') log.info(dataFrame.head(5)) if not xtest.empty: if targetFeature != '': non_null_index = ytest.notna() ytest = ytest[non_null_index] xtest = xtest[non_null_index] if profilerObj.force_numeric_conv: xtest[ profilerObj.force_numeric_conv] = xtest[profilerObj.force_numeric_conv].apply(pd.to_numeric,errors='coerce') xtest.astype(profilerObj.train_features_type) if unpreprocessed_columns: xtest_unprocessed = xtest[unpreprocessed_columns] xtest = preprocess_pipe.transform(xtest) if not isinstance(xtest, np.ndarray): xtest = xtest.toarray() xtest = pd.DataFrame(xtest, columns=preprocess_out_columns) if unpreprocessed_columns: xtest[unpreprocessed_columns] = xtest_unprocessed if survival_analysis_status: xtest.astype({x:'float' for x in unpreprocessed_columns}) xtrain.astype({x:'float' for x in unpreprocessed_columns}) #task 11997 removed setting datetime column as index of dataframe code as it is already done before if label_encoder: ytest = label_encoder.transform(ytest) if preprocess_pipe: if self.textFeatures: from text.textProfiler import reset_pretrained_model reset_pretrained_model(preprocess_pipe) # pickle is not possible for fasttext model ( binary) joblib.dump(preprocess_pipe, Path(deployLocation)/'model'/'preprocess_pipe.pkl') self.features=topFeatures if targetColumn in topFeatures: topFeatures.remove(targetColumn) self.topFeatures=topFeatures if normalizer != None: normalizer_file_path = os.path.join(deployLocation,'model','normalizer_pipe.sav') normalizer_pickle_file = 'normalizer_pipe.sav' pickle.dump(normalizer, open(normalizer_file_path,'wb')) log.info('Status:-|... AION feature transformation completed') dp_mlexecutionTime=time.time() - dp_mlstart log.info('-------> COMPUTING: Total Data Profiling Execution Time '+str(dp_mlexecutionTime)) log.info('================== Data Profiling completed ==================\n') else: datacolumns=list(dataFrame.columns) if targetFeature in datacolumns: datacolumns.remove(targetFeature) if not timeseriesStatus and not anomalyDetectionStatus and not inputDriftStatus and not outputDriftStatus and not imageClassificationStatus and not associationRuleStatus and not objectDetectionStatus and not stateTransitionStatus and not textsummarizationStatus: self.textFeatures,self.vectorizerFeatures,pipe,column_merge_flag,merge_columns = profilerObj.checkForTextClassification(dataFrame) self.topFeatures =datacolumns if(pipe is not None): preprocessing_pipe = 'pppipe'+iterName+'_'+iterVersion+'.sav' ppfilename = os.path.join(deployLocation,'model','pppipe'+iterName+'_'+iterVersion+'.sav') pickle.dump(pipe, open(ppfilename, 'wb')) status, msg = save_csv_compressed(dataFrame,profiled_data_file) if not status: log.info('CSV File Error: ' + str(msg)) if selector_status: log.info("\n================== Feature Selector has started ==================") log.info("Status:-|... AION feature engineering started") fs_mlstart = time.time() selectorJson = config_obj.getEionSelectorConfiguration() if self.textFeatures: config_obj.updateFeatureSelection(selectorJson, codeConfigure, self.textFeatures) log.info("-------> For vectorizer 'feature selection' is disabled and all the features will be used for training") from feature_engineering.featureSelector import featureSelector selectorObj = featureSelector() dataFrame,targetColumn,self.topFeatures,self.modelSelTopFeatures,self.allFeatures,self.targetType,self.similarGroups,numericContinuousFeatures,discreteFeatures,nonNumericFeatures,categoricalFeatures,pcaModel,bpca_features,apca_features,featureEngineeringSelector = selectorObj.startSelector(dataFrame, selectorJson,self.textFeatures,targetFeature,problem_type) if(str(pcaModel) != 'None'): featureReduction = 'True' status, msg = save_csv(dataFrame,reduction_data_file) if not status: log.info('CSV File Error: ' + str(msg)) pcaFileName = os.path.join(deployLocation,'model','pca'+iterName+'_'+iterVersion+'.sav') pcaModel_pickle_file = 'pca'+iterName+'_'+iterVersion+'.sav' pickle.dump(pcaModel, open(pcaFileName, 'wb')) if not xtest.empty: xtest = pd.DataFrame(pcaModel.transform(xtest),columns= apca_features) if targetColumn in self.topFeatures: self.topFeatures.remove(targetColumn) fs_mlexecutionTime=time.time() - fs_mlstart log.info('-------> COMPUTING: Total Feature Selection Execution Time '+str(fs_mlexecutionTime)) log.info('================== Feature Selection completed ==================\n') log.info("Status:-|... AION feature engineering completed") if deeplearner_status or learner_status: log.info('Status:-|... AION training started') ldp_mlstart = time.time() balancingMethod = config_obj.getAIONDataBalancingMethod() from learner.machinelearning import machinelearning mlobj = machinelearning() modelType = problem_type.lower() targetColumn = targetFeature if modelType == "na": if self.targetType == 'categorical': modelType = 'classification' elif self.targetType == 'continuous': modelType = 'regression' else: modelType='clustering' datacolumns=list(dataFrame.columns) if targetColumn in datacolumns: datacolumns.remove(targetColumn) features =datacolumns featureData = dataFrame[features] if(modelType == 'clustering') or (modelType == 'topicmodelling'): xtrain = featureData ytrain = pd.DataFrame() xtest = featureData ytest = pd.DataFrame() elif (targetColumn!=''): xtrain = dataFrame[features] ytrain = dataFrame[targetColumn] else: pass categoryCountList = [] if modelType == 'classification': if(mlobj.checkForClassBalancing(ytrain) >= 1): xtrain,ytrain = mlobj.ExecuteClassBalancing(xtrain,ytrain,balancingMethod) valueCount=targetData.value_counts() categoryCountList=valueCount.tolist() ldp_mlexecutionTime=time.time() - ldp_mlstart log.info('-------> COMPUTING: Total Learner data preparation Execution Time '+str(ldp_mlexecutionTime)) if learner_status: base_model_score=0 log.info('\n================== ML Started ==================') log.info('-------> Memory Usage by DataFrame During Learner Status '+str(dataFrame.memory_usage(deep=True).sum())) mlstart = time.time() log.info('-------> Target Problem Type:'+ self.targetType) learner_type = 'ML' learnerJson = config_obj.getEionLearnerConfiguration() from learner.machinelearning import machinelearning mlobj = machinelearning() anomalyDetectionStatus = False anomalyMethod =config_obj.getEionanomalyModels() if modelType.lower() == "anomalydetection" or modelType.lower() == "timeseriesanomalydetection": #task 11997 anomalyDetectionStatus = True if anomalyDetectionStatus == True : datacolumns=list(dataFrame.columns) if targetColumn in datacolumns: datacolumns.remove(targetColumn) if datetimeFeature in datacolumns: datacolumns.remove(datetimeFeature) self.features = datacolumns from learner.anomalyDetector import anomalyDetector anomalyDetectorObj=anomalyDetector() model_type ="anomaly_detection" saved_model = model_type+'_'+iterName+'_'+iterVersion+'.sav' if problem_type.lower() == "timeseriesanomalydetection": #task 11997 anomalyconfig = config_obj.getAIONTSAnomalyDetectionConfiguration() modelType = "TimeSeriesAnomalyDetection" else: anomalyconfig = config_obj.getAIONAnomalyDetectionConfiguration() testPercentage = config_obj.getAIONTestTrainPercentage() ##Multivariate feature based anomaly detection status from gui (true/false) mv_featurebased_selection = config_obj.getMVFeaturebasedAD() mv_featurebased_ad_status=str(mv_featurebased_selection['uniVariate']) model,estimator,matrix,trainmatrix,score,labelMaps=anomalyDetectorObj.startanomalydetector(dataFrame,targetColumn,labelMaps,inlierLabels,learnerJson,model_type,saved_model,anomalyMethod,deployLocation,predicted_data_file,testPercentage,anomalyconfig,datetimeFeature,mv_featurebased_ad_status) #Unnati score = 'NA' if(self.matrix != '{'): self.matrix += ',' self.matrix += matrix if(self.trainmatrix != '{'): self.trainmatrix += ',' self.trainmatrix += trainmatrix scoreParam = 'NA' scoredetails = f'{{"Model":"{model}","Score":"{score}"}}' if model_tried != '': model_tried += ',' model_tried += scoredetails model = anomalyMethod else: log.info('-------> Target Problem Type:'+ self.targetType) log.info('-------> Target Model Type:'+ modelType) if(modelType == 'regression'): allowedmatrix = ['mse','r2','rmse','mae'] if(scoreParam.lower() not in allowedmatrix): scoreParam = 'mse' if(modelType == 'classification'): allowedmatrix = ['accuracy','recall','f1_score','precision','roc_auc'] if(scoreParam.lower() not in allowedmatrix): scoreParam = 'accuracy' scoreParam = scoreParam.lower() codeConfigure.update_config('scoring_criteria',scoreParam) modelParams,modelList = config_obj.getEionLearnerModelParams(modelType) status,model_type,model,saved_model,matrix,trainmatrix,featureDataShape,model_tried,score,filename,self.features,threshold,pscore,rscore,self.method,loaded_model,xtrain1,ytrain1,xtest1,ytest1,topics,params=mlobj.startLearning(learnerJson,modelType,modelParams,modelList,scoreParam,targetColumn,dataFrame,xtrain,ytrain,xtest,ytest,categoryCountList,self.topFeatures,self.modelSelTopFeatures,self.allFeatures,self.targetType,deployLocation,iterName,iterVersion,trained_data_file,predicted_data_file,labelMaps,'MB',codeConfigure,featureEngineeringSelector,config_obj.getModelEvaluationConfig(),imageFolderLocation) #Getting model,data for ensemble calculation e_model=loaded_model base_model_score=score if(self.matrix != '{'): self.matrix += ',' if(self.trainmatrix != '{'): self.trainmatrix += ',' self.trainmatrix += trainmatrix self.matrix += matrix mlexecutionTime=time.time() - mlstart log.info('-------> Total ML Execution Time '+str(mlexecutionTime)) log.info('================== ML Completed ==================\n') if deeplearner_status: learner_type = 'DL' log.info('Status:- |... AION DL training started') from dlearning.deeplearning import deeplearning dlobj = deeplearning() from learner.machinelearning import machinelearning mlobj = machinelearning() log.info('\n================== DL Started ==================') dlstart = time.time() deeplearnerJson = config_obj.getEionDeepLearnerConfiguration() targetColumn = targetFeature method = deeplearnerJson['optimizationMethod'] optimizationHyperParameter = deeplearnerJson['optimizationHyperParameter'] cvSplit = optimizationHyperParameter['trainTestCVSplit'] roundLimit=optimizationHyperParameter['roundLimit'] if 'randomMethod' in optimizationHyperParameter: randomMethod = optimizationHyperParameter['randomMethod'] else: randomMethod = 'Quantum' modelType = problem_type.lower() modelParams = deeplearnerJson['modelParams'] modelParamsFile=deeplearnerJson['modelparamsfile'] if roundLimit =="": roundLimit=None else: roundLimit=int(roundLimit) if len(self.modelSelTopFeatures) !=0: dl_features=self.modelSelTopFeatures best_feature_model = 'ModelBased' elif len(self.topFeatures) != 0: dl_features=self.topFeatures if featureEngineeringSelector.lower() == 'true': best_feature_model = 'DimensionalityReduction' else: best_feature_model = 'StatisticalBased' elif len(self.allFeatures) != 0: dl_features=self.allFeatures best_feature_model = 'AllFeatures' else: datacolumns=list(dataFrame.columns) datacolumns.remove(targetColumn) dl_features =datacolumns best_feature_model = 'AllFeatures' log.info('-------> Features Used For Modeling: '+(str(dl_features))[:500]) if cvSplit == "": cvSplit =None else: cvSplit =int(cvSplit) xtrain = xtrain[dl_features] xtest = xtest[dl_features] df_test = xtest.copy() df_test['actual'] = ytest modelParams,modelList = config_obj.getEionDeepLearnerModelParams(modelType) if modelType.lower() == 'classification': scoreParam = dlobj.setScoreParams(scoreParam,modelType) featureDataShape = xtrain.shape model_type = 'Classification' log.info('\n------ Training DL: Classification ----') elif modelType.lower() == 'regression': model_type = "Regression" if scoreParam == 'None': scoreParam = None log.info('\n------ Training DL: Regression ----') featureDataShape = xtrain.shape model_dl,score_dl,best_model_dl,params_dl,X1,XSNN,model_tried_dl,loss_matrix,optimizer,saved_model_dl,filename_dl,dftrain,df_test,performancematrix,trainingperformancematrix = dlobj.startLearning(model_type,modelList, modelParams, scoreParam, cvSplit, xtrain,ytrain,xtest,ytest,method,randomMethod,roundLimit,labelMaps,df_test,deployLocation,iterName,iterVersion,best_feature_model) if model_tried != '': model_tried += ',' model_tried += model_tried_dl bestDL = True if learner_status: if score_dl <= score: bestDL = False log.info("\n----------- Machine Learning is Good ---") log.info("-------> Model: "+str(model) +" Score: "+str(score)) log.info("---------------------------------------\n") else: os.remove(filename) os.remove(predicted_data_file) log.info("\n------------ Deep Learning is Good---") log.info("-------> Model: "+str(model_dl)+" Score: "+str(score_dl)) log.info("---------------------------------------\n") if bestDL: model = model_dl score = score_dl best_model = best_model_dl params = params_dl filename = filename_dl status, msg = save_csv(df_test,predicted_data_file) if not status: log.info('CSV File Error: ' + str(msg)) saved_model = saved_model_dl self.matrix = '{'+performancematrix self.trainmatrix = '{'+trainingperformancematrix self.features = dl_features else: learner_type = 'ML' shutil.rmtree(filename_dl) dlexecutionTime=time.time() - dlstart log.info('-------> DL Execution Time '+str(dlexecutionTime)) log.info('Status:- |... AION DL training completed') log.info('================== Deep Completed ==================\n') if deeplearner_status or learner_status: log.info('Status:-|... AION training completed') if stateTransitionStatus: log.info('Status:-|... AION State Transition start') learner_type = modelType = model_type = 'StateTransition' model = 'MarkovClustering' scoreParam = 'NA' score = 0 from state_transition.pattern import pattern patternobj = pattern(modelFeatures,targetFeature) model_tried,probabilityfile,clusteringfile = patternobj.training(dataFrame,outputLocation) deploy_status = False visualizationstatus = False log.info('Status:-|... AION State Transition completed') if associationRuleStatus: log.info('\n================== Association Rule Started ==================') log.info('Status:-|... AION Association Rule start') learner_type = 'AR' modelType = 'Association Rule' model = 'apriori' scoreParam = 'NA' score = 'NA' model_type = modelType associationRuleJson = config_obj.getEionAssociationRuleConfiguration() modelparams,modelList = config_obj.getEionAssociationRuleModelParams() invoiceNoFeature,itemFeature = config_obj.getAssociationRuleFeatures() if model in modelparams: modelparam = modelparams[model] log.info('\n-------- Assciation Rule Start -----') from association_rules.associationrules import associationrules associationrulesobj = associationrules(dataFrame,associationRuleJson,modelparam,invoiceNoFeature,itemFeature) model_tried = associationrulesobj.apply_associationRules(outputLocation) log.info('-------- Association Rule End -----\n') log.info('<--------Association Rule Completed----->') log.info('Status:-|... AION Association Rule completed') deploy_status = False if textSimilarityStatus: log.info('================ Text Similarity Started ====================') log.info('Status:-|... AION Text Similarity started') learner_type = 'Text Similarity' model_type = 'Text Similarity' scoreParam = 'Accuracy' modelType = model_type firstDocFeature = config_obj.getFirstDocumentFeature() secondDocFeature = config_obj.getSecondDocumentFeature() textSimilarityCongig = config_obj.getEionTextSimilarityConfig() testPercentage = config_obj.getAIONTestTrainPercentage() #Unnati from recommender.text_similarity import eion_similarity_siamese objTextSimilarity = eion_similarity_siamese() model,score,matrix,trainmatrix,modeltried,saved_model,filename,padding_length,threshold = objTextSimilarity.siamese_model(dataFrame,firstDocFeature,secondDocFeature,targetFeature,textSimilarityCongig,pipe,deployLocation,iterName,iterVersion,testPercentage,predicted_data_file) if(self.matrix != '{'): self.matrix += ',' self.matrix += matrix if model_tried != '': model_tried += ',' model_tried += modeltried if(self.trainmatrix != '{'): self.trainmatrix += ',' self.trainmatrix += trainmatrix log.info('Status:-|... AION Text Similarity completed') log.info('================ Text Similarity Started End====================') if timeseriesStatus: log.info('================ Time Series Forecasting Started ====================') #task 11997 log.info('Status:-|... AION TimeSeries Forecasting started') #task 11997 modelType = 'TimeSeriesForecasting' #task 11997 model_type = 'TimeSeriesForecasting' #task 11997 learner_type = 'TS' modelName='ARIMA' numericContinuousFeatures = targetFeature.split(",") profilerJson = config_obj.getEionTimeSeriesConfiguration() modelParams,modelList = config_obj.getEionTimeSeriesModelParams() modelName = modelList testPercentage = config_obj.getAIONTestTrainPercentage() #Unnati from time_series.timeseries import timeseries allowedmatrix = ['mse','r2','rmse','mae'] if(scoreParam.lower() not in allowedmatrix): scoreParam = 'rmse' objTS = timeseries(profilerJson,modelParams,modelList,dataFrame,targetFeature,datetimeFeature,modelName,testPercentage,iterName,iterVersion,deployLocation,scoreParam) modelName,model,scoreParam,score,best_model,sfeatures,errormatrix,model_tried,dictDiffCount,pred_freq,additional_regressors,filename,saved_model,lag_order,scalertransformationFile = objTS.timeseries_learning(trained_data_file,predicted_data_file,deployLocation) xtrain = dataFrame self.matrix += errormatrix log.info("Best model to deploy: \n"+str(model)) ## Below part is for var,arima,fbprophet try: with open(filename, 'rb') as f: loaded_model = pickle.load(f) f.close() except: loaded_model=best_model pass df_l=len(dataFrame) pred_threshold=0.1 max_pred_by_user= round((df_l)*pred_threshold) #prediction for 24 steps or next 24 hours if noofforecasts == -1: noofforecasts = max_pred_by_user no_of_prediction=noofforecasts if (no_of_prediction > max_pred_by_user): log.info("-------> Forecast beyond the threshold.So, Reset to Maximum:" +str(max_pred_by_user)) no_of_prediction=max_pred_by_user noofforecasts = no_of_prediction log.info("-------> Number of Forecast Records: "+str(no_of_prediction)) log.info("\n------ Forecast Prediction Start -------------") if(model.lower() == 'var'): sfeatures.remove(datetimeFeature) self.features = sfeatures originalPredictions=objTS.var_prediction(no_of_prediction) log.info("-------> Predictions") log.info(originalPredictions) predictions=originalPredictions forecast_output = predictions.to_json(orient='records') else: if (model.lower() == 'fbprophet'): self.features = sfeatures if not pred_freq: sessonal_freq = 'H' else: sessonal_freq=pred_freq ts_prophet_future = best_model.make_future_dataframe(periods=no_of_prediction,freq=sessonal_freq,include_history = False) #If additional regressor given by user. if (additional_regressors): log.info("------->Prophet additional regressor given by user: "+str(additional_regressors)) ts_prophet_future[additional_regressors] = dataFrame[additional_regressors] ts_prophet_future.reset_index(drop=True) ts_prophet_future=ts_prophet_future.dropna() else: pass train_forecast = best_model.predict(ts_prophet_future) train_forecast = train_forecast.round(2) prophet_forecast_tail=train_forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']] prophet_forecast_tail['ds'] = prophet_forecast_tail['ds'].dt.strftime('%Y-%m-%d %H:%i:%s') log.info("------->Prophet Predictions") log.info(prophet_forecast_tail) forecast_output = prophet_forecast_tail.to_json(orient='records') elif (model.lower() == 'arima'): predictions = loaded_model.predict(n_periods=no_of_prediction) predictions = predictions.round(2) self.features = sfeatures col = targetFeature.split(",") pred = pd.DataFrame(predictions,columns=col) predictionsdf = pred log.info("-------> Predictions") log.info(predictionsdf) forecast_output = predictionsdf.to_json(orient='records') elif (model.lower() == 'encoder_decoder_lstm_mvi_uvo'): log.info(datetimeFeature) log.info(sfeatures) self.features = sfeatures if len(sfeatures) == 1: xt = xtrain[self.features].values else: xt = xtrain[self.features].values with open(scalertransformationFile, 'rb') as f: loaded_scaler_model = pickle.load(f) f.close() xt = xt.astype('float32') xt = loaded_scaler_model.transform(xt) pred_data = xt y_future = [] featuerlen = len(sfeatures) targetColIndx = (xtrain.columns.get_loc(targetFeature)) #in case of lstm multivariate input and univariate out prediction only one sequence can be predicted #consider the last xtrain window as input sequence pdata = pred_data[-lag_order:] pdata = pdata.reshape((1,lag_order, featuerlen)) pred = loaded_model.predict(pdata) pred_1d = pred.ravel() #pred_1d = pred_1d.reshape(len(pred_1d),1) pdata_2d = pdata.ravel().reshape(len(pdata) * lag_order, featuerlen) pdata_2d[:,targetColIndx] = pred_1d pred_2d_inv = loaded_scaler_model.inverse_transform(pdata_2d) predout = pred_2d_inv[:, targetColIndx] predout = predout.reshape(len(pred_1d),1) #y_future.append(predout) col = targetFeature.split(",") pred = pd.DataFrame(index=range(0,len(predout)),columns=col) for i in range(0, len(predout)): pred.iloc[i] = predout[i] predictions = pred log.info("-------> Predictions") log.info(predictions) forecast_output = predictions.to_json(orient='records') elif (model.lower() == 'mlp' or model.lower() == 'lstm'): sfeatures.remove(datetimeFeature) self.features = sfeatures if len(sfeatures) == 1: xt = xtrain[self.features].values else: xt = xtrain[self.features].values with open(scalertransformationFile, 'rb') as f: loaded_scaler_model = pickle.load(f) f.close() xt = xt.astype('float32') xt = loaded_scaler_model.transform(xt) pred_data = xt y_future = [] for i in range(no_of_prediction): pdata = pred_data[-lag_order:] if model.lower() == 'mlp': pdata = pdata.reshape((1,lag_order)) else: pdata = pdata.reshape((1,lag_order, len(sfeatures))) if (len(sfeatures) > 1): pred = loaded_model.predict(pdata) predout = loaded_scaler_model.inverse_transform(pred) y_future.append(predout) pred_data=np.append(pred_data,pred,axis=0) else: pred = loaded_model.predict(pdata) predout = loaded_scaler_model.inverse_transform(pred) y_future.append(predout.flatten()[-1]) pred_data = np.append(pred_data,pred) col = targetFeature.split(",") pred = pd.DataFrame(index=range(0,len(y_future)),columns=col) for i in range(0, len(y_future)): pred.iloc[i] = y_future[i] predictions = pred log.info("-------> Predictions") log.info(predictions) forecast_output = predictions.to_json(orient='records') else: pass log.info('Status:-|... AION TimeSeries Forecasting completed') #task 11997 log.info("------ Forecast Prediction End -------------\n") log.info('================ Time Series Forecasting Completed ================\n') #task 11997 if recommenderStatus: log.info('\n================ Recommender Started ================ ') log.info('Status:-|... AION Recommender started') learner_type = 'RecommenderSystem' model_type = 'RecommenderSystem' modelType = model_type model = model_type targetColumn='' datacolumns=list(dataFrame.columns) self.features=datacolumns svd_params = config_obj.getEionRecommenderConfiguration() from recommender.item_rating import recommendersystem recommendersystemObj = recommendersystem(modelFeatures,svd_params) testPercentage = config_obj.getAIONTestTrainPercentage() #Unnati saved_model,rmatrix,score,trainingperformancematrix,model_tried = recommendersystemObj.recommender_model(dataFrame,outputLocation) scoreParam = 'NA' #Task 11190 log.info('Status:-|... AION Recommender completed') log.info('================ Recommender Completed ================\n') if textsummarizationStatus: log.info('\n================ text Summarization Started ================ ') log.info('Status:-|... AION text Summarization started') modelType = 'textsummarization' model_type = 'textsummarization' learner_type = 'Text Summarization' modelName='TextSummarization' from sklearn.preprocessing import LabelEncoder from sklearn.ensemble import RandomForestClassifier from scipy import spatial model = model_type dataLocationTS,deployLocationTS,KeyWordsTS,pathForKeywordFileTS = config_obj.getEionTextSummarizationConfig() #print("dataLocationTS",dataLocationTS) #print("deployLocationTS",deployLocationTS) #print("KeyWordsTS",KeyWordsTS) #print("pathForKeywordFileTS",pathForKeywordFileTS) #PreTrained Model Download starts------------------------- from appbe.dataPath import DATA_DIR preTrainedModellocation = Path(DATA_DIR)/'PreTrainedModels'/'TextSummarization' preTrainedModellocation = Path(DATA_DIR)/'PreTrainedModels'/'TextSummarization' models = {'glove':{50:'glove.6B.50d.w2vformat.txt'}} supported_models = [x for y in models.values() for x in y.values()] modelsPath = Path(DATA_DIR)/'PreTrainedModels'/'TextSummarization' Path(modelsPath).mkdir(parents=True, exist_ok=True) p = Path(modelsPath).glob('**/*') modelsDownloaded = [x.name for x in p if x.name in supported_models] selected_model="glove.6B.50d.w2vformat.txt" if selected_model not in modelsDownloaded: print("Model not in folder, downloading") import urllib.request location = Path(modelsPath) local_file_path = location/f"glove.6B.50d.w2vformat.txt" urllib.request.urlretrieve(f'https://aion-pretrained-models.s3.ap-south-1.amazonaws.com/text/glove.6B.50d.w2vformat.txt', local_file_path) from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-cnn-12-6") tokenizer.save_pretrained(preTrainedModellocation) model.save_pretrained(preTrainedModellocation) #PreTrained Model Download ends----------------------- deployLocationData=deployLocation+"\\data\\" modelLocation=Path(DATA_DIR)/'PreTrainedModels'/'TextSummarization'/'glove.6B.50d.w2vformat.txt' KeyWordsTS=KeyWordsTS.replace(",", " ") noOfKeyword = len(KeyWordsTS.split()) keywords = KeyWordsTS.split() embeddings = {} word = '' with open(modelLocation, 'r', encoding="utf8") as f: header = f.readline() header = header.split(' ') vocab_size = int(header[0]) embed_size = int(header[1]) for i in range(vocab_size): data = f.readline().strip().split(' ') word = data[0] embeddings[word] = [float(x) for x in data[1:]] readData=pd.read_csv(pathForKeywordFileTS,encoding='utf-8',encoding_errors= 'replace') for i in range(noOfKeyword): terms=(sorted(embeddings.keys(), key=lambda word: spatial.distance.euclidean(embeddings[word], embeddings[keywords[i]])) )[1:6] readData = readData.append({'Keyword': keywords[i]}, ignore_index=True) for j in range(len(terms)): readData = readData.append({'Keyword': terms[j]}, ignore_index=True) deployLocationDataKwDbFile=deployLocationData+"keywordDataBase.csv" readData.to_csv(deployLocationDataKwDbFile,encoding='utf-8',index=False) datalocation_path=dataLocationTS path=Path(datalocation_path) fileList=os.listdir(path) textExtraction = pd.DataFrame() textExtraction['Sentences']="" rowIndex=0 for i in range(len(fileList)): fileName=str(datalocation_path)+"\\"+str(fileList[i]) if fileName.endswith(".pdf"): print("\n files ",fileList[i]) from pypdf import PdfReader reader = PdfReader(fileName) number_of_pages = len(reader.pages) text="" textOutputForFile="" OrgTextOutputForFile="" for i in range(number_of_pages) : page = reader.pages[i] text1 = page.extract_text() text=text+text1 import nltk tokens = nltk.sent_tokenize(text) for sentence in tokens: sentence=sentence.replace("\n", " ") if (len(sentence.split()) < 4 ) or (len(str(sentence.split(',')).split()) < 8)or (any(chr.isdigit() for chr in sentence)) : continue textExtraction.at[rowIndex,'Sentences']=str(sentence.strip()) rowIndex=rowIndex+1 if fileName.endswith(".txt"): print("\n txt files",fileList[i]) data=[] with open(fileName, "r",encoding="utf-8") as f: data.append(f.read()) str1 = "" for ele in data: str1 += ele sentences=str1.split(".") count=0 for sentence in sentences: count += 1 textExtraction.at[rowIndex+i,'Sentences']=str(sentence.strip()) rowIndex=rowIndex+1 df=textExtraction #print("textExtraction",textExtraction) deployLocationDataPreProcessData=deployLocationData+"preprocesseddata.csv" save_csv_compressed(deployLocationDataPreProcessData, df, encoding='utf-8') df['Label']=0 kw=pd.read_csv(deployLocationDataKwDbFile,encoding='utf-8',encoding_errors= 'replace') Keyword_list = kw['Keyword'].tolist() for i in df.index: for x in Keyword_list: if (str(df["Sentences"][i])).find(x) != -1: df['Label'][i]=1 break deployLocationDataPostProcessData=deployLocationData+"postprocesseddata.csv" #df.to_csv(deployLocationDataPostProcessData,encoding='utf-8') save_csv_compressed(deployLocationDataPostProcessData, df, encoding='utf-8') labelledData=df train_df=labelledData labelencoder = LabelEncoder() train_df['Sentences'] = labelencoder.fit_transform(train_df['Sentences']) X = train_df.drop('Label',axis=1) y = train_df['Label'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) Classifier = RandomForestClassifier(n_estimators = 10, random_state = 42) modelTs=Classifier.fit(X, y) import pickle deployLocationTS=deployLocation+"\\model\\"+iterName+'_'+iterVersion+'.sav' deployLocationTS2=deployLocation+"\\model\\"+"classificationModel.sav" pickle.dump(modelTs, open(deployLocationTS, 'wb')) pickle.dump(modelTs, open(deployLocationTS2, 'wb')) print("\n trainModel Ends") saved_model = 'textsummarization_'+iterName+'_'+iterVersion log.info('Status:-|... AION text summarization completed') model = learner_type log.info('================ text summarization Completed ================\n') if survival_analysis_status: sa_method = config_obj.getEionanomalyModels() labeldict = {} log.info('\n================ SurvivalAnalysis Started ================ ') log.info('Status:-|... AION SurvivalAnalysis started') log.info('\n================ SurvivalAnalysis DataFrame ================ ') log.info(dataFrame) from survival import survival_analysis from learner.machinelearning import machinelearning sa_obj = survival_analysis.SurvivalAnalysis(dataFrame, preprocess_pipe, sa_method, targetFeature, datetimeFeature, filter_expression, profilerObj.train_features_type) if sa_obj != None: predict_json = sa_obj.learn() if sa_method.lower() in ['kaplanmeierfitter','kaplanmeier','kaplan-meier','kaplan meier','kaplan','km','kmf']: predicted = sa_obj.models[0].predict(dataFrame[datetimeFeature]) status, msg = save_csv(predicted,predicted_data_file) if not status: log.info('CSV File Error: ' + str(msg)) self.features = [datetimeFeature] elif sa_method.lower() in ['coxphfitter','coxregression','cox-regression','cox regression','coxproportionalhazard','coxph','cox','cph']: predicted = sa_obj.models[0].predict_cumulative_hazard(dataFrame) datacolumns = list(dataFrame.columns) targetColumn = targetFeature if targetColumn in datacolumns: datacolumns.remove(targetColumn) self.features = datacolumns score = sa_obj.score scoreParam = 'Concordance_Index' status,msg = save_csv(predicted,predicted_data_file) if not status: log.info('CSV File Error: ' + str(msg)) model = sa_method modelType = "SurvivalAnalysis" model_type = "SurvivalAnalysis" modelName = sa_method i = 1 for mdl in sa_obj.models: saved_model = "%s_%s_%s_%d.sav"%(model_type,sa_method,iterVersion,i) pickle.dump(mdl, open(os.path.join(deployLocation,'model',saved_model), 'wb')), i+=1 p = 1 for plot in sa_obj.plots: img_name = "%s_%d.png"%(sa_method,p) img_location = os.path.join(imageFolderLocation,img_name) plot.savefig(img_location,bbox_inches='tight') sa_images.append(img_location) p+=1 log.info('Status:-|... AION SurvivalAnalysis completed') log.info('\n================ SurvivalAnalysis Completed ================ ') if visualizationstatus: visualizationJson = config_obj.getEionVisualizationConfiguration() log.info('\n================== Visualization Recommendation Started ==================') visualizer_mlstart = time.time() from visualization.visualization import Visualization visualizationObj = Visualization(iterName,iterVersion,dataFrame,visualizationJson,datetimeFeature,deployLocation,dataFolderLocation,numericContinuousFeatures,discreteFeatures,categoricalFeatures,self.features,targetFeature,model_type,original_data_file,profiled_data_file,trained_data_file,predicted_data_file,labelMaps,self.vectorizerFeatures,self.textFeatures,self.numericalFeatures,self.nonNumericFeatures,self.emptyFeatures,self.dfrows,self.dfcols,saved_model,scoreParam,learner_type,model,featureReduction,reduction_data_file) visualizationObj.visualizationrecommandsystem() visualizer_mlexecutionTime=time.time() - visualizer_mlstart log.info('-------> COMPUTING: Total Visualizer Execution Time '+str(visualizer_mlexecutionTime)) log.info('================== Visualization Recommendation Started ==================\n') if similarityIdentificationStatus or contextualSearchStatus: datacolumns=list(dataFrame.columns) features = modelFeatures.split(",") if indexFeature != '' and indexFeature != 'NA': iFeature = indexFeature.split(",") for ifea in iFeature: if ifea not in features: features.append(ifea) for x in features: dataFrame[x] = similaritydf[x] #get vectordb(chromadb) status selected if similarityIdentificationStatus: learner_type = 'similarityIdentification' else: learner_type = 'contextualSearch' vecDBCosSearchStatus = config_obj.getVectorDBCosSearchStatus(learner_type) if vecDBCosSearchStatus: status, msg = save_chromadb(dataFrame, config_obj, trained_data_file, modelFeatures) if not status: log.info('Vector DB File Error: '+str(msg)) else: status, msg = save_csv(dataFrame,trained_data_file) if not status: log.info('CSV File Error: '+str(msg)) self.features = datacolumns model_type = config_obj.getAlgoName(problem_type) model = model_type #bug 12833 model_tried = '{"Model":"'+model_type+'","FeatureEngineering":"NA","Score":"NA","ModelUncertainty":"NA"}' modelType = learner_type saved_model = learner_type score = 'NA' if deploy_status: if str(model) != 'None': log.info('\n================== Deployment Started ==================') log.info('Status:-|... AION Creating Prediction Service Start') deployer_mlstart = time.time() deployJson = config_obj.getEionDeployerConfiguration() deploy_name = iterName+'_'+iterVersion from prediction_package.model_deploy import DeploymentManager if textsummarizationStatus : deploy = DeploymentManager() deploy.deployTSum(deployLocation,preTrainedModellocation) codeConfigure.save_config(deployLocation) deployer_mlexecutionTime=time.time() - deployer_mlstart log.info('-------> COMPUTING: Total Deployer Execution Time '+str(deployer_mlexecutionTime)) log.info('Status:-|... AION Deployer completed') log.info('================== Deployment Completed ==================') else: deploy = DeploymentManager() deploy.deploy_model(deploy_name,deployJson,learner_type,model_type,model,scoreParam,saved_model,deployLocation,self.features,self.profilerAction,dataLocation,labelMaps,column_merge_flag,self.textFeatures,self.numericalFeatures,self.nonNumericFeatures,preprocessing_pipe,numericToLabel_json,threshold,loss_matrix,optimizer,firstDocFeature,secondDocFeature,padding_length,trained_data_file,dictDiffCount,targetFeature,normalizer_pickle_file,normFeatures,pcaModel_pickle_file,bpca_features,apca_features,self.method,deployFolder,iterName,iterVersion,self.wordToNumericFeatures,imageconfig,sessonal_freq,additional_regressors,grouperbyjson,rowfilterexpression,xtrain,profiled_data_file,conversion_method,modelFeatures,indexFeature,lag_order,scalertransformationFile,noofforecasts,preprocess_pipe,preprocess_out_columns, label_encoder,datetimeFeature,usecaseLocation,deploy_config) codeConfigure.update_config('deploy_path',os.path.join(deployLocation,'publish')) codeConfigure.save_config(deployLocation) deployer_mlexecutionTime=time.time() - deployer_mlstart log.info('-------> COMPUTING: Total Deployer Execution Time '+str(deployer_mlexecutionTime)) log.info('Status:-|... AION Creating Prediction Service completed') log.info('================== Deployment Completed ==================') if not outputDriftStatus and not inputDriftStatus: from transformations.dataProfiler import set_features self.features = set_features(self.features,profilerObj) self.matrix += '}' self.trainmatrix += '}' print(model_tried) model_tried = eval('['+model_tried+']') matrix = eval(self.matrix) trainmatrix = eval(self.trainmatrix) deployPath = deployLocation.replace(os.sep, '/') if survival_analysis_status: output_json = {"status":"SUCCESS","data":{"ModelType":modelType,"deployLocation":deployPath,"BestModel":model,"BestScore":str(score),"ScoreType":str(scoreParam).upper(),"matrix":matrix,"survivalProbability":json.loads(predict_json),"featuresused":str(self.features),"targetFeature":str(targetColumn),"EvaluatedModels":model_tried,"imageLocation":str(sa_images),"LogFile":logFileName}} elif not timeseriesStatus: try: json.dumps(params) output_json = {"status":"SUCCESS","data":{"ModelType":modelType,"deployLocation":deployPath,"BestModel":model,"BestScore":str(score),"ScoreType":str(scoreParam).upper(),"matrix":matrix,"trainmatrix":trainmatrix,"featuresused":str(self.features),"targetFeature":str(targetColumn),"params":params,"EvaluatedModels":model_tried,"LogFile":logFileName}} except: output_json = {"status":"SUCCESS","data":{"ModelType":modelType,"deployLocation":deployPath,"BestModel":model,"BestScore":str(score),"ScoreType":str(scoreParam).upper(),"matrix":matrix,"trainmatrix":trainmatrix,"featuresused":str(self.features),"targetFeature":str(targetColumn),"params":"","EvaluatedModels":model_tried,"LogFile":logFileName}} else: if config_obj.summarize: modelType = 'Summarization' output_json = {"status":"SUCCESS","data":{"ModelType":modelType,"deployLocation":deployPath,"BestModel":model,"BestScore":str(score),"ScoreType":str(scoreParam).upper(),"matrix":matrix,"featuresused":str(self.features),"targetFeature":str(targetColumn),"EvaluatedModels":model_tried,'forecasts':json.loads(forecast_output),"LogFile":logFileName}} if bool(topics) == True: output_json['topics'] = topics with open(outputjsonFile, 'w',encoding='utf-8') as f: json.dump(output_json, f) f.close() output_json = json.dumps(output_json) log.info('\n------------- Summary ------------') log.info('------->No of rows & columns in data:('+str(self.dfrows)+','+str(self.dfcols)+')') log.info('------->No of missing Features :'+str(len(self.mFeatures))) log.info('------->Missing Features:'+str(self.mFeatures)) log.info('------->Text Features:'+str(self.textFeatures)) log.info('------->No of Nonnumeric Features :'+str(len(self.nonNumericFeatures))) log.info('------->Non-Numeric Features :' +str(self.nonNumericFeatures)) if threshold == -1: log.info('------->Threshold: NA') else: log.info('------->Threshold: '+str(threshold)) log.info('------->Label Maps of Target Feature for classification :'+str(labelMaps)) for i in range(0,len(self.similarGroups)): log.info('------->Similar Groups '+str(i+1)+' '+str(self.similarGroups[i])) if((learner_type != 'TS') & (learner_type != 'AR')): log.info('------->No of columns and rows used for Modeling :'+str(featureDataShape)) log.info('------->Features Used for Modeling:'+str(self.features)) log.info('------->Target Feature: '+str(targetColumn)) log.info('------->Best Model Score :'+str(score)) log.info('------->Best Parameters:'+str(params)) log.info('------->Type of Model :'+str(modelType)) log.info('------->Best Model :'+str(model)) log.info('------------- Summary ------------\n') log.info('Status:-|... AION Model Training Successfully Done') except Exception as inst: log.info('server code execution failed !....'+str(inst)) log.error(inst, exc_info = True) output_json = {"status":"FAIL","message":str(inst).strip('"'),"LogFile":logFileName} output_json = json.dumps(output_json) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) executionTime = timeit.default_timer() - startTime log.info('\nTotal execution time(sec) :'+str(executionTime)) log.info('\n------------- Output JSON ------------') log.info('aion_learner_status:'+str(output_json)) log.info('------------- Output JSON ------------\n') for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): hdlr.close() log.removeHandler(hdlr) return output_json def split_into_train_test_data(self,featureData,targetData,testPercentage,log,modelType='classification'): #Unnati log.info('\n-------------- Test Train Split ----------------') if testPercentage == 0 or testPercentage == 100: #Unnati xtrain=featureData ytrain=targetData xtest=pd.DataFrame() ytest=pd.DataFrame() else: testSize= testPercentage/100 #Unnati if modelType == 'regression': log.info('-------> Split Type: Random Split') xtrain,xtest,ytrain,ytest=train_test_split(featureData,targetData,test_size=testSize,shuffle=True,random_state=42) else: try: log.info('-------> Split Type: Stratify Split') xtrain,xtest,ytrain,ytest=train_test_split(featureData,targetData,stratify=targetData,test_size=testSize,random_state=42) except Exception as ValueError: count_unique = targetData.value_counts() feature_with_single_count = count_unique[ count_unique == 1].index.tolist() error = f"The least populated class in {feature_with_single_count} has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2" raise Exception(error) from ValueError except: log.info('-------> Split Type: Random Split') xtrain,xtest,ytrain,ytest=train_test_split(featureData,targetData,test_size=testSize,shuffle=True,random_state=42) log.info('Status:- !... Train / test split done: '+str(100-testPercentage)+'% train,'+str(testPercentage)+'% test') #Unnati log.info('-------> Train Data Shape: '+str(xtrain.shape)+' ---------->') log.info('-------> Test Data Shape: '+str(xtest.shape)+' ---------->') log.info('-------------- Test Train Split End ----------------\n') return(xtrain,ytrain,xtest,ytest) def aion_train_model(arg): warnings.filterwarnings('ignore') config_path = Path( arg) with open( config_path, 'r') as f: config = json.load( f) log = set_log_handler(config['basic']) log.info('************* Version - v'+AION_VERSION+' *************** \n') msg = '-------> Execution Start Time: '+ datetime.datetime.now(timezone("Asia/Kolkata")).strftime('%Y-%m-%d %H:%M:%S' + ' IST') log.info(msg) try: config_validate(arg) valid, msg = pushRecordForTraining() if valid: serverObj = server() configObj = AionConfigManager() codeConfigure = code_configure() codeConfigure.create_config(config) readConfistatus,msg = configObj.readConfigurationFile(config) if(readConfistatus == False): raise ValueError( msg) output = serverObj.startScriptExecution(configObj, codeConfigure, log) else: output = {"status":"LicenseVerificationFailed","message":str(msg).strip('"')} output = json.dumps(output) print( f"\naion_learner_status:{output}\n") log.info( f"\naion_learner_status:{output}\n") except Exception as inst: output = {"status":"FAIL","message":str(inst).strip('"')} output = json.dumps(output) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) print(f"\naion_learner_status:{output}\n") log.info( f"\naion_learner_status:{output}\n") return output if __name__ == "__main__": aion_train_model( sys.argv[1]) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import joblib import time import pandas as pd import numpy as np import argparse import json import os import pathlib from pathlib import Path from uncertainties.uq_main import aionUQ import os from datetime import datetime from os.path import expanduser import platform import logging class run_uq: def __init__(self,modelfeatures,modelFile,csvFile,target): self.modelfeatures=modelfeatures self.modelFile=modelFile self.csvFile=csvFile self.target=target ##UQ classification fn def getUQclassification(self,model,ProblemName,Params): df = pd.read_csv(self.csvFile) # # object_cols = [col for col, col_type in df.dtypes.iteritems() if col_type == 'object'] -- Fix for python 3.8.11 update (in 2.9.0.8) object_cols = [col for col, col_type in zip(df.columns,df.dtypes) if col_type == 'object'] df = df.drop(object_cols, axis=1) df = df.dropna(axis=1) df = df.reset_index(drop=True) modelfeatures = self.modelfeatures #tar = args.target # target = df[tar] y=df[self.target].values y = y.flatten() X = df.drop(self.target, axis=1) try: uqObj=aionUQ(df,X,y,ProblemName,Params,model,modelfeatures,self.target) accuracy,uq_ece,output_jsonobject=uqObj.uqMain_BBMClassification() except Exception as e: print("uq error",e) # print("UQ Classification: \n",output_jsonobject) # print(accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertaitny_per) #print(output_jsonobject) return accuracy,uq_ece,output_jsonobject ##UQ regression fn def getUQregression(self,model,ProblemName,Params): df = pd.read_csv(self.csvFile) modelfeatures = self.modelfeatures dfp = df[modelfeatures] tar = self.target target = df[tar] uqObj=aionUQ(df,dfp,target,ProblemName,Params,model,modelfeatures,tar) total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject=uqObj.uqMain_BBMRegression() return total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject def uqMain(self,model): #print("inside uq main.\n") reg_status="" class_status="" algorithm_status="" try: model=model if Path(self.modelFile).is_file(): ProblemName = model.__class__.__name__ if ProblemName in ['LogisticRegression','SGDClassifier','SVC','DecisionTreeClassifier','RandomForestClassifier','GaussianNB','KNeighborsClassifier','GradientBoostingClassifier']: Problemtype = 'Classification' elif ProblemName in ['LinearRegression','Lasso','Ridge','DecisionTreeRegressor','RandomForestRegressor']: Problemtype = 'Regression' else: Problemtype = "None" if Problemtype.lower() == 'classification': try: Params = model.get_params() accuracy,uq_ece,output = self.getUQclassification(model,ProblemName,Params) class_status="SUCCESS" #print(output) except Exception as e: print(e) class_status="FAILED" output = {'Problem':'None','msg':str(e)} output = json.dumps(output) elif Problemtype.lower() == 'regression' : try: Params = model.get_params() total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,output = self.getUQregression(model,ProblemName,Params) #print(uq_jsonobject) reg_status="SUCCESS" except Exception as e: output = {'Problem':'None','msg':str(e)} output = json.dumps(output) reg_status="FAILED" else: try: output={} output['Problem']="None" output['msg']="Uncertainty Quantification not supported for this algorithm." output = json.dumps(output) algorithm_status="FAILED" except: algorithm_status="FAILED" except Exception as e: print(e) reg_status="FAILED" class_status="FAILED" algorithm_status="FAILED" output = {'Problem':'None','msg':str(e)} output = json.dumps(output) return class_status,reg_status,algorithm_status,output def aion_uq(modelFile,dataFile,features,targetfeatures): try: from appbe.dataPath import DEPLOY_LOCATION uqLogLocation = os.path.join(DEPLOY_LOCATION,'logs') try: os.makedirs(uqLogLocation) except OSError as e: if (os.path.exists(uqLogLocation)): pass else: raise OSError('uqLogLocation error.') filename_uq = 'uqlog_'+str(int(time.time())) filename_uq=filename_uq+'.log' filepath = os.path.join(uqLogLocation, filename_uq) print(filepath) logging.basicConfig(filename=filepath, format='%(message)s',filemode='w') log = logging.getLogger('aionUQ') log.setLevel(logging.INFO) log.info('************* Version - v1.7.0 *************** \n') if isinstance(features, list): modelfeatures = features else: if ',' in features: modelfeatures = [x.strip() for x in features.split(',')] else: modelfeatures = features.split(',') model = joblib.load(modelFile) uqobj = run_uq(modelfeatures,modelFile,dataFile,targetfeatures) class_status,reg_status,algorithm_status,output=uqobj.uqMain(model) if (class_status.lower() == 'failed'): log.info('uq classifiction failed./n') elif (class_status.lower() == 'success'): log.info('uq classifiction success./n') else: log.info('uq classifiction not used../n') if (reg_status.lower() == 'failed'): log.info('uq regression failed./n') elif (reg_status.lower() == 'success'): log.info('uq regression success./n') else: log.info('uq regression not used./n') if (algorithm_status.lower() == 'failed'): log.info('Problem type issue, UQ only support classification and regression. May be selected algorithm not supported by Uncertainty Quantification currently./n') except Exception as e: log.info('uq test failed.n'+str(e)) #print(e) output = {'Problem':'None','msg':str(e)} output = json.dumps(output) return(output) #Sagemaker main fn call if __name__=='__main__': try: parser = argparse.ArgumentParser() parser.add_argument('savFile') parser.add_argument('csvFile') parser.add_argument('features') parser.add_argument('target') args = parser.parse_args() home = expanduser("~") if platform.system() == 'Windows': uqLogLocation = os.path.join(home,'AppData','Local','HCLT','AION','uqLogs') else: uqLogLocation = os.path.join(home,'HCLT','AION','uqLogs') try: os.makedirs(uqLogLocation) except OSError as e: if (os.path.exists(uqLogLocation)): pass else: raise OSError('uqLogLocation error.') # self.sagemakerLogLocation=str(sagemakerLogLocation) filename_uq = 'uqlog_'+str(int(time.time())) filename_uq=filename_uq+'.log' # filename = 'mlopsLog_'+Time() filepath = os.path.join(uqLogLocation, filename_uq) logging.basicConfig(filename=filepath, format='%(message)s',filemode='w') log = logging.getLogger('aionUQ') log.setLevel(logging.DEBUG) if ',' in args.features: args.features = [x.strip() for x in args.features.split(',')] else: args.features = args.features.split(',') modelFile = args.savFile modelfeatures = args.features csvFile = args.csvFile target=args.target model = joblib.load(args.savFile) ##Main uq function call uqobj = run_uq(modelfeatures,modelFile,csvFile,target) class_status,reg_status,algorithm_status,output=uqobj.uqMain(model) if (class_status.lower() == 'failed'): log.info('uq classifiction failed./n') elif (class_status.lower() == 'success'): log.info('uq classifiction success./n') else: log.info('uq classifiction not used../n') if (reg_status.lower() == 'failed'): log.info('uq regression failed./n') elif (reg_status.lower() == 'success'): log.info('uq regression success./n') else: log.info('uq regression not used./n') if (algorithm_status.lower() == 'failed'): msg = 'Uncertainty Quantification not supported for this algorithm' log.info('Algorithm not supported by Uncertainty Quantification./n') output = {'Problem':'None','msg':str(msg)} output = json.dumps(output) except Exception as e: log.info('uq test failed.n'+str(e)) output = {'Problem':'None','msg':str(e)} output = json.dumps(output) #print(e) print(output) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import requests import json import os from datetime import datetime import socket import getmac def telemetry_data(operation,Usecase,data): now = datetime.now() ID = datetime.timestamp(now) record_date = now.strftime("%y-%m-%d %H:%M:%S") try: user = os.getlogin() except: user = 'NA' computername = socket.getfqdn() macaddress = getmac.get_mac_address() item = {} item['ID'] = str(int(ID)) item['record_date'] = record_date item['UseCase'] = Usecase item['user'] = str(user) item['operation'] = operation item['remarks'] = data item['hostname'] = computername item['macaddress'] = macaddress url = 'https://l5m119j6v9.execute-api.ap-south-1.amazonaws.com/default/aion_telemetry' record = {} record['TableName'] = 'AION_OPERATION' record['Item'] = item record = json.dumps(record) try: response = requests.post(url, data=record,headers={"x-api-key":"Obzt8ijfOT3dgBYma9JCt1tE3W6tzHaV8rVuQdMK","Content-Type":"application/json",}) check_telemetry_file() except Exception as inst: filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),'telemetry.txt') f=open(filename, "a+") f.write(record+'\n') f.close() def check_telemetry_file(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),'telemetry.txt') if(os.path.isfile(file_path)): f = open(file_path, 'r') file_content = f.read() f.close() matched_lines = file_content.split('\n') write_lines = [] url = 'https://l5m119j6v9.execute-api.ap-south-1.amazonaws.com/default/aion_telemetry' for record in matched_lines: try: response = requests.post(url, data=record,headers={"x-api-key":"Obzt8ijfOT3dgBYma9JCt1tE3W6tzHaV8rVuQdMK","Content-Type":"application/json",}) except: write_lines.append(record) f = open(file_path, "a") f.seek(0) f.truncate() for record in write_lines: f.write(record+'\n') f.close() return True else: return True ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import shutil import subprocess import sys import glob import json def publish(data): if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) model = jsonData['modelName'] version = jsonData['modelVersion'] deployFolder = jsonData['deployLocation'] model = model.replace(" ", "_") deployedPath = os.path.join(deployFolder,model+'_'+version) deployedPath = os.path.join(deployedPath,'WHEELfile') whlfilename='na' if os.path.isdir(deployedPath): for file in os.listdir(deployedPath): if file.endswith(".whl"): whlfilename = os.path.join(deployedPath,file) if whlfilename != 'na': subprocess.check_call([sys.executable, "-m", "pip", "uninstall","-y",model]) subprocess.check_call([sys.executable, "-m", "pip", "install", whlfilename]) status,pid,ip,port = check_service_running(jsonData['modelName'],jsonData['serviceFolder']) if status == 'Running': service_stop(json.dumps(jsonData)) service_start(json.dumps(jsonData)) output_json = {'status':"SUCCESS"} output_json = json.dumps(output_json) else: output_json = {'status':'Error','Msg':'Installation Package not Found'} output_json = json.dumps(output_json) return(output_json) def check_service_running(model,serviceFolder): model = model.replace(" ", "_") filename = model+'_service.py' modelservicefile = os.path.join(serviceFolder,filename) status = 'File Not Exist' ip = '' port = '' pid = '' if os.path.exists(modelservicefile): status = 'File Exist' import psutil for proc in psutil.process_iter(): pinfo = proc.as_dict(attrs=['pid', 'name', 'cmdline','connections']) if 'python' in pinfo['name']: if filename in pinfo['cmdline'][1]: status = 'Running' pid = pinfo['pid'] for x in pinfo['connections']: ip = x.laddr.ip port = x.laddr.port return(status,pid,ip,port) def service_stop(data): if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) status,pid,ip,port = check_service_running(jsonData['modelName'],jsonData['serviceFolder']) if status == 'Running': import psutil p = psutil.Process(int(pid)) p.terminate() time.sleep(2) output_json = {'status':'SUCCESS'} output_json = json.dumps(output_json) return(output_json) def service_start(data): if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) model = jsonData['modelName'] version = jsonData['modelVersion'] ip = jsonData['ip'] port = jsonData['port'] deployFolder = jsonData['deployLocation'] serviceFolder = jsonData['serviceFolder'] model = model.replace(" ", "_") deployLocation = os.path.join(deployFolder,model+'_'+version) org_service_file = os.path.abspath(os.path.join(os.path.dirname(__file__),'model_service.py')) filename = model+'_service.py' modelservicefile = os.path.join(serviceFolder,filename) status = 'File Not Exist' if os.path.exists(modelservicefile): status = 'File Exist' r = ([line.split() for line in subprocess.check_output("tasklist").splitlines()]) for i in range(len(r)): if filename in r[i]: status = 'Running' if status == 'File Not Exist': shutil.copy(org_service_file,modelservicefile) with open(modelservicefile, 'r+') as file: content = file.read() file.seek(0, 0) line = 'from '+model+' import aion_performance' file.write(line+"\n") line = 'from '+model+' import aion_drift' file.write(line+ "\n") line = 'from '+model+' import featureslist' file.write(line+ "\n") line = 'from '+model+' import aion_prediction' file.write(line+ "\n") file.write(content) file.close() status = 'File Exist' if status == 'File Exist': status,pid,ipold,portold = check_service_running(jsonData['modelName'],jsonData['serviceFolder']) if status != 'Running': command = "python "+modelservicefile+' '+str(port)+' '+str(ip) os.system('start cmd /c "'+command+'"') time.sleep(2) status = 'Running' output_json = {'status':'SUCCESS','Msg':status} output_json = json.dumps(output_json) return(output_json) if __name__ == "__main__": aion_publish(sys.argv[1]) import json import logging import os import shutil import time import sys from sys import platform from distutils.util import strtobool from config_manager.pipeline_config import AionConfigManager from summarizer import Summarizer # Base class for EION configuration Manager which read the needed f params from eion.json, initialize the parameterlist, read the respective params, store in variables and return back to caller function or external modules. class AionTextManager: def __init__(self): self.log = logging.getLogger('eion') self.data = '' self.problemType = '' self.basic = [] self.advance=[] def readTextfile(self,dataPath): #dataPath=self.[baisc][] file = open(dataPath, "r") data = file.read() return data #print(data) def generateSummary(self,data,algo,stype): bert_model = Summarizer() if stype == "large": bert_summary = ''.join(bert_model(data, min_length=300)) return(bert_summary) elif stype == "medium": bert_summary = ''.join(bert_model(data, min_length=150)) return(bert_summary) elif stype == "small": bert_summary = ''.join(bert_model(data, min_length=60)) return(bert_summary) def aion_textsummary(arg): Obj = AionTextManager() configObj = AionConfigManager() readConfistatus,msg = configObj.readConfigurationFile(arg) dataPath = configObj.getTextlocation() text_data = Obj.readTextfile(dataPath) getAlgo, getMethod = configObj.getTextSummarize() summarize = Obj.generateSummary(text_data, getAlgo, getMethod) output = {'status':'Success','summary':summarize} output_json = json.dumps(output) return(output_json) if __name__ == "__main__": aion_textsummary(sys.argv[1]) import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__)))) #from BaseHTTPServer import BaseHTTPRequestHandler,HTTPServer from http.server import BaseHTTPRequestHandler,HTTPServer #from SocketServer import ThreadingMixIn from socketserver import ThreadingMixIn from functools import partial from http.server import SimpleHTTPRequestHandler, test import base64 from appbe.dataPath import DEPLOY_LOCATION ''' from augustus.core.ModelLoader import ModelLoader from augustus.strict import modelLoader ''' import pandas as pd import os,sys from os.path import expanduser import platform import numpy as np import configparser import threading import subprocess import argparse from functools import partial import re import cgi from datetime import datetime import json import sys from datetime import datetime user_records = {} class LocalModelData(object): models = {} class HTTPRequestHandler(BaseHTTPRequestHandler): def __init__(self, *args, **kwargs): username = kwargs.pop("username") password = kwargs.pop("password") self._auth = base64.b64encode(f"{username}:{password}".encode()).decode() super().__init__(*args) def do_HEAD(self): self.send_response(200) self.send_header("Content-type", "text/html") self.end_headers() def do_AUTHHEAD(self): self.send_response(401) self.send_header("WWW-Authenticate", 'Basic realm="Test"') self.send_header("Content-type", "text/html") self.end_headers() def do_POST(self): print("PYTHON ######## REQUEST ####### STARTED") if None != re.search('/AION/', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': if self.headers.get("Authorization") == None: self.do_AUTHHEAD() resp = "Authentication Failed: Auth Header Not Present" resp=resp.encode() self.wfile.write(resp) elif self.headers.get("Authorization") == "Basic " + self._auth: length = int(self.headers.get('content-length')) #data = cgi.parse_qs(self.rfile.read(length), keep_blank_values=1) data = self.rfile.read(length) #print(data) #keyList = list(data.keys()) #print(keyList[0]) model = self.path.split('/')[-2] operation = self.path.split('/')[-1] home = expanduser("~") #data = json.loads(data) dataStr = data model_path = os.path.join(DEPLOY_LOCATION,model) isdir = os.path.isdir(model_path) if isdir: if operation.lower() == 'predict': predict_path = os.path.join(model_path,'aion_predict.py') outputStr = subprocess.check_output([sys.executable,predict_path,dataStr]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() resp = outputStr elif operation.lower() == 'spredict': try: predict_path = os.path.join(model_path,'aion_spredict.py') print(predict_path) outputStr = subprocess.check_output([sys.executable,predict_path,dataStr]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() resp = outputStr except Exception as e: print(e) elif operation.lower() == 'features': predict_path = os.path.join(model_path,'featureslist.py') outputStr = subprocess.check_output([sys.executable,predict_path,dataStr]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() resp = outputStr elif operation.lower() == 'explain': predict_path = os.path.join(model_path,'explainable_ai.py') outputStr = subprocess.check_output([sys.executable,predict_path,'local',dataStr]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_ai_explanation:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() elif operation.lower() == 'monitoring': predict_path = os.path.join(model_path,'aion_ipdrift.py') outputStr = subprocess.check_output([sys.executable,predict_path,dataStr]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'drift:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() elif operation.lower() == 'performance': predict_path = os.path.join(model_path,'aion_opdrift.py') outputStr = subprocess.check_output([sys.executable,predict_path,dataStr]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'drift:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() elif operation.lower() == 'pattern_anomaly_predict': data = json.loads(data) anomaly = False remarks = '' clusterid = -1 configfilename = os.path.join(model_path,'datadetails.json') filename = os.path.join(model_path,'clickstream.json') clusterfilename = os.path.join(model_path,'stateClustering.csv') probfilename = os.path.join(model_path,'stateTransitionProbability.csv') dfclus = pd.read_csv(clusterfilename) dfprod = pd.read_csv(probfilename) f = open(configfilename, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) activity = configSettingsJson['activity'] sessionid = configSettingsJson['sessionid'] f = open(filename, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) groupswitching = configSettingsJson['groupswitching'] page_threshold = configSettingsJson['transitionprobability'] chain_count = configSettingsJson['transitionsequence'] chain_probability = configSettingsJson['sequencethreshold'] currentactivity = data[activity] if bool(user_records): sessionid = data[sessionid] if sessionid != user_records['SessionID']: user_records['SessionID'] = sessionid prevactivity = '' user_records['probarry'] = [] user_records['prevclusterid'] = -1 user_records['NoOfClusterHopping'] = 0 user_records['pageclicks'] = 1 else: prevactivity = user_records['Activity'] user_records['Activity'] = currentactivity pageswitch = True if prevactivity == currentactivity or prevactivity == '': probability = 0 pageswitch = False remarks = '' else: user_records['pageclicks'] += 1 df1 = dfprod[(dfprod['State'] == prevactivity) & (dfprod['NextState'] == currentactivity)] if df1.empty: remarks = 'Anomaly Detected - User in unusual state' anomaly = True clusterid = -1 probability = 0 user_records['probarry'].append(probability) n=int(chain_count) num_list = user_records['probarry'][-n:] avg = sum(num_list)/len(num_list) for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] else: probability = df1['Probability'].iloc[0] user_records['probarry'].append(probability) n=int(chain_count) num_list = user_records['probarry'][-n:] davg = sum(num_list)/len(num_list) for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] remarks = '' if user_records['prevclusterid'] != -1: if probability == 0 and user_records['prevclusterid'] != clusterid: user_records['NoOfClusterHopping'] = user_records['NoOfClusterHopping']+1 if user_records['pageclicks'] == 1: remarks = 'Anomaly Detected - Frequent Cluster Hopping' anomaly = True else: remarks = 'Cluster Hopping Detected' user_records['pageclicks'] = 0 if user_records['NoOfClusterHopping'] > int(groupswitching) and anomaly == False: remarks = 'Anomaly Detected - Multiple Cluster Hopping' anomaly = True elif probability == 0: remarks = 'Anomaly Detected - Unusual State Transition Detected' anomaly = True elif probability <= float(page_threshold): remarks = 'Anomaly Detected - In-frequent State Transition Detected' anomaly = True else: if pageswitch == True: if probability == 0: remarks = 'Anomaly Detected - Unusual State Transition Detected' anomaly = True elif probability <= float(page_threshold): remarks = 'Anomaly Detected - In-frequent State Transition Detected' anomaly = True else: remarks = '' if davg < float(chain_probability): if anomaly == False: remarks = 'Anomaly Detected - In-frequent Pattern Detected' anomaly = True else: user_records['SessionID'] = data[sessionid] user_records['Activity'] = data[activity] user_records['probability'] = 0 user_records['probarry'] = [] user_records['chainprobability'] = 0 user_records['prevclusterid'] = -1 user_records['NoOfClusterHopping'] = 0 user_records['pageclicks'] = 1 for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] user_records['prevclusterid'] = clusterid outputStr = '{"status":"SUCCESS","data":{"Anomaly":"'+str(anomaly)+'","Remarks":"'+str(remarks)+'"}}' elif operation.lower() == 'pattern_anomaly_settings': data = json.loads(data) groupswitching = data['groupswitching'] transitionprobability = data['transitionprobability'] transitionsequence = data['transitionsequence'] sequencethreshold = data['sequencethreshold'] filename = os.path.join(model_path,'clickstream.json') data = {} data['groupswitching'] = groupswitching data['transitionprobability'] = transitionprobability data['transitionsequence'] = transitionsequence data['sequencethreshold'] = sequencethreshold updatedConfig = json.dumps(data) with open(filename, "w") as fpWrite: fpWrite.write(updatedConfig) fpWrite.close() outputStr = '{"Status":"SUCCESS"}' else: outputStr = "{'Status':'Error','Msg':'Operation not supported'}" else: outputStr = "{'Status':'Error','Msg':'Model Not Present'}" resp = outputStr resp=resp+"\n" resp=resp.encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(resp) else: self.do_AUTHHEAD() self.wfile.write(self.headers.get("Authorization").encode()) resp = "Authentication Failed" resp=resp.encode() self.wfile.write(resp) else: print("python ==> else1") self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() print("PYTHON ######## REQUEST ####### ENDED") return def getModelFeatures(self,modelSignature): datajson = {'Body':'Gives the list of features'} home = expanduser("~") if platform.system() == 'Windows': predict_path = os.path.join(home,'AppData','Local','HCLT','AION','target',modelSignature,'featureslist.py') else: predict_path = os.path.join(home,'HCLT','AION','target',modelSignature,'featureslist.py') if(os.path.isfile(predict_path)): outputStr = subprocess.check_output([sys.executable,predict_path]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'features:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() displaymsg = outputStr #displaymsg = json.dumps(displaymsg) return(True,displaymsg) else: displaymsg = "{'status':'ERROR','msg':'Unable to fetch featuers'}" return(False,displaymsg) def getFeatures(self,modelSignature): datajson = {'Body':'Gives the list of features'} urltext = '/AION/UseCase_Version/features' if modelSignature != '': status,displaymsg = self.getModelFeatures(modelSignature) if status: urltext = '/AION/'+modelSignature+'/features' else: displaymsg = json.dumps(datajson) else: displaymsg = json.dumps(datajson) msg=""" URL:{url} RequestType: POST Content-Type=application/json Output: {displaymsg}. """.format(url=urltext,displaymsg=displaymsg) return(msg) def features_help(self,modelSignature): home = expanduser("~") if platform.system() == 'Windows': display_path = os.path.join(home,'AppData','Local','HCLT','AION','target',modelSignature,'display.json') else: display_path = os.path.join(home,'HCLT','AION','target',modelSignature,'display.json') #display_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),'target',model,'display.json') datajson = {'Body':'Data Should be in JSON Format'} if(os.path.isfile(display_path)): with open(display_path) as file: config = json.load(file) file.close() datajson={} for feature in config['numericalFeatures']: if feature != config['targetFeature']: datajson[feature] = 'Numeric Value' for feature in config['nonNumericFeatures']: if feature != config['targetFeature']: datajson[feature] = 'Category Value' for feature in config['textFeatures']: if feature != config['targetFeature']: datajson[feature] = 'Category Value' displaymsg = json.dumps(datajson) return(displaymsg) def predict_help(self,modelSignature): if modelSignature != '': displaymsg = self.features_help(modelSignature) urltext = '/AION/'+modelSignature+'/predict' else: datajson = {'Body':'Data Should be in JSON Format'} displaymsg = json.dumps(datajson) urltext = '/AION/UseCase_Version/predict' msg=""" URL:{url} RequestType: POST Content-Type=application/json Body: {displaymsg} Output: prediction,probability(if Applicable),remarks corresponding to each row. """.format(url=urltext,displaymsg=displaymsg) return(msg) def performance_help(self,modelSignature): if modelSignature != '': urltext = '/AION/'+modelSignature+'/performance' else: urltext = '/AION/UseCase_Version/performance' datajson = {"trainingDataLocation":"Reference Data File Path","currentDataLocation":"Latest Data File Path"} displaymsg = json.dumps(datajson) msg=""" URL:{url} RequestType: POST Content-Type=application/json Body: {displaymsg} Output: HTML File Path.""".format(url=urltext,displaymsg=displaymsg) return(msg) def monitoring_help(self,modelSignature): if modelSignature != '': urltext = '/AION/'+modelSignature+'/monitoring' else: urltext = '/AION/UseCase_Version/monitoring' datajson = {"trainingDataLocation":"Reference Data File Path","currentDataLocation":"Latest Data File Path"} displaymsg = json.dumps(datajson) msg=""" URL:{url} RequestType: POST Content-Type=application/json Body: {displaymsg} Output: Affected Columns. HTML File Path.""".format(url=urltext,displaymsg=displaymsg) return(msg) def explain_help(self,modelSignature): if modelSignature != '': displaymsg = self.features_help(modelSignature) urltext = '/AION/'+modelSignature+'/explain' else: datajson = {'Body':'Data Should be in JSON Format'} displaymsg = json.dumps(datajson) urltext = '/AION/UseCase_Version/explain' msg=""" URL:{url} RequestType: POST Content-Type=application/json Body: {displaymsg} Output: anchor (Local Explanation),prediction,forceplot,multidecisionplot.""".format(url=urltext,displaymsg=displaymsg) return(msg) def help_text(self,modelSignature): predict_help = self.predict_help(modelSignature) explain_help = self.explain_help(modelSignature) features_help = self.getFeatures(modelSignature) monitoring_help = self.monitoring_help(modelSignature) performance_help = self.performance_help(modelSignature) msg=""" Following URL: Prediction {predict_help} Local Explaination {explain_help} Features {features_help} Monitoring {monitoring_help} Performance {performance_help} """.format(predict_help=predict_help,explain_help=explain_help,features_help=features_help,monitoring_help=monitoring_help,performance_help=performance_help) return msg def do_GET(self): print("PYTHON ######## REQUEST ####### STARTED") if None != re.search('/AION/', self.path): self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() helplist = self.path.split('/')[-1] print(helplist) if helplist.lower() == 'help': model = self.path.split('/')[-2] if model.lower() == 'aion': model ='' msg = self.help_text(model) elif helplist.lower() == 'predict': model = self.path.split('/')[-2] if model.lower() == 'aion': model ='' msg = self.predict_help(model) elif helplist.lower() == 'explain': model = self.path.split('/')[-2] if model.lower() == 'aion': model ='' msg = self.explain_help(model) elif helplist.lower() == 'monitoring': model = self.path.split('/')[-2] if model.lower() == 'aion': model ='' msg = self.monitoring_help(model) elif helplist.lower() == 'performance': model = self.path.split('/')[-2] if model.lower() == 'aion': model ='' msg = self.performance_help(model) elif helplist.lower() == 'features': model = self.path.split('/')[-2] if model.lower() == 'aion': model ='' status,msg = self.getModelFeatures(model) else: model = self.path.split('/')[-2] if model.lower() == 'aion': model =helplist msg = self.help_text(model) self.wfile.write(msg.encode()) else: self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() return class ThreadedHTTPServer(ThreadingMixIn, HTTPServer): allow_reuse_address = True def shutdown(self): self.socket.close() HTTPServer.shutdown(self) class SimpleHttpServer(): def __init__(self, ip, port,username,password): handler_class = partial(HTTPRequestHandler,username=username,password=password,) self.server = ThreadedHTTPServer((ip,port), handler_class) def start(self): self.server_thread = threading.Thread(target=self.server.serve_forever) self.server_thread.daemon = True self.server_thread.start() def waitForThread(self): self.server_thread.join() def stop(self): self.server.shutdown() self.waitForThread() def start_server(ip,port,username,password): server = SimpleHttpServer(ip,int(port),username,password) print('HTTP Server Running...........') server.start() server.waitForThread() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import sys import json import datetime, time, timeit import logging logging.getLogger('tensorflow').disabled = True import shutil import warnings from config_manager.online_pipeline_config import OTAionConfigManager from records import pushrecords import logging import mlflow from pathlib import Path from pytz import timezone def pushRecordForOnlineTraining(): try: from appbe.pages import getversion status,msg = pushrecords.enterRecord(AION_VERSION) except Exception as e: print("Exception", e) status = False msg = str(e) return status,msg def mlflowSetPath(path,experimentname): import mlflow url = "file:" + str(Path(path).parent.parent) + "/mlruns" mlflow.set_tracking_uri(url) mlflow.set_experiment(str(experimentname)) class server(): def __init__(self): self.response = None self.dfNumCols=0 self.dfNumRows=0 self.features=[] self.mFeatures=[] self.emptyFeatures=[] self.vectorizerFeatures=[] self.wordToNumericFeatures=[] self.profilerAction = [] self.targetType = '' self.matrix1='{' self.matrix2='{' self.matrix='{' self.trainmatrix='{' self.numericalFeatures=[] self.nonNumericFeatures=[] self.similarGroups=[] self.method = 'NA' self.pandasNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] self.modelSelTopFeatures=[] self.topFeatures=[] self.allFeatures=[] def startScriptExecution(self, config_obj): rowfilterexpression = '' grouperbyjson = '' model_tried='' learner_type = '' topics = {} numericContinuousFeatures='' discreteFeatures='' threshold=-1 targetColumn = '' categoricalFeatures='' dataFolderLocation = '' original_data_file = '' profiled_data_file = '' trained_data_file = '' predicted_data_file='' featureReduction = 'False' reduction_data_file='' params={} score = 0 labelMaps={} featureDataShape=[] self.riverModels = [] self.riverAlgoNames = ['Online Logistic Regression', 'Online Softmax Regression', 'Online Decision Tree Classifier', 'Online KNN Classifier', 'Online Linear Regression', 'Online Bayesian Linear Regression', 'Online Decision Tree Regressor','Online KNN Regressor'] #ConfigSettings iterName,iterVersion,dataLocation,deployLocation,delimiter,textqualifier = config_obj.getAIONLocationSettings() scoreParam = config_obj.getScoringCreteria() datetimeFeature,indexFeature,modelFeatures=config_obj.getFeatures() iterName = iterName.replace(" ", "_") deployLocation,dataFolderLocation,original_data_file,profiled_data_file,trained_data_file,predicted_data_file,logFileName,outputjsonFile = config_obj.createDeploymentFolders(deployLocation,iterName,iterVersion) #Mlflow mlflowSetPath(deployLocation,iterName+'_'+iterVersion) #Logger filehandler = logging.FileHandler(logFileName, 'w','utf-8') formatter = logging.Formatter('%(message)s') filehandler.setFormatter(formatter) log = logging.getLogger('eion') log.propagate = False for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): log.removeHandler(hdlr) log.addHandler(filehandler) log.setLevel(logging.INFO) log.info('************* Version - v2.2.5 *************** \n') msg = '-------> Execution Start Time: '+ datetime.datetime.now(timezone("Asia/Kolkata")).strftime('%Y-%m-%d %H:%M:%S' + ' IST') log.info(msg) startTime = timeit.default_timer() try: output = {'bestModel': '', 'bestScore': 0, 'bestParams': {}} #ConfigSetting problemType,targetFeature,profilerStatus,selectorStatus,learnerStatus,visualizationstatus,deployStatus = config_obj.getModulesDetails() selectorStatus = False if(problemType.lower() in ['classification','regression']): if(targetFeature == ''): output = {"status":"FAIL","message":"Target Feature is Must for Classification and Regression Problem Type"} return output #DataReading from transformations.dataReader import dataReader objData = dataReader() if os.path.isfile(dataLocation): dataFrame = objData.csvTodf(dataLocation,delimiter,textqualifier) dataFrame.rename(columns=lambda x:x.strip(), inplace=True) #FilterDataframe filter = config_obj.getfilter() if filter != 'NA': dataFrame,rowfilterexpression = objData.rowsfilter(filter,dataFrame) #GroupDataframe timegrouper = config_obj.gettimegrouper() grouping = config_obj.getgrouper() if grouping != 'NA': dataFrame,grouperbyjson = objData.grouping(grouping,dataFrame) elif timegrouper != 'NA': dataFrame,grouperbyjson = objData.timeGrouping(timegrouper,dataFrame) #KeepOnlyModelFtrs dataFrame = objData.removeFeatures(dataFrame,datetimeFeature,indexFeature,modelFeatures,targetFeature) log.info('\n-------> First Ten Rows of Input Data: ') log.info(dataFrame.head(10)) self.dfNumRows=dataFrame.shape[0] self.dfNumCols=dataFrame.shape[1] dataLoadTime = timeit.default_timer() - startTime log.info('-------> COMPUTING: Total dataLoadTime time(sec) :'+str(dataLoadTime)) if profilerStatus: log.info('\n================== Data Profiler has started ==================') log.info('Status:-|... AION feature transformation started') dp_mlstart = time.time() profilerJson = config_obj.getEionProfilerConfigurarion() log.info('-------> Input dataFrame(5 Rows): ') log.info(dataFrame.head(5)) log.info('-------> DataFrame Shape (Row,Columns): '+str(dataFrame.shape)) from incremental.incProfiler import incProfiler incProfilerObj = incProfiler() dataFrame,targetColumn,self.mFeatures,self.numericalFeatures,self.nonNumericFeatures,labelMaps,self.configDict,self.textFeatures,self.emptyFeatures,self.wordToNumericFeatures = incProfilerObj.startIncProfiler(dataFrame,profilerJson,targetFeature,deployLocation,problemType) self.features = self.configDict['allFtrs'] log.info('-------> Data Frame Post Data Profiling(5 Rows): ') log.info(dataFrame.head(5)) log.info('Status:-|... AION feature transformation completed') dp_mlexecutionTime=time.time() - dp_mlstart log.info('-------> COMPUTING: Total Data Profiling Execution Time '+str(dp_mlexecutionTime)) log.info('================== Data Profiling completed ==================\n') dataFrame.to_csv(profiled_data_file,index=False) selectorStatus = False if learnerStatus: log.info('Status:-|... AION Learner data preparation started') ldp_mlstart = time.time() testPercentage = config_obj.getAIONTestTrainPercentage() balancingMethod = config_obj.getAIONDataBalancingMethod() from learner.machinelearning import machinelearning mlobj = machinelearning() modelType = problemType.lower() targetColumn = targetFeature if modelType == "na": if self.targetType == 'categorical': modelType = 'classification' elif self.targetType == 'continuous': modelType = 'regression' datacolumns=list(dataFrame.columns) if targetColumn in datacolumns: datacolumns.remove(targetColumn) features =datacolumns featureData = dataFrame[features] if targetColumn != '': targetData = dataFrame[targetColumn] xtrain,ytrain,xtest,ytest = mlobj.split_into_train_test_data(featureData,targetData,testPercentage,modelType) categoryCountList = [] if modelType == 'classification': if(mlobj.checkForClassBalancing(ytrain) >= 1): xtrain,ytrain = mlobj.ExecuteClassBalancing(xtrain,ytrain,balancingMethod) valueCount=targetData.value_counts() categoryCountList=valueCount.tolist() ldp_mlexecutionTime=time.time() - ldp_mlstart log.info('-------> COMPUTING: Total Learner data preparation Execution Time '+str(ldp_mlexecutionTime)) log.info('Status:-|... AION Learner data preparation completed') if learnerStatus: log.info('\n================== ML Started ==================') log.info('Status:-|... AION training started') log.info('-------> Memory Usage by DataFrame During Learner Status '+str(dataFrame.memory_usage(deep=True).sum())) mlstart = time.time() log.info('-------> Target Problem Type:'+ self.targetType) learner_type = 'ML' learnerJson = config_obj.getEionLearnerConfiguration() log.info('-------> Target Model Type:'+ modelType) modelParams,modelList = config_obj.getEionLearnerModelParams(modelType) if(modelType == 'regression'): allowedmatrix = ['mse','r2','rmse','mae'] if(scoreParam.lower() not in allowedmatrix): scoreParam = 'mse' if(modelType == 'classification'): allowedmatrix = ['accuracy','recall','f1_score','precision','roc_auc'] if(scoreParam.lower() not in allowedmatrix): scoreParam = 'accuracy' scoreParam = scoreParam.lower() from incremental.incMachineLearning import incMachineLearning incMlObj = incMachineLearning(mlobj) self.configDict['riverModel'] = False status,model_type,model,saved_model,matrix,trainmatrix,featureDataShape,model_tried,score,filename,self.features,threshold,pscore,rscore,self.method,loaded_model,xtrain1,ytrain1,xtest1,ytest1,topics,params=incMlObj.startLearning(learnerJson,modelType,modelParams,modelList,scoreParam,self.features,targetColumn,dataFrame,xtrain,ytrain,xtest,ytest,categoryCountList,self.targetType,deployLocation,iterName,iterVersion,trained_data_file,predicted_data_file,labelMaps) if model in self.riverAlgoNames: self.configDict['riverModel'] = True if(self.matrix != '{'): self.matrix += ',' if(self.trainmatrix != '{'): self.trainmatrix += ',' self.trainmatrix += trainmatrix self.matrix += matrix mlexecutionTime=time.time() - mlstart log.info('-------> Total ML Execution Time '+str(mlexecutionTime)) log.info('Status:-|... AION training completed') log.info('================== ML Completed ==================\n') if visualizationstatus: visualizationJson = config_obj.getEionVisualizationConfiguration() log.info('Status:-|... AION Visualizer started') visualizer_mlstart = time.time() from visualization.visualization import Visualization visualizationObj = Visualization(iterName,iterVersion,dataFrame,visualizationJson,datetimeFeature,deployLocation,dataFolderLocation,numericContinuousFeatures,discreteFeatures,categoricalFeatures,self.features,targetFeature,model_type,original_data_file,profiled_data_file,trained_data_file,predicted_data_file,labelMaps,self.vectorizerFeatures,self.textFeatures,self.numericalFeatures,self.nonNumericFeatures,self.emptyFeatures,self.dfNumRows,self.dfNumCols,saved_model,scoreParam,learner_type,model,featureReduction,reduction_data_file) visualizationObj.visualizationrecommandsystem() visualizer_mlexecutionTime=time.time() - visualizer_mlstart log.info('-------> COMPUTING: Total Visualizer Execution Time '+str(visualizer_mlexecutionTime)) log.info('Status:-|... AION Visualizer completed') try: os.remove(os.path.join(deployLocation,'aion_xai.py')) except: pass if deployStatus: if str(model) != 'None': log.info('\n================== Deployment Started ==================') log.info('Status:-|... AION Deployer started') deployPath = deployLocation deployer_mlstart = time.time() src = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','utilities','useCaseFiles') shutil.copy2(os.path.join(src,'incBatchLearning.py'),deployPath) os.rename(os.path.join(deployPath,'incBatchLearning.py'),os.path.join(deployPath,'aion_inclearning.py')) shutil.copy2(os.path.join(src,'incBatchPrediction.py'),deployPath) os.rename(os.path.join(deployPath,'incBatchPrediction.py'),os.path.join(deployPath,'aion_predict.py')) self.configDict['modelName'] = str(model) self.configDict['modelParams'] = params self.configDict['problemType'] = problemType.lower() self.configDict['score'] = score self.configDict['metricList'] = [] self.configDict['metricList'].append(score) self.configDict['trainRowsList'] = [] self.configDict['trainRowsList'].append(featureDataShape[0]) self.configDict['scoreParam'] = scoreParam self.configDict['partialFit'] = 0 with open(os.path.join(deployLocation,'production', 'Config.json'), 'w', encoding='utf8') as f: json.dump(self.configDict, f, ensure_ascii=False) deployer_mlexecutionTime=time.time() - deployer_mlstart log.info('-------> COMPUTING: Total Deployer Execution Time '+str(deployer_mlexecutionTime)) log.info('Status:-|... AION Batch Deployment completed') log.info('================== Deployment Completed ==================') # self.features = profilerObj.set_features(self.features,self.textFeatures,self.vectorizerFeatures) self.matrix += '}' self.trainmatrix += '}' matrix = eval(self.matrix) trainmatrix = eval(self.trainmatrix) model_tried = eval('['+model_tried+']') try: json.dumps(params) output_json = {"status":"SUCCESS","data":{"ModelType":modelType,"deployLocation":deployPath,"BestModel":model,"BestScore":str(score),"ScoreType":str(scoreParam).upper(),"matrix":matrix,"trainmatrix":trainmatrix,"featuresused":str(self.features),"targetFeature":str(targetColumn),"params":params,"EvaluatedModels":model_tried,"LogFile":logFileName}} except: output_json = {"status":"SUCCESS","data":{"ModelType":modelType,"deployLocation":deployPath,"BestModel":model,"BestScore":str(score),"ScoreType":str(scoreParam).upper(),"matrix":matrix,"trainmatrix":trainmatrix,"featuresused":str(self.features),"targetFeature":str(targetColumn),"params":"","EvaluatedModels":model_tried,"LogFile":logFileName}} print(output_json) if bool(topics) == True: output_json['topics'] = topics with open(outputjsonFile, 'w') as f: json.dump(output_json, f) output_json = json.dumps(output_json) log.info('\n------------- Summary ------------') log.info('------->No of rows & columns in data:('+str(self.dfNumRows)+','+str(self.dfNumCols)+')') log.info('------->No of missing Features :'+str(len(self.mFeatures))) log.info('------->Missing Features:'+str(self.mFeatures)) log.info('------->Text Features:'+str(self.textFeatures)) log.info('------->No of Nonnumeric Features :'+str(len(self.nonNumericFeatures))) log.info('------->Non-Numeric Features :' +str(self.nonNumericFeatures)) if threshold == -1: log.info('------->Threshold: NA') else: log.info('------->Threshold: '+str(threshold)) log.info('------->Label Maps of Target Feature for classification :'+str(labelMaps)) if((learner_type != 'TS') & (learner_type != 'AR')): log.info('------->No of columns and rows used for Modeling :'+str(featureDataShape)) log.info('------->Features Used for Modeling:'+str(self.features)) log.info('------->Target Feature: '+str(targetColumn)) log.info('------->Best Model Score :'+str(score)) log.info('------->Best Parameters:'+str(params)) log.info('------->Type of Model :'+str(modelType)) log.info('------->Best Model :'+str(model)) log.info('------------- Summary ------------\n') except Exception as inst: log.info('server code execution failed !....'+str(inst)) output_json = {"status":"FAIL","message":str(inst).strip('"')} output_json = json.dumps(output_json) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) executionTime = timeit.default_timer() - startTime log.info('\nTotal execution time(sec) :'+str(executionTime)) log.info('\n------------- Output JSON ------------') log.info('-------> Output :'+str(output_json)) log.info('------------- Output JSON ------------\n') for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): hdlr.close() log.removeHandler(hdlr) return output_json def aion_ot_train_model(arg): warnings.filterwarnings('ignore') try: valid, msg = pushRecordForOnlineTraining() if valid: serverObj = server() configObj = OTAionConfigManager() jsonPath = arg readConfistatus,msg = configObj.readConfigurationFile(jsonPath) if(readConfistatus == False): output = {"status":"FAIL","message":str(msg).strip('"')} output = json.dumps(output) print("\n") print("aion_learner_status:",output) print("\n") return output output = serverObj.startScriptExecution(configObj) else: output = {"status":"LicenseVerificationFailed","message":str(msg).strip('"')} output = json.dumps(output) print("\n") print("aion_learner_status:",output) print("\n") return output except Exception as inst: output = {"status":"FAIL","message":str(inst).strip('"')} output = json.dumps(output) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) print("\n") print("aion_learner_status:",output) print("\n") return output if __name__ == "__main__": aion_ot_train_model(sys.argv[1]) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import os from pathlib import Path os.chdir(Path(__file__).parent) import json import shutil from mlac.timeseries import app as ts_app from mlac.ml import app as ml_app import traceback def create_test_file(config): code_file = 'aionCode.py' text = """ from pathlib import Path import subprocess import sys import json import argparse def run_pipeline(data_path): print('Data Location:', data_path) cwd = Path(__file__).parent monitor_file = str(cwd/'ModelMonitoring'/'{code_file}') load_file = str(cwd/'DataIngestion'/'{code_file}') transformer_file = str(cwd/'DataTransformation'/'{code_file}') selector_file = str(cwd/'FeatureEngineering'/'{code_file}') train_folder = cwd register_file = str(cwd/'ModelRegistry'/'{code_file}') deploy_file = str(cwd/'ModelServing'/'{code_file}') print('Running modelMonitoring') cmd = [sys.executable, monitor_file, '-i', data_path] result = subprocess.check_output(cmd) result = result.decode('utf-8') print(result) result = json.loads(result[result.find('{search}'):]) if result['Status'] == 'Failure': exit() print('Running dataIngestion') cmd = [sys.executable, load_file] result = subprocess.check_output(cmd) result = result.decode('utf-8') print(result) result = json.loads(result[result.find('{search}'):]) if result['Status'] == 'Failure': exit() print('Running DataTransformation') cmd = [sys.executable, transformer_file] result = subprocess.check_output(cmd) result = result.decode('utf-8') print(result) result = json.loads(result[result.find('{search}'):]) if result['Status'] == 'Failure': exit() print('Running FeatureEngineering') cmd = [sys.executable, selector_file] result = subprocess.check_output(cmd) result = result.decode('utf-8') print(result) result = json.loads(result[result.find('{search}'):]) if result['Status'] == 'Failure': exit() train_models = [f for f in train_folder.iterdir() if 'ModelTraining' in f.name] for model in train_models: print('Running',model.name) cmd = [sys.executable, str(model/'{code_file}')] train_result = subprocess.check_output(cmd) train_result = train_result.decode('utf-8') print(train_result) print('Running ModelRegistry') cmd = [sys.executable, register_file] result = subprocess.check_output(cmd) result = result.decode('utf-8') print(result) result = json.loads(result[result.find('{search}'):]) if result['Status'] == 'Failure': exit() print('Running ModelServing') cmd = [sys.executable, deploy_file] result = subprocess.check_output(cmd) result = result.decode('utf-8') print(result) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-i', '--inputPath', help='path of the input data') args = parser.parse_args() if args.inputPath: filename = args.inputPath else: filename = r"{filename}" try: print(run_pipeline(filename)) except Exception as e: print(e) """.format(filename=config['dataLocation'],search='{"Status":',code_file=code_file) deploy_path = Path(config["deploy_path"])/'MLaC' deploy_path.mkdir(parents=True, exist_ok=True) py_file = deploy_path/"run_pipeline.py" with open(py_file, "w") as f: f.write(text) def is_module_in_req_file(mod, folder): status = False if (Path(folder)/'requirements.txt').is_file(): with open(folder/'requirements.txt', 'r') as f: status = mod in f.read() return status def copy_local_modules(config): deploy_path = Path(config["deploy_path"]) local_modules_location = config.get("local_modules_location", None) if local_modules_location: folder_loc = local_modules_location else: folder_loc = Path(__file__).parent/'local_modules' if not folder_loc.exists(): folder_loc = None if folder_loc: file = folder_loc/'config.json' if file.exists(): with open(file, 'r') as f: data = json.load(f) for key, values in data.items(): local_module = folder_loc/key if local_module.exists(): for folder in values: target_folder = Path(deploy_path)/'MLaC'/folder if target_folder.is_dir(): if is_module_in_req_file(key, target_folder): shutil.copy(local_module, target_folder) def validate(config): error = '' if 'error' in config.keys(): error = config['error'] return error def generate_mlac_code(config): with open(config, 'r') as f: config = json.load(f) error = validate(config) if error: raise ValueError(error) if config['problem_type'] in ['classification','regression']: return generate_mlac_ML_code(config) elif config['problem_type'].lower() == 'timeseriesforecasting': #task 11997 return generate_mlac_TS_code(config) def generate_mlac_ML_code(config): try: ml_app.run_loader(config) ml_app.run_transformer(config) ml_app.run_selector(config) ml_app.run_trainer(config) ml_app.run_register(config) ml_app.run_deploy(config) ml_app.run_drift_analysis(config) copy_local_modules(config) create_test_file(config) status = {'Status':'SUCCESS','MLaC_Location':str(Path(config["deploy_path"])/'MLaC')} except Exception as Inst: status = {'Status':'Failure','msg':str(Inst)} traceback.print_exc() status = json.dumps(status) return(status) def generate_mlac_TS_code(config): try: ts_app.run_loader(config) ts_app.run_transformer(config) ts_app.run_selector(config) ts_app.run_trainer(config) ts_app.run_register(config) ts_app.run_deploy(config) ts_app.run_drift_analysis(config) create_test_file(config) status = {'Status':'SUCCESS','MLaC_Location':str(Path(config["deploy_path"])/'MLaC')} except Exception as Inst: status = {'Status':'Failure','msg':str(Inst)} traceback.print_exc() status = json.dumps(status) return(status) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import logging logging.getLogger('tensorflow').disabled = True #from autogluon.tabular import TabularDataset, TabularPredictor #from autogluon.core.utils.utils import setup_outputdir #from autogluon.core.utils.loaders import load_pkl #from autogluon.core.utils.savers import save_pkl import datetime, time, timeit from datetime import datetime as dt import os.path import json import io import shutil import sys #from Gluon_MultilabelPredictor import MultilabelPredictor class MultilabelPredictor(): """ Tabular Predictor for predicting multiple columns in table. Creates multiple TabularPredictor objects which you can also use individually. You can access the TabularPredictor for a particular label via: `multilabel_predictor.get_predictor(label_i)` Parameters ---------- labels : List[str] The ith element of this list is the column (i.e. `label`) predicted by the ith TabularPredictor stored in this object. path : str Path to directory where models and intermediate outputs should be saved. If unspecified, a time-stamped folder called "AutogluonModels/ag-[TIMESTAMP]" will be created in the working directory to store all models. Note: To call `fit()` twice and save all results of each fit, you must specify different `path` locations or don't specify `path` at all. Otherwise files from first `fit()` will be overwritten by second `fit()`. Caution: when predicting many labels, this directory may grow large as it needs to store many TabularPredictors. problem_types : List[str] The ith element is the `problem_type` for the ith TabularPredictor stored in this object. eval_metrics : List[str] The ith element is the `eval_metric` for the ith TabularPredictor stored in this object. consider_labels_correlation : bool Whether the predictions of multiple labels should account for label correlations or predict each label independently of the others. If True, the ordering of `labels` may affect resulting accuracy as each label is predicted conditional on the previous labels appearing earlier in this list (i.e. in an auto-regressive fashion). Set to False if during inference you may want to individually use just the ith TabularPredictor without predicting all the other labels. kwargs : Arguments passed into the initialization of each TabularPredictor. """ multi_predictor_file = 'multilabel_predictor.pkl' def __init__(self, labels, path, problem_types=None, eval_metrics=None, consider_labels_correlation=True, **kwargs): if len(labels) < 2: raise ValueError("MultilabelPredictor is only intended for predicting MULTIPLE labels (columns), use TabularPredictor for predicting one label (column).") self.path = setup_outputdir(path, warn_if_exist=False) self.labels = labels #print(self.labels) self.consider_labels_correlation = consider_labels_correlation self.predictors = {} # key = label, value = TabularPredictor or str path to the TabularPredictor for this label if eval_metrics is None: self.eval_metrics = {} else: self.eval_metrics = {labels[i] : eval_metrics[i] for i in range(len(labels))} problem_type = None eval_metric = None for i in range(len(labels)): label = labels[i] path_i = self.path + "Predictor_" + label if problem_types is not None: problem_type = problem_types[i] if eval_metrics is not None: eval_metric = self.eval_metrics[i] self.predictors[label] = TabularPredictor(label=label, problem_type=problem_type, eval_metric=eval_metric, path=path_i, **kwargs) def fit(self, train_data, tuning_data=None, **kwargs): """ Fits a separate TabularPredictor to predict each of the labels. Parameters ---------- train_data, tuning_data : str or autogluon.tabular.TabularDataset or pd.DataFrame See documentation for `TabularPredictor.fit()`. kwargs : Arguments passed into the `fit()` call for each TabularPredictor. """ if isinstance(train_data, str): train_data = TabularDataset(train_data) if tuning_data is not None and isinstance(tuning_data, str): tuning_data = TabularDataset(tuning_data) train_data_og = train_data.copy() if tuning_data is not None: tuning_data_og = tuning_data.copy() save_metrics = len(self.eval_metrics) == 0 for i in range(len(self.labels)): label = self.labels[i] predictor = self.get_predictor(label) if not self.consider_labels_correlation: labels_to_drop = [l for l in self.labels if l!=label] else: labels_to_drop = [self.labels[j] for j in range(i+1,len(self.labels))] train_data = train_data_og.drop(labels_to_drop, axis=1) if tuning_data is not None: tuning_data = tuning_data_og.drop(labels_to_drop, axis=1) print(f"Fitting TabularPredictor for label: {label} ...") predictor.fit(train_data=train_data, tuning_data=tuning_data, **kwargs) self.predictors[label] = predictor.path if save_metrics: self.eval_metrics[label] = predictor.eval_metric self.save() def eval_metrics(self): return(self.eval_metrics) def predict(self, data, **kwargs): """ Returns DataFrame with label columns containing predictions for each label. Parameters ---------- data : str or autogluon.tabular.TabularDataset or pd.DataFrame Data to make predictions for. If label columns are present in this data, they will be ignored. See documentation for `TabularPredictor.predict()`. kwargs : Arguments passed into the predict() call for each TabularPredictor. """ return self._predict(data, as_proba=False, **kwargs) def predict_proba(self, data, **kwargs): """ Returns dict where each key is a label and the corresponding value is the `predict_proba()` output for just that label. Parameters ---------- data : str or autogluon.tabular.TabularDataset or pd.DataFrame Data to make predictions for. See documentation for `TabularPredictor.predict()` and `TabularPredictor.predict_proba()`. kwargs : Arguments passed into the `predict_proba()` call for each TabularPredictor (also passed into a `predict()` call). """ return self._predict(data, as_proba=True, **kwargs) def evaluate(self, data, **kwargs): """ Returns dict where each key is a label and the corresponding value is the `evaluate()` output for just that label. Parameters ---------- data : str or autogluon.tabular.TabularDataset or pd.DataFrame Data to evalate predictions of all labels for, must contain all labels as columns. See documentation for `TabularPredictor.evaluate()`. kwargs : Arguments passed into the `evaluate()` call for each TabularPredictor (also passed into the `predict()` call). """ data = self._get_data(data) eval_dict = {} for label in self.labels: print(f"Evaluating TabularPredictor for label: {label} ...") predictor = self.get_predictor(label) eval_dict[label] = predictor.evaluate(data, **kwargs) if self.consider_labels_correlation: data[label] = predictor.predict(data, **kwargs) return eval_dict def save(self): """ Save MultilabelPredictor to disk. """ for label in self.labels: if not isinstance(self.predictors[label], str): self.predictors[label] = self.predictors[label].path save_pkl.save(path=self.path+self.multi_predictor_file, object=self) print(f"MultilabelPredictor saved to disk. Load with: MultilabelPredictor.load('{self.path}')") @classmethod def load(cls, path): """ Load MultilabelPredictor from disk `path` previously specified when creating this MultilabelPredictor. """ path = os.path.expanduser(path) if path[-1] != os.path.sep: path = path + os.path.sep return load_pkl.load(path=path+cls.multi_predictor_file) def get_predictor(self, label): """ Returns TabularPredictor which is used to predict this label. """ predictor = self.predictors[label] if isinstance(predictor, str): return TabularPredictor.load(path=predictor) return predictor def _get_data(self, data): if isinstance(data, str): return TabularDataset(data) return data.copy() def _predict(self, data, as_proba=False, **kwargs): data = self._get_data(data) if as_proba: predproba_dict = {} for label in self.labels: print(f"Predicting with TabularPredictor for label: {label} ...") predictor = self.get_predictor(label) if as_proba: predproba_dict[label] = predictor.predict_proba(data, as_multiclass=True, **kwargs) data[label] = predictor.predict(data, **kwargs) if not as_proba: return data[self.labels] else: return predproba_dict def aion_train_gluon(arg): configFile = arg with open(configFile, 'rb') as cfile: data = json.load(cfile) cfile.close() rootElement = data['basic'] modelname = rootElement['modelName'] version = rootElement['modelVersion'] dataLocation = rootElement['dataLocation'] deployFolder = rootElement['deployLocation'] analysisType = rootElement['analysisType'] testPercentage = data['advance']['testPercentage'] deployLocation = os.path.join(deployFolder,modelname+'_'+version) try: os.makedirs(deployLocation) except OSError as e: shutil.rmtree(deployLocation) os.makedirs(deployLocation) logLocation = os.path.join(deployLocation,'log') try: os.makedirs(logLocation) except OSError as e: pass etcLocation = os.path.join(deployLocation,'etc') try: os.makedirs(etcLocation) except OSError as e: pass logFileName=os.path.join(deployLocation,'log','model_training_logs.log') filehandler = logging.FileHandler(logFileName, 'w','utf-8') formatter = logging.Formatter('%(message)s') filehandler.setFormatter(formatter) log = logging.getLogger('eion') log.propagate = False for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): log.removeHandler(hdlr) log.addHandler(filehandler) log.setLevel(logging.INFO) log.info('************* Version - v1.2.0 *************** \n') msg = '-------> Execution Start Time: '+ dt.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S') log.info(msg) dataLabels = rootElement['targetFeature'].split(',') # Create and Write the config file used in Prediction # ----------------------------------------------------------------------------# tdata = TabularDataset(dataLocation) #train_data = tdata train_data = tdata.sample(frac = 0.8) test_data = tdata.drop(train_data.index) if rootElement['trainingFeatures'] != '': trainingFeatures = rootElement['trainingFeatures'].split(',') else: trainingFeatures = list(train_data.columns) features = trainingFeatures for x in dataLabels: if x not in features: features.append(x) indexFeature = rootElement['indexFeature'] if indexFeature != '': indexFeature = indexFeature.split(',') for x in indexFeature: if x in features: features.remove(x) dateTimeFeature = rootElement['dateTimeFeature'] if dateTimeFeature != '': dateTimeFeature = dateTimeFeature.split(',') for x in dateTimeFeature: if x in features: features.remove(x) train_data = train_data[features] test_data = test_data[features] configJsonFile = {"targetFeature":dataLabels,"features":",".join([feature for feature in features])} configJsonFilePath = os.path.join(deployLocation,'etc','predictionConfig.json') if len(dataLabels) == 1 and analysisType['multiLabelPrediction'] == "False": dataLabels = rootElement['targetFeature'] with io.open(configJsonFilePath, 'w', encoding='utf8') as outfile: str_ = json.dumps(configJsonFile, ensure_ascii=False) outfile.write(str_) # ----------------------------------------------------------------------------# if analysisType['multiLabelPrediction'] == "True": # Copy and Write the Predictiion script file into deployment location # ----------------------------------------------------------------------------# srcFile = os.path.join(os.path.dirname(__file__),'gluon','AION_Gluon_MultiLabelPrediction.py') dstFile = os.path.join(deployLocation,'aion_predict.py') shutil.copy(srcFile,dstFile) # ----------------------------------------------------------------------------# labels = dataLabels # which columns to predict based on the others #problem_types = dataProblem_types # type of each prediction problem save_path = os.path.join(deployLocation,'ModelPath') # specifies folder to store trained models time_limit = 5 # how many seconds to train the TabularPredictor for each label log.info('Status:-|... AION Gluon Start') try: if len(labels) < 2: log.info('Status:-|... AION Evaluation Error: Target should be multiple column') # ----------------------------------------------------------------------------# output = {'status':'FAIL','message':'Number of target variable should be 2 or more than 2'} else: multi_predictor = MultilabelPredictor(labels=labels, path=save_path) multi_predictor.fit(train_data, time_limit=time_limit) log.info('Status:-|... AION Gluon Stop') log.info('Status:-|... AION Evaluation Start') trainevaluations = multi_predictor.evaluate(train_data) testevaluations = multi_predictor.evaluate(test_data) best_model = {} for label in labels: predictor_class = multi_predictor.get_predictor(label) predictor_class.get_model_best() best_model[label] = predictor_class.get_model_best() log.info('Status:-|... AION Evaluation Stop') # ----------------------------------------------------------------------------# output = {'status':'SUCCESS','data':{'ModelType':'MultiLabelPrediction','EvaluatedModels':'','featuresused':'','BestModel':'AutoGluon','BestScore': '0', 'ScoreType': 'ACCURACY','deployLocation':deployLocation,'matrix':trainevaluations,'testmatrix':testevaluations,'BestModel':best_model, 'LogFile':logFileName}} except Exception as inst: log.info('Status:-|... AION Gluon Error') output = {"status":"FAIL","message":str(inst).strip('"')} if analysisType['multiModalLearning'] == "True": from autogluon.core.utils.utils import get_cpu_count, get_gpu_count from autogluon.text import TextPredictor # check the system and then set the equivelent flag # ----------------------------------------------------------------------------# os.environ["AUTOGLUON_TEXT_TRAIN_WITHOUT_GPU"] = "0" if get_gpu_count() == 0: os.environ["AUTOGLUON_TEXT_TRAIN_WITHOUT_GPU"] = "1" # ----------------------------------------------------------------------------# # Copy and Write the Predictiion script file into deployment location # ----------------------------------------------------------------------------# srcFile = os.path.join(os.path.dirname(__file__),'gluon','AION_Gluon_MultiModalPrediction.py') dstFile = os.path.join(deployLocation,'aion_predict.py') shutil.copy(srcFile,dstFile) time_limit = None # set to larger value in your applications save_path = os.path.join(deployLocation,'text_prediction') predictor = TextPredictor(label=dataLabels, path=save_path) predictor.fit(train_data, time_limit=time_limit) log.info('Status:-|... AION Gluon Stop') log.info('Status:-|... AION Evaluation Start') trainevaluations = predictor.evaluate(train_data) log.info('Status:-|... AION Evaluation Stop') # ----------------------------------------------------------------------------# output = {'status':'SUCCESS','data':{'ModelType':'MultiModelLearning','EvaluatedModels':'','featuresused':'','BestModel':'AutoGluon','BestScore': '0', 'ScoreType': 'SCORE','deployLocation':deployLocation,'matrix':trainevaluations,'LogFile':logFileName}} output = json.dumps(output) print("\n") print("aion_learner_status:",output) print("\n") log.info('\n------------- Output JSON ------------') log.info('-------> Output :'+str(output)) log.info('------------- Output JSON ------------\n') for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): hdlr.close() log.removeHandler(hdlr) return(output) if __name__ == "__main__": aion_train_gluon(sys.argv[1]) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import joblib import time from pandas import json_normalize import pandas as pd import numpy as np import argparse import json import os import pathlib from pathlib import Path from sagemaker.aionMlopsService import aionMlopsService import logging import os.path from os.path import expanduser import platform,sys from pathlib import Path from sklearn.model_selection import train_test_split def getAWSConfiguration(mlops_params,log): awsId=mlops_params['awsSagemaker']['awsID'] if ((not awsId) or (awsId is None)): awsId="" log.info('awsId error. ') awsAccesskeyid=mlops_params['awsSagemaker']['accesskeyID'] if ((not awsAccesskeyid) or (awsAccesskeyid is None)): awsAccesskeyid="" log.info('awsAccesskeyid error. ') awsSecretaccesskey=mlops_params['awsSagemaker']['secretAccesskey'] if ((not awsSecretaccesskey) or (awsSecretaccesskey is None)): awsSecretaccesskey="" log.info('awsSecretaccesskey error. ') awsSessiontoken=mlops_params['awsSagemaker']['sessionToken'] if ((not awsSessiontoken) or (awsSessiontoken is None)): awsSessiontoken="" log.info('awsSessiontoken error. ') awsRegion=mlops_params['awsSagemaker']['region'] if ((not awsRegion) or (awsRegion is None)): awsRegion="" log.info('awsRegion error. ') IAMSagemakerRoleArn=mlops_params['awsSagemaker']['IAMSagemakerRoleArn'] if ((not IAMSagemakerRoleArn) or (IAMSagemakerRoleArn is None)): IAMSagemakerRoleArn="" log.info('IAMSagemakerRoleArn error. ') return awsId,awsAccesskeyid,awsSecretaccesskey,awsSessiontoken,awsRegion,IAMSagemakerRoleArn def getMlflowParams(mlops_params,log): modelInput = mlops_params['modelInput'] data = mlops_params['data'] mlflowtosagemakerDeploy=mlops_params['sagemakerDeploy'] if ((not mlflowtosagemakerDeploy) or (mlflowtosagemakerDeploy is None)): mlflowtosagemakerDeploy="True" mlflowtosagemakerPushOnly=mlops_params['deployExistingModel']['status'] if ((not mlflowtosagemakerPushOnly) or (mlflowtosagemakerPushOnly is None)): mlflowtosagemakerPushOnly="False" mlflowtosagemakerPushImageName=mlops_params['deployExistingModel']['dockerImageName'] if ((not mlflowtosagemakerPushImageName) or (mlflowtosagemakerPushImageName is None)): mlflowtosagemakerPushImageName="mlops_image" mlflowtosagemakerdeployModeluri=mlops_params['deployExistingModel']['deployModeluri'] if ((not mlflowtosagemakerdeployModeluri) or (mlflowtosagemakerdeployModeluri is None)): mlflowtosagemakerdeployModeluri="None" log.info('mlflowtosagemakerdeployModeluri error. ') cloudInfrastructure = mlops_params['modelOutput']['cloudInfrastructure'] if ((not cloudInfrastructure) or (cloudInfrastructure is None)): cloudInfrastructure="Sagemaker" endpointName=mlops_params['endpointName'] if ((not endpointName) or (endpointName is None)): sagemakerAppName="aion-demo-app" log.info('endpointName not given, setting default one. ') experimentName=str(endpointName) mlflowContainerName=str(endpointName) return modelInput,data,mlflowtosagemakerDeploy,mlflowtosagemakerPushOnly,mlflowtosagemakerPushImageName,mlflowtosagemakerdeployModeluri,cloudInfrastructure,endpointName,experimentName,mlflowContainerName def getPredictionParams(mlops_params,log): predictStatus=mlops_params['prediction']['status'] if ((not predictStatus) or (predictStatus is None)): predictStatus="False" modelInput = mlops_params['modelInput'] data = mlops_params['data'] if (predictStatus == "True" or predictStatus.lower()== "true"): if ((not modelInput) or (modelInput is None)): log.info('prediction model input error.Please check given model file or its path for prediction ') if ((not data) or (data is None)): log.info('prediction data input error.Please check given data file or its path for prediction ') targetFeature=mlops_params['prediction']['target'] return predictStatus,targetFeature def sagemakerPrediction(mlopsobj,data,log): df = json_normalize(data) model=None predictionStatus=False try: endpointPrediction=mlopsobj.predict_sm_app_endpoint(df) if (endpointPrediction is None): log.info('Sagemaker endpoint application prediction Issue.') outputjson = {"status":"Error","msg":"Sagemaker endpoint application prediction Issue"} outputjson = json.dumps(outputjson) #print("predictions: "+str(outputjson)) predictionStatus=False else: log.info("sagemaker end point Prediction: \n"+str(endpointPrediction)) df['prediction'] = endpointPrediction outputjson = df.to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} outputjson = json.dumps(outputjson) #print("predictions: "+str(outputjson)) predictionStatus=True except Exception as e: #log.info("sagemaker end point Prediction error: \n") outputjson = {"status":"Error","msg":str(e)} outputjson=None predictionStatus=False return outputjson,predictionStatus ## Main aion sagemaker fn call def sagemaker_exec(mlops_params,log): #mlops_params = json.loads(config) mlops_params=mlops_params modelInput,data,mlflowtosagemakerDeploy,mlflowtosagemakerPushOnly,mlflowtosagemakerPushImageName,mlflowtosagemakerdeployModeluri,cloudInfrastructure,endpointName,experimentName,mlflowContainerName = getMlflowParams(mlops_params,log) mlflowModelname=None awsId,awsAccesskeyid,awsSecretaccesskey,awsSessiontoken,awsRegion,IAMSagemakerRoleArn = getAWSConfiguration(mlops_params,log) predictStatus,targetFeature = getPredictionParams(mlops_params,log) sagemakerDeployOption='create' deleteAwsecrRepository='False' sagemakerAppName=str(endpointName) ecrRepositoryName='aion-ecr-repo' #aws ecr model app_name should contain only [[a-zA-Z0-9-]], again rechecking here. import re if sagemakerAppName: pattern = re.compile("[A-Za-z0-9-]+") # if found match (entire string matches pattern) if pattern.fullmatch(sagemakerAppName) is not None: #print("Found match: ") pass else: log.info('wrong sagemaker Application Name, Nmae should contains only [A-Za-z0-9-] .') app_name = 'aion-demo-app' else: app_name = 'aion-demo-app' #Following 3 aws parameter values are now hard coded , because currently we are not using. If aion using the options, please make sure to get the values from GUI . sagemakerDeployOption="create" deleteAwsecrRepository="False" ecrRepositoryName="aion_test_repo" log.info('mlops parameter check done.') # predictionStatus=False deploystatus = 'SUCCESS' try: log.info('cloudInfrastructure: '+str(cloudInfrastructure)) if(cloudInfrastructure.lower() == "sagemaker"): ## sagemaker app prediction call if (predictStatus.lower() == "true"): # df = json_normalize(data) model=None mlopsobj = aionMlopsService(model,mlflowtosagemakerDeploy,mlflowtosagemakerPushOnly,mlflowtosagemakerPushImageName,mlflowtosagemakerdeployModeluri,experimentName,mlflowModelname,awsAccesskeyid,awsSecretaccesskey,awsSessiontoken,mlflowContainerName,awsRegion,awsId,IAMSagemakerRoleArn,sagemakerAppName,sagemakerDeployOption,deleteAwsecrRepository,ecrRepositoryName) outputjson,predictionStatus = sagemakerPrediction(mlopsobj,data,log) print("predictions: "+str(outputjson)) predictionStatus=predictionStatus return(outputjson) else: if Path(modelInput).is_file(): msg = '' model = joblib.load(modelInput) ProblemName = model.__class__.__name__ mlflowModelname=str(ProblemName) log.info('aion mlops Model name: '+str(mlflowModelname)) df=None mlopsobj = aionMlopsService(model,mlflowtosagemakerDeploy,mlflowtosagemakerPushOnly,mlflowtosagemakerPushImageName,mlflowtosagemakerdeployModeluri,experimentName,mlflowModelname,awsAccesskeyid,awsSecretaccesskey,awsSessiontoken,mlflowContainerName,awsRegion,awsId,IAMSagemakerRoleArn,sagemakerAppName,sagemakerDeployOption,deleteAwsecrRepository,ecrRepositoryName) mlflow2sm_status,localhost_container_status=mlopsobj.mlflow2sagemaker_deploy() log.info('mlflow2sm_status: '+str(mlflow2sm_status)) log.info('localhost_container_status: '+str(localhost_container_status)) # Checking deploy status if (mlflowtosagemakerPushOnly.lower() == "true" ): if (mlflow2sm_status.lower() == "success"): deploystatus = 'SUCCESS' msg = 'Endpoint succesfully deployed in sagemaker' log.info('Endpoint succesfully deployed in sagemaker (Push eisting model container).\n ') elif(mlflow2sm_status.lower() == "failed"): deploystatus = 'ERROR' msg = 'Endpoint failed to deploy in sagemaker' log.info('Endpoint failed to deploy in sagemaker. (Push eisting model container).\n ') else: pass elif(mlflowtosagemakerDeploy.lower() == "true"): if (mlflow2sm_status.lower() == "success"): deploystatus='SUCCESS' msg = 'Endpoint succesfully deployed in sagemaker' log.info('Endpoint succesfully deployed in sagemaker') elif(mlflow2sm_status.lower() == "failed"): deploystatus = 'ERROR' msg = 'Endpoint failed to deploy in sagemaker' log.info('Endpoint failed to deploy in sagemaker.\n ') elif (mlflow2sm_status.lower() == "Notdeployed"): deploystatus= 'ERROR' msg = 'Sagemaker compatible container created' log.info('sagemaker endpoint not deployed, check aws connection and credentials. \n') elif (mlflowtosagemakerDeploy.lower() == "false"): if(localhost_container_status.lower() == "success"): deploystatus = 'SUCCESS' msg = 'Localhost mlops docker created successfully' log.info('Localhost mlops docker created successfully. \n') elif(localhost_container_status.lower() == "failed"): deploystatus = 'ERROR' msg = 'Localhost mlops docker created failed' log.info('Localhost mlops docker creation failed. \n') elif (localhost_container_status.lower() == "Notdeployed"): deploystatus= 'ERROR' log.info('Localhost mlops docker not deployed, check local docker status. \n') else: pass else: pass else: deploystatus = 'ERROR' msg = 'Model Path not Found' print('Error: Model Path not Found') outputjson = {"status":str(deploystatus),"data":str(msg)} outputjson = json.dumps(outputjson) print("predictions: "+str(outputjson)) return(outputjson) except Exception as inst: outputjson = {"status":str(deploystatus),"data":str(msg)} outputjson = json.dumps(outputjson) print("predictions: "+str(outputjson)) return(outputjson) def aion_sagemaker(config): try: mlops_params = config print(mlops_params) from appbe.dataPath import LOG_LOCATION sagemakerLogLocation = LOG_LOCATION try: os.makedirs(sagemakerLogLocation) except OSError as e: if (os.path.exists(sagemakerLogLocation)): pass else: raise OSError('sagemakerLogLocation error.') filename_mlops = 'mlopslog_'+str(int(time.time())) filename_mlops=filename_mlops+'.log' filepath = os.path.join(sagemakerLogLocation, filename_mlops) logging.basicConfig(filename=filepath, format='%(message)s',filemode='w') log = logging.getLogger('aionMLOps') log.setLevel(logging.DEBUG) output = sagemaker_exec(mlops_params,log) return output except Exception as inst: print(inst) deploystatus = 'ERROR' output = {"status":str(deploystatus),"data":str(inst)} output = json.dumps(output) print("predictions: "+str(output)) return(output) #Sagemaker main fn call if __name__=='__main__': json_config = str(sys.argv[1]) output = aion_sagemaker(json.loads(json_config)) import json from pathlib import Path import shutil class mergeLogs(): def __init__(self, folders, dataLocation=None): self.folders = [Path(x) for x in folders] self.dataLocation = dataLocation self.baseFolder = "" self.outputData = {} def readOutputStr(self, data): text = "-------> Output :" output = data.find(text) def keywithmaxval(self, d): """ a) create a list of the dict's keys and values; b) return the key with the max value""" v=list(d.values()) k=list(d.keys()) return k[v.index(max(v))] def getBestScore(self, data): text = "-------> Output :" output = [x[len(text):-1] for x in data if text in x] self.outputData = json.loads(output[0]) return self.outputData['data']['BestScore'] def getModelParams(self, data): capture = False startText = "---------- ClassifierModel has started ----------" endText = "---------- ClassifierModel End ---------- " modelBasedText = "Models Based Selected Features Start" CorrelationBased = "Top/CorrelationBased Features Start" removableText = "Status:- |... Search Optimization Method applied: random\n" modelsParam = [] modelcorrelation = None output = {} for x in data: if endText in x: capture = False output[modelcorrelation] = ''.join(modelsParam) modelcorrelation = None modelsParam = [] elif capture: if x != removableText: modelsParam.append(x) elif startText in x: capture = True elif modelBasedText in x: modelcorrelation = 'modelBased' elif CorrelationBased in x: modelcorrelation = 'correlationBased' return output def mergeConfigFiles(self, bestScoreFolder): # path is already updated with open(bestScoreFolder/'etc'/'code_config.json', 'r') as f: config = json.load(f) if self.dataLocation: config['dataLocation'] = self.dataLocation if 'modelVersion' in config.keys(): config['modelVersion'] = '_'.join(config['modelVersion'].split('_')[:-1]) with open(bestScoreFolder/'etc'/'code_config.json', 'w') as f: json.dump(config, f, indent=4) with open(bestScoreFolder/'etc'/'display.json', 'r') as f: config = json.load(f) if 'version' in config.keys(): config['version'] = '_'.join(config['version'].split('_')[:-1]) with open(bestScoreFolder/'etc'/'display.json', 'w') as f: json.dump(config, f, indent=4) if len(self.folders) > 1: with open(bestScoreFolder/'etc'/'output.json', 'r') as f: config = json.load(f) evaluated_models = config['data']['EvaluatedModels'] for folder in self.folders: if folder != bestScoreFolder: with open(folder/'etc'/'output.json', 'r') as f: sub_config = json.load(f) for evaluated_model in sub_config['data']['EvaluatedModels']: evaluated_models.append(evaluated_model) with open(bestScoreFolder/'etc'/'output.json', 'w') as f: config['data']['EvaluatedModels'] = evaluated_models json.dump(config, f, indent=4) def mergeLogFiles(self, bestScoreFolder, data): startText = "---------- ClassifierModel has started ----------\n" endText = "---------- ClassifierModel End ---------- \n" modelBasedText = "Models Based Selected Features Start" CorrelationBased = "Top/CorrelationBased Features Start" with open(bestScoreFolder/'log'/'model_training_logs.log', 'r') as f: text = f.read() CorrelationBasedIndex = text.find(CorrelationBased) modelBasedTextIndex = text.find(modelBasedText) firstendIndex = text.find(endText) numOfMethods = 0 if CorrelationBasedIndex > 0: numOfMethods += 1 if modelBasedTextIndex > 0: numOfMethods += 1 if numOfMethods == 2: secondendIndex = text[firstendIndex+ len(endText):].find(endText) +firstendIndex+len(endText) # assuming correlation is always first for k,v in data.items(): if k != bestScoreFolder: if 'correlationBased' in v.keys(): text = text[:firstendIndex] + v['correlationBased'] + text[firstendIndex:] firstendIndex += len(v['correlationBased']) if numOfMethods == 2: secondendIndex += len(v['correlationBased']) if 'modelBased' in v.keys(): if numOfMethods == 2: text = text[:secondendIndex] + v['modelBased'] + text[secondendIndex:] secondendIndex += len(v['modelBased']) else: text = text[:firstendIndex] + v['modelBased'] + text[firstendIndex:] firstendIndex += len(v['modelBased']) with open(bestScoreFolder/'log'/'model_training_logs.log', 'w') as f: text = text.replace(str(bestScoreFolder), str(self.baseFolder)) f.write(text) def mergeFolder(self): bestScoreInFile = {} modelsTrainOutput = {} self.baseFolder = self.folders[0].parent/"_".join(self.folders[0].name.split('_')[:-1]) if len(self.folders) == 1: if self.baseFolder.exists(): shutil.rmtree(self.baseFolder) self.folders[0].rename(self.baseFolder) else: for folder in self.folders: with open(folder/'log'/'model_training_logs.log', 'r') as f: data = f.readlines() bestScoreInFile[folder] = self.getBestScore(data) modelsTrainOutput[folder] = self.getModelParams(data) bestScoreFolder = self.keywithmaxval(bestScoreInFile) self.mergeLogFiles(bestScoreFolder, modelsTrainOutput ) self.mergeConfigFiles(bestScoreFolder) if self.baseFolder.exists(): shutil.rmtree(self.baseFolder) bestScoreFolder.rename(self.baseFolder) #remove extra folders for folder in self.folders: if folder.exists(): shutil.rmtree(folder) return self.outputData import json import sys,os from pathlib import Path, PurePosixPath from fabric import Connection import tarfile import copy from hyperscalers.cloudServer import awsGPUTraining import time import shutil import logging import multiprocessing from hyperscalers.mergeLogs import mergeLogs class AION(awsGPUTraining): def __init__(self, config): config['AMAZON_EC2']['InstanceIds'] = [] #removing the support for Instance Id super().__init__(config) self.remoteUpload = {} def copyDataOnServer(self, index): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) client.run( 'mkdir -p {}'.format(self.remoteUpload['remoteDeployLocation'])) client.put(self.remoteUpload['configFile'], self.remoteUpload['remoteConfigLoc']) if not Path(self.remoteUpload['dataLoc']).exists(): raise ValueError(" data location {} does not exist".format(self.remoteUpload['dataLoc'])) if Path(self.remoteUpload['dataLoc']).is_file(): client.put(self.remoteUpload['dataLoc'], self.remoteUpload['remoteDataLoc']) else: client.run( 'mkdir -p {}'.format(self.remoteUpload['remoteDataLoc'])) p = Path(self.remoteUpload['dataLoc']).glob('**/*') files = [x for x in p if x.is_file()] for file in files: client.put(file, self.remoteUpload['remoteDataLoc']) if self.remoteUpload.get('imgCsvLoc', None): client.put(self.remoteUpload['imgCsvLoc'], self.remoteUpload['remoteDataLoc']) except Exception as e: raise ValueError("Error in copying data to cloud server. " + str(e)) def executeCode(self): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) cmd = '{} {} {}'.format("/home/ubuntu/aws/venv/aion-env/bin/python3.8", "/home/ubuntu/aws/venv/aion-env/lib/python3.8/site-packages/AION/aion.py", self.remoteUpload['remoteConfigLoc']) output = client.run( cmd, warn=True) except Exception as e: raise ValueError("Error in running code on cloud server. " + str(e)) def downloadAndExtractModel(self): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) remote = PurePosixPath(self.remoteUpload['remoteDeployLocation']) fileName = self.remoteUpload['deployName'] local = Path(self.remoteUpload['localDeployLocation']) tarFileName = fileName+".tar.gz" cmd = 'cd {};tar -czvf {} -C {}/ {}'.format(remote, tarFileName, remote, fileName) client.run( cmd) extractFile = str(local/tarFileName) client.get( str(remote/tarFileName), extractFile) with tarfile.open(extractFile, "r:gz") as tar: tar.extractall(local) Path(extractFile).unlink() client.run( 'rm -r {}'.format(remote/fileName)) client.run( 'rm {}'.format(remote/tarFileName)) except Exception as e: raise ValueError("Error in downloading file from server. " + str(e)) def deleteDataOnServer(self): client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) dataPaths = [self.remoteUpload['remoteDataLoc'], self.remoteUpload['remoteDeployLocation'], self.remoteUpload['remoteConfigLoc']] for loc in dataPaths: if Path(loc).is_file(): client.run( 'rm {}'.format(loc)) else: client.run( 'rm -r {}'.format(loc)) # only for csv files def updateConfigGetRemoteLoc(self, config, index=0): remote_location = '/home/ubuntu/aws/usecase' remoteInputLoc = PurePosixPath(remote_location)/"input" remoteOutputLoc = PurePosixPath(remote_location)/"target" if Path(config['basic']['dataLocation']).is_dir(): if Path(config['basic']['folderSettings']['labelDataFile']).parent !=Path(config['basic']['dataLocation']): self.remoteUpload['imgCsvLoc'] = config['basic']['folderSettings']['labelDataFile'] config['basic']['folderSettings']['labelDataFile'] = Path(config['basic']['folderSettings']['labelDataFile']).name csvFile = Path(config['basic']['dataLocation']).name localFile = config['basic']['dataLocation'] localDeployLoc = config['basic']['deployLocation'] config['basic']['dataLocation'] = str(remoteInputLoc/csvFile) config['basic']['deployLocation'] = str(remoteOutputLoc) jsonFile = Path(__file__).parent/'remote_{}.json'.format(index) with open(jsonFile,"w") as f: json.dump(config, f) self.remoteUpload['remoteDataLoc'] = config['basic']['dataLocation'] self.remoteUpload['remoteConfigLoc'] = str(remoteInputLoc)+ "/temp.json" self.remoteUpload['remoteDeployLocation'] = config['basic']['deployLocation'] self.remoteUpload['dataLoc'] = localFile self.remoteUpload['configFile'] = str(jsonFile) self.remoteUpload['localDeployLocation'] = localDeployLoc self.remoteUpload['deployName'] = "{}_{}".format(config['basic']['modelName'],config['basic']['modelVersion']) def updateDeployPath(self): import fileinput logFile = Path(self.remoteUpload['localDeployLocation'])/self.remoteUpload['deployName']/"model_training_logs.log" self.remoteUpload['localDeployLocation'] = self.remoteUpload['localDeployLocation'].replace('\\','/') if Path(logFile).exists(): with fileinput.FileInput(logFile, inplace=True, backup='.bak') as file: for line in file: remoteLoc = self.remoteUpload['remoteDeployLocation'] +'/'+ self.remoteUpload['deployName'] localLoc = self.remoteUpload['localDeployLocation'] +'/'+ "_".join(self.remoteUpload['deployName'].split('_')[:-1]) print(line.replace(remoteLoc, localLoc), end='') logFile = Path(self.remoteUpload['localDeployLocation'])/self.remoteUpload['deployName']/"output.json" if Path(logFile).exists(): with fileinput.FileInput(logFile, inplace=True, backup='.bak') as file: for line in file: remoteLoc = self.remoteUpload['remoteDeployLocation'] +'/'+ self.remoteUpload['deployName'] localLoc = self.remoteUpload['localDeployLocation'] +'/'+ "_".join(self.remoteUpload['deployName'].split('_')[:-1]) print(line.replace(remoteLoc, localLoc), end='') logFile = Path(self.remoteUpload['localDeployLocation'])/self.remoteUpload['deployName']/"display.json" if Path(logFile).exists(): with fileinput.FileInput(logFile, inplace=True, backup='.bak') as file: for line in file: remoteLoc = self.remoteUpload['remoteDeployLocation'] +'/'+ self.remoteUpload['deployName'] localLoc = self.remoteUpload['localDeployLocation'] +'/'+ "_".join(self.remoteUpload['deployName'].split('_')[:-1]) print(line.replace(remoteLoc, localLoc), end='') def updateUserServerConfig(aws_config): aws_config['ssh']['keyFilePath'] = str(Path(__file__).parent/"AION_GPU.pem") return aws_config def getKeyByValue(dictionary, refValue): for key, value in dictionary.items(): if value == refValue: return key return None def getKeysByValue(dictionary, refValue): keys = [] for key, value in dictionary.items(): if value == refValue: keys.append(key) return keys class openInstancesStatus(): def __init__(self): pass def addInstance(self, instanceId, args=None): fileName = instanceId + '.ec2instance' data = {} data[instanceId] = args with open(fileName, "w") as f: json.dump( data, f, indent=4) #TODO do we need to encrypt def removeInstance(self, instanceId): fileName = instanceId + '.ec2instance' if Path(fileName).exists(): Path(fileName).unlink() def clearPreviousInstancesState(self): # check and stop the previous instance openInstances = Path().glob("*.ec2instance") for file in openInstances: with open(file, 'r') as f: data = json.load(f) prevConfig = list(data.values())[0] key = Path(file).stem if prevConfig['AMAZON_EC2']['amiId']: prevConfig['AMAZON_EC2']['InstanceIds'] = [key] prevConfig['AMAZON_EC2']['amiId'] = "" # clear amiId instance = awsGPUTraining(prevConfig) if len(prevConfig['AMAZON_EC2']['InstanceIds']) > 0: try: if instance.is_instance_running(prevConfig['AMAZON_EC2']['InstanceIds'][0]): instance.stop_server_instance() except: pass self.removeInstance(key) class prepareConfig(): def __init__(self, config,noOfInstance,ComputeInfrastructure): if isinstance(config, dict): self.config = config self.configDir = Path(__file__).parent elif isinstance(config, str): with open(config, 'r') as f: self.config = json.load(f) self.configDir = Path(config).parent else: raise TypeError("{} type object is not supported for config".format(type(config))) self.problemType = getKeyByValue(self.config['basic']['analysisType'] ,"True") self.algorithms = getKeysByValue(self.config['basic']['algorithms'][self.problemType] ,"True") self.numInstances = int(noOfInstance) self.computeInfrastructure = ComputeInfrastructure self.isMultiInstance = False self.validateMultiInstance() self.newConfigs = [] def isRemoteTraining(self): return True if(self.computeInfrastructure == "True") else False def validateMultiInstance(self): if self.isRemoteTraining(): if self.problemType == 'classification' or self.problemType == 'regression': if self.numInstances > len(self.algorithms): self.numInstances = len(self.algorithms) if len(self.algorithms) > 1 and self.numInstances > 1: self.isMultiInstance = True def createNewConfigs(self): configs = [] algos = self.algorithms if len(algos) <= self.numInstances: self.numInstances = len(algos) algosPerInstances = (len(algos)+(self.numInstances - 1))//self.numInstances remainingAlgos = len(algos) for i in range(self.numInstances): newConfig = copy.deepcopy(self.config) for k,v in newConfig['basic']['algorithms'][self.problemType].items(): newConfig['basic']['algorithms'][self.problemType][k] = "False" algosPerInstances = remainingAlgos // (self.numInstances - i) for j in range(algosPerInstances): newConfig['basic']['algorithms'][self.problemType][algos[len(algos) - remainingAlgos + j]] = "True" newConfig['basic']['modelVersion'] = newConfig['basic']['modelVersion'] + "_{}".format(i) newFileName = str(self.configDir/"splittedConfig_{}.json".format(i)) with open(newFileName, 'w') as jsonFile: json.dump(newConfig, jsonFile, indent=4) configs.append(newFileName) remainingAlgos -= algosPerInstances return configs class Process(multiprocessing.Process): def __init__(self, aws_config, configFile, index, openInstancesLog): super(Process, self).__init__() self.index = index self.aws_config = aws_config self.configFile = configFile self.openInstances = openInstancesLog def run(self): log = logging.getLogger('eion') serverStart = False try: server = AION(self.aws_config) with open(self.configFile,"r") as f: config = json.load(f) server.updateConfigGetRemoteLoc(config, self.index) instanceId = server.start_instance() log.info('Status:-|... start instance: {}'.format(instanceId)) serverStart = True self.openInstances.addInstance(instanceId, self.aws_config) time.sleep(40) log.info('Status:-|... copying data on instance: {}'.format(instanceId)) server.copyDataOnServer( config) log.info('Status:-|... Training on instance: {}'.format(instanceId)) server.executeCode() log.info('Status:-|... downloading data from instance: {}'.format(instanceId)) server.downloadAndExtractModel() server.deleteDataOnServer() log.info('Status:-|... stopping instance: {}'.format(instanceId)) server.stop_server_instance() serverStart = False self.openInstances.removeInstance(instanceId) server.updateDeployPath() except Exception as e: print(e) pass finally: if serverStart: log.info('Status:-|... stopping instance: {}'.format(instanceId)) server.stop_server_instance() self.openInstances.removeInstance(instanceId) def awsTraining(configPath): try: # This function responsible for starting the training with AWS with open(configPath, "r") as f: config = json.load(f) ec2 = boto3.resource('ec2',region_name=AWS_Region) instance_id= instance[0].instance_id deployFolder = config['basic']['deployLocation'] iterName = config['basic']['modelName'] iterVersion = config['basic']['modelVersion'] dataLocation = config['basic']['dataLocation'] usecaseLocation = os.path.join(deployFolder,iterName) if not Path(usecaseLocation).exists(): os.makedirs(usecaseLocation) deployLocation = os.path.join(usecaseLocation,iterVersion) if Path(deployLocation).exists(): shutil.rmtree(deployLocation) os.makedirs(deployLocation) logLocation = os.path.join(deployLocation,'log') if not Path(logLocation).exists(): os.makedirs(logLocation) #read the server config logFileName=os.path.join(logLocation,'model_training_logs.log') filehandler = logging.FileHandler(logFileName, 'w','utf-8') formatter = logging.Formatter('%(message)s') filehandler.setFormatter(formatter) log = logging.getLogger('eion') log.propagate = False for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): log.removeHandler(hdlr) log.addHandler(filehandler) log.setLevel(logging.INFO) log.info('Status:-|... Compute Infrastructure:AMAZON EC2') with open(Path(__file__).parent/"../config/compute.conf", "r") as f: aws_config = json.load(f) aws_config = updateUserServerConfig(aws_config) configSplitter = prepareConfig(sys.argv[1],aws_config['AMAZON_EC2']['NoOfInstance'],aws_config['ComputeInfrastructure']) newConfigs = configSplitter.createNewConfigs() print(newConfigs) openInstances = openInstancesStatus() openInstances.clearPreviousInstancesState() folders = [] processes = [0] * len(newConfigs) for index, config in enumerate(newConfigs): processes[index] = Process(aws_config, config, index, openInstances) processes[index].start() for index, config in enumerate(newConfigs): processes[index].join() folders.append(deployLocation + '_{}'.format(index)) if Path(deployLocation+'_0').exists(): filehandler.close() log.removeHandler(filehandler) merge = mergeLogs(folders) merge.mergeFolder() else: output = {"status":"FAIL","message":"Please check cloud server configuration."} output = json.dumps(output) log.info('server code execution failed !....') log.info('\n------------- Output JSON ------------') log.info('-------> Output :'+str(output)) log.info('------------- Output JSON ------------\n') print("\n") print("aion_learner_status:",output) print("\n") except Exception as inst: output = {"status":"FAIL","message":str(inst).strip('"')} output = json.dumps(output) log.info('server code execution failed !....'+str(inst)) log.info('\n------------- Output JSON ------------') log.info('-------> Output :'+str(output)) log.info('------------- Output JSON ------------\n') print("\n") print("aion_learner_status:",output) print("\n") import json import sys,os from pathlib import Path, PurePosixPath from fabric import Connection import tarfile import copy from hyperscalers.cloudServer import awsGPUTraining import time import shutil import logging import multiprocessing from hyperscalers.mergeLogs import mergeLogs class AION(awsGPUTraining): def __init__(self, config): config['AMAZON_EC2']['InstanceIds'] = [] #removing the support for Instance Id super().__init__(config) self.remoteUpload = {} def copyDataOnServer(self, index): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) client.run( 'mkdir -p {}'.format(self.remoteUpload['remoteDeployLocation'])) client.put(self.remoteUpload['configFile'], self.remoteUpload['remoteConfigLoc']) if not Path(self.remoteUpload['dataLoc']).exists(): raise ValueError(" data location {} does not exist".format(self.remoteUpload['dataLoc'])) if Path(self.remoteUpload['dataLoc']).is_file(): client.put(self.remoteUpload['dataLoc'], self.remoteUpload['remoteDataLoc']) else: client.run( 'mkdir -p {}'.format(self.remoteUpload['remoteDataLoc'])) p = Path(self.remoteUpload['dataLoc']).glob('**/*') files = [x for x in p if x.is_file()] for file in files: client.put(file, self.remoteUpload['remoteDataLoc']) if self.remoteUpload.get('imgCsvLoc', None): client.put(self.remoteUpload['imgCsvLoc'], self.remoteUpload['remoteDataLoc']) except Exception as e: raise ValueError("Error in copying data to cloud server. " + str(e)) def executeCode(self): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) cmd = '{} {} {}'.format("/home/ubuntu/aws/venv/aion-env/bin/python3.8", "/home/ubuntu/aws/venv/aion-env/lib/python3.8/site-packages/AION/aion.py", self.remoteUpload['remoteConfigLoc']) output = client.run( cmd, warn=True) except Exception as e: raise ValueError("Error in running code on cloud server. " + str(e)) def downloadAndExtractModel(self): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) remote = PurePosixPath(self.remoteUpload['remoteDeployLocation']) fileName = self.remoteUpload['deployName'] local = Path(self.remoteUpload['localDeployLocation']) tarFileName = fileName+".tar.gz" cmd = 'cd {};tar -czvf {} -C {}/ {}'.format(remote, tarFileName, remote, fileName) client.run( cmd) extractFile = str(local/tarFileName) client.get( str(remote/tarFileName), extractFile) with tarfile.open(extractFile, "r:gz") as tar: tar.extractall(local) Path(extractFile).unlink() client.run( 'rm -r {}'.format(remote/fileName)) client.run( 'rm {}'.format(remote/tarFileName)) except Exception as e: raise ValueError("Error in downloading file from server. " + str(e)) def deleteDataOnServer(self): client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) dataPaths = [self.remoteUpload['remoteDataLoc'], self.remoteUpload['remoteDeployLocation'], self.remoteUpload['remoteConfigLoc']] for loc in dataPaths: if Path(loc).is_file(): client.run( 'rm {}'.format(loc)) else: client.run( 'rm -r {}'.format(loc)) # only for csv files def updateConfigGetRemoteLoc(self, config, index=0): remote_location = '/home/ubuntu/aws/usecase' remoteInputLoc = PurePosixPath(remote_location)/"input" remoteOutputLoc = PurePosixPath(remote_location)/"target" if Path(config['basic']['dataLocation']).is_dir(): if Path(config['basic']['folderSettings']['labelDataFile']).parent !=Path(config['basic']['dataLocation']): self.remoteUpload['imgCsvLoc'] = config['basic']['folderSettings']['labelDataFile'] config['basic']['folderSettings']['labelDataFile'] = Path(config['basic']['folderSettings']['labelDataFile']).name csvFile = Path(config['basic']['dataLocation']).name localFile = config['basic']['dataLocation'] localDeployLoc = config['basic']['deployLocation'] config['basic']['dataLocation'] = str(remoteInputLoc/csvFile) config['basic']['deployLocation'] = str(remoteOutputLoc) jsonFile = Path(__file__).parent/'remote_{}.json'.format(index) with open(jsonFile,"w") as f: json.dump(config, f) self.remoteUpload['remoteDataLoc'] = config['basic']['dataLocation'] self.remoteUpload['remoteConfigLoc'] = str(remoteInputLoc)+ "/temp.json" self.remoteUpload['remoteDeployLocation'] = config['basic']['deployLocation'] self.remoteUpload['dataLoc'] = localFile self.remoteUpload['configFile'] = str(jsonFile) self.remoteUpload['localDeployLocation'] = localDeployLoc self.remoteUpload['deployName'] = "{}_{}".format(config['basic']['modelName'],config['basic']['modelVersion']) def updateDeployPath(self): import fileinput logFile = Path(self.remoteUpload['localDeployLocation'])/self.remoteUpload['deployName']/"model_training_logs.log" self.remoteUpload['localDeployLocation'] = self.remoteUpload['localDeployLocation'].replace('\\','/') if Path(logFile).exists(): with fileinput.FileInput(logFile, inplace=True, backup='.bak') as file: for line in file: remoteLoc = self.remoteUpload['remoteDeployLocation'] +'/'+ self.remoteUpload['deployName'] localLoc = self.remoteUpload['localDeployLocation'] +'/'+ "_".join(self.remoteUpload['deployName'].split('_')[:-1]) print(line.replace(remoteLoc, localLoc), end='') logFile = Path(self.remoteUpload['localDeployLocation'])/self.remoteUpload['deployName']/"output.json" if Path(logFile).exists(): with fileinput.FileInput(logFile, inplace=True, backup='.bak') as file: for line in file: remoteLoc = self.remoteUpload['remoteDeployLocation'] +'/'+ self.remoteUpload['deployName'] localLoc = self.remoteUpload['localDeployLocation'] +'/'+ "_".join(self.remoteUpload['deployName'].split('_')[:-1]) print(line.replace(remoteLoc, localLoc), end='') logFile = Path(self.remoteUpload['localDeployLocation'])/self.remoteUpload['deployName']/"display.json" if Path(logFile).exists(): with fileinput.FileInput(logFile, inplace=True, backup='.bak') as file: for line in file: remoteLoc = self.remoteUpload['remoteDeployLocation'] +'/'+ self.remoteUpload['deployName'] localLoc = self.remoteUpload['localDeployLocation'] +'/'+ "_".join(self.remoteUpload['deployName'].split('_')[:-1]) print(line.replace(remoteLoc, localLoc), end='') def updateUserServerConfig(aws_config): aws_config['ssh']['keyFilePath'] = str(Path(__file__).parent/"AION_GPU.pem") return aws_config def getKeyByValue(dictionary, refValue): for key, value in dictionary.items(): if value == refValue: return key return None def getKeysByValue(dictionary, refValue): keys = [] for key, value in dictionary.items(): if value == refValue: keys.append(key) return keys class openInstancesStatus(): def __init__(self): pass def addInstance(self, instanceId, args=None): fileName = instanceId + '.ec2instance' data = {} data[instanceId] = args with open(fileName, "w") as f: json.dump( data, f, indent=4) #TODO do we need to encrypt def removeInstance(self, instanceId): fileName = instanceId + '.ec2instance' if Path(fileName).exists(): Path(fileName).unlink() def clearPreviousInstancesState(self): # check and stop the previous instance openInstances = Path().glob("*.ec2instance") for file in openInstances: with open(file, 'r') as f: data = json.load(f) prevConfig = list(data.values())[0] key = Path(file).stem if prevConfig['AMAZON_EC2']['amiId']: prevConfig['AMAZON_EC2']['InstanceIds'] = [key] prevConfig['AMAZON_EC2']['amiId'] = "" # clear amiId instance = awsGPUTraining(prevConfig) if len(prevConfig['AMAZON_EC2']['InstanceIds']) > 0: try: if instance.is_instance_running(prevConfig['AMAZON_EC2']['InstanceIds'][0]): instance.stop_server_instance() except: pass self.removeInstance(key) class prepareConfig(): def __init__(self, config,noOfInstance,ComputeInfrastructure): if isinstance(config, dict): self.config = config self.configDir = Path(__file__).parent elif isinstance(config, str): with open(config, 'r') as f: self.config = json.load(f) self.configDir = Path(config).parent else: raise TypeError("{} type object is not supported for config".format(type(config))) self.problemType = getKeyByValue(self.config['basic']['analysisType'] ,"True") self.algorithms = getKeysByValue(self.config['basic']['algorithms'][self.problemType] ,"True") self.numInstances = int(noOfInstance) self.computeInfrastructure = ComputeInfrastructure self.isMultiInstance = False self.validateMultiInstance() self.newConfigs = [] def isRemoteTraining(self): return True if(self.computeInfrastructure == "True") else False def validateMultiInstance(self): if self.isRemoteTraining(): if self.problemType == 'classification' or self.problemType == 'regression': if self.numInstances > len(self.algorithms): self.numInstances = len(self.algorithms) if len(self.algorithms) > 1 and self.numInstances > 1: self.isMultiInstance = True def createNewConfigs(self): configs = [] algos = self.algorithms if len(algos) <= self.numInstances: self.numInstances = len(algos) algosPerInstances = (len(algos)+(self.numInstances - 1))//self.numInstances remainingAlgos = len(algos) for i in range(self.numInstances): newConfig = copy.deepcopy(self.config) for k,v in newConfig['basic']['algorithms'][self.problemType].items(): newConfig['basic']['algorithms'][self.problemType][k] = "False" algosPerInstances = remainingAlgos // (self.numInstances - i) for j in range(algosPerInstances): newConfig['basic']['algorithms'][self.problemType][algos[len(algos) - remainingAlgos + j]] = "True" newConfig['basic']['modelVersion'] = newConfig['basic']['modelVersion'] + "_{}".format(i) newFileName = str(self.configDir/"splittedConfig_{}.json".format(i)) with open(newFileName, 'w') as jsonFile: json.dump(newConfig, jsonFile, indent=4) configs.append(newFileName) remainingAlgos -= algosPerInstances return configs class Process(multiprocessing.Process): def __init__(self, aws_config, configFile, index, openInstancesLog): super(Process, self).__init__() self.index = index self.aws_config = aws_config self.configFile = configFile self.openInstances = openInstancesLog def run(self): log = logging.getLogger('eion') serverStart = False try: server = AION(self.aws_config) with open(self.configFile,"r") as f: config = json.load(f) server.updateConfigGetRemoteLoc(config, self.index) instanceId = server.start_instance() log.info('Status:-|... start instance: {}'.format(instanceId)) serverStart = True self.openInstances.addInstance(instanceId, self.aws_config) time.sleep(40) log.info('Status:-|... copying data on instance: {}'.format(instanceId)) server.copyDataOnServer( config) log.info('Status:-|... Training on instance: {}'.format(instanceId)) server.executeCode() log.info('Status:-|... downloading data from instance: {}'.format(instanceId)) server.downloadAndExtractModel() server.deleteDataOnServer() log.info('Status:-|... stopping instance: {}'.format(instanceId)) server.stop_server_instance() serverStart = False self.openInstances.removeInstance(instanceId) server.updateDeployPath() except Exception as e: print(e) pass finally: if serverStart: log.info('Status:-|... stopping instance: {}'.format(instanceId)) server.stop_server_instance() self.openInstances.removeInstance(instanceId) def training(config_path): try: # read the aion configuration file for server enabled with open(config_path, "r") as f: config = json.load(f) deployFolder = config['basic']['deployLocation'] iterName = config['basic']['modelName'] iterVersion = config['basic']['modelVersion'] dataLocation = config['basic']['dataLocation'] deployLocation = os.path.join(deployFolder,iterName+'_'+iterVersion) if Path(deployLocation).exists(): shutil.rmtree(deployLocation) os.makedirs(deployLocation) #read the server config logFileName=os.path.join(deployLocation,'model_training_logs.log') filehandler = logging.FileHandler(logFileName, 'w','utf-8') formatter = logging.Formatter('%(message)s') filehandler.setFormatter(formatter) log = logging.getLogger('eion') log.propagate = False for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): log.removeHandler(hdlr) log.addHandler(filehandler) log.setLevel(logging.INFO) log.info('Status:-|... Compute Infrastructure:AMAZON EC2') with open(Path(__file__).parent/"../config/compute.conf", "r") as f: aws_config = json.load(f) aws_config = updateUserServerConfig(aws_config) configSplitter = prepareConfig(config_path,aws_config['AMAZON_EC2']['NoOfInstance'],aws_config['ComputeInfrastructure']) newConfigs = configSplitter.createNewConfigs() print(newConfigs) openInstances = openInstancesStatus() openInstances.clearPreviousInstancesState() folders = [] processes = [0] * len(newConfigs) for index, config in enumerate(newConfigs): processes[index] = Process(aws_config, config, index, openInstances) processes[index].start() for index, config in enumerate(newConfigs): processes[index].join() folders.append(deployLocation + '_{}'.format(index)) if Path(deployLocation+'_0').exists(): filehandler.close() log.removeHandler(filehandler) merge = mergeLogs(folders) merge.mergeFolder() else: output = {"status":"FAIL","message":"Please check cloud server configuration."} output = json.dumps(output) log.info('server code execution failed !....') log.info('\n------------- Output JSON ------------') log.info('-------> Output :'+str(output)) log.info('------------- Output JSON ------------\n') print("\n") print("aion_learner_status:",output) print("\n") except Exception as inst: output = {"status":"FAIL","message":str(inst).strip('"')} output = json.dumps(output) log.info('server code execution failed !....'+str(inst)) log.info('\n------------- Output JSON ------------') log.info('-------> Output :'+str(output)) log.info('------------- Output JSON ------------\n') print("\n") print("aion_learner_status:",output) print("\n") ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import boto3 import json import time import requests import datetime import uuid import shutil from websocket import create_connection from botocore.exceptions import ClientError import tarfile from pathlib import Path, PurePosixPath from stat import S_ISDIR from fabric import Connection import time import logging class awsGPUTraining(): def __init__(self, config): local_config = {"location":{"data":"aion/data/od", "code":"", "pretrainedModel":"aion/pretrainedModels"}, "jupyter":{"header":{"Authorization":"Token f3af05d5348301997fb014f245569e872d27bb9018fd70d2"}, "portNo":"8888", "notebook_path":"aion/code/AWS_GPU_OD_Training.ipynb"}} self.serverConfig = config["server"] self.sshConfig = config["ssh"] self.log = logging.getLogger('eion') self.codeLocation = local_config["location"]["code"] self.dataLocation = local_config["location"]["data"] self.pretrainedModelLocation = local_config["location"]["pretrainedModel"] self.jupyterConfig = local_config["jupyter"] self.serverIP = "" if self.serverConfig["awsAccessKeyId"] == "" or self.serverConfig["awsSecretAccessKey"] == "": raise ValueError("Cloud server configuration is not available.") if len(self.serverConfig["InstanceIds"]) == 0 and self.serverConfig["amiId"] == "": raise ValueError("Please provide either InstanceIds or amiId in server config") self.instanceId = [] self.separate_instance = False if self.serverConfig["amiId"] != "": self.separate_instance = True else: if len(self.serverConfig["InstanceIds"]): if isinstance(self.serverConfig["InstanceIds"], list): self.instanceId = self.serverConfig["InstanceIds"] elif isinstance(self.serverConfig["InstanceIds"], str): self.instanceId = [self.serverConfig["InstanceIds"]] self.ec2_client = boto3.client(self.serverConfig["serverName"], region_name=self.serverConfig["regionName"], aws_access_key_id=self.serverConfig["awsAccessKeyId"], aws_secret_access_key=self.serverConfig["awsSecretAccessKey"]) def __sftp_exists(self, sftp, path): try: sftp.stat(path) return True except:# IOError, e: #if e.errno == errno.ENOENT: return False def __rmtree(self, sftp, remotepath, level=0): for f in sftp.listdir_attr(remotepath): rpath = str(PurePosixPath(remotepath)/f.filename) if S_ISDIR(f.st_mode): self.__rmtree(sftp, rpath, level=(level + 1)) sftp.rmdir(rpath) else: rpath = str(PurePosixPath(remotepath)/f.filename) sftp.remove(rpath) def copy_files_to_server(self, location): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) client.sudo('rm -rf {}/*'.format(self.dataLocation)) tarFile = str((PurePosixPath(self.dataLocation).parent/PurePosixPath(self.dataLocation).name).with_suffix(".tar.gz")) client.put(location+'/test.tfrecord', self.dataLocation+'/test.tfrecord') client.put(location+'/train.tfrecord', self.dataLocation+'/train.tfrecord') client.put(location+'/pipeline.config', self.dataLocation+'/pipeline.config') client.put(location+'/label_map.pbtxt', self.dataLocation+'/label_map.pbtxt') client.put(location+'/model.config', self.dataLocation+'/model.config') if self.jupyterConfig != "": client.run("touch {}".format(self.dataLocation+'/log.txt')) except Exception as e: raise ValueError("Error in copying data to cloud server. " + str(e)) def __myexec(self, ssh, cmd, timeout, want_exitcode=False): # one channel per command stdin, stdout, stderr = ssh.exec_command(cmd) # get the shared channel for stdout/stderr/stdin channel = stdout.channel # we do not need stdin. stdin.close() # indicate that we're not going to write to that channel anymore channel.shutdown_write() # read stdout/stderr in order to prevent read block hangs stdout_chunks = [] stdout_chunks.append(stdout.channel.recv(len(stdout.channel.in_buffer))) # chunked read to prevent stalls while not channel.closed or channel.recv_ready() or channel.recv_stderr_ready(): # stop if channel was closed prematurely, and there is no data in the buffers. got_chunk = False readq, _, _ = select.select([stdout.channel], [], [], timeout) for c in readq: if c.recv_ready(): stdout_chunks.append(stdout.channel.recv(len(c.in_buffer))) got_chunk = True if c.recv_stderr_ready(): # make sure to read stderr to prevent stall stderr.channel.recv_stderr(len(c.in_stderr_buffer)) got_chunk = True ''' 1) make sure that there are at least 2 cycles with no data in the input buffers in order to not exit too early (i.e. cat on a >200k file). 2) if no data arrived in the last loop, check if we already received the exit code 3) check if input buffers are empty 4) exit the loop ''' if not got_chunk \ and stdout.channel.exit_status_ready() \ and not stderr.channel.recv_stderr_ready() \ and not stdout.channel.recv_ready(): # indicate that we're not going to read from this channel anymore stdout.channel.shutdown_read() # close the channel stdout.channel.close() break # exit as remote side is finished and our bufferes are empty # close all the pseudofiles stdout.close() stderr.close() if want_exitcode: # exit code is always ready at this point return (''.join(stdout_chunks), stdout.channel.recv_exit_status()) return ''.join(stdout_chunks) def __myexec1(self, ssh, cmd, timeout, want_exitcode=False): # one channel per command stdin, stdout, stderr = ssh.exec_command(cmd, get_pty=True) for line in iter(stderr.readline, ""): print(line, end="") stdin.close() stdout.close() stderr.close() def executeCode(self): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) cmd = 'python3.8 {} {} {}'.format(self.codeLocation, self.dataLocation, self.pretrainedModelLocation) client.run( cmd) except Exception as e: raise ValueError("Error in running code on cloud server. " + str(e)) def start_executing_notebook(self): try: publicIp_Port = self.serverIP + ":" + self.jupyterConfig["portNo"] conURL = "ws://" + publicIp_Port base = 'http://' + publicIp_Port + '' headers = self.jupyterConfig["header"] url = base + '/api/kernels' flag = True while flag: # deadlock need to add timeout response = requests.post(url, headers=headers) flag = False kernel = json.loads(response.text) # Load the notebook and get the code of each cell url = base + '/api/contents/' + self.jupyterConfig["notebook_path"] response = requests.get(url, headers=headers) file = json.loads(response.text) code = [c['source'] for c in file['content']['cells'] if len(c['source']) > 0 and c['cell_type']=='code' ] ws = create_connection(conURL + "/api/kernels/" + kernel["id"] + "/channels", header=headers) def send_execute_request(code): msg_type = 'execute_request'; content = {'code': code, 'silent': False} hdr = {'msg_id': uuid.uuid1().hex, 'username': 'test', 'session': uuid.uuid1().hex, 'data': datetime.datetime.now().isoformat(), 'msg_type': msg_type, 'version': '5.0'} msg = {'header': hdr, 'parent_header': hdr, 'metadata': {}, 'content': content} return msg for c in code: ws.send(json.dumps(send_execute_request(c))) # We ignore all the other messages, we just get the code execution output # (this needs to be improved for production to take into account errors, large cell output, images, etc.) error_msg = '' traceback_msg = '' for i in range(0, len(code)): msg_type = ''; while msg_type != "stream": rsp = json.loads(ws.recv()) msg_type = rsp["msg_type"] if msg_type == 'error': raise ValueError("Error on Cloud machine: "+rsp['content']['evalue']) ws.close() self.log.info('Status:- |...Execution Started`') except ClientError as e: raise ValueError(e) def __wait_for_completion(self, sftp, remoteLogFile, localLogFile): waiting = True error_msg = "" while waiting: time.sleep(5 * 60) try: sftp.get(str(remoteLogFile), str(localLogFile)) with open(localLogFile, "r") as f: content = f.readlines() for x in content: if "Error" in x: waiting = False error_msg = x if "success" in x: waiting = False except: raise (str(e)) return error_msg def copy_file_from_server(self, localPath): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) remoteLogFile = PurePosixPath(self.dataLocation)/'log.txt' localLogFile = Path(localPath)/'remote_log.txt' client.get(str(remoteLogFile), str(localLogFile)) tarFile = (PurePosixPath(self.dataLocation).parent/PurePosixPath(self.dataLocation).name).with_suffix(".tar.gz") client.get(str(tarFile), str(Path(localPath)/tarFile.name)) except: raise return str(Path(localPath)/tarFile.name) def create_instance(self): instances = self.ec2_client.run_instances( ImageId=self.serverConfig["amiId"], MinCount=1, MaxCount=1, InstanceType="t2.xlarge", KeyName="AION_GPU", SecurityGroupIds = ["sg-02c3a6c8dd67edb74"] ) self.instanceId = [instances['Instances'][0]['InstanceId']] def start_instance(self): if self.separate_instance: self.create_instance() try: response = self.ec2_client.start_instances(InstanceIds=self.instanceId, DryRun=True) except Exception as e: if 'DryRunOperation' not in str(e): raise ValueError("Error in starting the EC2 instance, check server configuration. " + str(e)) try: running_state_code = 16 response = self.ec2_client.start_instances(InstanceIds=self.instanceId, DryRun=False) instance_status_code = 0 while instance_status_code != running_state_code: response = self.ec2_client.describe_instances(InstanceIds=self.instanceId) instance_status_code = response['Reservations'][0]['Instances'][0]['State']['Code'] if instance_status_code == running_state_code: self.serverIP = response['Reservations'][0]['Instances'][0]['PublicIpAddress'] break except ClientError as e: raise ValueError("Error in starting the EC2 instance. " + str(e)) def terminate_instance(self): ec2 = boto3.resource(self.serverConfig["serverName"], region_name=self.serverConfig["regionName"], aws_access_key_id=self.serverConfig["awsAccessKeyId"], aws_secret_access_key=self.serverConfig["awsSecretAccessKey"]) ec2.instances.filter(InstanceIds=self.instanceId).terminate() # for terminating an ec2 instance def stop_server_instance(self): try: self.ec2_client.stop_instances(InstanceIds=self.instanceId, DryRun=True) except Exception as e: if 'DryRunOperation' not in str(e): raise stopped_state_code = 80 # Dry run succeeded, call stop_instances without dryrun try: response = self.ec2_client.stop_instances(InstanceIds=self.instanceId, DryRun=False) response = self.ec2_client.describe_instances(InstanceIds=self.instanceId) instance_status_code = 0 while instance_status_code != stopped_state_code: response = self.ec2_client.describe_instances(InstanceIds=self.instanceId) instance_status_code = response['Reservations'][0]['Instances'][0]['State']['Code'] if instance_status_code == stopped_state_code: break except: raise ValueError("Error in stopping the EC2 instance {}.Please stop it manually ".format(self.instanceId[0])) if self.separate_instance: try: self.terminate_instance() except: raise ValueError("Error in terminating the EC2 instance {}.Please terminate it manually ".format(self.instanceId[0])) #!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'appfe.ux.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main() """mpgWebApp URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path from django.urls import include, re_path from appfe.api import inferenceApis from django.urls import path, re_path urlpatterns = [ #path('predict', inferenceApis.apipredict,name='PredictAPI'), path('predict', inferenceApis.apipredict,name='PredictAPI'), path('spredict',inferenceApis.apispredict,name='SecurePredictAPI'), path('monitoring', inferenceApis.apiinputdrift,name='MonitoringAPI'), path('performance', inferenceApis.apioutputdrift,name='Performance'), path('xplain', inferenceApis.apixplain,name='Xplain'), path('features',inferenceApis.apifeatures,name='Features'), path('uploadfile',inferenceApis.uploadfile,name='uploadfile'), path('retrain',inferenceApis.retrain,name='retrain'), path('trainstatus',inferenceApis.trainstatus,name='trainstatus'), path('publish',inferenceApis.publish,name='publish'), path('geteda',inferenceApis.geteda,name='geteda'), path('pattern_anomaly_settings',inferenceApis.apiPatternAnomalySettings,name='PatternAnomalySettings'), path('pattern_anomaly_predict',inferenceApis.apiPatternAnomalyPredict,name='PatternAnomalyPredict') ] #df=pd.read_csv("C:\Project\Analytics\Deployment\germancredit_9\germancreditdata.csv") # #bool_cols = [col for col in df if np.isin(df[col].dropna().unique(), [0, 1]).all()] # #bool_cols from django.shortcuts import render from django.http import HttpResponse from appbe.dataPath import DEPLOY_LOCATION from rest_framework import status from django.db.models import Max, F import os,sys import time import json import re import pandas as pd from rest_framework.permissions import IsAuthenticated from django.views.decorators.csrf import csrf_exempt from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appfe.modelTraining.models import usecasedetails from appfe.modelTraining.models import Existusecases import subprocess from pathlib import Path user_records = {} @csrf_exempt def geteda(request): if request.method == 'POST': if request.content_type == 'application/json': output = {} try: data=request.body.decode('utf-8') data = json.loads(data) file_id = data['fileid'] edaOptions = 'All' if 'options' in data: edaOptions = data['options'] dataFile = os.path.join(DATA_FILE_PATH,file_id) from appbe.eda import ux_eda eda_obj = ux_eda(dataFile) if 'dataoverview' in edaOptions.lower() or 'all' in edaOptions.lower(): dataDistributionDF = eda_obj.dataDistribution() dataDistributionJson = dataDistributionDF.to_json(orient = 'records') output['DataOverview'] = json.loads(dataDistributionJson) if 'top10records' in edaOptions.lower() or 'all' in edaOptions.lower(): top10df = eda_obj.getTopRows(10) top10dfJson = top10df.to_json(orient = 'records') output['Top10Records'] = json.loads(top10dfJson) if 'datadistribution' in edaOptions.lower() or 'all' in edaOptions.lower(): distributionJson = eda_obj.getDistribution() output['DataDistribution'] = distributionJson if "featureimportance" in edaOptions.lower() or 'all' in edaOptions.lower(): pca_map = eda_obj.getPCATop10Features() pca_details = pca_map pca_df=pd.DataFrame() if len(pca_details) > 0: pca_df = pd.DataFrame({'Feature':pca_details.index, 'Explained Variance Ratio':pca_details.values}).round(2) pca_json = pca_df.to_json(orient="records") output['FeatureImportance'] = json.loads(pca_json) else: pca_json = 'Error During feature importance processing' output['FeatureImportance'] = pca_json if "correlationanalysis" in edaOptions.lower() or 'all' in edaOptions.lower(): corr_mat = eda_obj.getCorrelationMatrix() if not corr_mat.empty: corr_mat = corr_mat.to_json(orient="columns") output['CorrelationAnalysis'] = json.loads(corr_mat) else: output['CorrelationAnalysis'] = 'Error during correlation analysis' if "unsupervisedclustering" in edaOptions.lower() or 'all' in edaOptions.lower(): clusteringDetails,hopkins_val = eda_obj.getClusterDetails() output['UnsupervisedClustering'] = clusteringDetails output['HopkinsValue'] = hopkins_val except Exception as e: print(e) return HttpResponse(json.dumps({"status":"Success","output":output}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong Content Type"}),content_type="application/json") @csrf_exempt def publish(request): usecaseid = request.GET["usecaseid"] currentVersion = request.GET["version"] if request.method == 'POST': if request.content_type == 'application/json': try: from appbe.models import publishmodel status,msg,url = publishmodel(request,usecaseid,currentVersion,Existusecases,usecasedetails) return HttpResponse(json.dumps({"status":status,"msg":msg,"url":url}),content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"model training exception"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong Content Type"}),content_type="application/json") else: msg = help_text(request,usecaseid,version) return HttpResponse(msg,content_type="text/plain") @csrf_exempt def trainstatus(request): usecaseid = request.GET["usecaseid"] currentVersion = request.GET["version"] if request.method == 'POST': if request.content_type == 'application/json': try: data=request.body.decode('utf-8') data = json.loads(data) trainingid = int(data['trainingid']) model = Existusecases.objects.get(id=trainingid) if model.Status.lower() == 'success': return HttpResponse(json.dumps({"status":"success","trainingStatus":"Trained","usecaseid":str(usecaseid),"version":str(model.Version)}),content_type="application/json") else: from appbe.training import checkversionrunningstatus status = checkversionrunningstatus(trainingid,usecasedetails,Existusecases) if status.lower() == 'success': return HttpResponse(json.dumps({"status":"success","trainingStatus":"Trained","usecaseid":str(usecaseid),"version":str(model.Version)}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"success","trainingStatus":status,"usecaseid":str(usecaseid),"version":str(model.Version)}),content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"model training exception"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong Content Type"}),content_type="application/json") else: msg = help_text(request,usecaseid,version) return HttpResponse(msg,content_type="text/plain") @csrf_exempt def retrain(request): usecaseid = request.GET["usecaseid"] currentVersion = request.GET["version"] if request.method == 'POST': if request.content_type == 'application/json': try: data=request.body.decode('utf-8') data = json.loads(data) file_id = data['fileid'] p = usecasedetails.objects.get(usecaseid=usecaseid) s1 = Existusecases.objects.filter(ModelName=p).annotate(maxver=Max('ModelName__existusecases__Version')) config_list = s1.filter(Version=F('maxver')) if config_list.count() > 0: Version = config_list[0].Version Version = Version + 1 model = Existusecases.objects.filter(ModelName=p,Version=currentVersion) indexVal = 0 configfile = str(model[indexVal].ConfigPath) f = open(configfile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) configSettingsJson['basic']['modelVersion'] = str(Version) dataFile = configSettingsJson['basic']['dataLocation'] if os.path.isfile(dataFile): data = pd.read_csv(dataFile,encoding='utf-8',skipinitialspace = True,na_values=['-','?'],encoding_errors= 'replace') dataFile = os.path.join(DATA_FILE_PATH,file_id) data2 = pd.read_csv(dataFile,encoding='utf-8',skipinitialspace = True,na_values=['-','?'],encoding_errors= 'replace') data = data.append(data2,ignore_index=True) data.to_csv(dataFile,index=False) dataFile = os.path.join(DATA_FILE_PATH,file_id) configSettingsJson['basic']['dataLocation'] = str(dataFile) updatedConfigSettings = json.dumps(configSettingsJson) filetimestamp = str(int(time.time())) outputfile = os.path.join(CONFIG_FILE_PATH, 'AION_OUTPUT_' + filetimestamp + '.json') config_json_filename = os.path.join(CONFIG_FILE_PATH, 'AION_' + filetimestamp + '.json') with open(config_json_filename, "w") as fpWrite: fpWrite.write(updatedConfigSettings) fpWrite.close() ps = Existusecases(DataFilePath=str(dataFile), DeployPath='', Status='Not Trained',ConfigPath=str(config_json_filename), Version=Version, ModelName=p,TrainOuputLocation=str(outputfile)) ps.save() scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','aion.py')) outputStr = subprocess.Popen([sys.executable, scriptPath,'-m','training','-c',config_json_filename]) ps.Status = 'Running' ps.trainingPID = outputStr.pid ps.save() return HttpResponse(json.dumps({"status":"success","trainingid":str(ps.id),"version":str(ps.Version),"usecaseid":usecaseid}),content_type="application/json") ''' outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_learner_status:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() resultJsonObj = json.loads(outputStr) ps.Status = resultJsonObj['status'] if resultJsonObj['status'] == 'SUCCESS': ps.modelType = resultJsonObj['data']['ModelType'] ps.DeployPath = str(resultJsonObj['data']['deployLocation']) if resultJsonObj['data']['ModelType'] in ['clustering','anomalydetection', 'timeSeriesAnomalyDetection']: #task 11997 ps.ProblemType = 'unsupervised' else: ps.ProblemType = 'supervised' ps.save() ''' else: return HttpResponse(json.dumps({"status":"error","msg":'Existing trained model not found'}),content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"model training exception"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong Content Type"}),content_type="application/json") else: msg = help_text(request,usecaseid,version) return HttpResponse(msg,content_type="text/plain") @csrf_exempt def uploadfile(request): try: if 'file' not in request.FILES: msg = 'No file part in the request' return HttpResponse(json.dumps({"status":"error","msg":msg}),content_type="application/json") else: file = request.FILES['file'] if file.size > 31457280: msg = 'Upload limit is 30 MB only' ext = str(file).split('.')[-1] if ext.lower() in ['csv','tsv','tar','zip','avro','parquet']: filetimestamp = str(int(time.time())) file_id = 'AION_' + filetimestamp+'.'+ext dataFile = os.path.join(DATA_FILE_PATH,file_id) with open(dataFile, 'wb+') as destination: for chunk in file.chunks(): destination.write(chunk) destination.close() return HttpResponse(json.dumps({"status":"success","fileid":file_id}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"File extension not supported"}),content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"File upload exception"}),content_type="application/json") def help_text(request,usecaseid,version): hosturl =request.get_host() url='http://'+hosturl+'/api/' msg = """ Request_Type: Post Content_Type: applicattion/json For Prediction URL: {url}'predict?usecaseid={usecaseid}&version={version} For Explanations URL: {url}xplain?usecaseid={usecaseid}&version={version} For Input Drift URL: {url}monitoring?usecaseid={usecaseid}&version={version} For Output Drift URL: {url}performance?usecaseid={usecaseid}&version={version} BODY: Data in json format """.format(url=url,usecaseid=usecaseid,version=version) return msg @csrf_exempt def apispredict(request): usecaseid = request.GET["usecaseid"] version = request.GET["version"] if request.method == 'POST': if request.content_type == 'application/json': model_path = os.path.join(DEPLOY_LOCATION,usecaseid,str(version)) isdir = os.path.isdir(model_path) if isdir: try: data=request.body.decode('utf-8') predict_path = os.path.join(model_path,'aion_spredict.py') outputStr = subprocess.check_output([sys.executable,predict_path,data]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)',str(outputStr), re.IGNORECASE).group(1) resp = outputStr.strip() return HttpResponse(resp,content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"Bad Request"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong UseCaseID or Version"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong Content Type"}),content_type="application/json") else: msg = help_text(request,usecaseid,version) return HttpResponse(msg,content_type="text/plain") @csrf_exempt def apipredict(request): usecaseid = request.GET["usecaseid"] version = request.GET["version"] #print(request.content_type) if request.method == 'POST': if request.content_type in ['application/json','multipart/form-data']: model_path = os.path.join(DEPLOY_LOCATION,usecaseid,str(version)) isdir = os.path.isdir(model_path) if isdir: try: data = '' msg = 'Bad request' if 'file' not in request.FILES: data=request.body.decode('utf-8') else: file = request.FILES['file'] if file.size > 31457280: msg = 'Upload limit is 30 MB only' else: ext = str(file).split('.')[-1] if ext.lower() in ['csv','tsv','tar','zip','avro','parquet']: urlData = file.read() import io rawData = pd.read_csv(io.StringIO(urlData.decode('utf-8'))) data = rawData.to_json(orient='records') else: msg = 'Extension not supported' if data != '': predict_path = os.path.join(model_path,'aion_predict.py') outputStr = subprocess.check_output([sys.executable,predict_path,data]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)',str(outputStr), re.IGNORECASE).group(1) resp = outputStr.strip() return HttpResponse(resp,content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":msg}),content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"Bad Request"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong UseCaseID or Version"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong Content Type"}),content_type="application/json") else: msg = help_text(request,usecaseid,version) return HttpResponse(msg,content_type="text/plain") @csrf_exempt def apiinputdrift(request): usecaseid = request.GET["usecaseid"] version = request.GET["version"] if request.method == 'POST': if request.content_type == 'application/json': model_path = os.path.join(DEPLOY_LOCATION,usecaseid,str(version)) isdir = os.path.isdir(model_path) if isdir: try: data=request.body.decode('utf-8') predict_path = os.path.join(model_path,'aion_ipdrift.py') outputStr = subprocess.check_output([sys.executable,predict_path,data]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'drift:(.*)',str(outputStr), re.IGNORECASE).group(1) resp = outputStr.strip() return HttpResponse(resp,content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"Bad Request"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong UseCaseID or Version"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong Content Type"}),content_type="application/json") else: msg = help_text(request,usecaseid,version) return HttpResponse(msg,content_type="text/plain") @csrf_exempt def apioutputdrift(request): usecaseid = request.GET["usecaseid"] version = request.GET["version"] if request.method == 'POST': if request.content_type == 'application/json': model_path = os.path.join(DEPLOY_LOCATION,usecaseid,str(version)) isdir = os.path.isdir(model_path) if isdir: try: data=request.body.decode('utf-8') predict_path = os.path.join(model_path,'aion_opdrift.py') outputStr = subprocess.check_output([sys.executable,predict_path,data]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'drift:(.*)',str(outputStr), re.IGNORECASE).group(1) resp = outputStr.strip() return HttpResponse(resp,content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"Bad Request"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong UseCaseID or Version"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong Content Type"}),content_type="application/json") else: msg = help_text(request,usecaseid,version) return HttpResponse(msg,content_type="text/plain") @csrf_exempt def apixplain(request): usecaseid = request.GET["usecaseid"] version = request.GET["version"] if request.method == 'POST': if request.content_type == 'application/json': model_path = (Path(DEPLOY_LOCATION)/usecaseid)/str(version) if model_path.is_dir(): try: with open( (model_path/'etc')/'display.json', 'r') as f: disp_data = json.load(f) is_explainable = not disp_data.get('textFeatures') except: is_explainable = True try: if not is_explainable: return HttpResponse(json.dumps({"status":"error","msg":"explain api is not supported when text features are used for training"}),content_type="application/json") data=request.body.decode('utf-8') predict_path = model_path/'aion_xai.py' outputStr = subprocess.check_output([sys.executable,predict_path,'local',data]) #BugId:13304 outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_ai_explanation:(.*)',str(outputStr), re.IGNORECASE).group(1) resp = outputStr.strip() return HttpResponse(resp,content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"Bad Request"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong UseCaseID or Version"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong Content Type"}),content_type="application/json") else: msg = help_text(request,usecaseid,version) return HttpResponse(msg,content_type="text/plain") #@api_view(['POST','GET']) def apifeatures(request): usecaseid = request.GET["usecaseid"] version = request.GET["version"] if request.content_type == 'application/json': model_path = os.path.join(DEPLOY_LOCATION,usecaseid,str(version)) isdir = os.path.isdir(model_path) if isdir: try: data=request.body.decode('utf-8') predict_path = os.path.join(model_path,'featureslist.py') outputStr = subprocess.check_output([sys.executable,predict_path,data]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)',str(outputStr), re.IGNORECASE).group(1) resp = outputStr.strip() return HttpResponse(resp,content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"Bad Request"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong UseCaseID or Version"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Wrong Content Type"}),content_type="application/json") @csrf_exempt def apiPatternAnomalySettings(request): usecaseid = request.GET["usecaseid"] version = request.GET["version"] if request.content_type == 'application/json': model_path = os.path.join(DEPLOY_LOCATION,usecaseid,str(version)) isdir = os.path.isdir(model_path) if isdir: try: data=request.body.decode('utf-8') data = json.loads(data) groupswitching = data['groupswitching'] transitionprobability = data['transitionprobability'] transitionsequence = data['transitionsequence'] sequencethreshold = data['sequencethreshold'] filename = os.path.join(model_path,'clickstream.json') print(filename) data = {} data['groupswitching'] = groupswitching data['transitionprobability'] = transitionprobability data['transitionsequence'] = transitionsequence data['sequencethreshold'] = sequencethreshold updatedConfig = json.dumps(data) with open(filename, "w") as fpWrite: fpWrite.write(updatedConfig) fpWrite.close() return HttpResponse(json.dumps({"status":'Success'}),content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"Bad Request"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Bad Request"}),content_type="application/json") else: msg = help_text(request,usecaseid,version) return HttpResponse(msg,content_type="text/plain") #@api_view(['POST']) @csrf_exempt def apiPatternAnomalyPredict(request): import pandas as pd usecaseid = request.GET["usecaseid"] version = request.GET["version"] if request.content_type == 'application/json': model_path = os.path.join(DEPLOY_LOCATION,usecaseid,str(version)) isdir = os.path.isdir(model_path) if isdir: try: data=request.body.decode('utf-8') data = json.loads(data) anomaly = False remarks = '' clusterid = -1 configfilename = os.path.join(model_path,'datadetails.json') filename = os.path.join(model_path,'clickstream.json') clusterfilename = os.path.join(model_path,'stateClustering.csv') probfilename = os.path.join(model_path,'stateTransitionProbability.csv') dfclus = pd.read_csv(clusterfilename) dfprod = pd.read_csv(probfilename) f = open(configfilename, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) activity = configSettingsJson['activity'] sessionid = configSettingsJson['sessionid'] f = open(filename, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) groupswitching = configSettingsJson['groupswitching'] page_threshold = configSettingsJson['transitionprobability'] chain_count = configSettingsJson['transitionsequence'] chain_probability = configSettingsJson['sequencethreshold'] currentactivity = data[activity] if bool(user_records): sessionid = data[sessionid] if sessionid != user_records['SessionID']: user_records['SessionID'] = sessionid prevactivity = '' user_records['probarry'] = [] user_records['prevclusterid'] = -1 user_records['NoOfClusterHopping'] = 0 user_records['pageclicks'] = 1 else: prevactivity = user_records['Activity'] user_records['Activity'] = currentactivity pageswitch = True if prevactivity == currentactivity or prevactivity == '': probability = 0 pageswitch = False remarks = '' else: user_records['pageclicks'] += 1 df1 = dfprod[(dfprod['State'] == prevactivity) & (dfprod['NextState'] == currentactivity)] if df1.empty: remarks = 'Anomaly Detected - User in unusual state' anomaly = True clusterid = -1 probability = 0 user_records['probarry'].append(probability) n=int(chain_count) num_list = user_records['probarry'][-n:] avg = sum(num_list)/len(num_list) for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] else: probability = df1['Probability'].iloc[0] user_records['probarry'].append(probability) n=int(chain_count) num_list = user_records['probarry'][-n:] davg = sum(num_list)/len(num_list) for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] remarks = '' if user_records['prevclusterid'] != -1: if probability == 0 and user_records['prevclusterid'] != clusterid: user_records['NoOfClusterHopping'] = user_records['NoOfClusterHopping']+1 if user_records['pageclicks'] == 1: remarks = 'Anomaly Detected - Frequent Cluster Hopping' anomaly = True else: remarks = 'Cluster Hopping Detected' user_records['pageclicks'] = 0 if user_records['NoOfClusterHopping'] > int(groupswitching) and anomaly == False: remarks = 'Anomaly Detected - Multiple Cluster Hopping' anomaly = True elif probability == 0: remarks = 'Anomaly Detected - Unusual State Transition Detected' anomaly = True elif probability <= float(page_threshold): remarks = 'Anomaly Detected - In-frequent State Transition Detected' anomaly = True else: if pageswitch == True: if probability == 0: remarks = 'Anomaly Detected - Unusual State Transition Detected' anomaly = True elif probability <= float(page_threshold): remarks = 'Anomaly Detected - In-frequent State Transition Detected' anomaly = True else: remarks = '' if davg < float(chain_probability): if anomaly == False: remarks = 'Anomaly Detected - In-frequent Pattern Detected' anomaly = True else: user_records['SessionID'] = data[sessionid] user_records['Activity'] = data[activity] user_records['probability'] = 0 user_records['probarry'] = [] user_records['chainprobability'] = 0 user_records['prevclusterid'] = -1 user_records['NoOfClusterHopping'] = 0 user_records['pageclicks'] = 1 for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] user_records['prevclusterid'] = clusterid outputStr = {'status':'SUCCESS','data':{'Anomaly':str(anomaly),'Remarks':str(remarks)}} return HttpResponse(json.dumps(outputStr),content_type="application/json") except Exception as e: print(e) return HttpResponse(json.dumps({"status":"error","msg":"Bad Request"}),content_type="application/json") else: return HttpResponse(json.dumps({"status":"error","msg":"Bad Request"}),content_type="application/json") else: msg = help_text(request,usecaseid,version) return HttpResponse(msg,content_type="text/plain") """mpgWebApp URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path from django.urls import include, re_path from appfe.modelTraining import views from appfe.modelTraining import upload_views from appfe.modelTraining import bc_views from appfe.modelTraining import mltest_views from appfe.modelTraining import train_views from appfe.modelTraining import dg_views from appfe.modelTraining import settings_views from appfe.modelTraining import drift_views from appfe.modelTraining import landing_views from appfe.modelTraining import mllite_views from appfe.modelTraining import trustedAI_views from appfe.modelTraining import llm_views from appfe.modelTraining import visualizer_views as v from appfe.modelTraining import prediction_views from django.urls import path, re_path urlpatterns = [ path('admin/', admin.site.urls), path('api/', include('appfe.api.urls')), path('', views.index, name="index"), re_path('^$',views.index,name='Homepage'), re_path('prediction', prediction_views.Prediction, name="Prediction"), path('edit/<int:id>', views.edit), path('update/<int:id>', views.update), path('opentraining/<int:id>/<int:currentVersion>',views.opentraining), path('opentraininglogs/<int:id>/<int:currentVersion>',landing_views.opentraininglogs), path('show',views.show,name="show"), path('ucdetails/<int:id>',views.ucdetails,name='ucdetails'), path('delete/<int:id>', views.destroy,name='DeleteUseCase'), path('deleteversion/<int:id>',views.remove_version,name='RemoveVersion'), path('deletes3Bucket/<str:name>', settings_views.removes3bucket,name='removes3bucket'), path('deleteGcsBucket/<str:name>', settings_views.removegcsbucket,name='removegcsbucket'), path('deleteAzureBucket/<str:name>', settings_views.removeazurebucket,name='removeazurebucket'), path('publish/<int:id>',views.publish), path('createpackagedocker/<int:id>/<int:version>',views.createpackagedocker), path('stoptraining',train_views.stoptraining), path('downloadPackage/<int:id>/<int:version>',views.downloadpackage), re_path('startmodelservice',views.startmodelservice,name="startmodelservice"), re_path('stopmodelservice',views.stopmodelservice,name="stopmodelservice"), path('retrain/<int:id>/<int:currentVersion>', landing_views.retrain), re_path('computetoAWS',settings_views.computetoAWS,name='computeInfrastructure'), re_path('computetoLLaMMA7b',settings_views.computetoLLaMMA7b,name='computeInfrastructure'), re_path('computetoGCPLLaMA13B',settings_views.computetoGCPLLaMA13B,name='computeInfrastructure'), re_path('help',views.help,name = "help"), re_path('mlac_userguide',views.mlac_userguide,name = "mlac_userguide"), path('launchmodel/<int:id>/<int:version>', landing_views.launchmodel), path('modxplain/<int:id>/<int:version>', landing_views.modxplain), path('moddrift/<int:id>/<int:version>',landing_views.moddrift), re_path('ConvertOnnx', mllite_views.ConvertOnnx, name="ConvertOnnx"), re_path('runtimeutility', mllite_views.runtimeutility, name="runtimeutility"), re_path('sagepredict', mllite_views.sageprediction, name="sageprediction"), re_path('mlstyles', views.mlstyles, name="mlstyles"), re_path('mltrain', views.mltrain, name="mltrain"), re_path('usecasefilter', views.usecasefilter, name="usecasefilter"), re_path('mlpredict', views.mlpredict, name="mlpredict"), re_path('getdataclasses',views.getdataclasses,name="getdataclasses"), re_path('usecases', views.AIusecases, name="AIusecases"), re_path('modelkafka',views.modelkafka,name="ModelKafka"), re_path('AionProblem', views.AionProblem, name="AionProblem"), re_path('UQTesting', mltest_views.UQTesting, name="UQTesting"), re_path('maaccommand',views.maaccommand,name='MAAC'), re_path('GCSbucketAdd',settings_views.GCSbucketAdd,name="gcsbucket"), re_path('adds3bucket',settings_views.adds3bucket,name="adds3bucket"), re_path('azurestorageAdd',settings_views.azurestorageAdd,name="azurestorageAdd"), re_path('features', views.features, name="features"), re_path('downloadedareport',upload_views.downloadedareport,name="downloadedareport"), re_path('downloadxplainreport',views.downloadxplainreport,name="downloadxplainreport"), re_path('downlpredictreport',views.downlpredictreport,name="DownloadPrediction"), re_path('LoadBasicConfiguration',views.LoadBasicConfiguration,name='LoadBasicConfiguration'), re_path('LoadAdvanceConfiguration',views.LoadAdvanceConfiguration,name='LoadAdvanceConfiguration'), re_path('uploaddatafromscript',upload_views.uploaddatafromscript,name='uploaddatafromscript'), re_path('features', views.features, name="features"), re_path('uploadDatafromSatandardDataset',upload_views.uploadDatafromSatandardDataset,name="uploadDatafromSatandardDataset"), re_path('uploadDatafromunsupervisedmodel',views.uploadDatafromunsupervisedmodel,name="uploadDatafromunsupervisedmodel"), re_path('mltesting',mltest_views.mltesting,name='mltesting'), re_path('mllite',views.mllite,name="MLLite"), re_path('settings',settings_views.settings_page,name="settings"), re_path('openneural',mllite_views.openneural,name="openneural"), re_path('Tfliteneural',mllite_views.Tfliteneural,name="Tfliteneural"), re_path('encryptedpackage',views.encryptedpackage,name='encryptedpackage'), re_path('ABtesting', mltest_views.ABtest, name="ABtesting"), re_path('uploadedData', upload_views.uploadedData, name='uploadedData'), # Text Data Labelling using LLM related changes # -------------------------------------------------------- re_path('uploadedTextData', llm_views.uploadedTextData, name='uploadedTextData'), re_path('getTextLabel', llm_views.getTextLabel, name='getTextLabel'), re_path('downloadTextLabelReport',llm_views.downloadTextLabelReport,name="downloadTopicReport"), # -------------------------------------------------------- # QnA Generator using LLM related changes # -------------------------------------------------------- re_path('genearateQA', llm_views.genearateQA, name='genearateQA'), re_path('downloadQnAReport',llm_views.downloadQnAReport,name="downloadQnAReport"), # -------------------------------------------------------- re_path('advanceconfig', bc_views.savebasicconfig, name='Advance'), re_path('edaReport',upload_views.EDAReport,name='edareport'), re_path('readlogfile',views.readlogfile,name="readlogfile"), re_path('flcommand',views.flcommand,name="flcommand"), re_path('openmlflow',views.mlflowtracking,name="MLflow"), re_path('basicconfig',bc_views.basicconfig,name='basicConfig'), re_path('Advance',views.Advance,name='Advance'), re_path('uploaddata', views.uploaddata, name='uploaddata'), re_path('dataupload', views.Dataupload, name='dataupload'), re_path('trainmodel', train_views.trainmodel, name='next'), #Sagemaker re_path('Sagemaker',mllite_views.Sagemaker,name="Sagemaker"), re_path('batchlearning',views.batchlearning,name="batchlearning"), # EDA Reports changes re_path('gotoreport', views.gotoreport, name='report'), re_path('llmmodelevaluate',train_views.llmmodelevaluate, name='llmmodelevaluate'), # EDA Visualization changes re_path('getgraph',views.getgraph,name="getgraph"), # Fairness Metrics changes re_path('getmetrics',views.getmetrics,name="getmetrics"), re_path('getDeepDiveData',views.getDeepDiveData,name="getDeepDiveData"), # 12686:Data Distribution related Changes re_path('getDataDistribution',views.getDataDistribution,name="getDataDistribution"), re_path('licensekey',views.licensekey,name="licensekey"), # -------------------------------- Graviton-Integration Changes S T A R T -------------------------------- re_path('getuserdata',views.getuserdata,name="getuserdata"), re_path('getdataservice',views.getdataservice,name="getdataservice"), # ------------------------------------------------ E N D ------------------------------------------------- re_path('getdataimbalance',views.getdataimbalance,name="getdataimbalance"), re_path('trainresult',train_views.trainresult,name='trainresult'), re_path('LoadDataForSingleInstance',views.LoadDataForSingleInstance,name='LoadDataForSingleInstance'), re_path('PredictForSingleInstance',prediction_views.PredictForSingleInstance,name='PredictForSingleInstance'), re_path('stateTransitionSettings',views.stateTransitionSettings,name='stateTransitionSettings'), re_path('instancepredict',views.instancepredict,name='predict'), re_path('onnxruntime',views.onnxruntime,name='onnxruntime'), re_path('home',views.Dataupload,name='manage'), re_path('show',views.show,name="show"), re_path('delete',views.show,name="delete"), re_path('inputdrift', landing_views.inputdrift, name='inputdrift'), re_path('dotextSummarization',views.dotextSummarization,name='textSummarization'), re_path('outputdrift', views.outputdrift, name='outputdrift'), re_path('xplain', v.xplain, name='xplain'), re_path('sensitivity', trustedAI_views.sensitivityAnalysis, name='sensitivity'), re_path('fairnesmetrics', trustedAI_views.fairnesmetrics, name='fairnesmetrics'), re_path('handlefairness', trustedAI_views.handlefairness, name='handlefairness'), re_path('performance', trustedAI_views.performance_metrics, name='performance'), re_path('uquncertainty', trustedAI_views.uquncertainty, name='uquncertainty'), re_path('uqtransparency', trustedAI_views.uqtransparency, name='uqtransparency'), re_path('RLpath',views.RLpath,name='RLpath'), path('opendetailedlogs/<int:id>/<int:currentVersion>', views.opendetailedlogs, name='logfile'), path('downloadlogfile/<int:id>/<int:currentVersion>',views.downloadlogfile), path('openmodelevaluation/<int:id>',views.openmodelevaluation,name='openmodelevaluation'), re_path('startPublishServices',settings_views.startPublishServices,name="PublishService"), re_path('startKafka',settings_views.startKafka,name='startKafka'), re_path('startService',views.startService,name='startService'), re_path('startTracking',views.startTracking,name="startTracking"), re_path('Drift', drift_views.Drift, name='Drift'), re_path('Distribution', drift_views.Distribution, name='Distribution'), re_path('newfile', views.newfile, name='newfile'), re_path('Evaluate', drift_views.Evaluate, name='Evaluate'), re_path('qlearning',views.qlearning,name='qlearning'), re_path('listfiles',upload_views.listfiles,name='listfiles'), #url('actionavalanche',views.actionavalanche,name='actionavalanche'), re_path('sqlAlchemy',upload_views.sqlAlchemy,name='sqlAlchemy'), re_path('submitquery',upload_views.submitquery,name='submitquery'), re_path('validatecsv',upload_views.validatecsv,name='validatecsv'), path('ObjLabelAdd/<int:id>',views.ObjLabelAdd), path('objectlabel/<int:id>',views.objectlabel), path('imagelabel/<int:id>',views.imagelabel), path('ObjLabelRemove/<int:id>',views.ObjLabelRemove), re_path('objectlabelling',views.objectlabelling,name='objectlabelling'), re_path('imagelabelling',views.imagelabelling,name='imagelabelling'), re_path('ObjLabelDiscard',views.ObjLabelDiscard,name='ObjLabelDiscard'), re_path('ObjLabelNext',views.ObjLabelNext,name='ObjLabelNext'), re_path('ObjLabelPrev',views.ObjLabelPrev,name="ObjLabelPrev"), re_path('saveaionconfig',settings_views.saveaionconfig,name='saveaionconfig'), re_path('savegravitonconfig',settings_views.savegravitonconfig,name='savegravitonconfig'), re_path('saveopenaiconfig',settings_views.saveopenaiconfig,name="saveopenaiconfig"), re_path('getvalidateddata',views.getvalidateddata,name="getvalidateddata"), re_path('updateawsconfig',settings_views.amazonec2settings,name="amazonec2settings"), re_path('updategcpconfig',settings_views.gcpcomputesettings,name="gcpcomputesettings"), re_path('localsetings',views.localsetings,name="localsetings"), re_path('ImgLabelNext',views.ImgLabelNext,name='ImgLabelNext'), re_path('objectlabeldone',views.objectlabeldone,name='ObjectLabeling'), re_path(r'^get_table_list', upload_views.get_table_list, name='get_table_list'), re_path(r'^getdatasetname', views.getdatasetname, name='getdatasetname'), re_path(r'^get_tables_fields_list', upload_views.get_tables_fields_list, name='get_tables_fields_list'), re_path(r'^validate_query', upload_views.validate_query, name='validate_query'), re_path(r'^trigger_DAG', views.trigger_DAG, name = 'trigger_DAG'), # The home page path('dataoperations', views.dataoperations, name='dataoperations'), path('summarization',views.summarization,name='summarization'), path('datalabel', views.datalabel, name='datalabel'), path('upload_and_read_file_data', views.upload_and_read_file_data, name='upload_and_read_file_data'), path('apply_rule', views.apply_rule, name='apply_rule'), path('apply_rule_ver2', views.apply_rule_ver2, name='apply_rule_ver2'), path('download_result_dataset', views.download_result_dataset, name='download_result_dataset'), path('get_sample_result_of_individual_rule', views.get_sample_result_of_individual_rule, name='get_sample_result_of_individual_rule'), path('get_sample_result_of_individual_rule_ver2', views.get_sample_result_of_individual_rule_ver2, name='get_sample_result_of_individual_rule_ver2'), path('upload_and_read_test_data', views.upload_and_read_test_data, name='upload_and_read_test_data'), path('get_label_and_weightage', views.get_label_and_weightage, name='get_label_and_weightage'), path('datagenrate', dg_views.datagenrate, name='datagenrate'), path('generateconfig', dg_views.generateconfig, name='generateconfig'), path('StationarySeasonalityTest', views.StationarySeasonalityTest, name='StationarySeasonalityTest'), path('modelcompare', views.modelcompare, name='modelcompare'), path('textsummarization', views.textsummarization, name='textsummarization'), path('azureOpenAiDavinci', llm_views.azureOpenAiDavinci, name='azureOpenAiDavinci'), path('azureOpenAiDavinciSumarization', llm_views.azureOpenAiDavinciSumarization, name='azureOpenAiDavinciSumarization'), # LLM Testing path('llmtesting', views.llmtesting, name='llmtesting'), path('llmtestingresult', views.llmtestingresult, name='llmtestingresult'), re_path('llmtestreport',views.llmtestreport,name="llmtestreport"), # Code Clone Detection path('codeclonedetectionresult', views.codeclonedetectionresult, name='codeclonedetectionresult'), re_path('codeclonereport',views.codeclonereport,name="codeclonereport"), re_path('evaluateprompt',views.evaluatepromptmetrics,name="evaluatepromptmetrics"), path('libraries', views.libraries, name='libraries'), #To display libraries ] #df=pd.read_csv("C:\Project\Analytics\Deployment\germancredit_9\germancreditdata.csv") # #bool_cols = [col for col in df if np.isin(df[col].dropna().unique(), [0, 1]).all()] # #bool_cols from django.http import HttpResponse from django.conf import settings import traceback class ErrorHandlerMiddleware: def __init__(self, get_response): self.get_response = get_response def __call__(self, request): response = self.get_response(request) return response def process_exception(self, request, exception): if not settings.DEBUG: if exception: # Format your message here message = "**{url}**\n\n{error}\n\n````{tb}````".format( url=request.build_absolute_uri(), error=repr(exception), tb=traceback.format_exc() ) # Do now whatever with this message # e.g. requests.post(<slack channel/teams channel>, data=message) return HttpResponse("Error processing the request.", status=500) """ WSGI config for ux project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'ux.settings') application = get_wsgi_application() """ ASGI config for ux project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'ux.settings') application = get_asgi_application() """ Django settings for mpgWebApp project. Generated by 'django-admin startproject' using Django 3.0.3. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os from os.path import expanduser import platform from appbe.dataPath import DATA_DIR #from cloghandler import ConcurrentRotatingFileHandler sql_database_path = os.path.join(DATA_DIR,'sqlite') if os.path.isdir(sql_database_path) == False: os.makedirs(sql_database_path) DATA_UPLOAD_MAX_NUMBER_FIELDS = None DATA_UPLOAD_MAX_MEMORY_SIZE = None # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) #BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath())) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'y8d*&k0jv4c*zu^ykqz$=yyv@(lcmz495uj^()hthjs=x&&g0y' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['*'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'appfe.modelTraining', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'appfe.ux.error_handler.ErrorHandlerMiddleware' ] ROOT_URLCONF = 'appfe.ux.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR,'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'appfe.ux.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(sql_database_path, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT=os.path.join(BASE_DIR,'static') from django import forms from modelTraining.models import usecasedetails import os class usecasedetailsForm(forms.ModelForm): class Meta: model = usecasedetails fields = "__all__" from modelTraining.models import Existusecases class ExistusecasesForm(forms.ModelForm): class Meta: model = Existusecases fields = "__all__" from django import forms from appfe.modelTraining.models import usecasedetails from appfe.modelTraining.models import Existusecases class usecasedetailsForm(forms.ModelForm): class Meta: model = usecasedetails fields = "__all__" class ExistusecasesForm(forms.ModelForm): class Meta: model = Existusecases fields = "__all__" from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect import time from django.template import loader from django import template from django.views.decorators.csrf import csrf_exempt from os import walk from plotly.subplots import make_subplots import plotly.graph_objects as go from appbe import help_Text as ht import random from appbe import service_url from appbe import compute from appbe import installPackage from appbe.pages import getusercasestatus from appbe import images_analysis as ia from django.db.models import Max, F from appbe.aion_config import settings from appbe.aion_config import get_graviton_data from appbe.aion_config import get_llm_data from appbe.aion_config import get_edafeatures from appbe.training import calculate_total_activities from appbe.training import calculate_total_interations from appbe.training import checkModelUnderTraining from appbe.training import checkversionrunningstatus from appbe.training import changeModelStatus from appbe.training import getStatusCount from appbe.training import getModelStatus from appbe.training import check_unsupported_col from appbe.publish import chech_publish_info from appbe.publish import check_input_data import uuid import numpy as np from appbe.aion_config import kafka_setting from appbe.aion_config import running_setting from appbe.validatecsv import csv_validator from appbe.aion_config import addKafkaModel from appbe.aion_config import getrunningstatus from appbe.aion_config import aion_service from appbe.pages import getversion from appbe.s3bucketsDB import get_s3_bucket from appbe.s3bucketsDB import read_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.gcsbucketsDB import read_gcs_bucket from appbe.azureStorageDB import get_azureStorage from appbe.azureStorageDB import read_azureStorage from appbe.dataIngestion import getcommonfields from appbe.dataIngestion import ingestDataFromFile from appbe.dataIngestion import delimitedsetting import pdfplumber from docx import Document from appbe.trainresult import ParseResults import pandas as pd import numpy as np import re import xml.etree.ElementTree as ET import json import glob from appbe import dataPath from pathlib import Path import urllib, base64 import os from os.path import expanduser import platform import time import sys import csv import subprocess import base64 from appfe.modelTraining.models import usecasedetails from appfe.modelTraining.forms import usecasedetailsForm from appfe.modelTraining.models import Existusecases from django.shortcuts import get_list_or_404, get_object_or_404 from pandas import json_normalize from django.contrib.sessions.models import Session import logging from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from utils.file_ops import read_df_compressed from appbe.dataPath import LOG_LOCATION from appbe.log_ut import logg LOG_FILE_NAME = 'model_training_logs.log' LOG_FOLDER = 'log' if os.path.isdir(DATA_FILE_PATH) == False: os.makedirs(DATA_FILE_PATH) if os.path.isdir(CONFIG_FILE_PATH) == False: os.makedirs(CONFIG_FILE_PATH) if os.path.isdir(DEPLOY_LOCATION) == False: os.makedirs(DEPLOY_LOCATION) # EION_SCRIPT_PATH = 'C:\\Project\\Analytics\\eion\\eion\\eion.py' PYTHON_PATH = 'python.exe' AION_VERSION = getversion() usecasetab = settings() #AION_VERSION # MainPage logg_obj = logg(LOG_LOCATION) log = logg_obj.create_log(AION_VERSION) def index(request): from appbe.pages import index_page status,context,action = index_page(request,usecasedetails,Existusecases) context['version'] = AION_VERSION return render(request,action,context) def localsetings(request): from appbe.pages import get_usecase_page try: compute.updatelocalsetings(request) time.sleep(2) request.session['IsRetraining'] = 'No' #print(1) status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['version'] = AION_VERSION #print(2) return render(request,action,context) except Exception as e: print(e) return render(request, 'usecases.html',{'error': 'Fail to update localsetings','version':AION_VERSION}) def computetoAWS(request): from appbe.pages import get_usecase_page try: compute.updateToComputeSettings(request) time.sleep(2) #print(1) request.session['IsRetraining'] = 'No' status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['version'] = AION_VERSION return render(request,action,context) except Exception as e: print(e) return render(request, 'usecases.html',{'error': 'Fail to update ComputeSettings','version':AION_VERSION}) def licensekey(request): try: command = request.POST['licensesubmit'] if command.lower() == 'generatelicense': userkey = request.POST['userkey'] from records import pushrecords msg = pushrecords.generateLicenseKey(userkey) context = {'msg':msg} context['selected'] = 'License' print(context) return render(request,'licenseexpired.html',context) else: licensekey = request.POST['licensekey'] from records import pushrecords pushrecords.updateLicense(licensekey) from appbe.pages import get_usecase_page status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['version'] = AION_VERSION return render(request,action,context) except Exception as e: print(e) return render(request, 'usecases.html',{'error': 'Fails in loading the page','version':AION_VERSION}) def help(request): context = {'selected': 'userguide', 'usecasetab': usecasetab} context['version'] = AION_VERSION return render(request, 'help.html', context) def mlac_userguide(request): context = {'selected': 'mlac_userguide', 'usecasetab': usecasetab} context['version'] = AION_VERSION return render(request, 'help.html', context) def AionProblem(request): if request.method == "POST": AionProblem = request.POST["Algorithm"] request.session["AionProblem"] = AionProblem return HttpResponse(AionProblem) def features(request): if request.method == "POST": typedata = request.POST['datatype'] if typedata == "datapath": datapath = request.POST['datap'] if(os.path.isfile(datapath) and os.path.isfile(datapath)): df = pd.read_csv(datapath) modelfeature = df.columns.tolist() modelfeatures = json.dumps(modelfeature) return HttpResponse(modelfeatures) else: return HttpResponse(json.dumps("Data path does not exist "), content_type="application/error") elif typedata == "scriptpath": scriptPath = request.POST['scriptp'] #print(scriptPath) f = open(scriptPath, "r") pythoncode = f.read() f.close() ldict = {} exec(pythoncode, globals(), ldict) df = ldict['dfpy'] filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') df.to_csv(dataFile, index=False) modelfeature = df.columns.tolist() output = {'features':modelfeature,'datafile':dataFile} output = json.dumps(output) # return render(request,'prediction.html',{'modelfeatures':modelfeatures,'test':'test'}) return HttpResponse(output) def mllite(request): from appbe.pages import mllite_page context = mllite_page(request) context['version'] = AION_VERSION return render(request, 'ConvertOnnx.html',context) def usecasefilter(request): from appbe import mlstyles as mls selectedoption = request.GET["selectedoption"] context = mls.Aiusecases(request,selectedoption) context['listtype'] = selectedoption context['version'] = AION_VERSION return render(request, 'aiUseCases.html',context) def AIusecases(request): from appbe import mlstyles as mls context = mls.Aiusecases(request,'Implemented') context['listtype'] = 'Implemented' context['version'] = AION_VERSION return render(request, 'aiUseCases.html',context) def mlstyles(request): from appbe import mlstyles as mls context = mls.mlstyles(request) context['selected'] = 'DataOperations' context['version'] = AION_VERSION return render(request, 'mlstyles.html',context) def mlpredict(request): from appbe import mlstyles as mls context, button_flag = mls.mlpredict(request) context['selected'] = 'DataOperations' context['version'] = AION_VERSION if button_flag in ['prediction','predictsingle']: return render(request, 'mlstyles.html', context) else: return context def mltrain(request): from appbe import mlstyles as mls context, button_flag = mls.mltrain(request) context['selected'] = 'DataOperations' context['version'] = AION_VERSION if button_flag == 'training': return render(request, 'mlstyles.html', context) else: return context def getdatasetname(request): try: from appbe.dataPath import DATA_DIR from appbe.sqliteUtility import sqlite_db file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') temp_data = sqlite_obj.read_data('dataingest') data = [] for x in temp_data: data_dict = {} data_dict['datasetname'] = x[1] data.append(data_dict) except Exception as e: print(e) data = [] return HttpResponse(json.dumps(data)) def outputdrift(request): try: selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) computeinfrastructure = compute.readComputeConfig() if ModelStatus != 'SUCCESS': context = {'error': 'Please train the model first or launch an existing trained model', 'selected_use_case': selected_use_case,'usecasetab':usecasetab, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'monitoring','computeinfrastructure':computeinfrastructure} else: updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) problemtypes = configSettingsJson['basic']['analysisType'] problem_type = "" for k in problemtypes.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break problem = problem_type ser_url = service_url.read_performance_service_url_params() iterName = request.session['UseCaseName'].replace(" ", "_") ModelVersion = request.session['ModelVersion'] ser_url = ser_url+'performance?usecaseid='+iterName+'&version='+str(ModelVersion) if problem.lower() not in ['classification','regression']: context = {'error': 'Output drift only available for classification and regression problems type', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'monitoring','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab} else: context = {'SUCCESS': 'Model is trained', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'usecasetab':usecasetab, 'ModelVersion': ModelVersion, 'selected': 'monitoring','computeinfrastructure':computeinfrastructure,'ser_url':ser_url,'trainingDataLocation':request.session['datalocation']} return render(request, 'outputdrif.html', context) except: return render(request, 'outputdrif.html', {'error':'Fail to do outputdrift analysis','usecasetab':usecasetab}) # -------------------------------- Graviton-Integration Changes S T A R T -------------------------------- def getuserdata(request): import requests data = [] try: graviton_url,graviton_userid = get_graviton_data() gravitonURL = graviton_url gravitonUserId = graviton_userid # url = 'https://xenius.azurewebsites.net/api/dataservices?userid='+ url = gravitonURL + 'dataservices?userid=' + gravitonUserId print(url) response = requests.get(url) statuscode = response.status_code print(statuscode) if statuscode == 200: json_dictionary = json.loads(response.content) data = json_dictionary['result'] print(data) except Exception as e: print(e) data = [] data_json = json.dumps(data) return HttpResponse(data_json) def getdataservice(request): import requests data = [] dataServiceId = request.GET.get('DataServiceId') try: graviton_url,graviton_userid = get_graviton_data() gravitonURL = graviton_url gravitonUserId = graviton_userid # url = 'https://xenius.azurewebsites.net/api/getmetadata?userid=1&dataserviceid='+str(dataServiceId) url = gravitonURL + 'getmetadata?userid=' + gravitonUserId +'&dataserviceid='+str(dataServiceId) response = requests.get(url) statuscode = response.status_code if statuscode == 200: json_dictionary = json.loads(response.content) data = json_dictionary['result'] except Exception as e: print(e) data = [] data_json = json.dumps(data) return HttpResponse(data_json) # ------------------------------------------------ E N D ------------------------------------------------- def getvalidateddata(request): import requests computeinfrastructure = compute.readComputeConfig() taskid = request.POST.get('elixirdatataskid') try: url = 'http://'+elixir_ip+':'+elixir_port+'/api/get_validation_result?task_id='+str(taskid) #print(url) response = requests.get(url) statuscode = response.status_code if statuscode == 200: json_dictionary = json.loads(response.content) data = json_dictionary['Result'] else: data = [] except Exception as e: print(e) data = [] try: df = pd.DataFrame.from_dict(data) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') request.session['datalocation'] = str(dataFile) df.to_csv(dataFile, index=False) df_top = df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) statusmsg = 'Data File Uploaded Successfully ' selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] request.session['currentstate'] = 0 request.session['finalstate'] = 0 request.session['datatype'] = 'Normal' context = {'tab': 'tabconfigure','data': df_json,'status_msg': statusmsg,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning', 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'exploratory':False,'computeinfrastructure':computeinfrastructure} return render(request, 'upload.html', context) except: context = {'tab': 'tabconfigure','selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,"usecaseerror":"Error in validating data!"} return render(request, 'upload.html', context) def trigger_DAG(request): from appfe.modelTraining import AirflowLib response = AirflowLib.TriggerDag("example_complex", "") return HttpResponse(response, content_type="application/json") def Airflow(request): try: selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] context = {'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'monitoring', 'airflow': True} return render(request, 'upload.html', context) except: return render(request, 'upload.html', {'error':'interrupted error'}) def Results(request): return render(request, 'modeltraning.html', context) def uploadnext(request): return render(request, 'basicconfig.html', {'selected': 'modeltraning','version':AION_VERSION}) def basicconfignext(request): from appbe import advance_Config as ac context = ac.basicconfignex(request) computeinfrastructure = compute.readComputeConfig() context['computeinfrastructure'] = computeinfrastructure context['version'] = AION_VERSION return render(request, 'advancedconfig.html', context) def updateRunConfig(_trainingTime, _filesize, _features, _modelname, _problem_type): returnVal = 'Success' try: import psutil memInGB = round(psutil.virtual_memory().total / (1024 * 1024 * 1024)) _resource = str(memInGB) + " GB" _time = str(_trainingTime) + " Mins" new_record = { "sampleSize": _filesize, "features": _features, "algorithm": _modelname, "machineResource": _resource, "trainingTime": _time, "problemtype": _problem_type } configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','config','training_runs.json') if(os.path.isfile(configfilepath)): with open(configfilepath,'r+') as file: # load existing data into a dict. file_data = json.load(file) # join new_record with file_data inside runs file_data["runs"].append(new_record) # sets file's current position at offset. file.seek(0) # convert back to json. json.dump(file_data, file, indent = 4) except Exception as inst: returnVal = 'Fail' pass return returnVal def objectlabeldone(request): try: computeinfrastructure = compute.readComputeConfig() request.session['datatype'] = 'Object' request.session['csvfullpath'] = request.session['objectLabelFileName'] df = pd.read_csv(request.session['csvfullpath']) df1 = df.groupby(['Label']).agg({"File":{"count","nunique"}}) df1.columns = df1.columns.droplevel(0) df1 = df1.reset_index() class_count = [] for i in range(len(df1)): dct = {} dct['Label'] = df1.loc[i, "Label"] dct['TotalAnnotations'] = df1.loc[i, "count"] dct['Images'] = df1.loc[i, "nunique"] class_count.append(dct) #orxml_file in glob.glob(request.session['datalocation'] + '/*.xml'): status_msg = 'Successfully Done' wordcloudpic = '' bargraph = '' firstFile = pd.DataFrame() #print(class_count) context = {'tab': 'upload','firstFile':firstFile,'dataa': class_count,'textdetails':wordcloudpic,'featuregraph': bargraph,'status_msg': status_msg,'validcsv': True,'computeinfrastructure':computeinfrastructure} return render(request, 'upload.html', context) except: context = {'tab': 'upload','computeinfrastructure':computeinfrastructure,"usecaseerror":"Error in labeling object!"} return render(request, 'upload.html', context) def ObjLabelDiscard(request): return redirect(reverse('objectlabelling')) def ObjLabelAdd(request,id): angle = request.GET.get("angle") gid = request.GET.get("gid") xMin = min(int(request.GET.get("xMin")),int(request.GET.get("xMax"))) xMax =max(int(request.GET.get("xMin")),int(request.GET.get("xMax"))) yMin = min(int(request.GET.get("yMin")),int(request.GET.get("yMax"))) yMax = max(int(request.GET.get("yMin")),int(request.GET.get("yMax"))) height = request.GET.get("height") width = request.GET.get("width") #print("=====> "+str(angle) +" "+ str(gid) +" "+ str(xMin) + " " + str(xMax) + " " +str(yMin) +" "+ str(yMax)+" "+str(width)) # with open("out.csv", 'w') as f: # # writer = csv.writer(f) # # writer.writerow([angle, id, gid, xMin, xMax, yMin, yMax]) # f.write(angle +" "+ gid +" "+ xMin + " " + xMax + " " +yMin +" "+ yMax) labels = request.session['labels'] labels.append({"id":id, "name":"", "xMin":xMin, "xMax":xMax, "yMin":yMin, "yMax":yMax, "height":height,"width":width, "angle":angle}) request.session['labels'] = labels return redirect(reverse('objectlabelling')) def imageeda(request): try: computeinfrastructure = compute.readComputeConfig() request.session['datatype'] = 'Image' filename = request.session['csvfullpath'] os.remove(filename) request.session['csvfullpath'] = request.session['LabelFileName'] df = pd.read_csv(request.session['csvfullpath']) eda_result = '' duplicate_img = '' color_plt = '' df2 = df.groupby('Label', as_index=False)['File'].count().reset_index() df_json = df2.to_json(orient="records") df_json = json.loads(df_json) cfig = go.Figure() xaxis_data = df2['Label'].tolist() yaxis_data = df2['File'].tolist() cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data)) cfig.update_layout(barmode='stack', xaxis_title='Label', yaxis_title='File') bargraph = cfig.to_html(full_html=False, default_height=450, default_width=520) firstFile = df.groupby('Label').first().reset_index() #firstFile['FilePath'] = firstFile['File'].apply(lambda x: os.path.join(request.session['datalocation'], x)) images = [] qualityscore,eda_result,duplicate_img,color_plt = ia.analysis_images(request.session['datalocation']) for i in range(len(firstFile)): filename = firstFile.loc[i, "File"] filePath = os.path.join(request.session['datalocation'], filename) string = base64.b64encode(open(filePath, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) firstFile.loc[i, "Image"] = image_64 firstFile.loc[i, "Quality"] = qualityscore[filename] status_msg = 'Successfully Done' selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] context = {'tab': 'upload', 'featuregraph': bargraph,'dataa': df_json, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'validcsv': True,'eda_result':eda_result,'duplicate_img':duplicate_img,'color_plt':color_plt, 'firstFile': firstFile, 'status_msg': status_msg,'computeinfrastructure':computeinfrastructure} return(context) except: context={'error':'Fail to load Eda result'} return (context) def imagelabelling(request): if (request.session['currentIndex']) == (request.session['endIndex']+1): try: context = imageeda(request) return render(request, 'upload.html', context) except: context = {'error': 'Image labeling error'} return render(request, 'upload.html', context) else: try: df = pd.read_csv(request.session['csvfullpath']) filePath = os.path.join(request.session['datalocation'],df["File"].iloc[request.session['currentIndex']]) string = base64.b64encode(open(filePath, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) context = {'tab': 'upload','id':request.session['currentIndex'],'labels': request.session['labels'],'image':image_64,'head':request.session['currentIndex']+1,'len':len(df)} return render(request, 'imagelabelling.html', context) except: context = {'error': 'Image labeling error'} return render(request, 'upload.html', context) def objecteda(request): request.session['datatype'] = 'Object' filename = request.session['csvfullpath'] try: os.remove(filename) except: pass try: request.session['csvfullpath'] = request.session['LabelFileName'] df = pd.read_csv(request.session['csvfullpath']) df1 = df.groupby(['Label']).agg({"File":{"count","nunique"}}) df1.columns = df1.columns.droplevel(0) df1 = df1.reset_index() class_count = [] for i in range(len(df1)): dct = {} dct['Label'] = df1.loc[i, "Label"] dct['TotalAnnotations'] = df1.loc[i, "count"] dct['Images'] = df1.loc[i, "nunique"] class_count.append(dct) #orxml_file in glob.glob(request.session['datalocation'] + '/*.xml'): status_msg = 'Successfully Done' wordcloudpic = '' bargraph = '' firstFile = pd.DataFrame() context = {'tab': 'upload','firstFile':firstFile,'dataa': class_count,'textdetails':wordcloudpic,'featuregraph': bargraph,'status_msg': status_msg,'validcsv': True} return(context) except: context={'tab': 'upload','error':'Fail to load Eda result'} return(context) def objectlabelling(request): if (request.session['currentIndex']) == (request.session['endIndex']+1): try: context = objecteda(request) context['version'] = AION_VERSION return render(request, 'upload.html', context) except: return render(request, 'upload.html', {'error':'objectlabelling error','version':AION_VERSION}) else: try: df = pd.read_csv(request.session['csvfullpath']) filePath = os.path.join(request.session['datalocation'],df["File"].iloc[request.session['currentIndex']]) string = base64.b64encode(open(filePath, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) bounds = [] context = {'tab': 'upload','bounds':bounds,'labels': request.session['labels'],'directory':request.session['datalocation'],'image':image_64,'head':request.session['currentIndex']+1,'len':len(df),'filelist':df,'selectedfile':df["File"].iloc[request.session['currentIndex']]} context['version'] = AION_VERSION return render(request, 'objectlabelling.html',context) except: return render(request, 'objectlabelling.html',{'tab': 'upload','error':'Objectlabelling Error','version':AION_VERSION}) def imagelabel(request,id): request.session['labels'] = request.GET.get("name") return redirect(reverse('imagelabelling')) def objectlabel(request,id): name = request.GET.get("name") labels = request.session['labels'] labels[int(id) - 1]["name"] = name request.session['labels'] = labels return redirect(reverse('objectlabelling')) def ObjLabelRemove(request,id): index = int(id) - 1 labels = request.session['labels'] del labels[index] for label in labels[index:]: label["id"] = str(int(label["id"]) - 1) request.session['labels'] = labels return redirect(reverse('objectlabelling')) def ImgLabelNext(request): df = pd.read_csv(request.session['csvfullpath']) filePath = df["File"].iloc[request.session['currentIndex']] if request.session['labels'] != '': dataFile = request.session['LabelFileName'] #print(dataFile) with open(dataFile,'a') as f: f.write(filePath + "," + request.session['labels'] + "\n") f.close() request.session['currentIndex'] = request.session['currentIndex']+1 request.session['labels'] = '' return redirect(reverse('imagelabelling')) def ObjLabelPrev(request): df = pd.read_csv(request.session['csvfullpath']) imagePath = df["File"].iloc[request.session['currentIndex']] request.session['currentIndex'] = request.session['currentIndex'] - 1 process_marked_area_on_image(imagePath,request) return redirect(reverse('objectlabelling')) def remove_labelling_from_csv(imagePath,request): dataFile = request.session['LabelFileName'] df = pd.read_csv(dataFile) if not df.empty: if imagePath in df.values: df = df.set_index("File") df = df.drop(imagePath, axis=0) df.to_csv(dataFile, index=True) def process_marked_area_on_image(imagePath,request): df = pd.read_csv(request.session['csvfullpath']) dataFile = request.session['LabelFileName'] remove_labelling_from_csv(imagePath,request) write_coordinates_and_label_to_csv(imagePath,request) if request.session['currentIndex'] < len(df): image = df["File"].iloc[request.session['currentIndex']] request.session['labels'] = [] with open(dataFile, 'r') as file: reader = csv.reader(file) for row in reader: if row[0] == image: labels = request.session['labels'] labels.append({"id":row[1], "name":row[9], "xMin": row[3], "xMax":row[4], "yMin":row[5], "yMax":row[6], "height":row[7],"width":row[8], "angle":row[2]}) request.session['labels'] = labels labels = request.session['labels'] return True def write_coordinates_and_label_to_csv(imagePath,request): dataFile = request.session['LabelFileName'] with open(dataFile, 'a') as f: for label in request.session['labels']: f.write(imagePath + "," + str(round(float(label["id"]))) + "," + str(label["angle"]) + "," + str(round(float(label["xMin"]))) + "," + str(round(float(label["xMax"]))) + "," + str(round(float(label["yMin"]))) + "," + str(round(float(label["yMax"]))) + "," + str(round(float(label["height"]))) + "," + str(round(float(label["width"]))) + "," + label["name"] + "\n") f.close() def ObjLabelSelect(request): selectedimage=request.GET.get('file') df = pd.read_csv(request.session['csvfullpath']) filePath = df["File"].iloc[request.session['currentIndex']] remove_labelling_from_csv(filePath,request) dataFile = request.session['LabelFileName'] with open(dataFile,'a') as f: for label in request.session['labels']: f.write(filePath + "," + str(round(float(label["id"]))) + "," + str(label["angle"]) + "," + str(round(float(label["xMin"]))) + "," + str(round(float(label["xMax"]))) + "," + str(round(float(label["yMin"]))) + "," + str(round(float(label["yMax"]))) + "," + str(round(float(label["height"]))) + "," + str(round(float(label["width"]))) + "," + label["name"] + "\n") f.close() currentIndex = 0 for index,row in df.iterrows(): #print(row['File']) if row['File'] == selectedimage: break else: currentIndex = currentIndex+1 request.session['currentIndex'] = currentIndex if request.session['currentIndex'] < len(df): image = df["File"].iloc[request.session['currentIndex']] request.session['labels'] = [] with open(dataFile, 'r') as file: reader = csv.reader(file) for row in reader: if row[0] == image: labels = request.session['labels'] labels.append({"id":row[1], "name":row[9], "xMin": row[3], "xMax":row[4], "yMin":row[5], "yMax":row[6], "height":row[7],"width":row[8], "angle":row[2]}) request.session['labels'] = labels labels = request.session['labels'] return redirect(reverse('objectlabelling')) def ObjLabelNext(request): df = pd.read_csv(request.session['csvfullpath']) filePath = df["File"].iloc[request.session['currentIndex']] remove_labelling_from_csv(filePath,request) dataFile = request.session['LabelFileName'] with open(dataFile,'a') as f: for label in request.session['labels']: f.write(filePath + "," + str(round(float(label["id"]))) + "," + str(label["angle"]) + "," + str(round(float(label["xMin"]))) + "," + str(round(float(label["xMax"]))) + "," + str(round(float(label["yMin"]))) + "," + str(round(float(label["yMax"]))) + "," + str(round(float(label["height"]))) + "," + str(round(float(label["width"]))) + "," + label["name"] + "\n") f.close() request.session['currentIndex'] = request.session['currentIndex']+1 if request.session['currentIndex'] < len(df): image = df["File"].iloc[request.session['currentIndex']] request.session['labels'] = [] with open(dataFile, 'r') as file: reader = csv.reader(file) for row in reader: if row[0] == image: labels = request.session['labels'] labels.append({"id":row[1], "name":row[9], "xMin": row[3], "xMax":row[4], "yMin":row[5], "yMax":row[6], "height":row[7],"width":row[8], "angle":row[2]}) request.session['labels'] = labels labels = request.session['labels'] return redirect(reverse('objectlabelling')) def encryptedpackage(request): from appbe.encryptedPackage import encrptpackage_command from appbe.encryptedPackage import download_sclient context = encrptpackage_command(request,Existusecases,usecasedetails) context['version'] = AION_VERSION try: return download_sclient(request,context) #Task 9981 except Exception as e: print(e) return render(request, 'usecases.html', context) def StationarySeasonalityTest(request): from appbe.stationarity_seasonality_check import StationarySeasonalityTest as sst datapath = request.GET.get('datapath') datetimefeature = request.GET.get('datefeature') featurename = request.GET.get('targetfeature') seasonality_status = request.GET.get('seasonality_status') stationarity_status = request.GET.get('stationarity_status') df=pd.read_csv(datapath) ss_obj=sst(df,featurename,datetimefeature) result_dict=ss_obj.analysis(seasonality_status,stationarity_status) return HttpResponse(json.dumps(result_dict), content_type="application/json") def dataoverframe(df): from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator gfsg = GenericFeatureStatisticsGenerator() proto = gfsg.ProtoFromDataFrames([{'name': 'train', 'table': df}]) protostr = base64.b64encode(proto.SerializeToString()).decode("utf-8") return protostr def getimpfeatures(dataFile, numberoffeatures): imp_features = [] if numberoffeatures > 20: from appbe.eda import ux_eda eda_obj = ux_eda(dataFile, optimize=1) pca_map = eda_obj.getPCATop10Features() imp_features = pca_map.index.values.tolist() return imp_features def uploaddata(request): from appbe import exploratory_Analysis as ea from appbe.aion_config import eda_setting # context={'test':'test'} selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) computeinfrastructure = compute.readComputeConfig() try: if selected_use_case == 'Not Defined': context = {'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'tab': 'tabconfigure', 'usecaseerror': 'Please create a new use case for training the model or select an existing use case for retraining', 'selected_use_case': selected_use_case,'computeinfrastructure':computeinfrastructure,'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'azurestorage':get_azureStorage() ,'usecasetab':usecasetab,'version':AION_VERSION} return render(request, 'upload.html', context) if 'ModelVersion' in request.session: ModelVersion = request.session['ModelVersion'] else: ModelVersion = 0 if 'ModelStatus' in request.session: ModelStatus = request.session['ModelStatus'] else: ModelStatus = 'Not Trained' if request.session['finalstate'] > 0: if request.session['datatype'] in ['Video', 'Image','Document','Object']: folderLocation = str(request.session['datalocation']) dataFile = os.path.join(folderLocation, request.session['csvfullpath']) df = pd.read_csv(dataFile, encoding='utf8',encoding_errors= 'replace') if df['Label'].isnull().sum() > 0: if request.session['datatype'] == 'Document': dataDf = pd.DataFrame() dataDict = {} keys = ["text"] for key in keys: dataDict[key] = [] for i in range(len(df)): filename = os.path.join(request.session['datalocation'],df.loc[i,"File"]) with open(filename, "r",encoding="utf-8") as f: dataDict["text"].append(f.read()) f.close() dataDf = pd.DataFrame.from_dict(dataDict) tcolumns=['text'] wordcloudpic,df_text = ea.getWordCloud(dataDf,tcolumns) status_msg = 'Successfully Done' request.session['currentstate'] = 0 firstFile = pd.DataFrame() context = {'tab': 'upload','firstFile':firstFile,'validcsv': True,'singletextdetails':wordcloudpic,'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'computeinfrastructure':computeinfrastructure,'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'azurestorage':get_azureStorage() ,'usecasetab':usecasetab,'version':AION_VERSION} return render(request, 'upload.html', context) eda_result = '' duplicate_img = '' color_plt = '' df2 = df.groupby('Label', as_index=False)['File'].count().reset_index() df_json = df2.to_json(orient="records") df_json = json.loads(df_json) cfig = go.Figure() xaxis_data = df2['Label'].tolist() yaxis_data = df2['File'].tolist() cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data)) cfig.update_layout(barmode='stack', xaxis_title='Label', yaxis_title='File') bargraph = cfig.to_html(full_html=False, default_height=450, default_width=520) firstFile = df.groupby('Label').first().reset_index() images = [] if request.session['datatype'] == 'Image': qualityscore,eda_result,duplicate_img,color_plt = ia.analysis_images(request.session['datalocation']) for i in range(len(firstFile)): filename = firstFile.loc[i, "File"] filePath = os.path.join(request.session['datalocation'], filename) string = base64.b64encode(open(filePath, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) firstFile.loc[i, "Image"] = image_64 firstFile.loc[i, "Quality"] = qualityscore[filename] elif request.session['datatype'] == 'Document': dataDrift = '' dataDf = pd.DataFrame() dataDict = {} keys = ["text","Label"] for key in keys: dataDict[key] = [] for i in range(len(df)): filename = os.path.join(request.session['datalocation'],df.loc[i,"File"]) with open(filename, "r",encoding="utf-8") as f: dataDict["text"].append(f.read()) f.close() dataDict["Label"].append(df.loc[i,"Label"]) dataDf = pd.DataFrame.from_dict(dataDict) wordcloudpic = ea.getCategoryWordCloud(dataDf) status_msg = 'Successfully Done' context = {'tab': 'upload','dataa': df_json,'textdetails':wordcloudpic,'featuregraph': bargraph,'status_msg': status_msg,'validcsv': True,'computeinfrastructure':computeinfrastructure,'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket() ,'usecasetab':usecasetab,'azurestorage':get_azureStorage(),'version':AION_VERSION} return render(request, 'upload.html', context) status_msg = 'Successfully Done' selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] request.session['currentstate'] = 0 context = {'tab': 'upload', 'featuregraph': bargraph, 'validcsv': True, 'firstFile': firstFile, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(), 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'eda_result':eda_result,'duplicate_img':duplicate_img,'color_plt':color_plt,'azurestorage':get_azureStorage(), 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure, 'usecasetab':usecasetab,'version':AION_VERSION } return render(request, 'upload.html', context) elif request.session['datatype'].lower() in ['llm_document', 'llm_code']: request.session['currentstate'] = 0 dataFile = request.session['csvfullpath'] df = pd.read_csv(dataFile, encoding='utf8',encoding_errors= 'replace') filesCount = 0 filesSize = 0 files = [] for index, row in df.iterrows(): filename = row['File'] files.append(filename) filesCount = filesCount + 1 get_size = os.path.getsize(filename) filesSize = round(filesSize + get_size, 1) if filesSize > 1048576: size = round((filesSize / (1024 * 1024)), 1) filesSize = str(size) + ' M' elif filesSize > 1024: size = round((filesSize /1024), 1) filesSize = str(size) + ' K' else: filesSize = str(filesSize) + ' B' files = pd.DataFrame(files, columns=['File']) files.index = range(1, len(files) + 1) files.reset_index(level=0, inplace=True) files = files.to_json(orient="records") files = json.loads(files) from appbe.prediction import get_instance hypervisor, instanceid,region,image = get_instance(selected_use_case + '_' + str(ModelVersion)) if hypervisor != '': computeinfrastructure['computeInfrastructure'] = hypervisor else: computeinfrastructure['computeInfrastructure'] = 'AWS' context = {'tab': 'upload',"selected_use_case":selected_use_case,"selectedPath":request.session['datalocation'],"selectedfile":request.session['fileExtension'],'csvgenerated': True,'filesCount':filesCount,'filesSize':filesSize,'files':files, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(), 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'azurestorage':get_azureStorage(), 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'datatype':request.session['datatype'], 'usecasetab':usecasetab,'version':AION_VERSION,"selectedfile":request.session['fileExtension'],"selectedPath":request.session['datalocation'] } return render(request, 'upload.html', context) else: dataFile = str(request.session['datalocation']) check_df = pd.read_csv(dataFile, encoding='utf8',encoding_errors= 'replace') check_df.rename(columns=lambda x: x.strip(), inplace=True) featuresList = check_df.columns.tolist() numberoffeatures = len(featuresList) imp_features = getimpfeatures(dataFile, numberoffeatures) # check_df = pd.read_csv(dataFile) # check_df.rename(columns=lambda x: x.strip(), inplace=True) # ---------------------------- # EDA Performance change # ---------------------------- sample_size = int(eda_setting()) samplePercentage = 100 samplePercentval = 0 showRecommended = False #dflength = len(eda_obj.getdata()) dflength = len(check_df) if dflength > sample_size: samplePercentage = round(float((sample_size/dflength) * 100),2) samplePercentval = samplePercentage / 100 showRecommended = True # ---------------------------- # df_top = df.head(10) df_top = check_df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) statusmsg = '' selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] request.session['currentstate'] = 0 # EDA Subsampling changes context = {'range':range(1,101),'samplePercentage':samplePercentage,'samplePercentval':samplePercentval, 'showRecommended':showRecommended,'featuresList': featuresList, 'selected_use_case': selected_use_case,'data': df_json,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'usecasetab':usecasetab,'azurestorage':get_azureStorage(), 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'imp_features':imp_features,'numberoffeatures':numberoffeatures, 'version':AION_VERSION, 'selected': 'modeltraning','exploratory':False,'computeinfrastructure':computeinfrastructure} else: request.session['uploaddone'] = False request.session['currentstate'] = 0 request.session['finalstate'] = 0 clusteringModels = Existusecases.objects.filter(Status='SUCCESS',ProblemType='unsupervised').order_by('-id') context = {'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'usecasetab':usecasetab,'azurestorage':get_azureStorage(),'clusteringModels':clusteringModels, 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(), 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure } context['version'] = AION_VERSION return render(request, 'upload.html', context) except Exception as e: exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) print(e) return render(request, 'upload.html', {'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'error':'Fail to upload Data','usecasetab':usecasetab,'version':AION_VERSION}) def mlflowtracking(request): import requests response = requests.get("http://localhost:5000/") #response = requests.get(url) statuscode = response.status_code data = [] context = {'statuscode':statuscode} context['version'] = AION_VERSION return render(request, 'openmlflow.html', context) def readlogfile(request): file_path = request.session['logfilepath'] try: updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r+") configSettingsData = f.read() configSettings = json.loads(configSettingsData) f.close() if os.path.exists(file_path): my_file = open(file_path, 'r',encoding="utf-8") file_content = my_file.read() my_file.close() matched_lines = [line.replace('Status:-', '') for line in file_content.split('\n') if "Status:-" in line] matched_status_lines = matched_lines[::-1] if len(matched_status_lines) > 0: no_lines = len(matched_lines) if 'noflines' not in request.session: request.session['noflines'] = 0 request.session['noflines'] = request.session['noflines'] + 1 if request.session['ModelStatus'] != 'SUCCESS': numberoflines = request.session['noflines'] if numberoflines > no_lines: numberoflines = no_lines request.session['noflines'] = no_lines matched_lines = matched_lines[0:numberoflines] matched_status_lines = matched_status_lines[0] output = getStatusCount(matched_lines,request.session['total_steps']) matched_status_lines = matched_status_lines.split('...') matched_status_lines = matched_status_lines[1] output2=[] output2.append(matched_status_lines) from appbe import leaderboard import pandas result = leaderboard.get_leaderboard(file_content) if result.empty==False: result = result.to_html(classes='table',col_space='100px', index=False) else: result = 'Leaderboard is not available' data_details = {'status':output2,'logs':output,'log_file':file_content,'leaderboard': result,'trainingstatus':request.session['ModelStatus']} return HttpResponse(json.dumps(data_details), content_type="application/json") else: matched_lines = [] matched_lines.append('Initializing Training Engine') data_details = {'status':matched_lines,'logs':matched_lines,'log_file':matched_lines, 'leaderboard':matched_lines,'trainingstatus':matched_lines} return HttpResponse(json.dumps(data_details), content_type="application/json") else: stepsdone = 0 matched_lines = [] if request.session['ModelStatus'] == 'Running': matched_lines.append('Initializing Training Engine') else: matched_lines.append('Not Trained') data_details = {'status':matched_lines,'logs':matched_lines,'log_file':matched_lines, 'leaderboard':matched_lines,'trainingstatus':matched_lines} return HttpResponse(json.dumps(data_details), content_type="application/json") except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) matched_lines = [] if request.session['ModelStatus'] == 'Running': stepsdone = 0 matched_lines.append('Initializing Training Engine') data_details = {'status':matched_lines,'logs':matched_lines,'log_file':matched_lines, 'leaderboard':matched_lines,'trainingstatus':matched_lines} return HttpResponse(json.dumps(data_details), content_type="application/json") else: matched_lines.append('Not Trained') data_details = {'status':matched_lines,'logs':matched_lines,'log_file':matched_lines,'leaderboard':matched_lines,'trainingstatus':matched_lines} return HttpResponse(json.dumps(data_details), content_type="application/json") # EDA Visualization changes # ---------------------------- def getgraph(request): from appbe import exploratory_Analysis as ea output = ea.get_edaGraph(request) return HttpResponse(output) # ---------------------------- # --- 12686:Data Distribution related Changes S T A R T --- def getDataDistribution(request): from appbe import exploratory_Analysis as ea output = ea.get_DataDistribution(request) return HttpResponse(output) # ---------------------- E N D ---------------------- def getDeepDiveData(request): from appbe import exploratory_Analysis as ea output = ea.get_DeepDiveData(request) return HttpResponse(output) # Fairness Metrics changes # ---------------------------- def getmetrics(request): from appbe import exploratory_Analysis as ea output = ea.get_fairmetrics(request) return HttpResponse(output) # ---------------------------- def getdataimbalance(request): d3_url = request.GET.get('d3_url') mpld3_url = request.GET.get('mpld3_url') updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r+", encoding="utf-8") configSettingsData = f.read() configSettingsJson = json.loads(configSettingsData) df = pd.read_csv(configSettingsJson['basic']['dataLocation'],encoding='utf8') targetFeature = configSettingsJson['basic']['targetFeature'] df1 = df[targetFeature].value_counts().to_frame() if (len(df1) < 1): response = 'Data balancing detail is not available due to no class is found in target feature.' elif (len(df1) > 30): response = 'Data balancing detail is not available due to high number of classes in target feature.' else: dfStyler = df1.style.set_properties(**{'text-align': 'right'}) dfStyler.set_table_styles([dict(selector='th', props=[('text-align', 'right')])]) valueCount = dfStyler.to_html() import matplotlib.pyplot as plt import mpld3 fig, ax = plt.subplots(figsize=[6.5,6]) df2 = df[targetFeature].value_counts().sort_values() _ncol = 1 _radius = 0.5 if (len(df1) > 10): _radius = 0.4 _ncol = 1 else: _radius = 0.6 _ncol = 1 ax = df2.plot(kind = 'pie', ylabel='', title=targetFeature, labeldistance=None, radius=_radius, autopct='%1.0f%%') ax.legend(loc='right', bbox_to_anchor=(1, 0.8), ncol = _ncol) # ax.legend(bbox_to_anchor=(1,1), bbox_transform=plt.gcf().transFigure) plt.subplots_adjust(left=0.02, bottom=0.05, right=0.9) ax.get_yaxis().set_visible(False) html_graph = mpld3.fig_to_html(fig,d3_url=d3_url,mpld3_url=mpld3_url) response = valueCount + ' ' + html_graph return HttpResponse(response) def dotextSummarization(request): from appbe.textSummarization import startSummarization context = startSummarization(request,DEFAULT_FILE_PATH,CONFIG_FILE_PATH,DATA_FILE_PATH) context['version'] = AION_VERSION return render(request, 'summarization.html', context) def openmodelevaluation(request,id): deploypath = request.session['deploypath'] if id == 1: contentFile= os.path.join(deploypath,'log','boosting_overfit.html') if id == 2: contentFile= os.path.join(deploypath,'log','boosting_overfit_condition.html') if id == 3: contentFile= os.path.join(deploypath,'log','smc.html') if id == 4: contentFile= os.path.join(deploypath,'log','smc_condition.html') if id == 5: contentFile= os.path.join(deploypath,'log','mi.html') if id == 6: contentFile= os.path.join(deploypath,'log','mi_con.html') try: my_file = open(contentFile, 'r', encoding="utf-8") file_content = my_file.read() my_file.close() context = {'content': file_content,'status':request.session['ModelStatus']} context['version'] = AION_VERSION return render(request, 'deepcheck.html', context, content_type="text/html") except: context = {'content': 'Not available'} context['version'] = AION_VERSION return render(request, 'deepcheck.html', context, content_type="text/html") def downloadlogfile(request,id,currentVersion): import mimetypes from django.http import FileResponse p = usecasedetails.objects.get(id=id) model = Existusecases.objects.filter(ModelName=p,Version=currentVersion) if model[0].DeployPath != 'NA': file_path = os.path.join(str(model[0].DeployPath),'log','model_training_logs.log') else: file_path = os.path.join(DEPLOY_LOCATION,model[0].ModelName.usecaseid,str(currentVersion),'log','model_training_logs.log') try: if os.path.exists(file_path): my_file = open(file_path, 'r', encoding="utf-8") file_content = my_file.read() my_file.close() mime_type, _ = mimetypes.guess_type(file_path) response = HttpResponse(file_content, content_type=mime_type)#bugid 12513 # Set the HTTP header for sending to browser filename = p.usecaseid+'.log' response['Content-Disposition'] = "attachment; filename=%s" % filename return response else: response = HttpResponse('File Not Found')#bugid 12513 # Set the HTTP header for sending to browser filename = p.usecaseid+'.log' response['Content-Disposition'] = "attachment; filename=%s" % filename return response except Exception as e: response = HttpResponse('File Not Found')#bugid 12513 # Set the HTTP header for sending to browser filename = p.usecaseid+'.log' response['Content-Disposition'] = "attachment; filename=%s" % filename return response def opendetailedlogs(request,id,currentVersion): p = usecasedetails.objects.get(id=id) model = Existusecases.objects.filter(ModelName=p,Version=currentVersion) if model[0].DeployPath != 'NA': file_path = os.path.join(str(model[0].DeployPath),'log','model_training_logs.log') else: file_path = os.path.join(DEPLOY_LOCATION,model[0].ModelName.usecaseid,str(currentVersion),'log','model_training_logs.log') try: if os.path.exists(file_path): my_file = open(file_path, 'r', encoding="utf-8") file_content = my_file.read() my_file.close() context = {'content':file_content} return HttpResponse(json.dumps(context),content_type="application/json") else: context = {'content':'Status not available'} return HttpResponse(json.dumps(context),content_type="application/json") except Exception as e: print(e) context = {'content':'Status not available'} return HttpResponse(json.dumps(context),content_type="application/json") def batchlearning(request): from appbe.onlineLearning import startIncrementallearning action,context = startIncrementallearning(request,usecasedetails,Existusecases,DATA_FILE_PATH) context['version'] = AION_VERSION return render(request,action,context) def downlpredictreport(request): predictionResults = request.POST.get('predictionResults') predictionResults = pd.DataFrame.from_dict(eval(predictionResults)) usename = request.session['UseCaseName'].replace(" ", "_") + '_' + str(request.session['ModelVersion']) predictFileName = usename + '_prediction.xlsx' from io import BytesIO as IO excel_file = IO() excel_writer = pd.ExcelWriter(excel_file, engine="xlsxwriter") predictionResults.to_excel(excel_writer, sheet_name='Predictions') workbook = excel_writer.book #excel_writer.save() excel_writer.close() excel_file.seek(0) response = HttpResponse(excel_file.read(), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') response['Content-Disposition'] = 'attachment; filename=' + predictFileName return response # EDA Reports changes # ---------------------------- def downloadxplainreport(request): from appbe.xplain import global_explain status,msg,ale_view,sentences,bargraph,inputFields,nrows,ncols,targetFeature,dataPoints,target_classes,df_proprocessed,numberofclasses,modelfeatures,problemType,mfcount,topTwoFeatures,topFeaturesMsg,most_influencedfeature,interceppoint,anchorjson,labelMaps = global_explain(request) if status == 'Success': usename = request.session['UseCaseName'].replace(" ", "_") + '_' + str(request.session['ModelVersion']) predictFileName = usename + '_xplain.xlsx' df = pd.DataFrame({'What kind of data does the system learn from?': ['This dataset is a dataset of measurements taken for '+str(numberofclasses)+' categories of '+str(targetFeature),'The '+str(numberofclasses)+' different categories of '+str(targetFeature)+' as per the data are:']}) i = 1 df1 = [] for x in target_classes: df1.append({'What kind of data does the system learn from?':' '+str(i)+':'+str(x)}) i = i+1 df1.append({'What kind of data does the system learn from?':'The total number of data points is '+str(dataPoints)}) df = pd.concat([df, pd.DataFrame(df1)], ignore_index = True) from io import BytesIO as IO excel_file = IO() excel_writer = pd.ExcelWriter(excel_file, engine="xlsxwriter") df.to_excel(excel_writer, sheet_name='Dashboard',index=False) pd.DataFrame(df_proprocessed).to_excel(excel_writer, sheet_name='Top 5 Rows',index=False) df = pd.DataFrame({'What are the various features of the data used for model training?': ['The various features of the data are:']}) i = 1 df1 = [] for x in modelfeatures: df1.append({'What are the various features of the data used for model training?':' '+str(i)+': '+str(x)}) i = i+1 df = pd.concat( [df, pd.DataFrame( df1)], ignore_index = True) df.to_excel(excel_writer, sheet_name='Features',index=False) topFeaturesMsg = pd.DataFrame(topFeaturesMsg,columns=["Feature Importance"]) topFeaturesMsg.to_excel(excel_writer, sheet_name='Feature importance',index=False) achors = pd.DataFrame(anchorjson) achors.to_excel(excel_writer, sheet_name='Prediction',index=False) workbook = excel_writer.book #excel_writer.save() excel_writer.close() excel_file.seek(0) response = HttpResponse(excel_file.read(), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') response['Content-Disposition'] = 'attachment; filename=' + predictFileName return response else: response = HttpResponse() return response def gotoreport(request): report_button = request.POST.get('trainmodel') usename = request.session['UseCaseName'].replace(" ", "_") + '_' + str(request.session['ModelVersion']) if report_button == 'download_edafile': from appbe.reports import downloadtrainingfile edaFileName,excel_file = downloadtrainingfile(request,Existusecases) response = HttpResponse(excel_file.read(), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') response['Content-Disposition'] = 'attachment; filename=' + edaFileName return response def LoadBasicConfiguration(request): try: from appbe import exploratory_Analysis as ea configFile = DEFAULT_FILE_PATH + 'eion_config.json' f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) temp = {} temp['ModelName'] = request.session['UseCaseName'] temp['Version'] = request.session['ModelVersion'] dataLocation = str(request.session['datalocation']) df = pd.read_csv(dataLocation, encoding='latin1') featuresList = df.columns.values.tolist() datetimeFeatures = [] sequenceFeatures = [] unimportantFeatures = [] featuresRatio = {} for i in featuresList: check = ea.match_date_format(df[i]) if check == True: datetimeFeatures.append(i) unimportantFeatures.append(i) continue seq_check = ea.check_seq_feature(df[i]) if seq_check == True: sequenceFeatures.append(i) unimportantFeatures.append(i) continue ratio = ea.check_category(df[i]) if ratio != 0: featuresRatio[i] = ratio else: unimportantFeatures.append(i) targetFeature = min(featuresRatio, key=featuresRatio.get) unimportantFeatures.append(targetFeature) config = {} config['modelName'] = request.session['UseCaseName'] config['modelVersion'] = request.session['ModelVersion'] config['datetimeFeatures'] = datetimeFeatures config['sequenceFeatures'] = sequenceFeatures config['FeaturesList'] = featuresList config['unimportantFeatures'] = unimportantFeatures config['targetFeature'] = targetFeature context = {'tab': 'configure', 'temp': temp, 'config': config} context['version'] = AION_VERSION return render(request, 'modeltraning.html', context) except: return render(request, 'modeltraning.html', {'error':'Fail to load basic config file','version':AION_VERSION}) def LoadDataForSingleInstance(request): try: updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) problemtypes = configSettingsJson['basic']['analysisType'] #print(problemtypes.keys()) problem_type = "" for k in problemtypes.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break if problem_type == 'timeSeriesForecasting': #task 11997 inputFieldsDict = {'noofforecasts': 10} elif problem_type == 'recommenderSystem': inputFieldsDict = {"uid": 1, "iid": 31, "rating": 0} elif problem_type == 'videoForecasting': inputFieldsDict = {'VideoPath': 'person01_boxing_d1_uncomp.avi'} else: inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] inputFeaturesList = inputFeatures.split(',') if targetFeature in inputFeaturesList: inputFeaturesList.remove(targetFeature) dataFilePath = str(configSettingsJson['basic']['dataLocation']) df = pd.read_csv(dataFilePath, encoding='latin1') singleInstanceData = df.loc[0, inputFeaturesList] inputFieldsDict = singleInstanceData.to_dict() inputFields = [] inputFields.append(inputFieldsDict) selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] context = {'tab': 'predict', 'inputFields': inputFields, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'prediction'} return render(request, 'prediction.html', context=context) except: return render(request, 'prediction.html', {'tab': 'predict', 'error': 'Fail to load inputfields', 'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'prediction'}) def uploadDatafromunsupervisedmodel(request): computeinfrastructure = compute.readComputeConfig() try: modelid = request.POST.get('modelid') p = Existusecases.objects.get(id=modelid) dataFile = str(p.DataFilePath) deploypath = str(p.DeployPath) if(os.path.isfile(dataFile) == False): context = {'tab': 'tabconfigure', 'error': 'Data file does not exist','computeinfrastructure':computeinfrastructure} return render(request, 'prediction.html', context) predictionScriptPath = os.path.join(deploypath,'aion_predict.py') outputStr = subprocess.check_output([sys.executable, predictionScriptPath, dataFile]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() predict_dict = json.loads(outputStr) if (predict_dict['status'] == 'SUCCESS'): predictionResults = predict_dict['data'] df2 = pd.json_normalize(predictionResults) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') request.session['datalocation'] = str(dataFile) df2.to_csv(dataFile, index=False) request.session['datalocation'] = str(dataFile) from appbe.eda import ux_eda eda_obj = ux_eda(dataFile) featuresList,datetimeFeatures,sequenceFeatures,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catFeature = eda_obj.getFeatures() # ---------------------------- samplePercentage = 100 samplePercentval = 0 showRecommended = False df = pd.read_csv(dataFile,nrows=100) df_top = df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) statusmsg = 'Data File Uploaded Successfully ' selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] request.session['currentstate'] = 0 request.session['finalstate'] = 0 request.session['datatype'] = 'Normal' No_of_Permissible_Features_EDA = get_edafeatures() clusteringModels = Existusecases.objects.filter(Status='SUCCESS',ProblemType='unsupervised').order_by('-id') context = {'tab': 'tabconfigure','range':range(1,101),'FeturesEDA':No_of_Permissible_Features_EDA,'samplePercentage':samplePercentage,'computeinfrastructure':computeinfrastructure, 'samplePercentval':samplePercentval, 'showRecommended':showRecommended,'featuresList':featuresList,'data': df_json,'status_msg': statusmsg,'selected_use_case': selected_use_case,'clusteringModels':clusteringModels, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning', 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'exploratory':False} context['version'] = AION_VERSION return render(request, 'upload.html', context) except Exception as e: print(e) return render(request, 'upload.html', {'error':'Failed to upload Data','selected_use_case': selected_use_case,'computeinfrastructure':computeinfrastructure,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning','version':AION_VERSION}) def qlearning(request): return render(request, 'qlearning.html', {}) def RLpath(request): return render(request, 'rl_path.html', {}) def stateTransitionSettings(request): selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] import requests setting_url = service_url.read_service_url_params(request) usecasename = request.session['usecaseid'].replace(" ", "_") setting_url = setting_url+'pattern_anomaly_settings?usecaseid='+usecasename+'&version='+str(request.session['ModelVersion']) #print(setting_url) inputFieldsDict = {} inputFieldsDict['groupswitching'] = request.POST.get('groupswitching') inputFieldsDict['transitionprobability'] = request.POST.get('transitionprobability') inputFieldsDict['transitionsequence'] = request.POST.get('transitionsequence') inputFieldsDict['sequencethreshold'] = request.POST.get('sequencethreshold') # print(inputFieldsDict) inputFieldsJson = json.dumps(inputFieldsDict) #print(inputFieldsJson) try: response = requests.post(setting_url,data=inputFieldsJson,headers={"Content-Type":"application/json",}) if response.status_code != 200: outputStr=response.content context = {'tab': 'tabconfigure', 'error': outputStr.decode('utf-8'), 'selected': 'prediction'} return render(request, 'prediction.html', context) except Exception as inst: if 'Failed to establish a new connection' in str(inst): context = {'tab': 'tabconfigure', 'error': 'AION Service needs to be started', 'selected': 'prediction'} else: context = {'tab': 'tabconfigure', 'error': 'Prediction Error '+str(inst), 'selected': 'prediction'} return render(request, 'prediction.html', context) try: outputStr=response.content outputStr = outputStr.decode('utf-8') outputStr = outputStr.strip() #print(outputStr) predict_dict = json.loads(str(outputStr)) selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] inputFeaturesList = inputFeatures.split(',') inputFieldsDict = {inputFeatures:'session',targetFeature:'Activity'} inputFields = [] inputFields.append(inputFieldsDict) iterName = request.session['UseCaseName'].replace(" ", "_") settings_url = '' problemtypes = configSettingsJson['basic']['analysisType'] #print(problemtypes.keys()) problem_type = "" for k in problemtypes.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break if problem_type == 'StateTransition': ser_url = service_url.read_pattern_anomaly_url_params(request) settings_url = service_url.read_pattern_anomaly_setting_url_params(request) else: ser_url = service_url.read_service_url_params(request) ser_url = ser_url+'predict?usecaseid='+iterName+'&version='+str(ModelVersion) onnx_runtime = False if str(configSettingsJson['advance']['deployer']['edge_deployment']) == 'True': if str(configSettingsJson['advance']['deployer']['edge_format']['onnx']) == 'True': onnx_runtime = True analyticsTypes = problem_type imagedf = '' return render(request, 'prediction.html', {'inputFields': inputFields,'imagedf':imagedf, 'selected_use_case': selected_use_case,'ser_url':ser_url,'analyticsType':analyticsTypes,'settings_url':settings_url,'usecasetab':usecasetab, 'ModelStatus': ModelStatus,'onnx_edge':onnx_runtime,'ModelVersion': ModelVersion, 'selected': 'prediction'}) except Exception as e: print(e) return render(request, 'prediction.html', {'error': 'Fail to do state Transition Settings', 'selected_use_case': selected_use_case,'ModelStatus': ModelStatus,'ModelVersion': ModelVersion, 'selected': 'prediction'}) def flcommand(request): try: from appbe.flconfig import fl_command context = fl_command(request,Existusecases,usecasedetails) return render(request, 'usecases.html', context) except Exception as e: print(e) return render(request, 'models.html',{'error': 'Failed to generate federated learning client code'}) def maaccommand(request): from appbe.models import maac_command try: context,page = maac_command(request,Existusecases,usecasedetails) context['version'] = AION_VERSION return render(request,page,context) except Exception as e: print(e) return render(request, 'usecases.html',{'errormlac': 'Failed to generate code: '+str(e),'version':AION_VERSION}) def onnxruntime(request): try: onnx_scriptPath = os.path.join(request.session['deploypath'],'edge','onnxvalidation.py') outputStr = subprocess.check_output([sys.executable, onnx_scriptPath]) #print(outputStr) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() predict_dict = json.loads(outputStr) selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] context = {'tab': 'predict', 'predictionResults': predict_dict, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'prediction','onnx_edge':True,'version':AION_VERSION} return render(request, 'prediction.html', context=context) except Exception as inst: print('-------------------->'+str(inst)) context = {'tab': 'tabconfigure', 'error': 'Failed To Perform Prediction', 'selected': 'prediction','version':AION_VERSION} return render(request, 'prediction.html', context) def instancepredict(request): log = logging.getLogger('log_ux') from appbe.train_output import get_train_model_details modelType='' trainingStatus,modelType,bestmodel = get_train_model_details(DEPLOY_LOCATION,request) computeinfrastructure = compute.readComputeConfig() selected_use_case, ModelVersion, ModelStatus = getusercasestatus(request) try: t1 = time.time() if request.FILES: Datapath = request.FILES['DataFilePath'] from io import StringIO ext = str(Datapath).split('.')[-1] if ext.lower() in ['csv','tsv','tar','zip','avro','parquet','txt']: content = StringIO(Datapath.read().decode('utf-8')) reader = csv.reader(content) df = pd.DataFrame(reader) df.columns = df.iloc[0] df = df[1:] filetimestamp = str(int(time.time())) if ext.lower() in ['csv','tsv','tar','zip','avro','parquet','txt','pdf']: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext) else: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp) with open(dataFile, 'wb+') as destination: for chunk in Datapath.chunks(): destination.write(chunk) destination.close() dataPath = dataFile if(os.path.isfile(dataFile) == False): context = {'tab': 'tabconfigure', 'error': 'Data file does not exist','computeinfrastructure':computeinfrastructure,'version':AION_VERSION} log.info('Predict Batch : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Data file does not exist') return render(request, 'prediction.html', context) updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) predictionScriptPath = os.path.join(request.session['deploypath'], 'aion_predict.py') outputStr = subprocess.check_output([sys.executable, predictionScriptPath, dataFile]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() predict_dict = json.loads(outputStr) problemtypes = configSettingsJson['basic']['analysisType'] problem_type = '' for k in problemtypes.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break PredictionResultsOfTextSum = [] if (predict_dict['status'] == 'SUCCESS'): predictionResults = predict_dict['data'] predictionResultsTextSum= predict_dict['data'] if problem_type in ['similarityIdentification','contextualSearch']: for x in predictionResults: msg='' for y in x['prediction']: msg += str(y) msg += '\n' msg += '\n' msg += '\n' msg += '\n' msg += '\n' x['prediction'] = msg if problem_type == 'textSummarization': Results = {} Results['msg'] = predict_dict['msg'] PredictionResultsOfTextSum.append(Results) Results['prediction'] = predict_dict['data'] PredictionResultsOfTextSum.append(Results) t2 = time.time() log.info('Predict Batch : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + str( round(t2 - t1)) + ' sec' + ' : ' + 'Success') else: context = {'tab': 'tabconfigure', 'error': 'Failed To perform prediction','version':AION_VERSION} log.info('Predict Batch : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Failed To perform prediction') return render(request, 'prediction.html', context) selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] from appfe.modelTraining.train_views import getMLModels problem_type,dproblemtype,sc,mlmodels,dlmodels,smodelsize = getMLModels(configSettingsJson) from appbe.prediction import createInstanceFeatures ser_url = service_url.read_service_url_params(request) inputFields,ser_url = createInstanceFeatures(configSettingsJson,problem_type,mlmodels,request.session['usecaseid'],request.session['ModelVersion'],ser_url) from appfe.modelTraining.prediction_views import getTrainingStatus result = getTrainingStatus(request) context = {'tab': 'predict','ser_url':ser_url,'predictionResults': predictionResults, 'selected_use_case': selected_use_case,'problem_type':problem_type,'result':result, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'prediction','computeinfrastructure':computeinfrastructure,'bestmodel':bestmodel,'usecasetab':usecasetab,'version':AION_VERSION,'modelType':modelType,'inputFields':inputFields,'configSettingsJson':configSettingsJson} if problem_type == 'textSummarization': context={'tab': 'predict','predictionResultsTextSum': predictionResultsTextSum, 'PredictionResultsOfTextSum': PredictionResultsOfTextSum,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion, 'selected': 'prediction','problem_type':problem_type} return render(request, 'prediction.html', context=context) except Exception as inst: print(inst) context = {'tab': 'tabconfigure', 'error': 'Failed To perform prediction', 'selected': 'prediction','computeinfrastructure':computeinfrastructure,'version':AION_VERSION} log.info('Predict Batch :' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Failed To perform prediction, '+str(inst)) return render(request, 'prediction.html', context) def LoadAdvanceConfiguration(request): try: if request.method == 'POST': configFile = request.session['config_json'] f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) context = {'tab': 'advconfig', 'advconfig': configSettingsJson} context['version'] = AION_VERSION context['usecasetab'] = usecasetab return render(request, 'modeltraning.html', context) except: return render(request, 'modeltraning.html', {'error':'Fail to load advance config file','version':AION_VERSION,'usecasetab':usecasetab}) # advance def Advance(request): try: from appbe import advance_Config as ac request.session['defaultfilepath'] = DEFAULT_FILE_PATH context = ac.save(request) submittype = request.POST.get('AdvanceSubmit') computeinfrastructure = compute.readComputeConfig() if submittype != 'AdvanceDefault': from appfe.modelTraining.train_views import trainmodel return trainmodel(request) else: context['version'] = AION_VERSION context['usecasetab'] = usecasetab context['computeinfrastructure'] = computeinfrastructure return render(request, 'advancedconfig.html', context) except Exception as e: print(e) return render(request, 'advancedconfig.html', {'erroradvance':'Fail to save','version':AION_VERSION,'usecasetab':usecasetab,'computeinfrastructure':computeinfrastructure}) def templatepage(request): computeinfrastructure = compute.readComputeConfig() try: kafkaSetting = kafka_setting() ruuningSetting = running_setting() ser_url = service_url.read_service_url_params(request) packagetip=''' Call From Command Line 1. Click AION Shell 2. python {packageAbsolutePath}/aion_prediction.py {json_data} Call As a Package 1. Go To package_path\WHEELfile 2. python -m pip install {packageName}-py3-none-any.whl Call the predict function after wheel package installation 1. from {packageName} import aion_prediction as p1 2. p1.predict({json_data}) ''' usecase = usecasedetails.objects.all() models = Existusecases.objects.filter(Status='SUCCESS') selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) if len(usecase) > 0: nouc = usecasedetails.objects.latest('id') nouc = (nouc.id)+1 else: nouc = 1 context = {'usecasedetail': usecase, 'nouc': nouc,'models': models, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ser_url':ser_url,'packagetip':packagetip,'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting,'usecasetab':usecasetab} return (context) except: context = {'error':'Fail to load usecases details','usecasetab':usecasetab} return (context) def modelkafka(request): try: addKafkaModel(request,request.session['datalocation']) selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) computeinfrastructure = compute.readComputeConfig() kafkaSetting = kafka_setting() ruuningSetting = running_setting() ser_url = service_url.read_service_url_params(request) packagetip=''' Call From Command Line 1. Click AION Shell 2. python {packageAbsolutePath}/aion_prediction.py {json_data} Call As a Package 1. Go To package_path\WHEELfile 2. python -m pip install {packageName}-py3-none-any.whl Call the predict function after wheel package installation 1. from {packageName} import aion_prediction as p1 2. p1.predict({json_data}) ''' models = Existusecases.objects.filter(Status='SUCCESS').order_by('-id') usecase = usecasedetails.objects.all().order_by('-id') if len(usecase) > 0: nouc = usecasedetails.objects.latest('id') nouc = (nouc.id)+1 else: nouc = 1 return render(request, 'usecases.html', {'usecasedetail': usecase, 'nouc': nouc, 'models': models, 'selected_use_case': selected_use_case,'ser_url':ser_url,'packagetip':packagetip,'usecasetab':usecasetab, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting}) except: return render(request, 'usecases.html',{'selected': 'usecase', 'selected_use_case': selected_use_case,'error': 'Fail to load modelkafka'}) def startTracking(request): from appbe.aion_config import aion_tracking from appbe.aion_config import start_tracking try: status = aion_tracking() if status.lower() == 'error': start_tracking() status = 'MLflowSuccess' else: status = 'MLflowSuccess' context = {'selected':'DataOperations','usecasetab':usecasetab,'status':status} context['version'] = AION_VERSION return render(request, "dataoperations.html",context) except: context = {'selected':'DataOperations','usecasetab':usecasetab,'status':'Error'} context['version'] = AION_VERSION return render(request, "dataoperations.html",context) def startService(request): try: status = aion_service() if status == 'Running': status = 'AION service already running' elif status == 'Started': status = 'AION service started successfully' else: status = 'Error in starting' context = settings(request) context['status'] = status return render(request, 'settings_page.html', context) except: return render(request, 'settings_page.html', {'error':'Fail to start service'}) def Dataupload(request): from appbe.pages import usecases_page checkModelUnderTraining(request,usecasedetails,Existusecases) request.session['IsRetraining'] = 'No' status,context,action = usecases_page(request,usecasedetails,Existusecases) context['version'] = AION_VERSION context['currentstate'] =0 from appbe.aion_config import get_telemetryoptout telemetryoptout = get_telemetryoptout() if telemetryoptout == 'No': from appbe.telemetry import checkTelemtry checkTelemtry() return render(request,action,context) def show(request): try: models = Existusecases.objects.all() # print(models) return render(request, "usecases.html", {'models': models, 'selected': 'usecase'}) except: return render(request, "usecases.html", {'error': 'Error to show Usecases', 'selected': 'usecase'}) def edit(request, id): try: usecasedetail = usecasedetails.objects.get(id=id) return render(request, 'edit.html', {'usecasedetail': usecasedetail, 'selected': 'usecase'}) except: return render(request, "usecases.html", {'error': 'Error in editing usecase', 'selected': 'usecase'}) def opentraining(request, id,currentVersion): from appbe.pages import usecases_page try: p = usecasedetails.objects.get(id=id) model = Existusecases.objects.filter(ModelName=p,Version=currentVersion) Version = model[0].Version usecasename = p.UsecaseName request.session['ModelName'] = p.id request.session['UseCaseName'] = usecasename request.session['usecaseid'] = p.usecaseid request.session['ModelVersion'] = Version request.session['ModelStatus'] = 'Not Trained' request.session['finalstate'] = 0 usecase = usecasedetails.objects.all().order_by('-id') configfile = str(model[0].ConfigPath) dataFile = '' if configfile != '': request.session['finalstate'] = 2 f = open(configfile, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) dataFile = configSettings['basic']['dataLocation'] if configSettings['basic']['folderSettings']['fileType'] == 'Object': request.session['datatype'] = configSettings['basic']['folderSettings']['fileType'] request.session['objectLabelFileName'] = configSettings['basic']['folderSettings']['labelDataFile'] request.session['datalocation'] = configSettings['basic']['dataLocation'] return objectlabeldone(request) elif configSettings['basic']['folderSettings']['fileType'] in ['LLM_Document','LLM_Code']: request.session['datatype'] = configSettings['basic']['folderSettings']['fileType'] request.session['fileExtension'] = configSettings['basic']['folderSettings']['fileExtension'] request.session['csvfullpath'] = configSettings['basic']['folderSettings']['labelDataFile'] request.session['datalocation'] = configSettings['basic']['dataLocation'] else: request.session['datalocation'] = str(configSettings['basic']['dataLocation']) request.session['datatype'] = 'Normal' if 'fileSettings' in configSettings['basic'].keys(): fileSettings = configSettings['basic']['fileSettings'] if 'delimiters' in fileSettings.keys(): delimiters = configSettings['basic']['fileSettings']['delimiters'] textqualifier = configSettings['basic']['fileSettings']['textqualifier'] request.session['delimiter'] = delimiters request.session['textqualifier'] = textqualifier else: request.session['delimiter'] = ',' request.session['textqualifier'] = '"' if dataFile == '': dataFile = str(model[0].DataFilePath) if dataFile != '': request.session['finalstate'] = 2 request.session['datalocation'] = dataFile return uploaddata(request) except Exception as e: print(e) checkModelUnderTraining(request,usecasedetails,Existusecases) request.session['IsRetraining'] = 'No' status,context,action = usecases_page(request,usecasedetails,Existusecases) context['version'] = AION_VERSION context['Status'] = 'Error' context['Msg'] = 'Error in retraining usecase. Check log file for more details' return render(request,action,context) def stopmodelservice(request): try: kafkaSetting = kafka_setting() ruuningSetting = running_setting() computeinfrastructure = compute.readComputeConfig() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) id = request.POST.get('modelid') pid = request.POST.get('pid') installPackage.stopService(pid) time.sleep(5) usecasedetail = usecasedetails.objects.get(id=id) usecasename = usecasedetail.UsecaseName runningStatus,pid,ip,port = installPackage.checkModelServiceRunning(usecasename) installationStatus,modelName,modelVersion=installPackage.checkInstalledPackge(usecasename) models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS') for model in models: model.scoringCreteria = 'NA' model.score = 'NA' model.deploymodel = 'NA' model.maacsupport = 'False' model.flserversupport = 'False' if os.path.isdir(str(model.DeployPath)): modelPath = os.path.join(str(model.DeployPath), 'output.json') try: with open(modelPath) as file: outputconfig = json.load(file) file.close() if outputconfig['status'] == 'SUCCESS': model.scoringCreteria = outputconfig['data']['ScoreType'] model.score = outputconfig['data']['BestScore'] model.deploymodel = outputconfig['data']['BestModel'] supportedmodels = ["Logistic Regression", "Naive Bayes","Decision Tree","Support Vector Machine","K Nearest Neighbors","Gradient Boosting","Random Forest","Linear Regression","Lasso","Ridge"] if model.deploymodel in supportedmodels: model.maacsupport = 'True' else: model.maacsupport = 'False' supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"] if model.deploymodel in supportedmodels: model.flserversupport = 'True' else: model.flserversupport = 'False' except Exception as e: pass selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) nouc = 0 usecase = usecasedetails.objects.all() return render(request, 'models.html', {'tab': 'upload','nouc':nouc,'usecasedetail': usecase, 'models': models, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting,'installationStatus':installationStatus,'modelName':modelName,'modelVersion':modelVersion,'usecasename':usecasename,'runningStatus':runningStatus,'pid':pid,'ip':ip,'port':port,'usecaseid':id}) except: return render(request, 'models.html',{'error': 'Fail to stop model service'}) def startmodelservice(request): try: kafkaSetting = kafka_setting() ruuningSetting = running_setting() computeinfrastructure = compute.readComputeConfig() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) installPackage.startService(request.POST.get('modelName'),request.POST.get('ip'),request.POST.get('portNo')) time.sleep(5) id = request.POST.get('modelid') usecasedetail = usecasedetails.objects.get(id=id) usecasename = usecasedetail.UsecaseName runningStatus,pid,ip,port = installPackage.checkModelServiceRunning(usecasename) installationStatus,modelName,modelVersion=insallPackage.checkInstalledPackge(usecasename) models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS') for model in models: model.scoringCreteria = 'NA' model.score = 'NA' model.deploymodel = 'NA' model.maacsupport = 'False' model.flserversupport = 'False' if os.path.isdir(str(model.DeployPath)): modelPath = os.path.join(str(model.DeployPath),'etc', 'output.json') try: with open(modelPath) as file: outputconfig = json.load(file) file.close() if outputconfig['status'] == 'SUCCESS': model.scoringCreteria = outputconfig['data']['ScoreType'] model.score = outputconfig['data']['BestScore'] model.deploymodel = outputconfig['data']['BestModel'] supportedmodels = ["Logistic Regression", "Naive Bayes","Decision Tree","Support Vector Machine","K Nearest Neighbors","Gradient Boosting","Random Forest","Linear Regression","Lasso","Ridge"] if model.deploymodel in supportedmodels: model.maacsupport = 'True' else: model.maacsupport = 'False' supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"] if model.deploymodel in supportedmodels: model.flserversupport = 'True' else: model.flserversupport = 'False' except Exception as e: pass selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) nouc = 0 usecase = usecasedetails.objects.all() return render(request, 'models.html', {'tab': 'upload','nouc':nouc,'usecasedetail': usecase, 'models': models, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting,'installationStatus':installationStatus,'modelName':modelName,'modelVersion':modelVersion,'usecasename':usecasename,'runningStatus':runningStatus,'pid':pid,'ip':ip,'port':port,'usecaseid':id}) except: return render(request, 'models.html',{'error': 'Fail to start model service'}) def downloadpackage(request, id,version): return(installPackage.downloadPackage(request,id,version,usecasedetails,Existusecases)) def createpackagedocker(request, id,version): try: context = installPackage.createPackagePackage(request,id,version,usecasedetails,Existusecases) context['version'] = AION_VERSION return render(request, 'usecases.html',context) except Exception as e: return render(request, 'usecases.html',{'error': str(e)}) def publish(request, id): print("Inside Publish Tab") try: kafkaSetting = kafka_setting() ruuningSetting = running_setting() computeinfrastructure = compute.readComputeConfig() usecasedetail = usecasedetails.objects.get(id=id) usecasename = usecasedetail.UsecaseName publish_version,publish_status,publish_drift_status =chech_publish_info(usecasename) runningStatus,pid,ip,port = installPackage.checkModelServiceRunning(usecasename) installationStatus,modelName,modelVersion=installPackage.checkInstalledPackge(usecasename) models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS') for model in models: model.scoringCreteria = 'NA' model.score = 'NA' model.deploymodel = 'NA' model.maacsupport = 'False' model.flserversupport = 'False' if os.path.isdir(str(model.DeployPath)): modelPath = os.path.join(str(model.DeployPath),'etc', 'output.json') try: with open(modelPath) as file: outputconfig = json.load(file) file.close() if outputconfig['status'] == 'SUCCESS': model.scoringCreteria = outputconfig['data']['ScoreType'] model.score = outputconfig['data']['BestScore'] model.deploymodel = outputconfig['data']['BestModel'] model.featuresused = eval(outputconfig['data']['featuresused']) model.targetFeature = outputconfig['data']['targetFeature'] if 'params' in outputconfig['data']: model.modelParams = outputconfig['data']['params'] model.modelType = outputconfig['data']['ModelType'] model.dataPath = os.path.join(str(model.DeployPath),'data', 'postprocesseddata.csv') supportedmodels = ["Logistic Regression", "Naive Bayes","Decision Tree","Support Vector Machine","K Nearest Neighbors","Gradient Boosting","Random Forest","Linear Regression","Lasso","Ridge","Extreme Gradient Boosting (XGBoost)","Light Gradient Boosting (LightGBM)","Categorical Boosting (CatBoost)","LSTM"] print(model.deploymodel) if model.deploymodel in supportedmodels: model.maacsupport = 'True' else: model.maacsupport = 'False' supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"] if model.deploymodel in supportedmodels: model.flserversupport = 'True' else: model.flserversupport = 'False' supportedmodels = ["Extreme Gradient Boosting (XGBoost)"] if model.deploymodel in supportedmodels: model.encryptionsupport = 'True' else: model.encryptionsupport = 'False' except Exception as e: print(e) pass selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) nouc = 0 usecase = usecasedetails.objects.all() print(models) return render(request, 'models.html', {'tab': 'upload','nouc':nouc,'usecasedetail': usecase, 'models': models, 'selected_use_case': selected_use_case,'usecasetab':usecasetab,'publish_version':publish_version,'publish_status':publish_status,'publish_drift_status':publish_drift_status, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'installationStatus':installationStatus,'modelName':modelName,'modelVersion':modelVersion,'usecasename':usecasename,'runningStatus':runningStatus,'pid':pid,'ip':ip,'port':port,'usecaseid':id}) except Exception as e: print(e) return render(request, 'models.html',{'error': 'Fail to publish model'}) def remove_version(request, id): from appbe.pages import get_usecase_page try: kafkaSetting = kafka_setting() ruuningSetting = running_setting() computeinfrastructure = compute.readComputeConfig() if request.method == 'GET': try: model = Existusecases.objects.get(id=id) usecaseid = model.ModelName.id if os.path.isdir(str(model.DeployPath)): import shutil if DEPLOY_LOCATION != str(model.DeployPath): shutil.rmtree(str(model.DeployPath)) else: uname = model.ModelName.usecaseid.replace(" ", "_") usecaseversion = model.Version deployLocation = os.path.join(str(model.DeployPath),uname+'_'+str(usecaseversion)) if os.path.isdir(str(deployLocation)): shutil.rmtree(str(deployLocation)) model.delete() usecasedetail = usecasedetails.objects.get(id=model.ModelName.id) models = Existusecases.objects.filter(ModelName=usecasedetail) if len(models) == 0: usecasedetail.delete() Status = 'SUCCESS' Msg = 'Version Deleted Successfully' except Exception as e: print(e) Status = 'Error' Msg = str(e) status, context,page = get_usecase_page(request,usecasedetails,Existusecases) context['Status'] = Status context['Msg'] = Msg context['version'] = AION_VERSION return render(request, 'usecases.html',context) except Exception as e: print(e) status, context,page = get_usecase_page(request,usecasedetails,Existusecases) context['Status'] = 'Error' context['Msg'] = 'Usecase Version Deletion Error' context['version'] = AION_VERSION return render(request, 'usecases.html',context) def destroy(request, id): from appbe.pages import get_usecase_page try: kafkaSetting = kafka_setting() ruuningSetting = running_setting() computeinfrastructure = compute.readComputeConfig() if request.method == 'GET': try: usecasedetail = usecasedetails.objects.get(id=id) usecasename = usecasedetail.usecaseid models = Existusecases.objects.filter(ModelName=usecasedetail) for model in models: if os.path.isdir(str(model.DeployPath)): import shutil if DEPLOY_LOCATION != str(model.DeployPath): shutil.rmtree(str(model.DeployPath)) else: uname = usecasename.replace(" ", "_") usecaseversion = model.Version deployLocation = os.path.join(str(model.DeployPath),uname+'_'+str(usecaseversion)) if os.path.isdir(str(deployLocation)): shutil.rmtree(str(deployLocation)) usecasedetail.delete() Status = 'SUCCESS' Msg = 'Deleted Successfully' except Exception as e: print(e) Status = 'Error' Msg = str(e) else: usecasename = 'Not Defined' if 'UseCaseName' in request.session: if (usecasename == request.session['UseCaseName']): selected_use_case = 'Not Defined' request.session['UseCaseName'] = selected_use_case request.session['ModelVersion'] = 0 request.session['ModelStatus'] = 'Not Trained' else: selected_use_case = request.session['UseCaseName'] else: selected_use_case = 'Not Defined' status, context,page = get_usecase_page(request,usecasedetails,Existusecases) context['Status'] = Status context['Msg'] = Msg context['version'] = AION_VERSION return render(request, 'usecases.html',context) except: status, context,page = get_usecase_page(request,usecasedetails,Existusecases) context['Status'] = 'Error' context['Msg'] = 'Usecase Deletion Error' context['version'] = AION_VERSION return render(request, 'usecases.html',context) def update(request, id): try: lab = get_object_or_404(usecasedetails, id=id) if request.method == 'POST': form = usecasedetailsForm(request.POST, instance=lab) request.session['usecaseid'] = form['id'] # print(request.session['usecaseid']) if form.is_valid(): form.save() return redirect('/show') else: form = usecasedetailsForm(instance=lab) request.session['usecaseid'] = form['id'] # print(request.session['usecaseid']) return render(request, 'edit.html', {'form': form, 'selected': 'usecase'}) except: return render(request, 'edit.html', {'error': 'Error in updating usecase', 'selected': 'usecase'}) def newfile(request): selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] try: model = Existusecases.objects.get(ModelName=request.session['ModelName'], Version=request.session['ModelVersion']) output_train_json_filename = str(model.TrainOuputLocation) f = open(output_train_json_filename, "r+") training_output = f.read() f.close() training_output = json.loads(training_output) dataFile = request.POST.get('localfilePath') if(os.path.isfile(dataFile) == False): context = {'error': 'Data file does not exist', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion} return render(request, 'outputdrif.html', context) df = pd.read_csv(dataFile) request.session['drift_datalocations'] = dataFile request.session['Features_dr'] = df.columns.values.tolist() Featrs = request.session['Features_dr'] statusmsg = 'Data File Uploaded Successfully' context = {'tab': 'tabconfigure', 'status_msg': statusmsg, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'monitoring', 'z': Featrs} context['version'] = AION_VERSION return render(request, 'outputdrif.html', context) except Exception as Isnt: context = {'error': 'Error during output drift.'+str(Isnt), 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion} context['version'] = AION_VERSION return render(request, 'outputdrif.html', context) def summarization(request): context = {'selected':'DataOperations','usecasetab':usecasetab} context['version'] = AION_VERSION return render(request, "summarization.html",context) # ------------------ Debiasing Changes ------------------ def getdataclasses(request): updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r+", encoding="utf-8") configSettingsData = f.read() configSettingsJson = json.loads(configSettingsData) df = pd.read_csv(configSettingsJson['basic']['dataLocation'],encoding='utf8') classeslist = [] selectedFeature = request.GET.get('features') classeslist = df[selectedFeature].unique().tolist() _list = [] for item in classeslist: _list.append("<option value='"+ item +"'>" + item +"</option>") return HttpResponse(_list) # ------------------ ------------------ def ucdetails(request, id): from appbe.pages import usecases_page checkModelUnderTraining(request, usecasedetails, Existusecases) request.session['IsRetraining'] = 'No' status, context, action = usecases_page(request, usecasedetails, Existusecases, id) context['version'] = AION_VERSION return render(request, 'usecasedetails.html', context) def dataoperations(request): context = {'selected':'DataOperations','usecasetab':usecasetab} context['version'] = AION_VERSION return render(request, "dataoperations.html",context) # @login_required(login_url="/login/") def datalabel(request): context = {'selected':'DataOperations','usecasetab':usecasetab} context['version'] = AION_VERSION return render(request, "label_dataset_ver2.html",context) # @login_required(login_url="/login/") def pages(request): context = {} # All resource paths end in .html. # Pick out the html file name from the url. And load that template. try: load_template = request.path.split('/')[-1] html_template = loader.get_template(load_template) return HttpResponse(html_template.render(context, request)) except template.TemplateDoesNotExist: html_template = loader.get_template('page-404.html') return HttpResponse(html_template.render(context, request)) except: html_template = loader.get_template('page-500.html') return HttpResponse(html_template.render(context, request)) def delimitedsetting(delimiter='',textqualifier='',other=''): if delimiter != '': if delimiter.lower() == 'tab' or delimiter.lower() == '\t': delimiter = '\t' elif delimiter.lower() == 'semicolon' or delimiter.lower() == ';': delimiter = ';' elif delimiter.lower() == 'comma' or delimiter.lower() == ',': delimiter = ',' elif delimiter.lower() == 'space' or delimiter.lower() == ' ': delimiter = ' ' elif delimiter.lower() == 'other' or other.lower() != '': if other != '': delimiter = other else: delimiter = ',' elif delimiter != '': delimiter = delimiter else: delimiter = ',' else: delimiter = ',' if textqualifier == '': textqualifier = '"' return delimiter,textqualifier @csrf_exempt def upload_and_read_file_data(request): file_path, file_ext = handle_uploaded_file(path=DATA_FILE_PATH, file=request.FILES['uploaded_file']) file_delim = request.POST.get("file_delim") textqualifier = request.POST.get("qualifier") delimiters = request.POST.get("delimiters") delimiter,textqualifier = delimitedsetting(request.POST.get('file_delim'),request.POST.get('qualifier'),request.POST.get('delimiters_custom_value')) size_take = 100 if file_ext in ["csv", "tsv"]: num_records = sum(1 for line in open(file_path)) - 1 num_rows = num_records if num_records > size_take: skip = sorted(random.sample(range(1, num_records + 1), num_records - size_take)) else: skip = 0 # with open(file_path, 'r') as file: # data = file.readline(10) # from detect_delimiter import detect # row_delimiter = detect(text=data, default=None, whitelist=[',', ';', ':', '|', '\t', ' ']) # if file_delim == "custom" and request.POST["custom_delim"] != "": # row_delimiter = request.POST["custom_delim"] # print('row_delimiter',row_delimiter) file_content = pd.read_csv(file_path, sep=delimiter,quotechar=textqualifier, engine='python',skiprows=skip,encoding='utf-8-sig',skipinitialspace = True) elif file_path.endswith(".json"): file_content_df = pd.read_json(file_path) file_content = pd.json_normalize(file_content_df.to_dict("records")) num_rows = len(file_content) elif file_path.endswith(".avro"): import pandavro as pdx from avro.datafile import DataFileReader from avro.io import DatumReader reader = DataFileReader(open(file_path, "rb"), DatumReader()) schema = json.loads(reader.meta.get('avro.schema').decode('utf-8')) file_content = pdx.read_avro(file_path, schema=schema, na_dtypes=True) num_rows = len(file_content) elif file_path.endswith(".parquet"): from pyarrow.parquet import ParquetFile import pyarrow as pa import pyarrow.parquet as pq pf = ParquetFile(file_path) take_rows = next(pf.iter_batches(batch_size=size_take)) file_content = pa.Table.from_batches([take_rows]).to_pandas() table = pq.read_table(file_path, columns=[]) num_rows = table.num_rows # file_content = pd.read_parquet(file_path, engine="pyarrow") else: raise ValueError("Invalid file format") response = {} column_list = [] for key, val in dict(file_content.dtypes).items(): if str(val) == 'object': try: pd.to_datetime(file_content[str(key)]) column_list.append({"column_name": str(key), 'data_type': 'datetime64'}) except ValueError: column_list.append({"column_name": str(key), 'data_type': 'string'}) pass else: column_list.append({"column_name": str(key), 'data_type': str(val)}) response["column_list"] = column_list response["data_html"] = file_content.to_html(classes='table table-striped table-bordered table-hover dataTable no-footer', justify='left', index=False) response["record_count"] = num_rows response["file_ext"] = file_ext return HttpResponse(json.dumps(response), content_type="application/json") @csrf_exempt def handle_uploaded_file(path, file, test_dataset=False): print('path',path) if test_dataset: filename = os.path.join(path,"test_data_file." + file.name.split('.')[1]) with open(filename, 'wb+') as destination: for chunk in file.chunks(): destination.write(chunk) return filename, file.name.split('.')[1] else: filename = os.path.join(path,"uploaded_file." + file.name.split('.')[1]) with open(filename, 'wb+') as destination: for chunk in file.chunks(): destination.write(chunk) return filename, file.name.split('.')[1] @csrf_exempt def apply_rule(request): from appbe import labelling_utils as utils rule_list = json.loads(request.POST['rule_list']) file_ext = request.POST.get("file_ext") label_list = json.loads(request.POST['label_list']) not_satisfy_label = request.POST.get("non_satisfied_label") response = utils.label_dataset(rule_list, file_ext, label_list, not_satisfy_label) return HttpResponse(json.dumps(response), content_type="application/json") @csrf_exempt def get_sample_result_of_individual_rule(request): from appbe import labelling_utils as utils rule_json = json.loads(request.POST['rule_json']) file_ext = request.POST.get("file_ext") label_list = json.loads(request.POST['label_list']) not_satisfy_label = request.POST.get("non_satisfied_label") print("rule_json>>>", rule_json) print("file_ext>>>", file_ext) print("label_list>>>>", label_list) print("not_satisfied_label", not_satisfy_label) response = utils.get_sample_result_of_individual_rule(rule_json, file_ext, label_list, not_satisfy_label) return HttpResponse(json.dumps(response), content_type="application/json") def download_result_dataset(request): #file_name = request.GET.get("filename") file_name = request.session['AION_labelled_Dataset'] file_path = os.path.join(DATA_FILE_PATH, file_name) is_exist = os.path.exists(file_path) if is_exist: with open(file_path, "rb") as file: response = HttpResponse(file, content_type="application/force-download") response["Content-Disposition"] = "attachment; filename=%s" % file_name return response else: return HttpResponse(json.dumps("file not found"), content_type="application/error") @csrf_exempt def get_sample_result_of_individual_rule_ver2(request): from appbe import labelling_utils as utils rule_json = json.loads(request.POST['rule_json']) file_ext = request.POST.get("file_ext") label_list = json.loads(request.POST['label_list']) not_satisfy_label = request.POST.get("non_satisfied_label") response = utils.get_sample_result_of_individual_rule_ver2(rule_json, file_ext, label_list, not_satisfy_label) return HttpResponse(json.dumps(response), content_type="application/json") def get_label_list(label_json): label_list = [] label_weightage = [] for item in label_json: label_list.append(item["label_name"]) if item["label_weightage"] != "": weightage_perc = float(item["label_weightage"]) / 100 label_weightage.append(np.around(weightage_perc, 2)) else: label_weightage.append(100 / len(label_json)) return label_list, label_weightage @csrf_exempt def apply_rule_ver2(request): from appbe import labelling_utils as utils rule_list = json.loads(request.POST['rule_list']) file_ext = request.POST.get("file_ext") label_json = json.loads(request.POST['label_list']) label_list, label_weightage = get_label_list(label_json) not_satisfy_label = request.POST.get("non_satisfied_label") include_proba = request.POST.get("is_include_proba") == 'true' response = utils.label_dataset_ver2(request,rule_list, file_ext, label_list, not_satisfy_label, label_weightage, include_proba) return HttpResponse(json.dumps(response), content_type="application/json") @csrf_exempt def upload_and_read_test_data(request): file_path, file_ext = handle_uploaded_file(path=DATA_FILE_PATH, file=request.FILES['uploaded_file'], test_dataset=True) # file_path, file_ext = handle_uploaded_file(path=DATA_FILE_PATH, file=request.FILES['uploaded_file']) file_delim_test = request.POST.get("file_delim_test") size_take = 100 if file_ext in ["csv", "tsv"]: num_records = sum(1 for line in open(file_path)) - 1 num_rows = num_records if num_records > size_take: skip = sorted(random.sample(range(1, num_records + 1), num_records - size_take)) else: skip = 0 with open(file_path, 'r') as file: data = file.readline(10) from detect_delimiter import detect row_delimiter = detect(text=data, default=None, whitelist=[',', ';', ':', '|', '\t', ' ']) if file_delim_test == "custom" and request.POST["custom_test_delim"] != "": row_delimiter = request.POST["custom_test_delim"] file_content = pd.read_csv(file_path, sep=row_delimiter, quotechar="'", escapechar="/", engine='python',skiprows=skip,encoding='utf-8-sig',skipinitialspace = True) elif file_path.endswith(".json"): file_content_df = pd.read_json(file_path) file_content = pd.json_normalize(file_content_df.to_dict("records")) num_rows = len(file_content) elif file_path.endswith(".avro"): import pandavro as pdx from avro.datafile import DataFileReader from avro.io import DatumReader reader = DataFileReader(open(file_path, "rb"), DatumReader()) schema = json.loads(reader.meta.get('avro.schema').decode('utf-8')) file_content = pdx.read_avro(file_path, schema=schema, na_dtypes=True) num_rows = len(file_content) elif file_path.endswith(".parquet"): from pyarrow.parquet import ParquetFile import pyarrow as pa import pyarrow.parquet as pq pf = ParquetFile(file_path) take_rows = next(pf.iter_batches(batch_size=size_take)) file_content = pa.Table.from_batches([take_rows]).to_pandas() table = pq.read_table(file_path, columns=[]) num_rows = table.num_rows # file_content = pd.read_parquet(file_path, engine="pyarrow") else: raise ValueError("Invalid file format") response = {} column_list = [] for key, val in dict(file_content.dtypes).items(): if str(val) == 'object': try: pd.to_datetime(file_content[str(key)]) column_list.append({"column_name": str(key), 'data_type': 'datetime64'}) except ValueError: column_list.append({"column_name": str(key), 'data_type': 'string'}) pass else: column_list.append({"column_name": str(key), 'data_type': str(val)}) response["column_list"] = column_list response["data_html"] = file_content.to_html(classes='table table-striped text-left',table_id='testdata', justify='left', index=False) response["record_count"] = num_rows response["file_ext"] = file_ext response["file_delim_test"] = file_delim_test response["custom_test_delim"] = request.POST["custom_test_delim"] return HttpResponse(json.dumps(response), content_type="application/json") @csrf_exempt def get_label_and_weightage(request): from appbe import labelling_utils as utils test_file_ext = request.POST.get("test_file_ext") file_delim_test = request.POST.get("file_delim_test") marked_label_column = request.POST.get("marked_label_column") custom_test_delim = request.POST.get("custom_test_delim") label_list_with_weightage = utils.get_label_and_weightage(test_file_ext, marked_label_column, file_delim_test, custom_test_delim) return HttpResponse(json.dumps(label_list_with_weightage), content_type="application/json") def modelcompare(request): deploypath = request.GET.get('DeployLocation') filepath = os.path.join(deploypath,'etc','output.json') with open(filepath) as file: config = json.load(file) file.close() # training/testing data needs to be updated as below once it is available in deployment folder #trainingDataPath = os.path.join(deploypath,'data','trainData.csv') #testingDataPath = os.path.join(deploypath,'data','testData.csv') trainingDataPath = os.path.join(deploypath,'data','postprocesseddata.csv.gz') testingDataPath = os.path.join(deploypath,'data','postprocesseddata.csv.gz') featureUsedInTraining=config['data']['featuresused'] targetFeature= config['data']['targetFeature'] scoringCriteria=config['data']['ScoreType'] scoringCriteria=scoringCriteria.lower() problemType=config['data']['ModelType'] problemType=problemType.lower() tempFeatureUsedInTraining = featureUsedInTraining.split(',') finalFeatures=[] for i in range (len(tempFeatureUsedInTraining)) : tempFeatureUsedInTraining[i]=tempFeatureUsedInTraining[i].replace('[', '') tempFeatureUsedInTraining[i]=tempFeatureUsedInTraining[i].replace(']', '') tempFeatureUsedInTraining[i]=tempFeatureUsedInTraining[i].replace("'", '') tempFeatureUsedInTraining[i] = tempFeatureUsedInTraining[i].lstrip() tempFeatureUsedInTraining[i] = tempFeatureUsedInTraining[i].rstrip() finalFeatures.append(tempFeatureUsedInTraining[i]) featureUsedInTraining = finalFeatures #print("trainingDataPath----",trainingDataPath) #print("testingDataPath----",testingDataPath) #print("problemType----",problemType) #print("scoringCriteria----",scoringCriteria) #print("featureUsedInTraining----",featureUsedInTraining,type(featureUsedInTraining)) #print("targetFeature----",targetFeature) if problemType == 'classification': try: df1 = pd.read_csv(trainingDataPath,encoding='utf-8',skipinitialspace = True,compression='gzip') df2 = pd.read_csv(testingDataPath,encoding='utf-8',skipinitialspace = True,compression='gzip') trainX=df1[featureUsedInTraining] trainY=df1[targetFeature] testX=df2[featureUsedInTraining] testY=df2[targetFeature].to_numpy() from sklearn import linear_model estimator = linear_model.LogisticRegression() estimator.fit(trainX, trainY) predictedData = estimator.predict(testX) from learner.aion_matrix import aion_matrix scoring = aion_matrix() score = scoring.get_score(scoringCriteria, testY, predictedData) context = {'Model': 'Logistic regression','Testing Score': score, 'Confidence Score': "Not supported", 'Feature Engineering Method': "ModelBased"} return HttpResponse(json.dumps(context), content_type="application/json") except Exception as e: print("exception "+str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) context = {'Model': 'Logistic regression','Testing Score': "Exception Occured", 'Confidence Score': "Not supported", 'Feature Engineering Method': "ModelBased"} return HttpResponse(json.dumps(context), content_type="application/json") if problemType == 'regression': try: df1 = pd.read_csv(trainingDataPath,encoding='utf-8',skipinitialspace = True,compression='gzip') df2 = pd.read_csv(testingDataPath,encoding='utf-8',skipinitialspace = True,compression='gzip') trainX=df1[featureUsedInTraining] trainY=df1[targetFeature] testX=df2[featureUsedInTraining] testY=df2[targetFeature].to_numpy() from sklearn import linear_model estimator = linear_model.LinearRegression() estimator.fit(trainX, trainY) predictedData = estimator.predict(testX) from learner.aion_matrix import aion_matrix scoring = aion_matrix() score = scoring.get_score(scoringCriteria, testY, predictedData) context = {'Model': 'Linear regression','Testing Score': score, 'Confidence Score': "Not supported", 'Feature Engineering Method': "ModelBased"} return HttpResponse(json.dumps(context), content_type="application/json") except Exception as e: print("exception") context = {'Model': 'Linear regression','Testing Score': "Exception Occured", 'Confidence Score': "Not supported", 'Feature Engineering Method': "ModelBased"} return HttpResponse(json.dumps(context), content_type="application/json") def textsummarization(request): return render(request, "textsummarization.html",context={'version':AION_VERSION,'selected': 'textsummarization'}) # LLM Testing Task ID 14533 def validate_llm(prompts, reference_generation,temperature, similarity_threshold, perturbations_per_sample): default = {'temperature':{'default':0.9,'lower':0.0,'upper':1.0},'similarity_threshold':{'default':0.75,'lower':0.0,'upper':1.0},'perturbations_per_sample':5} if not isinstance( prompts, (list,str)): raise ValueError(f"Prompt should be of type str, got '{prompt}' of type {type(prompt)}") elif prompts == '': raise ValueError("Prompt field can not be empty") if not isinstance( reference_generation, str): raise ValueError(f"Reference Generated Answer should be of type str, got '{reference_generation}' of type {type(reference_generation)}") # elif reference_generation == '': # raise ValueError("Reference Generation field can not be empty") if not isinstance( temperature, float) or temperature < default['temperature']['lower'] or temperature > default['temperature']['upper']: if isinstance( temperature, str) and temperature == '': temperature = default['temperature']['default'] else: raise ValueError(f"Model Parameter Temperature should be of type float with range {default['temperature']['lower']} - {default['temperature']['upper']}, got {temperature} of type {type(temperature)}") if not isinstance( similarity_threshold, float) or similarity_threshold < default['similarity_threshold']['lower'] or similarity_threshold > default['similarity_threshold']['upper']: if isinstance( similarity_threshold, str) and similarity_threshold == '': similarity_threshold = default['similarity_threshold']['default'] else: raise ValueError(f"Similarity Threshold should be of type float with range {default['similarity_threshold']['lower']} - {default['similarity_threshold']['upper']}, got {similarity_threshold} of type {type(similarity_threshold)}") if not isinstance( perturbations_per_sample, int): if isinstance( perturbations_per_sample, str) and perturbations_per_sample == '': perturbations_per_sample = default['perturbations_per_sample'] else: raise ValueError(f"Perturbations Per Sample should be of type integer, got {perturbations_per_sample} of type {type(perturbations_per_sample)}") return prompts, reference_generation,temperature, similarity_threshold, perturbations_per_sample def llmtesting(request): ftmodels = [] usecase = usecasedetails.objects.all().order_by('-id') for x in usecase: #print(x.id) models = Existusecases.objects.filter(Status='SUCCESS',ModelName=x.id).order_by('-id') if len(models) > 0: for model in models: #print(str(model.ConfigPath)) version = model.Version if os.path.isdir(str(model.DeployPath)): modelPath = os.path.join(str(model.DeployPath),'etc','output.json') with open(modelPath) as file: outputconfig = json.load(file) problemType = outputconfig['data']['ModelType'] if problemType.lower() == 'llm fine-tuning': from appbe.models import get_instance hypervisor,instanceid,region,image,status = get_instance(x.usecaseid+ '_' + str(version)) with open(str(model.ConfigPath)) as file: configSettingsJson = json.load(file) file.close() from appbe.pages import getMLModels problem_type,dproblem_type,sc,mlmodels,dlmodels,smodelsize = getMLModels(configSettingsJson) ft = mlmodels+'-'+smodelsize+'-'+x.usecaseid+'_'+str(version) finetunedModel = {} finetunedModel['ft']=ft finetunedModel['basemodel'] = mlmodels+'-'+smodelsize finetunedModel['usecaseid'] = x.usecaseid+'_'+str(version) ftmodels.append(finetunedModel) return render(request, "llmtesting.html",context={'version':AION_VERSION,'selected': 'llmtesting','ftmodels':ftmodels}) # LLM Testing Result Task ID 14533 def llmtestingresult(request): try: context = {'result':result,'provider':provider,'tabledata':tabledata,'summary':summary,'modelName':modelName,'temperature':temperature,'similarity_threshold':similarity_threshold,'prompt':prompt,'reference_generation':reference_generation,'perturbations_per_sample':perturbations_per_sample,'version':AION_VERSION,'selected': 'llmtestingresults','success':'success'} return render(request, "llmtestingresults.html",context) except Exception as e: print(e) context = {'error': 'Fail to Generate LLM Testing Report '+str(e),'version':AION_VERSION,'selected': 'llmtestingresults','fail':'fail'} return render(request, "llmtestingresults.html",context) # LLM Testing Result Task ID 14533 def llmtestingresult(request): try: generate_test = request.POST['prompt_temp'] if generate_test == "generatetest": UseCaseName = request.POST['selectusecase'] ModelName = request.POST['selectmodel'] temperature = request.POST['modelparam'] similarity_threshold = request.POST['similarity_threshold'] perturbations_per_sample = request.POST['perturbations_per_sample'] selecttype = request.POST['selectquestion'] reference_generation = (request.POST['reference_generation']) baseModel = request.POST['basemodel'] from appbe.llmTesting import test_LLM if selecttype == "Single": prompts = request.POST['prompt'] else: data_file = request.POST['dataFilePath']#Task 16794 file_name = os.path.splitext(data_file)[0] file_extension = os.path.splitext(data_file)[-1].lower() if file_extension != ".csv": questions = [] answers = [] if file_extension == ".pdf": with pdfplumber.open(data_file) as pdf: for page in pdf.pages: text = page.extract_text() lines = text.split("\n") current_question = "" current_answer = "" reading_question = False for line in lines: line = line.strip() if line.endswith("?"): if reading_question: questions.append(current_question) answers.append(current_answer) current_question = "" current_answer = "" current_question = line reading_question = True elif reading_question: current_answer += " " + line if reading_question: questions.append(current_question) answers.append(current_answer) elif file_extension == ".docx": doc = Document(data_file) current_question = "" current_answer = "" reading_question = False for paragraph in doc.paragraphs: text = paragraph.text.strip() if text.endswith("?"): if reading_question: questions.append(current_question) answers.append(current_answer) current_question = "" current_answer = "" current_question = text reading_question = True elif reading_question: current_answer += " "+ text if reading_question: questions.append(current_question) answers.append(current_answer) else: print("unsupported file format. please provide a pdf or docx file.") faq = pd.DataFrame({'Question':questions, 'Answers':answers}) # print(faq) data_file_csv = file_name+".csv" faq.to_csv(data_file_csv, index=False, encoding='utf-8') else: faq = pd.read_csv(data_file,encoding='cp1252') rows = faq.shape[0] prompts = list(faq['Question']) try: temperature = float( temperature) similarity_threshold = float(similarity_threshold) perturbations_per_sample = int( perturbations_per_sample) except: pass prompts, reference_generation,temperature, similarity_threshold, perturbations_per_sample = validate_llm(prompts, reference_generation,temperature, similarity_threshold, perturbations_per_sample) from appbe.aion_config import get_llm_data llm_key,llm_url,api_type,api_version=get_llm_data() urls = { 'OPENAI_API_BASE' : llm_url, 'OPENAI_API_KEY' : llm_key, 'OPENAI_API_TYPE' :api_type, 'OPENAI_API_VERSION':api_version } llm_obj = test_LLM() llm_obj.set_params(urls) if selecttype == "Single": print(UseCaseName,ModelName) if ModelName.lower() == 'basemodel': result = llm_obj.run_offline_model( UseCaseName,baseModel,temperature, similarity_threshold, perturbations_per_sample, reference_generation, prompts,False ) llmModelName = baseModel else: result = llm_obj.run_offline_model( UseCaseName,ModelName,temperature, similarity_threshold, perturbations_per_sample, reference_generation, prompts,True ) llmModelName = ModelName+'-'+UseCaseName print(result) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'llmreport_' + filetimestamp+'.html') result = result.split("LLMTestingResultOutput:")[-1] output = json.loads(result) with open(dataFile,'w') as htmlfile: htmlfile.write(output['data']['html_file']) request.session['llmtestreport'] = str(dataFile) # provider = result.generation_kwargs['Provider'] provider = "" # metric_name = list(result.metric[0].keys())[0] metric_name = output['data']['metric_name'] # metric_values = output['data']['metric_values'] metric_values = eval(output['data']['metric_values']) passed_tests = output['data']['passed_tests'] total_tests = output['data']['total_tests'] summary = f'{passed_tests}/{total_tests}' tabledata = {} prompts = output['data']['prompts'] generations= output['data']['generations'] Generations = [] for sub in generations: Generations.append(sub.replace("\n", "")) metricvalues = metric_values text = [eval(x) for x in generations] gen = [x[0]['generated_text'].split('\n')[1:] for x in text] Generations = [' '.join(x) for x in gen] resultoutput = eval(output['data']['resultoutput'])[0] for index,val in enumerate(Generations): Generations[index]= Generations[index].strip() if len(Generations[index])<=2: metricvalues[index] = 0 resultoutput[index] = 0 tabledata = zip(prompts,Generations,metricvalues,resultoutput) context = {'result':result,'provider':provider,'tabledata':tabledata,'summary':summary,'modelName':llmModelName,'temperature':temperature,'similarity_threshold':similarity_threshold,'prompt':prompts,'reference_generation':reference_generation,'perturbations_per_sample':perturbations_per_sample,'single':'single','version':AION_VERSION,'selected': 'llmtestingresults','success':'success'} # context = {'result':result,'provider':"provider",'tabledata':"tabledata",'summary':"summary",'modelName':modelName,'temperature':temperature,'similarity_threshold':similarity_threshold,'prompt':prompts,'reference_generation':reference_generation,'perturbations_per_sample':perturbations_per_sample,'single':'single','version':AION_VERSION,'selected': 'llmtestingresults','success':'success'} else: if ModelName.lower() == 'basemodel': result_str =llm_obj.run_multiple_offline_model(UseCaseName,baseModel,temperature, similarity_threshold, perturbations_per_sample,faq,False) llmModelName = baseModel else: result_str =llm_obj.run_multiple_offline_model(UseCaseName,ModelName,temperature, similarity_threshold, perturbations_per_sample,faq,True) llmModelName = ModelName+'-'+UseCaseName result_str = result_str.split("LLMTestingResultOutput:")[-1] output = json.loads(result_str) # result will be df converted from output['data'] result = pd.DataFrame(json.loads(output['data'])) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'llmreport_' + filetimestamp+'.csv') request.session['llmtestreport'] = str(dataFile) result.rename(columns={'Perturbed Prompts':'PerturbedPrompts','Similarity [Generations]':'Similarity'},inplace=True) result_df = result.head(5) result.to_csv(dataFile, index=False) context={'result':result_df,'modelName':llmModelName,'temperature':temperature,'similarity_threshold':similarity_threshold,'perturbations_per_sample':perturbations_per_sample,'selected': 'llmtestingresults','multiple':'multiple','success':'success'} return render(request, "llmtestingresults.html",context) if generate_test == "download_prompt": csvdata= os.path.join(DEFAULT_FILE_PATH,"Prompt_template.csv") if os.path.isfile(csvdata) and os.path.exists(csvdata): df = pd.read_csv(csvdata,encoding='utf8') downloadFileName = 'llmreport.csv' response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename='+downloadFileName df.to_csv(response, index=False) return response else: context = {'error': 'Fail to Download File','version':AION_VERSION,'selected': 'llmtestingresults','fail':'fail'} return render(request, "llmtestingresults.html",context) except Exception as e: print(e) errormsg = str(e) if 'Invalid URL' in errormsg or 'No connection adapters' in errormsg or 'invalid subscription key' in errormsg: errormsg = 'Access denied due to invalid subscription key or wrong API endpoint. Please go to settings and make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.' if 'Max retries exceeded with url' in errormsg: errormsg = 'Please make sure you have good internet connection and access to API endpoint for your resource.' context = {'error':errormsg,'version':AION_VERSION,'selected': 'llmtestingresults','fail':'fail'} return render(request, "llmtestingresults.html",context) def llmtestreport(request): file_path = request.session['llmtestreport'] # file_path = "C:\AION\To_Kiran\To_Kiran\codeCloneReport\code_clone_report.txt" report_path = os.path.join(file_path) if os.path.exists(report_path): with open(report_path, 'rb') as fh: response = HttpResponse(fh.read(), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') response['Content-Disposition'] = 'attachment; filename=' + os.path.basename(report_path) return response else: return render(request, "llmtestingresults.html",context={"error":"Fail To Download File",'version':AION_VERSION,'result':'result','selected': 'llmtestingresults'}) ### To display libraries in UI #### def libraries(request): current_dir = os.path.dirname(os.path.abspath(__file__)) file_path = os.path.normpath(os.path.join(current_dir,'..','..','lic',"requirement.csv")) library_data = [] with open(file_path, 'r') as file: csv_reader = csv.DictReader(file) for row in csv_reader: library_info = { "library" :row["Library"] if row.get("Library") else "none", "version" :row["Version"] if row.get("Version") else "none", "license" :row["License"] if row.get("License") else "none" } library_data.append(library_info) # print(library_data) return render(request, "libraries.html", context={"data":library_data,'version':AION_VERSION,'selected': 'libraries'}) # For Code Clone Detection def codeclonedetectionresult(request): from appbe.codeclonedetection import CodeCloneDetectionFiles try: codecloneconfig = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','config','code_clone_config.json') f = open(codecloneconfig, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) rootdir = request.POST.get('rootdirectory') ccdmode = request.POST.get('ccdmode') if(os.path.isdir(rootdir)): llm_key,llm_url,api_type,api_version = get_llm_data() openai_apiKey = llm_key openai_baseUrl = llm_url try: openai_apiType = api_type openai_apiVersion = api_version except: openai_apiType = configSettings['openaiApiType'] openai_apiVersion = configSettings['openaiApiVersion'] openai_embeddingEngine = configSettings['codeCloneDetection']['openaiEmbeddingEngine'] openai_embeddingModel = configSettings['codeCloneDetection']['openaiEmbeddingModel'] openai_chatModel = configSettings['codeCloneDetection']['openaiChatModel'] openai_deploymentId = configSettings['codeCloneDetection']['openaiDeploymentId'] rootDirFilesType = configSettings['codeCloneDetection']['rootDirFilesType'] else: return render(request, "codeclone.html",context={"codeclonedetectionerror":"Please provide valid root directory file path.",'version':AION_VERSION,'result':'result','selected': 'codeclonedetectionresult'}) filetimestamp = str(int(time.time())) config_json_filename = os.path.join(CONFIG_FILE_PATH, 'code_clone_config_' + filetimestamp + '.json') updatedConfigSettings = json.dumps(configSettings) with open(config_json_filename, "w") as fpWrite: fpWrite.write(updatedConfigSettings) fpWrite.close() from appbe.dataPath import DEPLOY_LOCATION codeclonedir_path = os.path.join(DEPLOY_LOCATION,('codeCloneDetection_'+filetimestamp)) os.makedirs(codeclonedir_path,exist_ok=True) request.session['clonereport'] = str(codeclonedir_path) try: if (rootDirFilesType.lower() == "python" and ccdmode.lower() == "openai"): cdobj = CodeCloneDetectionFiles(rootdir,openai_baseUrl, openai_apiKey,openai_apiType,openai_apiVersion,codeclonedir_path,openai_embeddingEngine,openai_embeddingModel,openai_chatModel,openai_deploymentId) report_str,report_dict,report_df,report_json = cdobj.getCloneReport() clonetype = report_dict['Code_clones_count_by_clone_type'].to_dict() for i in clonetype: clonevalues = clonetype[i].values() clonekeys = clonetype[i].keys() clonetype = zip(clonekeys,clonevalues) return render(request, "codeclonedetectionresult.html",context={'report_json':json.loads(report_json),'report_dict':report_dict,'clonetype':clonetype,'clonefunctions':report_dict['clone_functions'],'version':AION_VERSION,'result':'result','selected': 'codeclonedetectionresult','openai':'openai'}) ## Pls uncomment below code if you need to use sklearn based code clone detection. # elif (ccdmode.lower() =="sklearn"): # from appbe.codeclonedetection_sklearn import codeCloneDetectionSklearn # chunk_size = 10000 # cdobj = codeCloneDetectionSklearn(rootdir,codeclonedir_path,chunk_size) # report_dict = cdobj.get_clone() # return render(request, "codeclonedetectionresult.html",context={'report_dict':report_dict,'function_df':report_dict['result_df'],'function_dict':report_dict['result_df'].to_dict(),'sklearn':'sklearn'}) else: raise Exception ("Invalid clonedetection input.") return render(request, "codeclone.html",context={"codeclonedetectionerror":"Python Files Are Only Supported."}) except Exception as e: return render(request, "codeclone.html",context={"codeclonedetectionerror":"OpenAI Model Connection Error",'version':AION_VERSION,'result':'result','selected': 'codeclonedetectionresult'}) except Exception as e: print('code clone detection interface issue.Error message: ',e) return render(request, "codeclone.html",context={"codeclonedetectionerror":"OpenAI Model Connection Error",'version':AION_VERSION,'result':'result','selected': 'codeclonedetectionresult'}) def codeclonereport(request): file_path = request.session['clonereport'] report_path = os.path.join(file_path, 'codeCloneReport','code_clone_report.txt') if os.path.exists(report_path): with open(report_path, 'rb') as fh: response = HttpResponse(fh.read(), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') response['Content-Disposition'] = 'attachment; filename=' + os.path.basename(report_path) return response else: return render(request, "codeclonedetectionresult.html",context={"codeclonedetectionerror":"Fail To Download File",'version':AION_VERSION,'result':'result','selected': 'codeclonedetectionresult'}) def evaluatepromptmetrics(request): """ Evaluate prompt only information for LLM Evaluation.""" import whylogs as why from langkit import light_metrics from whylogs.experimental.core.udf_schema import udf_schema from whylogs.experimental.core.udf_schema import register_dataset_udf from langkit import lang_config, response_column import json prompt_msg = request.GET.get('instruction') text_schema = udf_schema() llm_schema = light_metrics.init() df = pd.DataFrame({ "prompt": [ prompt_msg ]}) results = why.log(df, schema=udf_schema()) # .profile() view = results.view() # import pdb # pdb.set_trace() from appbe.evaluate_prompt import evaluate_prompt_metrics final_output_json,prompt_results = evaluate_prompt_metrics(prompt_msg) prompt_results_json = json.dumps(prompt_results, indent=4) # return prompt_results_json,prompt_results return HttpResponse(final_output_json) from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from appbe.pages import getversion from appbe.aion_config import running_setting from appfe.modelTraining.models import usecasedetails from appfe.modelTraining.models import Existusecases from appbe import compute AION_VERSION = getversion() def basicconfig(request): try: from appbe.aion_config import settings usecasetab = settings() from appbe import basic_Config as bc request.session['defaultfilepath'] = DEFAULT_FILE_PATH request.session['configfilepath'] = CONFIG_FILE_PATH request.session['deploylocation'] = DEPLOY_LOCATION computeinfrastructure = compute.readComputeConfig() submit_button = request.POST.get('upload_submit') ModelVersion = request.session['ModelVersion'] ruuningSetting = running_setting() selected_use_case = request.session['UseCaseName'] ModelStatus = request.session['ModelStatus'] #print(request.method) # Retraing settings changes if request.method == 'POST' and request.session['finalstate'] == 0: context = bc.gotoconf(request) else: context = bc.openbasicconf(request) context['computeinfrastructure'] = computeinfrastructure context['version'] = AION_VERSION context['usecasetab'] = usecasetab return render(request, 'basicconfig.html', context) except Exception as e: print(e) import sys,os exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return render(request, 'basicconfig.html', {'error':'Fail to retreive basic config file inputs','version':AION_VERSION,'usecasetab':usecasetab}) def savebasicconfig(request): from appbe import basic_Config as bc from appbe import advance_Config as ac from appfe.modelTraining.train_views import trainmodel try: if request.method != 'GET': status,msg,context =bc.save(request) else: status = 'pass' msg = '' except Exception as e: print(e) if status.lower()!='error': if request.method == 'GET': context = ac.basicconfignex(request) computeinfrastructure = compute.readComputeConfig() context['computeinfrastructure'] = computeinfrastructure context['version'] = AION_VERSION context['currentstate'] = 1 return render(request, 'advancedconfig.html', context) elif request.POST.get('BasicSubmit') == 'GotoAdvance': context = ac.basicconfignex(request) computeinfrastructure = compute.readComputeConfig() context['computeinfrastructure'] = computeinfrastructure context['version'] = AION_VERSION context['currentstate'] = 1 return render(request, 'advancedconfig.html', context) else: return trainmodel(request) else: context = bc.openbasicconf(request) computeinfrastructure = compute.readComputeConfig() context['computeinfrastructure'] = computeinfrastructure context['config_error']= msg context['version'] = AION_VERSION return render(request, 'basicconfig.html', context) from django.db import models class usecasedetails(models.Model): id = models.AutoField(primary_key=True) UsecaseName = models.CharField(max_length=50) usecaseid = models.CharField(max_length=10, default=UsecaseName) Description = models.CharField(max_length=200) class Meta: db_table = "usecasedetails" class Existusecases(models.Model): id = models.AutoField(primary_key=True) ModelName = models.ForeignKey(usecasedetails, on_delete=models.CASCADE) Version = models.IntegerField(default=0) DataFilePath = models.FileField(upload_to=None) ConfigPath = models.FileField(upload_to=None) DeployPath = models.FileField(upload_to=None) Status = models.CharField(max_length=200) publishStatus = models.CharField(max_length=20, default='') publishPID = models.IntegerField(default=0) trainingPID = models.IntegerField(default=0) driftStatus = models.CharField(max_length=20, default='') ProblemType = models.CharField(max_length=20, default='') modelType = models.CharField(max_length=40, default='') portNo = models.IntegerField(default=0) TrainOuputLocation = models.CharField(max_length=200, default='') class Meta: db_table = "Existusecases" from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect import json from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from appbe.pages import getusercasestatus import pandas as pd import numpy as np from appbe.pages import getversion import logging import json import time import os import subprocess import sys import base64 from appbe import compute import urllib AION_VERSION = getversion() def Sagemaker(request): if request.method == "POST": try: datafile = request.POST['datap'] endpoint = request.POST['endpoint'] awsaccountid = request.POST['awsaccountid'] accesskeyid = request.POST['accesskeyid'] secretaccesskey = request.POST['secretaccesskey'] sessionToken = request.POST['sessionToken'] region = request.POST['region'] if (awsaccountid != "" and accesskeyid != "" and secretaccesskey != "" and sessionToken != "" and endpoint != "") : awsSagemaker = {} awsSagemaker['awsID'] = awsaccountid awsSagemaker['accesskeyID'] = request.POST['accesskeyid'] awsSagemaker['secretAccesskey'] = request.POST['secretaccesskey'] awsSagemaker['sessionToken'] = request.POST['sessionToken'] awsSagemaker['region'] = request.POST['region'] configFile = os.path.join(DEFAULT_FILE_PATH, 'model_converter.json') f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) configSettingsJson['awsSagemaker'] = awsSagemaker if(os.path.exists(datafile)): inputDataType = datafile.rsplit('.', 1)[1] if inputDataType.lower() == 'csv': df = pd.read_csv(datafile) # df1 = df.iloc[0, :] df2 = df.head(1) df3 =df2.to_dict(orient='records')[0] inputFields = [] inputFields.append(df3) # models = models.rsplit('.', 1)[1] context = {'sagepredict':'sagepredict','endpoint':endpoint,'datafile':datafile,'inputFields':inputFields,'sagemaker':configSettingsJson,'version':AION_VERSION} else: context = {'exception':'exception','error':'Data File Error','version':AION_VERSION} else: context = {'error': 'Error: Please enter valid input','runtimeerror':'runtimeerror','version':AION_VERSION} except Exception as e: context = {'exception':'exception','error':'Exception :'+str(e),'sagepredict':'sagepredict','version':AION_VERSION} return render(request, 'ConvertOnnx.html',context) def Tfliteneural(request): try: if request.method == "POST": try: models = request.POST['model1'] datafile = request.POST['datafile1'] if(os.path.isfile(models)): modelformat = models.rsplit('.', 1)[1] if(os.path.isfile(models) and os.path.exists(datafile) and modelformat.lower()=='tflite'): inputDataType = datafile.rsplit('.', 1)[1] if inputDataType.lower() == 'csv': df = pd.read_csv(datafile) df2 = df.head(1) df3 =df2.to_dict(orient='records')[0] inputFields = [] inputFields.append(df3) context = {'mlalgotf':'mlalgotf','models':models,'datafile':datafile,'inputFields':inputFields,'selected':'mllite','version':AION_VERSION} elif inputDataType.lower() == 'jpg': from PIL import Image img = Image.open(datafile) string = base64.b64encode(open(datafile, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) context = {'dlalgotf':'dlalgotf','models':models,'datafile':datafile,'im':image_64,'selected':'mllite','version':AION_VERSION} else: context={'error':'Either model path or data path does not exists','runtimeerror':'runtimeerror','selected':'mllite','version':AION_VERSION} except Exception as e: context={'error':'Exception i.e., '+str(e),'runtimeerror':'runtimeerror','selected':'mllite','version':AION_VERSION} return render(request, 'ConvertOnnx.html',context) except: context={'error':'Failed to perform TFlite Runtime Prediction','runtimeerror':'runtimeerror','selected':'mllite'} return render(request, 'ConvertOnnx.html',context) def openneural(request): try: if request.method == "POST": models = request.POST['model'] datafile = request.POST['datafile'] if(os.path.isfile(models)): modelformat = models.rsplit('.', 1)[1] if(os.path.isfile(models) and os.path.exists(datafile)) and modelformat.lower()=='onnx': inputDataType = datafile.rsplit('.', 1)[1] if inputDataType.lower() == 'csv': df = pd.read_csv(datafile) df2 = df.head(1) df3 =df2.to_dict(orient='records')[0] inputFields = [] inputFields.append(df3) # models = models.rsplit('.', 1)[1] context = {'mlalgo':'mlalgo','models':models,'datafile':datafile,'selected':'mllite','inputFields':inputFields,'version':AION_VERSION} elif inputDataType.lower() == 'jpg': from PIL import Image img = Image.open(datafile) string = base64.b64encode(open(datafile, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) context = {'dlalgo':'dlalgo','models':models,'datafile':datafile,'im':image_64,'selected':'mllite','version':AION_VERSION} else: context={'error':'Either model path or data path does not exists','runtimeerror':'runtimeerror','selected':'mllite','version':AION_VERSION} return render(request, 'ConvertOnnx.html',context) except: context={'error':'Failed to perform ONNX Runtime Prediction','runtimeerror':'runtimeerror','selected':'mllite','version':AION_VERSION} return render(request, 'ConvertOnnx.html',context) def ConvertOnnx(request): try: if request.method == "POST": modelpath = request.POST['models'] deploypath = request.POST['deploy'] outputonnx = request.POST['outputonnx'] inputtonnx = request.POST['inputtonnx'] outputonnx = request.POST['outputonnx'] Features = request.POST['Features'] modelinput = inputtonnx modeloutput = outputonnx if (os.path.exists(modelpath) == False) and (outputonnx !="sagemaker") and (os.path.exists(deploypath) == False): context = {'modelpath':modelpath,'deploypath':deploypath,'inputtype':modelinput,'outputtype':modeloutput,'Features':Features,'error2':'error2','convert':'convert','logfile':'','selected':'mllite','version':AION_VERSION} elif outputonnx !="sagemaker": filetimestamp = str(int(time.time())) convjson = os.path.join(DEFAULT_FILE_PATH, 'conversion.json') with open(convjson, 'r+') as f: conv = json.load(f) f.close() conv['basic']['modelName'] = 'conversion_'+ str(filetimestamp) conv['basic']['modelVersion'] = "1" conv['advance']['aionConversionUtility']['modelpath'] = modelpath conv['advance']['aionConversionUtility']['deployedlocation'] = deploypath conv['advance']['aionConversionUtility']['numberoffeatures'] = Features temp = {} temp['inputModelType'] = inputtonnx temp['outputModelType'] = outputonnx inputtype = conv['advance']['aionConversionUtility']['inputModelType'] outputtype = conv['advance']['aionConversionUtility']['outputModelType'] for i in list(inputtype.keys()): conv['advance']['aionConversionUtility']['inputModelType'][i] = 'False' for i in list(outputtype.keys()): conv['advance']['aionConversionUtility']['outputModelType'][i] = 'False' conv['advance']['aionConversionUtility']['inputModelType'][temp['inputModelType'][0].lower() + temp['inputModelType'][1:]] = 'True' conv['advance']['aionConversionUtility']['outputModelType'][temp['outputModelType'][0].lower() + temp['outputModelType'][1:]] = 'True' conv = json.dumps(conv) config_json_filename = os.path.join(CONFIG_FILE_PATH, 'conv' + filetimestamp + '.json') with open(config_json_filename, "w") as fpWrite: fpWrite.write(conv) fpWrite.close() scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','aion.py')) try: outputStr = subprocess.check_output([sys.executable, scriptPath,'-m','convertmodel','-c',config_json_filename]) outputStr = outputStr.decode('utf-8') outputStr= outputStr.replace('\'','\"') #print('ou',outputStr) outputStr = outputStr.strip() MLlite = json.loads(outputStr) logsfile = MLlite['logfiles'] if MLlite['Convert'] == 'Success': context = {'modelpath':modelpath,'deploypath':deploypath,'inputtype':modelinput,'outputtype':modeloutput,'Features':Features,'convert1':'convert1','convert':'convert','logfile':MLlite['logfiles'],'selected':'mllite','version':AION_VERSION} else: logfile = logsfile.replace('\\','@') context = {'modelpath':modelpath,'deploypath':deploypath,'inputtype':modelinput,'outputtype':modeloutput,'Features':Features,'error1':'error1','convert':'convert','logfile':logfile,'selected':'mllite','version':AION_VERSION} except Exception as e: print(e) context = {'modelpath':modelpath,'deploypath':deploypath,'inputtype':modelinput,'outputtype':modeloutput,'Features':Features,'Notconvert':'Notconvert','convert':'convert','version':AION_VERSION} elif ( outputonnx =="sagemaker") : configFile = os.path.join(DEFAULT_FILE_PATH, 'model_converter.json') #print(configFile) f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) configSettingsJson['modelInput'] = request.POST.get('ModelInput') #print('pushonly:',request.POST.get('sagemaker')) if request.POST.get('sagemaker') == 'CreateDeploy': configSettingsJson['sagemakerDeploy'] = 'True' configSettingsJson['deployExistingModel']['status'] = 'False' else: configSettingsJson['sagemakerDeploy'] = 'False' if request.POST.get('sagemaker') == 'DeployOnly': configSettingsJson['deployExistingModel']['status'] = 'True' else: configSettingsJson['deployExistingModel']['status'] = 'False' #configSettingsJson['deployExistingModel']['status'] = request.POST.get('Status') configSettingsJson['deployExistingModel']['dockerImageName'] = request.POST.get('imagename') configSettingsJson['deployExistingModel']['deployModeluri'] = request.POST.get('deploymodel') configSettingsJson['modelOutput']['cloudInfrastructure'] = request.POST.get('problemname') configSettingsJson['endpointName'] = request.POST.get('endpointname') configSettingsJson['awsSagemaker']['awsID'] = request.POST.get('awskeyid1') configSettingsJson['awsSagemaker']['accesskeyID'] = request.POST.get('accesskey1') configSettingsJson['awsSagemaker']['secretAccesskey'] = request.POST.get('secretaccess1') configSettingsJson['awsSagemaker']['sessionToken'] = request.POST.get('token1') configSettingsJson['awsSagemaker']['region'] = request.POST.get('region1') configSettingsJson['awsSagemaker']['IAMSagemakerRoleArn'] = request.POST.get('fullaccess') conv = json.dumps(configSettingsJson) ''' filetimestamp = str(int(time.time())) config_json_filename = os.path.join(CONFIG_FILE_PATH, 'Sagemaker' + filetimestamp + '.json') with open(config_json_filename, "w") as fpWrite: fpWrite.write(conv) fpWrite.close() ''' from bin.aion_sagemaker import aion_sagemaker aion_sagemaker(configSettingsJson) #print(conv) #scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','bin','run_sagemaker.py')) #outputStr = subprocess.check_output([sys.executable, scriptPath, conv]) #outputStr = outputStr.decode('utf-8') #outputStr=outputStr.strip() #print('kir',outputStr) context = {'convert':'convert','sagemaker1':'sagemaker1','mlflow':'mlflow','inputtype':modelinput,'outputtype':modeloutput,'deploy':outputStr,'selected':'mllite','version':AION_VERSION} else: context={'exception':'exception','error':'Please Enter Valid Inputs','selected':'mllite','version':AION_VERSION} except Exception as e: print(e) context={'exception':'exception','error':'Error during Conversion','selected':'mllite','version':AION_VERSION} return render(request, 'ConvertOnnx.html',context) def sageprediction(request): #print("=========asdecdefefefefefefefef=======") values = request.POST['value'] keys = request.POST['keys'] endpoint = request.POST['endpointname'] x = keys.split(",") y = values.split(",") dictionary = {key:value for key, value in zip(x,y)} awsSagemaker={} awsSagemaker['awsID'] = request.POST['awsaccountid'] awsSagemaker['accesskeyID'] = request.POST['accesskeyid'] awsSagemaker['secretAccesskey'] = request.POST['secretaccesskey'] awsSagemaker['sessionToken'] = request.POST['sessionToken'] awsSagemaker['region'] = request.POST['region'] configFile = os.path.join(DEFAULT_FILE_PATH, 'model_converter.json') f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) awsSagemaker['IAMSagemakerRoleArn'] = configSettingsJson['awsSagemaker']['IAMSagemakerRoleArn'] configSettingsJson['awsSagemaker'] = awsSagemaker configSettingsJson['data'] = dictionary configSettingsJson['endpointName'] = endpoint configSettingsJson['prediction']['status'] = 'True' conv = json.dumps(configSettingsJson) scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','bin','run_sagemaker.py')) outputStr = subprocess.check_output([sys.executable, scriptPath, conv]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr=outputStr.strip() output = json.loads(outputStr) if output['status'] == 'SUCCESS': outputStr = output['data'] outputStr = pd.json_normalize(outputStr) outputStr = outputStr.to_html() else: outputStr = output['msg'] return HttpResponse(outputStr) def runtimeutility(request): if request.method == "POST": models = request.POST['model'] datafile = request.POST['datafile'] inputDataType = datafile.rsplit('.', 1)[1] if inputDataType.lower() == 'csv': values = request.POST['value'] keys = request.POST['keys'] x = keys.split(",") y = values.split(",") dictionary = {key:value for key, value in zip(x,y)} jsondata = json.dumps(dictionary, indent = 4) #print(jsondata) config_json_filename = os.path.join(DEFAULT_FILE_PATH, 'runtime.json') #print(config_json_filename) with open(config_json_filename, "w") as fpWrite: fpWrite.write(jsondata) fpWrite.close() from conversions.runtime_utility import runTimeTesting #scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','conversions', 'runtime_utility.py')) config_json_file = os.path.join(DEFAULT_FILE_PATH, 'runtime.json') #outputStr = subprocess.check_output([sys.executable, scriptPath, models, config_json_file]) #outputStr = outputStr.decode('utf-8') outputStr=runTimeTesting(models,config_json_file) # context = {'outputStr':outputStr,'modeltype':modeltype} else: from conversions.runtime_utility import runTimeTesting outputStr=runTimeTesting(models,datafile) return HttpResponse(outputStr) from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect import json from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from appbe.pages import getversion AION_VERSION = getversion() import os import time import subprocess import sys import re import pandas as pd def mltesting(request): from appbe.pages import mltesting_page context = mltesting_page(request) context['selected']='mltesting' context['version'] = AION_VERSION return render(request, 'mltesting.html',context) def ABtest(request): try: if request.method == "POST": models = request.POST["model"] data = request.POST["data"] #context['version'] = AION_VERSION if(os.path.isfile(models) and os.path.isfile(data)): AlgorithmNames={'LogisticRegression':'Logistic Regression','SGDClassifier':'Stochastic Gradient Descent','GaussianNB':'Naive Bayes','SVC':'Support Vector Machine','KNeighborsClassifier':'K Nearest Neighbors','DecisionTreeClassifier':'Decision Tree','RandomForestClassifier':'Random Forest','GradientBoostingClassifier':'Gradient Boosting','XGBClassifier':'Extreme Gradient Boosting (XGBoost)','DecisionTreeRegressor':'Decision Tree','LinearRegression':'Linear Regression','Lasso':'Lasso','Ridge':'Ridge','RandomForestRegressor':'Random Forest','XGBRegressor':'Extreme Gradient Boosting (XGBoost)'} filetimestamp = str(int(time.time())) mltestjson = os.path.join(DEFAULT_FILE_PATH, 'aion_config.json') with open(mltestjson, 'r+') as f: mltest = json.load(f) f.close() with open(request.session['MLTestResult'], 'r+') as f: mltestresult = json.load(f) f.close() models = mltestresult['models'] datapath = mltestresult['datap'] featurs = mltestresult['feature'] featurs = featurs.split(",") tar = mltestresult['target'] tar = tar.split(",") # models,datap,Problemtype,targ,feature,Problem,Parameters,Accuracy # models,datap,Problemtype,targ,feature,Problem,Parameters,Accuracy mltest['basic']['modelName'] = 'MLtest_'+ str(filetimestamp) mltest['basic']['modelVersion'] = "1" mltest['basic']['dataLocation'] = mltestresult['datap'] mltest['basic']['deployLocation'] = DEPLOY_LOCATION mltest['basic']['trainingFeatures'] = mltestresult['feature'] mltest['basic']['targetFeature'] = mltestresult['target'] mltest['advance']['profiler']['featureDict']=[] temp = {} Problemtype = mltestresult['Problemtype'] if Problemtype == 'Classification': Accuracyscore1 = mltestresult['Score'] Accuracyscore = float(Accuracyscore1)*100 temp['ScoringCriteria'] = 'Accuracy' else: R2_Score = round(float(mltestresult['Score']),2) temp['ScoringCriteria'] = 'R2' baselineparam = mltestresult['Params'] temp['algorithm'] = [] if request.session["AionProblem"] == 'Samebaseline': baselineprob = AlgorithmNames[mltestresult['ProblemName']] temp['algorithm'].append(baselineprob) else: baselineprob = request.session["AionProblem"] temp['algorithm'] = baselineprob.split(",") #print(baselineprob) temp['ProblemType'] = Problemtype #temp['algorithm'] = ['K Nearest Neighbors'] problemtyp = mltest['basic']['analysisType'] scoring = mltest['basic']['scoringCriteria'] for i in list(scoring.keys()): for x in list(mltest['basic']['scoringCriteria'][i].keys()): mltest['basic']['scoringCriteria'][i][x] = 'False' if temp['ProblemType'].lower() in ["classification","regression",]: mltest['basic']['scoringCriteria'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]][temp['ScoringCriteria']] = 'True' for i in list(problemtyp.keys()): mltest['basic']['analysisType'][i]='False' algorithm = mltest['basic']['algorithms'] for i in list(algorithm.keys()): for x in list(mltest['basic']['algorithms'][i].keys()): mltest['basic']['algorithms'][i][x] = 'False' mltest['basic']['analysisType'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]] = 'True' for X in temp['algorithm']: mltest['basic']['algorithms'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]][X] = 'True' mltest = json.dumps(mltest) config_json_filename = os.path.join(CONFIG_FILE_PATH, 'MLtest' + filetimestamp + '.json') with open(config_json_filename, "w") as fpWrite: fpWrite.write(mltest) fpWrite.close() scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','aion.py')) outputStr = subprocess.check_output([sys.executable, scriptPath,'-m','training','-c',config_json_filename]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_learner_status:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() decoded_data = json.loads(outputStr) #print(decoded_data) if decoded_data['data']['ScoreType'] == 'R2': decoded_data['data']['BestScore'] = str(round(float(decoded_data['data']['BestScore']),2)) if decoded_data['data']['ScoreType'].lower() == 'accuracy': decoded_data['data']['BestScore'] = str(round(float(decoded_data['data']['BestScore']),2)) #print(decoded_data) #print('123',Accuracyscore) if Problemtype == 'Classification': if Accuracyscore >= float(decoded_data['data']['BestScore']) : context = {'modelname': models,'datapath':datapath,'features':featurs,'target':tar,'abtest':'abtest','message':'message','msg':'Existing model is good to be used.','classification':'classification','classuccess':'classuccess','selected':'mltesting','version':AION_VERSION} else: context = {'modelname': models,'datapath':datapath,'features':featurs,'target':tar,'tableab':'tableab','abtest':'abtest','decoded_data':decoded_data,'score':Accuracyscore,'basealgo':baselineprob,'Problem':AlgorithmNames[mltestresult['ProblemName']],'baseparm':baselineparam,'classification':'classification','classuccess':'classuccess','selected':'mltesting','version':AION_VERSION} else: if R2_Score >= float(decoded_data['data']['BestScore']) : context = {'modelname': models,'datapath':datapath,'features':featurs,'target':tar,'abtest':'abtest','message':'message','msg':'Existing model is good to be used.','regression':'regression','regsuccess':'regsuccess','selected':'mltesting'} else: context = {'modelname': models,'datapath':datapath,'features':featurs,'target':tar,'tableab':'tableab','abtest':'abtest','decoded_data':decoded_data,'score':R2_Score,'basealgo':baselineprob,'Problem':AlgorithmNames[mltestresult['ProblemName']],'baseparm':baselineparam,'regression':'regression','regsuccess':'regsuccess','selected':'mltesting','version':AION_VERSION} else: context= {'error':'Error - Model file or Data file does not exist','abtesting':'abtesting','selected':'mltesting'} context['version'] = AION_VERSION return render(request, 'mltesting.html', context) except Exception as e: print(e) context= {'error':'Error - Fail to perform A/B Testing','abtesting':'abtesting','selected':'mltesting'} context['version'] = AION_VERSION return render(request, 'mltesting.html', context) def UQTesting(request): try: if request.method == "POST": models = request.POST['modeluq'] datap = request.POST['datauq'] if(os.path.isfile(models) and os.path.isfile(datap)): df = pd.read_csv(datap) trainfea = df.columns.tolist() featurs = request.POST.getlist('Traininguq') feature = ",".join(featurs) # features = ['PetalLengthCm','PetalWidthCm'] targ = request.POST['Targetuq'] tar =[targ] from bin.aion_uncertainties import aion_uq outputStr = aion_uq(models,datap,feature,tar) print(outputStr) uq_test = json.loads(outputStr) #print("==============") #print(uq_test) #print("==============") Problemtype= uq_test['Problem'] msg = uq_test['msg'] if Problemtype == 'Regression': # Confidence_Interval_Plot = uq_test['Confidence Interval Plot'] # #print(Confidence_Interval_Plot) # if Confidence_Interval_Plot != '': # string = base64.b64encode(open(Confidence_Interval_Plot, "rb").read()) # Confidence_Interval_Plot = 'data:image/png;base64,' + urllib.parse.quote(string) # PICP_Plot = uq_test['PICP Plot'] # if PICP_Plot != '': # string = base64.b64encode(open(PICP_Plot, "rb").read()) # PICP_Plot = 'data:image/png;base64,' + urllib.parse.quote(string) # Confidence_Plot = uq_test['Confidence Plot'] # if Confidence_Plot != '': # string = base64.b64encode(open(Confidence_Plot, "rb").read()) # Confidence_Plot = 'data:image/png;base64,' + urllib.parse.quote(string) if msg == "Good": context={'Uqtest':'Uqtest','regression':'regression','modelname':models,'datapath':datap,'features':featurs,'target':tar,'trainfea':trainfea,'uq_reg':uq_test,'uqregression':'uqregression','dfuqr':df,'Green':'Green','selected':'mllite','version':AION_VERSION} elif msg == "Bad": context={'Uqtest':'Uqtest','regression':'regression','modelname':models,'datapath':datap,'features':featurs,'target':tar,'trainfea':trainfea,'uq_reg':uq_test,'uqregression':'uqregression','dfuqr':df,'Red':'Red','selected':'mllite','version':AION_VERSION} else: context={'Uqtest':'Uqtest','regression':'regression','modelname':models,'datapath':datap,'features':featurs,'target':tar,'trainfea':trainfea,'uq_reg':uq_test,'uqregression':'uqregression','dfuqr':df,'orange':'orange','selected':'mllite','version':AION_VERSION} elif Problemtype == 'Classification': # df3 = pd.DataFrame.from_dict(uq_test,orient='index') df3 = pd.DataFrame.from_dict(uq_test, orient='index', columns=['value']) df3 = df3.rename_axis('Attributes').reset_index() # risk_plot = uq_test['Risk Plot'] # if risk_plot != '': # string = base64.b64encode(open(risk_plot, "rb").read()) # risk_plot = 'data:image/png;base64,' + urllib.parse.quote(string) # reliability_plot = uq_test['Reliability Plot'] # if reliability_plot != '': # string = base64.b64encode(open(reliability_plot, "rb").read()) # reliability_plot = 'data:image/png;base64,' + urllib.parse.quote(string) df3['Attributes'] = df3['Attributes'].str.replace(r'_', ' ') # df3.loc[(df3.Attributes == 'Model_total_confidence_score'),'Attributes']='Model Total Confidence' # df3.loc[(df3.Attributes == 'Expected_Calibration_Error'),'Attributes']='Expected Calibration Error' df3 = df3.iloc[4:, :] report = df3.to_html(index=False) if msg == "Good": context={'Uqtest':'Uqtest','classification':'classification','modelname':models,'datapath':datap,'features':featurs,'target':tar,'uqclassification':'uqclassification','uq_class':uq_test,'report':report,'selected':'mltesting','selected':'mllite','version':AION_VERSION} elif msg == "Bad": context={'Uqtest':'Uqtest','classification':'classification','modelname':models,'datapath':datap,'features':featurs,'target':tar,'uqclassification':'uqclassification','uq_class':uq_test,'report':report,'selected':'mltesting','selected':'mllite','version':AION_VERSION} else: context={'Uqtest':'Uqtest','classification':'classification','modelname':models,'datapath':datap,'features':featurs,'target':tar,'uqclassification':'uqclassification','uq_class':uq_test,'report':report,'selected':'mltesting','selected':'mllite','version':AION_VERSION} elif Problemtype == 'None': #print('hi') context={'Uqerror':'Uqerror','errormsg':"Error:"+str(msg),'version':AION_VERSION} else: context= {'error':'Please enter valid inputs','UQtesting':'UQtesting','selected':'mllite','version':AION_VERSION} return render(request, 'mltesting.html', context) except Exception as e: print("uqregression error: ",e) context= {'error':'Error - Fail to perform Uncertainty Quantification ','UQtesting':'UQtesting','selected':'mllite','version':AION_VERSION} return render(request, 'mltesting.html', context) from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect import json from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from appbe.pages import getusercasestatus import pandas as pd import numpy as np from appbe.pages import getversion import logging import json import time import os from appbe import compute AION_VERSION = getversion() def sensitivityAnalysis(request): #usnish from appbe.pages import usecases_page t1 = time.time() from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'TrustedAI','Yes') log = logging.getLogger('log_ux') computeinfrastructure = compute.readComputeConfig() selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] try: from trusted_ai.sensitivity_analysis import startSA # request.session['deploypath'] = str(p.DeployPath) sensitivitystr= startSA(request) sensitivitystr = json.loads(sensitivitystr) ModelStatus = request.session['ModelStatus'] if sensitivitystr['Status']=='Success': sensitivityGraph = sensitivitystr['graph'] t2 = time.time() log.info('Sensitivity Analysis : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + str(round(t2 - t1)) + ' sec' + ' : ' + 'Success') return HttpResponse(json.dumps(sensitivitystr)) else: error = sensitivitystr['reason'] raise Exception(error) except Exception as e: print(e) log.info('Sensitivity Analysis : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0' + 'sec' + ' : ' + 'Error : Failed to Perform Sensitivity Analysis, ' + str(e)) outputstr = json.dumps({'Status':'','msg':'Failed to Perform Sensitivity Analysis. '+str(e)}) return HttpResponse(outputstr) def handlefairness(request): from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'TrustedAI','Yes') updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) problemType = 'classification' for key in configSettings['basic']['analysisType']: if configSettings['basic']['analysisType'][key] == 'True': problemType = key break trainingfeature = configSettings['basic']['trainingFeatures'] targetfeature = configSettings['basic']['targetFeature'] featuretype = configSettings['advance']['profiler']['featureDict'] catfeature = [] for feat_conf in featuretype: colm = feat_conf.get('feature', '') if feat_conf['type'] == "categorical": catfeature.append(feat_conf['feature']) output={'targetfeature':targetfeature,'trainingfeature':trainingfeature,'catfeature':catfeature,'problemType':problemType} return HttpResponse(json.dumps(output)) def fairnesmetrics(request): #Richard--Task-13581 from appbe.pages import usecases_page from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'TrustedAI','Yes') t1 = time.time() log = logging.getLogger('log_ux') selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) try: from trusted_ai.fairness_metrics import get_metrics output = get_metrics(request) t2 = time.time() log.info('Fairness Metrics : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + str(round(t2 - t1)) + ' sec' + ' : ' + 'Success') return HttpResponse(output) except Exception as e: print(e) log.info('Fairness Metrics : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0' + 'sec' + ' : ' + 'Error : Failed to diaplay Fairness Metrics, ' + str(e)) return HttpResponse('') def performance_metrics(request): from appbe.pages import usecases_page from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'TrustedAI','Yes') t1 = time.time() log = logging.getLogger('log_ux') selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) try: from trusted_ai.performance import get_metrics output = get_metrics(request) t2 = time.time() log.info('Performance Metrics : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + str(round(t2 - t1)) + ' sec' + ' : ' + 'Success') print( output) return HttpResponse(json.dumps(output)) except Exception as e: print(e) log.info('Performance Metrics : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0' + 'sec' + ' : ' + 'Error : Failed to diaplay Performance Metrics, ' + str(e)) return HttpResponse('') def uquncertainty(request): from trusted_ai.trustedai_uq import trustedai_uq from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'TrustedAI','Yes') output = trustedai_uq(request) return HttpResponse(output) def uqtransparency(request): t1 = time.time() log = logging.getLogger('log_ux') from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'TrustedAI','Yes') selected_use_case = request.session['UseCaseName'] model_version = request.session['ModelVersion'] try: deploypath = request.session['deploypath'] configpath = os.path.join(deploypath,'etc','display.json') f = open(configpath, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) problemType = configSettings['problemType'] model_Features = configSettings['modelFeatures'] if problemType.lower() == 'classification': from trusted_ai.brier_score import get_brier_score problem_type, brier_score = get_brier_score(request) display_dict = {"ProblemType":problem_type.title(),"BrierScore":round(brier_score, 2),'model_Features':model_Features,'problemTypeuq':problemType} else: display_dict = {"ProblemType":problemType,"BrierScore":'','model_Features':model_Features,'problemTypeuq':problemType} display_json = json.dumps(display_dict) t2 = time.time() log.info('Brier Score : ' + str(selected_use_case) + ' : ' + str( model_version) + ' : ' + str(round(t2 - t1)) + ' sec' + ' : ' + 'Success') return HttpResponse(display_json, content_type="application/json") except Exception as e: print(e) log.info('Brier Score : ' + str(selected_use_case) + ' : ' + str( model_version) + ' : ' + '0' + 'sec' + ' : ' + 'Error : Failed to diaplay Brier Score, ' + str(e)) return HttpResponse('') from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect import json from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from appbe.pages import getusercasestatus import os import plotly.graph_objects as go import time import sys from pathlib import Path import csv import pandas as pd import numpy as np from appbe.pages import getversion AION_VERSION = getversion() def uploadedData(request): from appbe.dataIngestion import ingestDataFromFile context = ingestDataFromFile(request,DATA_FILE_PATH) context['version'] = AION_VERSION from appbe.aion_config import get_edafeatures No_of_Permissible_Features_EDA = get_edafeatures() context['FeturesEDA'] = No_of_Permissible_Features_EDA return render(request, 'upload.html', context) def uploaddatafromscript(request): from appbe.aion_config import settings usecasetab = settings() from appbe import compute computeinfrastructure = compute.readComputeConfig() from appfe.modelTraining.models import Existusecases clusteringModels = Existusecases.objects.filter(Status='SUCCESS',ProblemType='unsupervised').order_by('-id') selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] try: scriptPath = request.POST.get('pythonscriptPath') if(os.path.isfile(scriptPath) == False ): context = {'tab': 'upload', 'error': 'File does not exist', 'selected': 'modeltraning','clusteringModels':clusteringModels,'computeinfrastructure':computeinfrastructure,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion} context['version'] = AION_VERSION return render(request, 'upload.html', context) if(scriptPath != ''): try: f = open(scriptPath, "r") pythoncode = f.read() f.close() ldict = {} exec(pythoncode, globals(), ldict) except Exception as e: context = {'tab': 'upload', 'error': 'Error in script execution i.e., '+str(e), 'selected': 'modeltraning','usecasetab':usecasetab,'clusteringModels':clusteringModels,'computeinfrastructure':computeinfrastructure,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion} context['version'] = AION_VERSION return render(request, 'upload.html', context) if 'dfpy' not in ldict: context = {'tab': 'upload', 'error': 'dfpy dataset not found', 'selected': 'modeltraning','usecasetab':usecasetab,'clusteringModels':clusteringModels,'computeinfrastructure':computeinfrastructure,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion} context['version'] = AION_VERSION return render(request, 'upload.html', context) raw_data = '' if 'df_aion_raw' in ldict: df_raw = ldict['df_aion_raw'] raw_data = df_raw.to_json(orient="records") raw_data = json.loads(raw_data) df = ldict['dfpy'] filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') request.session['datalocation'] = str(dataFile) df.to_csv(dataFile, index=False) df_top = df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) statusmsg = 'Data File Uploaded Successfully ' request.session['currentstate'] = 0 request.session['finalstate'] = 0 request.session['datatype'] = 'Normal' from appbe.aion_config import get_edafeatures No_of_Permissible_Features_EDA = get_edafeatures() context = {'tab': 'tabconfigure','FeturesEDA':No_of_Permissible_Features_EDA,'computeinfrastructure':computeinfrastructure,'raw_data':raw_data,'data': df_json,'status_msg': statusmsg,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning', 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'exploratory':False,'usecasetab':usecasetab} return render(request, 'upload.html', context) else: from appfe.modelTraining.models import Existusecases clusteringModels = Existusecases.objects.filter(Status='SUCCESS',ProblemType='unsupervised').order_by('-id') context = {'tab': 'upload','computeinfrastructure':computeinfrastructure, 'error': 'Please enter script path', 'selected': 'modeltraning','usecasetab':usecasetab,'clusteringModels':clusteringModels,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion} return render(request, 'upload.html', context) except: from appfe.modelTraining.models import Existusecases clusteringModels = Existusecases.objects.filter(Status='SUCCESS',ProblemType='unsupervised').order_by('-id') return render(request, 'upload.html', {'tab': 'upload','clusteringModels':clusteringModels,'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'error':'Fail to upload data from script','selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion}) def listfiles(request): from appbe.labels import label_filename selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) from appbe import compute computeinfrastructure = compute.readComputeConfig() path = request.POST.get('scriptPath') print(path) dirstatus = os.path.isdir(path) import glob try: if(path != '' and dirstatus == True): radiovalue = request.POST.get('filetype') # create csv filetimestamp = str(int(time.time())) header = ['File', 'Label'] filename = 'AION_List_' + selected_use_case + '.csv' dataFile = os.path.join(DATA_FILE_PATH, filename) csvfilename = 'AION_List_' + filetimestamp request.session['csvfilename'] = dataFile request.session['datalocation'] = path type = 'NA' request.session['fileExtension'] = radiovalue if radiovalue in ['avi', 'wmv', 'mp4']: if request.POST.get('computeInfrastructure') in ['AWS','GCP']: request.session['datatype'] = 'LLM_Video' type = 'LLM_Video' else: request.session['datatype'] = 'Video' type = 'Video' elif radiovalue in ['jpeg', 'png', 'bmp']: if request.POST.get('computeInfrastructure') in ['AWS','GCP']: request.session['datatype'] = 'LLM_Image' type = 'LLM_Image' else: request.session['datatype'] = 'Image' type = 'Image' elif radiovalue in ['txt', 'log', 'pdf','docs','docx','doc']: if request.POST.get('computeInfrastructure') in ['AWS','GCP']: request.session['datatype'] = 'LLM_Document' type = 'LLM_Document' else: request.session['datatype'] = 'Document' type = 'Document' elif radiovalue in ['java','py']: if request.POST.get('computeInfrastructure') in ['AWS','GCP']: request.session['datatype'] = 'LLM_Code' type = 'LLM_Code' else: request.session['datatype'] = 'Code' type = 'Document' if type == 'NA': context = {'tab': 'upload', 'error': 'Please select the type', 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'version':AION_VERSION, 'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion} return render(request, 'upload.html', context) request.session['folderPath'] = path request.session['csvfullpath'] = dataFile file = open(dataFile, 'w', newline='') writer = csv.DictWriter(file, fieldnames=header) # writing data row-wise into the csv file writer.writeheader() #os.chdir(path) tifCounter = 0 if radiovalue == 'doc': tifCounter = len(glob.glob(os.path.join(path,"**/*."+'doc'),recursive=True)) tifCounter = tifCounter+len(glob.glob(os.path.join(path,"**/*."+'docx'),recursive=True) ) else: tifCounter = len(glob.glob(os.path.join(path, "**/*." + radiovalue), recursive=True)) if radiovalue == 'jpeg': tifCounter += len(glob.glob1(path,"*.jpg")) labelfileexists = False dflabels = pd.DataFrame() if type == 'Image': labelfilename = label_filename(request) labelfileexists = os.path.isfile(labelfilename) if labelfileexists == True: dflabels = pd.read_csv(labelfilename) if len(dflabels) == 0: labelfileexists = False else: dflabels = dflabels.head(5) if tifCounter == 0: context = {'tab': 'upload', 'error': 'No files in the folder with selected file type', 'selected': 'modeltraning','selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'labelfileexists':labelfileexists,'dflabels':dflabels,'computeinfrastructure':computeinfrastructure,'version':AION_VERSION} return render(request, 'upload.html', context) filesCount = 0 filesSize = 0 files=[] for filename in glob.iglob(os.path.join(path, "**/*." + radiovalue), recursive=True): files.append(filename) if radiovalue == 'doc': for filename in glob.iglob(os.path.join(path, "**/*." + 'docx'), recursive=True): files.append(filename) for filename in files: filesCount = filesCount+1 writer.writerow({'File': filename, 'Label': ''}) get_size = os.path.getsize(filename) filesSize = round(filesSize + get_size, 1) if filesSize > 1048576: size = round((filesSize / (1024 * 1024)), 1) filesSize = str(size) + ' M' elif filesSize > 1024: size = round((filesSize /1024), 1) filesSize = str(size) + ' K' else: filesSize = str(filesSize) + ' B' files = pd.DataFrame(files,columns=['File']) files.index = range(1, len(files) + 1) files.reset_index(level=0, inplace=True) files = files.to_json(orient="records") files = json.loads(files) if radiovalue == 'jpeg': for filename in glob.iglob(os.path.join(path,"**/*.jpg"), recursive=True): writer.writerow({'File': filename, 'Label': ''}) from appbe.aion_config import get_edafeatures No_of_Permissible_Features_EDA = get_edafeatures() #filesSize = str(filesSize)+' M' print(filesSize) print(filesCount) context = {'tab': 'upload','files':files,'filesCount':filesCount,'filesSize':filesSize,'filelist':dataFile,'finalstate':0, 'file': dataFile,'FeturesEDA':No_of_Permissible_Features_EDA, 'csvfilename': csvfilename,'type':type,'csvgenerated': True,'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'labelfileexists':labelfileexists,'dflabels':dflabels,'computeinfrastructure':computeinfrastructure,'version':AION_VERSION,"selectedfile":radiovalue,"selectedPath":path} return render(request, 'upload.html', context) else: context = {'tab': 'upload', 'error': 'Error: Folder path either not entered or does not exists.', 'modeltraning': 'prediction','selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'computeinfrastructure':computeinfrastructure,'version':AION_VERSION,"selectedfile":radiovalue,"selectedPath":path} return render(request, 'upload.html', context) except Exception as e: print(e) return render(request, 'upload.html', {'tab': 'upload','error':'Folder path is mandatory','version':AION_VERSION,'computeinfrastructure':computeinfrastructure, 'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion}) def validatecsv(request): from appbe.aion_config import settings usecasetab = settings() from appbe import exploratory_Analysis as ea from appbe.labels import label_filename try: selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) from appbe import compute computeinfrastructure = compute.readComputeConfig() #print(request.POST.get('validatesubmit')) if request.POST.get('validatesubmit') == 'ObjectDetection': df = pd.read_csv(request.session['csvfullpath']) dataFile = label_filename(request) request.session['LabelFileName'] = dataFile request.session['currentIndex'] = 0 request.session['endIndex'] = len(df)-1 not_end = not(request.session['currentIndex'] == request.session['endIndex']) filePath = os.path.join(request.session['datalocation'],df["File"].iloc[request.session['currentIndex']]) string = base64.b64encode(open(filePath, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) request.session['labels'] = [] if os.path.isfile(dataFile): image = df["File"].iloc[request.session['currentIndex']] with open(dataFile, 'r') as file: reader = csv.reader(file) for row in reader: if row[0] == image: labels = request.session['labels'] labels.append({"id":row[1], "name":row[9], "xMin": row[3], "xMax":row[4], "yMin":row[5], "yMax":row[6], "height":row[7],"width":row[8], "angle":row[2]}) request.session['labels'] = labels labels = request.session['labels'] else: with open(dataFile,'w') as f: f.write("File,id,angle,xmin,xmax,ymin,ymax,height,width,Label\n") f.close() bounds = [] context = {'tab': 'upload','bounds':bounds,'labels': request.session['labels'],'directory':request.session['datalocation'],'image':image_64,'head':request.session['currentIndex']+1,'len':len(df),'filelist':df,'computeinfrastructure':computeinfrastructure} context['version'] = AION_VERSION return render(request, 'objectlabelling.html', context) elif request.POST.get('validatesubmit') == 'bulkLabeling': type = 'BulkImage' dataFile = request.session['csvfullpath'] csvfilename = request.session['csvfullpath'] labelfileexists = False dflabels = pd.DataFrame() context = {'tab': 'upload', 'file': dataFile, 'csvfilename': csvfilename,'type':type,'csvgenerated': True,'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'labelfileexists':labelfileexists,'dflabels':dflabels,'computeinfrastructure':computeinfrastructure} context['version'] = AION_VERSION return render(request, 'upload.html', context) elif request.POST.get('validatesubmit') == 'ImageClassification': df = pd.read_csv(request.session['csvfullpath']) dataFile = label_filename(request) request.session['LabelFileName'] = dataFile with open(dataFile,'w') as f: f.write("File,Label\n") f.close() request.session['currentIndex'] = 0 request.session['endIndex'] = len(df)-1 not_end = not(request.session['currentIndex'] == request.session['endIndex']) filePath = os.path.join(request.session['datalocation'],df["File"].iloc[request.session['currentIndex']]) string = base64.b64encode(open(filePath, "rb").read()) request.session['labels'] = '' image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) context = {'tab': 'upload','id':request.session['currentIndex'],'labels': request.session['labels'],'image':image_64,'head':request.session['currentIndex']+1,'len':len(df),'computeinfrastructure':computeinfrastructure} context['version'] = AION_VERSION return render(request, 'imagelabelling.html', context) elif request.POST.get('validatesubmit') == 'submitpreviouslabel': dataFile = label_filename(request) request.session['LabelFileName'] = dataFile df = pd.read_csv(dataFile) if len(df.columns) == 2: context = imageeda(request) context['version'] = AION_VERSION return render(request, 'upload.html', context) else: context = objecteda(request) context['version'] = AION_VERSION return render(request, 'upload.html', context) else: df = pd.read_csv(request.session['csvfullpath']) if request.session['datatype'] in ['LLM_Document','LLM_Code']: from appfe.modelTraining.bc_views import basicconfig return basicconfig(request) else: if df['Label'].isnull().sum() > 0: # show error message if request.session['datatype'] == 'Document': dataDf = pd.DataFrame() dataDict = {} keys = ["text"] for key in keys: dataDict[key] = [] for i in range(len(df)): filename = os.path.join(request.session['datalocation'],df.loc[i,"File"]) if Path(filename).suffix == '.pdf': from appbe.dataIngestion import pdf2text text = pdf2text(filename) dataDict["text"].append(text) else: with open(filename, "r",encoding="utf-8") as f: dataDict["text"].append(f.read()) f.close() dataDf = pd.DataFrame.from_dict(dataDict) tcolumns=['text'] wordcloudpic,df_text = ea.getWordCloud(dataDf,tcolumns) status_msg = 'Successfully Done' firstFile = pd.DataFrame() context = {'tab': 'upload','firstFile':firstFile,'singletextdetails':wordcloudpic,'status_msg': status_msg,'validcsv': True,'computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion} else: errormessage = str(df['Label'].isnull().sum()) + " rows do not contain label values" context = {'error': errormessage} else: eda_result = '' duplicate_img = '' color_plt = '' df2 = df.groupby('Label', as_index=False)['File'].count().reset_index().rename(columns ={'File':'Number of Files'}) df_json = df2.to_json(orient="records") df_json = json.loads(df_json) cfig = go.Figure() xaxis_data = df2['Label'].tolist() yaxis_data = df2['Number of Files'].tolist() cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data)) cfig.update_layout(barmode='stack', xaxis_title='Label', yaxis_title='File') bargraph = cfig.to_html(full_html=False, default_height=450, default_width=520) firstFile = df.groupby('Label').first().reset_index() #firstFile['FilePath'] = firstFile['File'].apply(lambda x: os.path.join(request.session['datalocation'], x)) images = [] if request.session['datatype'] == 'Image': qualityscore,eda_result,duplicate_img,color_plt = ia.analysis_images(request.session['datalocation']) #print(qualityscore) for i in range(len(firstFile)): filename = firstFile.loc[i, "File"] filePath = os.path.join(request.session['datalocation'], filename) string = base64.b64encode(open(filePath, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) firstFile.loc[i, "Image"] = image_64 firstFile.loc[i, "Quality"] = qualityscore[filename] elif request.session['datatype'] == 'Document': dataDrift = '' dataDf = pd.DataFrame() dataDict = {} keys = ["text","Label"] for key in keys: dataDict[key] = [] for i in range(len(df)): filename = os.path.join(request.session['datalocation'],df.loc[i,"File"]) if Path(filename).suffix == '.pdf': from appbe.dataIngestion import pdf2text text = pdf2text(filename) dataDict["text"].append(text) dataDict["Label"].append(df.loc[i,"Label"]) else: with open(filename, "r",encoding="utf-8") as f: dataDict["text"].append(f.read()) f.close() dataDict["Label"].append(df.loc[i,"Label"]) dataDf = pd.DataFrame.from_dict(dataDict) wordcloudpic = ea.getCategoryWordCloud(dataDf) status_msg = 'Successfully Done' firstFile = pd.DataFrame() context = {'tab': 'upload','firstFile':firstFile,'dataa': df_json,'textdetails':wordcloudpic,'featuregraph': bargraph,'status_msg': status_msg,'validcsv': True,'computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion} return render(request, 'upload.html', context) status_msg = 'Successfully Done' selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] context = {'tab': 'upload', 'featuregraph': bargraph,'dataa': df_json, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'validcsv': True,'eda_result':eda_result,'duplicate_img':duplicate_img,'color_plt':color_plt, 'firstFile': firstFile, 'status_msg': status_msg,'computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab} context['version'] = AION_VERSION return render(request, 'upload.html', context) except UnicodeDecodeError: exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return render(request, 'upload.html', {'tab': 'upload','selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'error':'Only utf8 file encoding supported','computeinfrastructure':computeinfrastructure}) except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return render(request, 'upload.html', {'tab': 'upload','selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'error':'Validation Failed','computeinfrastructure':computeinfrastructure}) def file_successfully_created(request,dataFile): from appbe import compute computeinfrastructure = compute.readComputeConfig() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) try: request.session['datalocation'] = str(dataFile) request.session['delimiter'] = ',' request.session['textqualifier'] = '"' from appbe.eda import ux_eda eda_obj = ux_eda(dataFile,optimize=1) featuresList,datetimeFeatures,sequenceFeatures,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catFeature = eda_obj.getFeatures() # ---------------------------- numberoffeatures = len(featuresList) from appfe.modelTraining.views import getimpfeatures imp_features = getimpfeatures(dataFile,numberoffeatures) samplePercentage = 100 samplePercentval = 0 showRecommended = False from utils.file_ops import read_df status,df_top = read_df(dataFile,nrows=10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) statusmsg = 'Data File Uploaded Successfully ' request.session['currentstate'] = 0 request.session['finalstate'] = 0 request.session['datatype'] = 'Normal' from appbe.aion_config import get_edafeatures No_of_Permissible_Features_EDA = get_edafeatures() context = {'tab': 'tabconfigure','computeinfrastructure':computeinfrastructure,'range':range(1,101),'FeturesEDA':No_of_Permissible_Features_EDA,'samplePercentage':samplePercentage, 'samplePercentval':samplePercentval, 'showRecommended':showRecommended,'featuresList':featuresList,'data': df_json,'status_msg': statusmsg,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning', 'imp_features':imp_features, 'numberoffeatures':numberoffeatures, 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'exploratory':False} context['version'] = AION_VERSION return render(request, 'upload.html', context) except Exception as e: print(e) return render(request, 'upload.html', {'error':'Failed to upload Data','selected_use_case': selected_use_case,'computeinfrastructure':computeinfrastructure,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning'}) def uploadDatafromSatandardDataset(request): from appbe import compute computeinfrastructure = compute.readComputeConfig() try: dataobject = request.POST.get('dataset') if dataobject == 'Iris': from sklearn.datasets import load_iris data = load_iris() df = pd.DataFrame(data.data, columns=data.feature_names) df['Species']=data['target'] df['Species']=df['Species'].apply(lambda x: data['target_names'][x]) elif dataobject == 'Boston': from sklearn.datasets import load_boston df1 = load_boston() df = pd.DataFrame(data=df1.data, columns=df1.feature_names) df["target"] = df1.target elif dataobject == 'BreastCancer': from sklearn.datasets import load_breast_cancer cancer = load_breast_cancer() df = pd.DataFrame(np.c_[cancer['data'], cancer['target']],columns= np.append(cancer['feature_names'], ['target'])) elif dataobject == 'Diabetes': from sklearn.datasets import load_diabetes data = load_diabetes() df = pd.DataFrame(data.data, columns=data.feature_names) df['y']=data['target'] elif dataobject == 'Wine': from sklearn.datasets import load_wine data = load_wine() df = pd.DataFrame(data.data, columns=data.feature_names) df['class']=data['target'] df['class']=df['class'].apply(lambda x: data['target_names'][x]) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') request.session['datalocation'] = str(dataFile) df.to_csv(dataFile, index=False) request.session['delimiter'] = ',' request.session['textqualifier'] = '"' # EDA Subsampling changes # ---------------------------- from appbe.eda import ux_eda eda_obj = ux_eda(dataFile) featuresList,datetimeFeatures,sequenceFeatures,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catFeature = eda_obj.getFeatures() # ---------------------------- numberoffeatures = len(featuresList) from appfe.modelTraining.views import getimpfeatures imp_features = getimpfeatures(dataFile,numberoffeatures) samplePercentage = 100 samplePercentval = 0 showRecommended = False df_top = df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) statusmsg = 'Data File Uploaded Successfully ' selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] request.session['currentstate'] = 0 request.session['finalstate'] = 0 request.session['datatype'] = 'Normal' from appbe.aion_config import get_edafeatures No_of_Permissible_Features_EDA = get_edafeatures() from appfe.modelTraining.models import Existusecases clusteringModels = Existusecases.objects.filter(Status='SUCCESS',ProblemType='unsupervised').order_by('-id') context = {'tab': 'tabconfigure','computeinfrastructure':computeinfrastructure,'range':range(1,101),'FeturesEDA':No_of_Permissible_Features_EDA,'samplePercentage':samplePercentage, 'samplePercentval':samplePercentval, 'showRecommended':showRecommended,'featuresList':featuresList,'data': df_json,'status_msg': statusmsg,'selected_use_case': selected_use_case,'clusteringModels':clusteringModels, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning', 'imp_features':imp_features, 'numberoffeatures':numberoffeatures, 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'exploratory':False} context['version'] = AION_VERSION return render(request, 'upload.html', context) except Exception as e: print(e) return render(request, 'upload.html', {'error':'Failed to upload Data','selected_use_case': selected_use_case,'computeinfrastructure':computeinfrastructure,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning'}) def sqlAlchemy(request): from appbe import alchemy selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) dbType = request.POST.get('dbType') request.session['dbType'] = dbType from appbe import compute computeinfrastructure = compute.readComputeConfig() from appbe.aion_config import get_edafeatures No_of_Permissible_Features_EDA = get_edafeatures() if dbType.lower() == "sqlite": request.session['filepath'] = request.POST.get('filepath') request.session['tablenamesql'] = request.POST.get('tablenamesql') table_details = {"Database Type": dbType, "File Path": request.session['filepath']} if dbType.lower() in ["postgresql", "mysql", "mssql"]: if dbType.lower()=='mssql': db = "mssql" else: db = "postgresql" request.session['tablename'] = request.POST.get('tablename'+'_'+db) request.session['dbname'] = request.POST.get('dbname'+'_'+db) request.session['password'] = request.POST.get('password'+'_'+db) request.session['username'] = request.POST.get('username'+'_'+db) request.session['port'] = request.POST.get('port'+'_'+db) request.session['host'] = request.POST.get('host'+'_'+db) table_details = {"Database Type": dbType, "Database Name": request.session['dbname'], "Host": request.session['host'], "Port": request.session['port']} if dbType.lower() == "mssql": request.session['driver'] = request.POST.get('driver'+'_'+db) table_details.update({"driver": request.session['driver']}) request.session['currentstate'] = 0 request.session['finalstate'] = 0 request.session['datatype'] = 'Normal' #print(dbType) submit_button = request.POST.get('sql_submit') if submit_button == 'multitable': try: connection_string = alchemy.get_connection(request) import sqlalchemy as db engine = db.create_engine(connection_string) engine.connect() request.session['currentstate'] = 0 request.session['finalstate'] = 0 request.session['datatype'] = 'Normal' print(request.POST.get('dbType')) context = {'tab': 'tabconfigure','FeturesEDA':No_of_Permissible_Features_EDA,'computeinfrastructure':computeinfrastructure,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning', 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'version':AION_VERSION} context.update({'db_details':table_details}) return render(request, 'querybuildersql.html', context) except Exception as e: print(str(e)) if "No module named 'psycopg2'" in str(e): error = 'Not found module: psycopg2. Please install and try again' else: error = 'Error in connecting to the database' return render(request, 'upload.html', {'tab': 'tabconfigure', 'selected_use_case': selected_use_case,'computeinfrastructure':computeinfrastructure, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'modeltraning', 'version': AION_VERSION, 'error': error}) else: try: df = alchemy.getDataFromSingleTable(request) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') request.session['datalocation'] = str(dataFile) df.to_csv(dataFile, index=False) df_top = df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) statusmsg = 'Data File Uploaded Successfully ' request.session['currentstate'] = 0 request.session['finalstate'] = 0 request.session['datatype'] = 'Normal' context = {'tab': 'tabconfigure','data': df_json,'status_msg': statusmsg,'selected_use_case': selected_use_case,'computeinfrastructure':computeinfrastructure,'FeturesEDA':No_of_Permissible_Features_EDA, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning', 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'exploratory':False} context['version'] = AION_VERSION return render(request, 'upload.html', context) except Exception as e: print(e) if "No module named 'psycopg2'" in str(e): context = {'tab': 'upload','computeinfrastructure':computeinfrastructure,'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,"error":"Not found module: psycopg2. Please install and try again"} else: context = {'tab': 'upload','computeinfrastructure':computeinfrastructure,'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,"error":"Error in fetching the data from database."} context['version'] = AION_VERSION return render(request, 'upload.html', context) def get_table_list(request): from appbe import alchemy dbType = request.session['dbType'] table_list = alchemy.list_tables(request) #print(json.dumps(table_list)) return HttpResponse(json.dumps(table_list), content_type="application/json") def get_tables_fields_list(request): from appbe import alchemy table_list = request.GET.get("sel_tables") table_field_list = alchemy.list_tables_fields(request,table_list) return HttpResponse(table_field_list, content_type="application/json") def validate_query(request): from appbe import alchemy query = request.GET.get("query") table_details = request.GET.get("table_details") join_details = request.GET.get("join_details") where_details = request.GET.get("where_details") request.session["table_details"]=table_details request.session["join_details"]=join_details request.session["where_details"]=where_details df,msg = alchemy.validatequery(request,table_details,join_details,where_details) return HttpResponse(json.dumps(msg), content_type="application/json") def submitquery(request): from appbe import alchemy from appbe import compute selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] computeinfrastructure = compute.readComputeConfig() try: query = request.POST.get("txtfinalquery") table_details = request.session["table_details"] join_details = request.session["join_details"] where_details = request.session["where_details"] df,msg = alchemy.validatequery(request,table_details,join_details,where_details) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') request.session['datalocation'] = str(dataFile) df.to_csv(dataFile, index=False) df_top = df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) statusmsg = 'Data File Uploaded Successfully ' selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] request.session['currentstate'] = 0 request.session['finalstate'] = 0 request.session['datatype'] = 'Normal' context = {'tab': 'tabconfigure','data': df_json,'status_msg': statusmsg,'selected_use_case': selected_use_case,'computeinfrastructure':computeinfrastructure, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning', 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'exploratory':False} return render(request, 'upload.html', context) except: return render(request, 'upload.html', {'tab': 'tabconfigure','selected_use_case': selected_use_case,'computeinfrastructure':computeinfrastructure,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning','error':'Failed to upload datafile'}) def EDAReport(request): from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'EDA','Yes') from appbe import exploratory_Analysis as ea request.session['defaultfilepath'] = DEFAULT_FILE_PATH request.session['configfilepath'] = CONFIG_FILE_PATH request.session['deploylocation'] = DEPLOY_LOCATION from appbe import compute computeinfrastructure = compute.readComputeConfig() submit_button = request.POST.get('upload_submit') ModelVersion = request.session['ModelVersion'] #print(submit_button) if submit_button == 'data_eda': try: from appbe.aion_config import settings usecasetab = settings() from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage context = ea.get_eda(request) context['computeinfrastructure'] = computeinfrastructure context['s3buckets'] = get_s3_bucket() context['gcsbuckets'] = get_gcs_bucket() context['azurestorage'] = get_azureStorage() context['version'] = AION_VERSION context['usecasetab'] = usecasetab except Exception as e: print(e) context = {'error':'Error in doing the EDA','ModelVersion': ModelVersion,'version':AION_VERSION} return render(request, 'upload.html', context) def get_features_datatype(v,num_list,cat_list,text_list): """ To get exact datatype of the feature in Data Overview.""" if v in cat_list: return 'Categorical' elif v in num_list: return 'Numerical' elif v in text_list: return 'Text' def downloadedareport(request): des1 = json.loads(request.POST.get('des1')) des1 = pd.DataFrame(des1) cluster_df = json.loads(request.POST.get('cluster_df')) cluster_df = pd.DataFrame(cluster_df) pca_df = [] if request.POST.get('pca_df') != 'Empty DataFrame\r\nColumns: []\r\nIndex: []': pca_df = json.loads(request.POST.get('pca_df')) pca_df = pd.DataFrame(pca_df) cor_mat = json.loads(request.POST.get('cor_mat')) cor_mat = pd.DataFrame(cor_mat) cor_mat.replace(np.nan, 0, inplace=True) cor_mat.fillna('None',inplace=True) usename = request.session['UseCaseName'].replace(" ", "_") + '_' + str(request.session['ModelVersion']) edaFileName = usename + '_EDA.xlsx' from io import BytesIO as IO excel_file = IO() excel_writer = pd.ExcelWriter(excel_file, engine="xlsxwriter") ##For Task 17622 actual_df = json.loads(request.POST.get('data_deep_json')) actual_df = pd.DataFrame(actual_df) actual_df.replace(np.nan, 0,inplace=True) actual_df.fillna('None',inplace=True) top_10_rows = actual_df.head(10) top_10_rows.to_excel(excel_writer, sheet_name='Top 10 Rows',index=True) des1 = des1.fillna(0) #Write everything in one single column actual_df_numerical_features = actual_df.select_dtypes(exclude='object') actual_df_categorical_features = actual_df.select_dtypes(include='object') #For text features textFeature = json.loads(request.POST.get('textFeature')) textFeature_df = actual_df.filter(textFeature) actual_df_categorical_features = actual_df_categorical_features.drop(textFeature, axis=1) for i in des1['Features']: num_cols = actual_df_numerical_features.columns.to_list() cat_cols = actual_df_categorical_features.columns.to_list() text_cols = textFeature des1['Features Type'] = des1['Features'].apply(lambda x: get_features_datatype(x, num_cols,cat_cols,text_cols)) curr_columns = des1.columns.to_list() curr_columns.remove('Features Type') insert_i = curr_columns.index('Features')+1 curr_columns.insert(insert_i,'Features Type') des1 = des1[curr_columns] des1.to_excel(excel_writer, sheet_name='Data Overview',startrow=0, startcol=0,index=False) ## Hopkins value addition hopkins_value = str(request.POST.get('hopkins_val')) hopkins_tip = request.POST.get('hopkins_tip') hopkins_dict = {'Hopkins_value':[hopkins_value],"hopkins_information":[hopkins_tip]} hopkins_df = pd.DataFrame.from_dict(hopkins_dict) ##Data Distribution from appbe.eda import ux_eda eda_obj = ux_eda(actual_df) datadist_dict={} for k,v in enumerate(actual_df.columns.to_list()): distname, sse = eda_obj.DistributionFinder(actual_df[v]) datadist_dict[v]=[distname,sse] data_dist_df = pd.DataFrame(datadist_dict) data_dist_df = data_dist_df.T data_dist_df.reset_index(inplace=True) data_dist_df.columns = ['Features','Distribution','SSE'] data_dist_df.drop(['SSE'],axis=1,inplace=True) data_dist_df.fillna("NA",inplace = True) data_dist_df = data_dist_df.replace(['',None,pd.NaT],"NA") data_dist_df = data_dist_df.replace(["geom"],"geometric") data_dist_df.to_excel(excel_writer, sheet_name='Data Distribution',index=False) if len(pca_df) > 0: pca_df.to_excel(excel_writer, sheet_name='Feature Importance',index=False) cor_mat.to_excel(excel_writer, sheet_name='Correlation Analysis',index=False) #Unsupervised clustering cdf_start_row = 1+len(hopkins_df)+6 if not textFeature: import io hs_info = "Hopkins Statistics" hs_info_df = pd.read_csv(io.StringIO(hs_info), sep=",") hs_info_df.to_excel(excel_writer, sheet_name='Unsupervised Clustering',startrow=0, startcol=2,index=False) hopkins_df.to_excel(excel_writer, sheet_name='Unsupervised Clustering',startrow=2, startcol=0,index=False) else: # If text features available in data. import io hs_info = "Hopkins Statistics is not availble for data with text features. Unselect text features and retry EDA." hs_info_df = pd.read_csv(io.StringIO(hs_info), sep=",") hs_info_df.to_excel(excel_writer, sheet_name='Unsupervised Clustering',startrow=0, startcol=3,index=False) #cluster_df.to_excel(excel_writer, sheet_name='Unsupervised Clustering',startrow=cdf_start_row, startcol=1,index=True) cdf_start_row = 1+len(hopkins_df)+4 cluster_info = " Unsupervised clustering results (Excluding text features) " cluster_info_df = pd.read_csv(io.StringIO(cluster_info), sep=",") cluster_info_df.to_excel(excel_writer, sheet_name='Unsupervised Clustering',startrow=cdf_start_row-2, startcol=1,index=False) cluster_df.to_excel(excel_writer, sheet_name='Unsupervised Clustering',startrow=cdf_start_row, startcol=0,index=False) workbook = excel_writer.book #excel_writer.save() #Save() is deprecated,instead we need to use close(). excel_writer.close() excel_file.seek(0) response = HttpResponse(excel_file.read(), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') response['Content-Disposition'] = 'attachment; filename=' + edaFileName return response import os,sys import re import logging from django.http import HttpRequest, HttpResponse from django.conf import settings from django.shortcuts import render from appbe.pages import getversion import plotly.graph_objects as go import plotly.figure_factory as ff from django.shortcuts import render from plotly.subplots import make_subplots from django.contrib.sessions.models import Session from sklearn.metrics import confusion_matrix from IPython.core.display import HTML from IPython.core import display from django.template import Context, loader import pandas as pd import numpy as np import io import urllib, base64 from natsort import natsorted import matplotlib.pyplot as plt import plotly.express as px import json from IPython.core.display import display, HTML from appbe import compute import base64 import warnings warnings.filterwarnings('ignore') import subprocess from appbe import installPackage from appfe.modelTraining.models import usecasedetails from appfe.modelTraining.models import Existusecases from utils.file_ops import read_df_compressed from appbe.dataPath import LOG_LOCATION from appbe.log_ut import logg import time AION_VERSION = getversion() def getusercasestatus(request): if 'UseCaseName' in request.session: selected_use_case = request.session['UseCaseName'] else: selected_use_case = 'Not Defined' if 'ModelVersion' in request.session: ModelVersion = request.session['ModelVersion'] else: ModelVersion = 0 if 'ModelStatus' in request.session: ModelStatus = request.session['ModelStatus'] else: ModelStatus = 'Not Trained' return selected_use_case,ModelVersion,ModelStatus def xplain(request): log = logging.getLogger('log_ux') computeinfrastructure = compute.readComputeConfig() from appbe.aion_config import settings usecasetab = settings() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) if request.method == 'GET': try: if ModelStatus != 'SUCCESS': log.info('xplain :' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error:Please train the model first or launch an existing trained model') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please train the model first or launch an existing trained model','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) else: if 'ModelVersion' not in request.session: log.info('xplain : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error:Please train the model first') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please trained the model first','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) elif request.session['ModelVersion'] == 0: log.info('xplain : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please train the model first') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please trained the model first','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) elif 'ModelStatus' not in request.session: log.info('xplain : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please train the model first') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please trained the model first','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) elif request.session['ModelStatus'] != 'SUCCESS': log.info('xplain : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please train the model first') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please trained the model first','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) problemType = 'classification' for key in configSettingsJson['basic']['analysisType']: if configSettingsJson['basic']['analysisType'][key] == 'True': problemType = key break if problemType.lower() != 'classification' and problemType.lower() != 'regression': log.info('xplain:' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + 'sec' + ' : ' + 'Error:Explainable AI only available for classification and regression problem') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Explainable AI only available for classification and regression problem','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) displaypath = os.path.join( request.session['deploypath'],'etc','display.json') with open(displaypath) as file: config = json.load(file) file.close() inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] inputFeaturesList = inputFeatures.split(',') if targetFeature in inputFeaturesList: inputFeaturesList.remove(targetFeature) dataFilePath = str(configSettingsJson['basic']['dataLocation']) df = pd.read_csv(dataFilePath,nrows=10) df.rename(columns=lambda x: x.strip(), inplace=True) df = df[inputFeaturesList] inputFieldsDict = df.to_dict(orient='index')[5] inputFields = [] inputFields.append(inputFieldsDict) targetfeatures = targetFeature.split(",") ##### Bug 20649 starts featureDict = configSettingsJson['advance']['profiler']['featureDict'] catFeatureList = [] for feature in featureDict: if feature['type'] == 'categorical': catFeatureList.append(feature['feature']) for feature in targetfeatures: if feature in catFeatureList: catFeatureList.remove(feature) fairness_error = "" if len(catFeatureList)>0 else "Fairness metrics is not applicable as categorical feature(s) is not present." ##### Bug 20649 ends context = {"fairness_error":fairness_error,"catFeatureList":catFeatureList,'selected_use_case':selected_use_case,'configSettings':configSettingsJson,'targetfeatures':targetfeatures,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'selected':'visualizer','subselected':'businessview','inputFields':inputFields,'computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION,'problemType':problemType} return render(request, 'businessview.html', context) except Exception as e: log.info('xplain : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Unexpected error occur, '+str(e)) print(e) return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Unexpected error occur','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) if request.method == 'POST': if request.POST.get("submit") == 'modelxplain': return modelxplain(request) if request.POST.get("submit") == 'xplainprediction': return predictionxplain(request) def modelxplain(request): log = logging.getLogger('log_ux') computeinfrastructure = compute.readComputeConfig() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) from appbe.aion_config import settings usecasetab = settings() t1 = time.time() if 'UseCaseName' not in request.session: return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please create the use case first, trained the model and then visualize the data','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) else: if 'ModelVersion' not in request.session: log.info('Xplain Model : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please trained the model first') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please trained the model first','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) elif request.session['ModelVersion'] == 0: log.info('Xplain Model : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please trained the model first') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please trained the model first','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) elif 'ModelStatus' not in request.session: log.info('Xplain Model : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please trained the model first') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please trained the model first','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) elif request.session['ModelStatus'] != 'SUCCESS': log.info('Xplain Model : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please trained the model first') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please trained the model first','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) try: from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'TrustedAI','Yes') updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) problemType = 'classification' for key in configSettingsJson['basic']['analysisType']: if configSettingsJson['basic']['analysisType'][key] == 'True': problemType = key break if problemType.lower() != 'classification' and problemType.lower() != 'regression': log.info('Xplain Model : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Explainable AI only available for classification and regression problem') return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Explainable AI only available for classification and regression problem','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) displaypath = os.path.join( request.session['deploypath'],'etc','display.json') with open(displaypath) as file: config = json.load(file) file.close() targetFeature = configSettingsJson['basic']['targetFeature'] dataFilePath = str(configSettingsJson['basic']['dataLocation']) status, df = read_df_compressed(config['postprocessedData'], nrows=10) df.rename(columns=lambda x: x.strip(), inplace=True) if targetFeature in df.columns: df.drop( targetFeature, axis=1, inplace=True) inputFieldsDict = df.to_dict(orient='index')[5] inputFields = [] inputFields.append(inputFieldsDict) if 'nrows' in config: nrows = config['nrows'] else: nrows = 'Not Available' if 'ncols' in config: ncols = config['ncols'] else: ncols = 'Not Available' if 'targetFeature' in config: targetFeature = config['targetFeature'] else: targetFeature = '' labelMaps = config['labelMaps'] modelfeatures = configSettingsJson['basic']['trainingFeatures'].split(',') mfcount = len(modelfeatures) dataFilePath = str(configSettingsJson['basic']['dataLocation']) df_proprocessed = pd.read_csv(dataFilePath,nrows=1000) df_proprocessed.rename(columns=lambda x: x.strip(), inplace=True) if 'targetFeature' != '': target_classes = df_proprocessed[targetFeature].unique() numberofclasses = len(target_classes) else: target_classes = [] numberofclasses = 'Not Available' dataPoints = df_proprocessed.shape[0] df_proprocessed = df_proprocessed.head(5) df_proprocessed = df_proprocessed.to_json(orient="records") df_proprocessed = json.loads(df_proprocessed) expainableAIPath = os.path.join(request.session['deploypath'],'aion_xai.py') outputStr = subprocess.check_output([sys.executable,expainableAIPath,'global']) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_ai_explanation:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() ale_json = json.loads(str(outputStr)) ale_json = ale_json['data'] ale_view = ale_json['data'] sentences = ale_json['sentences'] scoreMessage = '' feature_importance = ale_json['feature_importance'] dfimp = pd.DataFrame.from_dict(feature_importance) dfimp = dfimp.sort_values(by=['values'],ascending=False).reset_index() yaxis_data = dfimp['values'].tolist() xaxis_data = dfimp['labels'].tolist() cfig = go.Figure() cfig.add_trace(go.Bar(x=xaxis_data,y=yaxis_data,name='Feature Importance')) cfig.update_layout(barmode='stack',xaxis_title='Features') bargraph = cfig.to_html(full_html=False, default_height=450,default_width=1000) dftoprecords = dfimp.head(2) topTwoFeatures = dfimp['labels'].tolist() topFeaturesMsg = [] for i in range(0,len(dfimp)): value = round(dfimp.loc[i, "values"],2)*100 value = round(value,2) tvalue = str(dfimp.loc[i, "labels"])+' contributing to '+ str(value)+'%' topFeaturesMsg.append(tvalue) most_influencedfeature = ale_json['most_influencedfeature'] interceppoint = ale_json['interceptionpoint'] anchorjson = ale_json['anchorjson'] t2 = time.time() context = {'ale_view':ale_view,'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'selected':'visualizer','subselected':'businessview','sentences':sentences,"bargraph":bargraph,'inputFields':inputFields,'nrows':nrows,'ncols':ncols,'targetFeature':targetFeature,'dataPoints':dataPoints,'target_classes':target_classes,'datarows':df_proprocessed,'numberofclasses':numberofclasses,'modelfeatures':modelfeatures,'problemType':problemType,'mfcount':mfcount,'topTwoFeatures':topTwoFeatures,'topFeaturesMsg':topFeaturesMsg,'most_influencedfeature':most_influencedfeature,'interceppoint':interceppoint,'achors':anchorjson,'labelMaps':labelMaps,'computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION} log.info('Xplain Model : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + str(round(t2 - t1)) + ' sec' + ' : ' + 'Success') return render(request, 'businessview.html', context) except Exception as Inst: exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) print(Inst) log.info('Xplain Model : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Failed to Xplain Model, '+str(Inst)) log.info('Details : '+str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error1':'Failed to Xplain Model','selected':'visualizer','subselected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION}) def predictionxplain(request): log = logging.getLogger('log_ux') from appbe.aion_config import settings usecasetab = settings() computeinfrastructure = compute.readComputeConfig() selected_use_case, ModelVersion, ModelStatus = getusercasestatus(request) try: from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'TrustedAI','Yes') t1 = time.time() displaypath = os.path.join( request.session['deploypath'],'etc','display.json') with open(displaypath) as file: config = json.load(file) file.close() updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] inputFeaturesList = inputFeatures.split(',') if targetFeature in inputFeaturesList: inputFeaturesList.remove(targetFeature) inputFieldsDict = {} problemType = 'classification' for key in configSettingsJson['basic']['analysisType']: if configSettingsJson['basic']['analysisType'][key] == 'True': problemType = key break if problemType.lower() == 'timeseriesforecasting': #task 11997 inputFieldsDict['noofforecasts'] = request.POST.get('noofforecasts') elif problemType == 'RecommenderSystem': inputFieldsDict['uid'] = request.POST.get('uid') inputFieldsDict['iid'] = request.POST.get('iid') inputFieldsDict['rating'] = request.POST.get('rating') else: for feature in inputFeaturesList: try: dataFilePath = str(configSettingsJson['basic']['dataLocation']) df = pd.read_csv(dataFilePath,nrows=10) df.rename(columns=lambda x: x.strip(), inplace=True) df = df[inputFeaturesList] inputFieldsDict = df.to_dict(orient='index')[5] except: inputFieldsDict[feature] = request.POST.get(feature) for key, value in inputFieldsDict.items(): if value == 'nan': inputFieldsDict[key] = '' inputFieldsJson = json.dumps(inputFieldsDict) expainableAIPath = os.path.join(request.session['deploypath'],'aion_xai.py') #print(inputFieldsJson) outputStr = subprocess.check_output([sys.executable,expainableAIPath,'local',inputFieldsJson]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_ai_explanation:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() #print(outputStr) predict_dict = json.loads(str(outputStr)) if (predict_dict['status'] == 'SUCCESS'): predict_dict = predict_dict['data'] prediction = predict_dict['prediction'] anchor = predict_dict['anchor'] precision = predict_dict['precision'] coverage = round(predict_dict['coverage'],2) confidence = '95%' forceplot_view = predict_dict['forceplot'] multidecisionplot_view = predict_dict['multidecisionplot'] waterfallplot_view = predict_dict['waterfallplot'] #Task12581 else: context={'tab':'tabconfigure','error':'Failed to xplain','selected':'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION,'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion} log.info('Xplain Prediction : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Failed to xplain') return render(request,'businessview.html',context) inputFields = [] inputFields.append(inputFieldsDict) selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] context={'tab' : 'predict','inputFields':inputFields,'prediction':prediction,'reason':anchor, 'precision': precision,'coverage':coverage,'confidence':confidence,'forceplot_view':forceplot_view,'multidecisionplot_view':multidecisionplot_view,'waterfallplot_view':waterfallplot_view,'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'selected' : 'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION} t2= time.time() log.info('Xplain Prediction : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + str(round(t2 - t1)) + ' sec' + ' : ' + 'Success') return render(request, 'businessview.html', context = context) except Exception as inst: print(inst) log.info('Xplain Prediction : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' +'0'+ 'sec' + ' : ' + 'Error : Failed to Xplain Prediction, ' + str(inst)) context={'tab' : 'tabconfigure','error' : 'Failed to Xplain Prediction','selected' : 'businessview','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION,'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion} return render(request,'businessview.html',context) from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect import json from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from appbe.pages import getversion from appbe.aion_config import running_setting from appbe.training import checkModelUnderTraining from appbe.training import calculate_total_activities from appbe.training import check_unsupported_col from appbe.training import check_granularity from appbe.training import checkversionrunningstatus from appbe.training import getModelStatus from appbe.training import changeModelStatus from appbe.training import calculate_total_interations from appbe.pages import getusercasestatus from utils.file_ops import read_df_compressed import plotly.graph_objects as go from appbe.pages import getMLModels from appfe.modelTraining.models import usecasedetails from appbe.training import getStatusCount from appfe.modelTraining.models import Existusecases import os,sys import urllib, base64 import subprocess import time import re import numpy as np import pandas as pd from pathlib import Path import importlib from appbe.log_ut import logg from appbe import compute import logging AION_VERSION = getversion() LOG_FILE_NAME = 'model_training_logs.log' LOG_FOLDER = 'log' def getPerformanceMatrix(deploypath,output_json): displaypath = os.path.join(deploypath,'etc','display.json') model_perf = [] try: with open(displaypath) as file: config = json.load(file) file.close() except Exception as e: print(e) import glob resultJsonObj = json.loads(output_json) if (resultJsonObj['data']['ModelType'] == 'anomalydetection' and resultJsonObj['data']['BestScore'] != 0) or resultJsonObj['data']['ModelType'].lower() == 'timeseriesanomalydetection': #task 11997 if resultJsonObj['data']['BestModel'].lower() == 'autoencoder' or resultJsonObj['data']['BestModel'].lower() == 'dbscan' : try: anomaly_plot_files = glob.glob(os.path.normpath(os.path.join(deploypath,'output','anomaly_plot','*.png'))) for plot in anomaly_plot_files: if(os.path.isfile(plot)): string = base64.b64encode(open(plot, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) model_perf.append(image_64) else: model_perf.append('nograph') except Exception as e: print("Anomaly plot exe error: \n",e) else: predictfile = os.path.join(deploypath,'data','predicteddata.csv') if(os.path.isfile(predictfile)): df = pd.read_csv(predictfile) outliers=df.loc[df['predict']==-1] outlier_index=list(outliers.index) normals=df.loc[df['predict']!=-1] normals_index=list(normals.index) featuresList = df.columns.values.tolist() #print(featuresList) if 'predict' in featuresList: featuresList.remove('predict') if 'score' in featuresList: featuresList.remove('score') if len(featuresList) == 1: xdata = df[featuresList[0]] ydata = df['score'] fig = go.Figure() fig.add_trace(go.Scatter(x=df[featuresList[0]].iloc[normals_index], y=df['score'].iloc[normals_index],mode='markers',name='Normal')) fig.add_trace(go.Scatter(x=df[featuresList[0]].iloc[outlier_index], y=df['score'].iloc[outlier_index],mode='markers',name='Predicted Outliers')) fig.update_layout(xaxis_title=featuresList[0],yaxis_title="Score") frgraph = fig.to_html(full_html=False, default_height=400, default_width=1100) model_perf.append(frgraph) if len(featuresList) == 2: fig = go.Figure() df = df.reset_index() fig.add_trace(go.Scatter(x=df[featuresList[0]], y=df[featuresList[1]],mode='markers',name='Normal Points')) fig.add_trace(go.Scatter(x=df[featuresList[0]].iloc[outlier_index], y=df[featuresList[1]].iloc[outlier_index],mode='markers',name='Predicted Outliers')) fig.update_xaxes(title_text=featuresList[0]) fig.update_yaxes(title_text=featuresList[1]) fig.update_layout(xaxis_title=featuresList[0],yaxis_title=featuresList[1]) frgraph = fig.to_html(full_html=False, default_height=400, default_width=1100) model_perf.append(frgraph) if len(featuresList) > 2: from sklearn.decomposition import PCA pca = PCA(2) pca.fit(df) res=pd.DataFrame(pca.transform(df)) Z = np.array(res) fig = go.Figure() fig.add_trace(go.Scatter(x=res[0], y=res[1],mode='markers',name='Normal Points')) fig.add_trace(go.Scatter(x=res.iloc[outlier_index,0], y=res.iloc[outlier_index,1],mode='markers',name='Predicted Outliers')) fig.update_xaxes(title_text="Principal Component 1") fig.update_yaxes(title_text="Principal Component 2") frgraph = fig.to_html(full_html=False, default_height=400, default_width=1100) model_perf.append(frgraph) return (model_perf) if config['problemType'].lower() == 'classification' or config['problemType'].lower() == 'anomaly_detection' or config['problemType'].lower() == 'timeseriesanomalydetection': displaypath = os.path.join(deploypath,'log','img') import glob for img in glob.glob(displaypath+"/*.png"): string = base64.b64encode(open(img, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) model_perf.append(image_64) #print(model_perf) elif config['problemType'].lower() == 'regression' or config['problemType'].lower() == 'recommendersystem' or \ config['problemType'].lower() == 'text similarity': dataPath = config['predictedData'] readstatus,predict_df=read_df_compressed(dataPath) regfig = go.Figure() regfig.add_trace(go.Scatter(x=np.arange(1, len(predict_df) + 1), y=predict_df['actual'], mode='lines', name='Actual')) regfig.add_trace(go.Scatter(x=np.arange(1, len(predict_df) + 1), y=predict_df['predict'], mode='lines', name='Predict')) frgraph = regfig.to_html(full_html=False, default_height=400, default_width=1100) rfgraph = '' model_perf.append(frgraph) elif config['problemType'].lower() == 'clustering': dataPath = config['predictedData'] readstatus,predict_df=read_df_compressed(dataPath) distinctCount = len(predict_df['predict'].unique()) clusterlist = predict_df['predict'].unique() color = ['green','blue','red','orange','green','blue','red','orange'] fig = go.Figure() for cluster in clusterlist: df_cluster = predict_df[predict_df['predict'] == cluster] modelFeatures = config['modelFeatures'] X1= df_cluster[modelFeatures[0]].tolist() X2= df_cluster[modelFeatures[1]].tolist() fig.add_trace(go.Scatter(x=X1, y=X2,mode='markers',name='cluster '+str(cluster))) fig.update_layout(title="Cluster Graph",xaxis_title=modelFeatures[0],yaxis_title=modelFeatures[1],) frgraph = fig.to_html(full_html=False, default_height=400, default_width=1100) model_perf.append(frgraph) elif config['problemType'].lower() == 'timeseriesforecasting': #task 11997 dataPath = config['predictedData'] predict_df = pd.read_csv(dataPath) modelFeatures = config['modelFeatures'] for feature in modelFeatures: feature_name = feature + '_actual' prediction = feature + '_pred' if feature_name in predict_df.columns: regfig = go.Figure() regfig.add_trace(go.Scatter(x=np.arange(1, len(predict_df) + 1), y=predict_df[feature_name], mode='lines', name=feature)) regfig.add_trace(go.Scatter(x=np.arange(1, len(predict_df) + 1), y=predict_df[prediction], mode='lines', name='Predict')) frgraph = regfig.to_html(full_html=False, default_height=400, default_width=1100) model_perf.append(frgraph) return (model_perf) def stoptraining(request): request.session['ModelStatus'] = 'Terminated' request.session.save() changeModelStatus(Existusecases,request.session['modelid'],'Terminated','NA','NA') return HttpResponse('Terminated') def kill_child_proc_rec(ppid): import psutil for process in psutil.process_iter(): _ppid = process.ppid() if _ppid == ppid: _pid = process.pid kill_child_proc_rec(_pid) print(f'Terminating: {_pid}') if sys.platform == 'win32': process.terminate() else: os.system('kill -9 {0}'.format(_pid)) def getDataFileCountAndSize(basicConfig): import glob path = basicConfig['dataLocation'] radiovalue = basicConfig['folderSettings']['fileExtension'] filesCount = 0 filesSize = 0 files = [] for filename in glob.iglob(os.path.join(path, "**/*." + radiovalue), recursive=True): files.append(filename) if radiovalue == 'doc': for filename in glob.iglob(os.path.join(path, "**/*." + 'docx'), recursive=True): files.append(filename) for filename in files: #for filename in glob.iglob(os.path.join(path, "**/*." + radiovalue), recursive=True): filesCount = filesCount + 1 get_size = os.path.getsize(filename) filesSize = round(filesSize + get_size, 1) if filesSize > 1048576: size = round((filesSize / (1024 * 1024)), 1) filesSize = str(size) + ' M' elif filesSize > 1024: size = round((filesSize /1024), 1) filesSize = str(size) + ' K' else: filesSize = str(filesSize) + ' B' return filesCount,filesSize # task 4343 Abort training def read_log_file( config_file): outputStr = 'aion_learner_status:{"status":"Fail","message":"Log file not found"}' if Path(config_file).exists(): with open(config_file, 'r', encoding='utf-8') as f: config = json.load(f) deployPath = Path(config['basic']['deployLocation']) log_file = deployPath/config['basic']['modelName'].replace(' ', '_')/config['basic']['modelVersion']/LOG_FOLDER/LOG_FILE_NAME if log_file.exists(): with open(log_file, 'r', encoding='utf-8') as f: outputStr = f.read() return outputStr def checkVectorDBPackage(embeddedDB): errorStatus = 'False' if embeddedDB.lower() == 'vectordb': status = importlib.util.find_spec('chromadb') if not status: errorStatus = 'True' return errorStatus def getModelSize(configSettings,model): modelSize = 'NA' if 'modelSize' in configSettings['basic']: selectedModelSize = configSettings['basic']['modelSize']['llmFineTuning'][model] for k in selectedModelSize.keys(): if configSettings['basic']['modelSize']['llmFineTuning'][model][k] == 'True': modelSize = k break return modelSize def llmmodelevaluate(request): selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) usecasename = request.session['usecaseid'].replace(" ", "_") from appbe.prediction import get_instance hypervisor,instanceid,region,image = get_instance(usecasename+'_'+str(ModelVersion)) scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','aion.py')) usecaseconfigfile = request.session['config_json'] f = open(usecaseconfigfile, "r+", encoding="utf-8") configSettingsData = f.read() f.close() configSettings = json.loads(configSettingsData) problem_type = '' modelSize = '' problemtypes = configSettings['basic']['analysisType'] for k in problemtypes.keys(): if configSettings['basic']['analysisType'][k] == 'True': problem_type = k break mlmodels ='' algorihtms = configSettings['basic']['algorithms'][problem_type] for k in algorihtms.keys(): if configSettings['basic']['algorithms'][problem_type][k] == 'True': if mlmodels != '': mlmodels += ', ' mlmodels += k if 'modelSize' in configSettings['basic']: selectedModelSize = configSettings['basic']['modelSize']['llmFineTuning'][mlmodels] for k in selectedModelSize.keys(): if configSettings['basic']['modelSize']['llmFineTuning'][mlmodels][k] == 'True': modelSize = k break eval = '' if configSettings['basic']['folderSettings']['fileType'] == 'LLM_Document': eval = 'doc' elif configSettings['basic']['folderSettings']['fileType'] == 'LLM_Code': eval = 'code' #print(sys.executable, scriptPath,hypervisor,instanceid,f'{mlmodels}-{modelSize}',selected_use_case+'_'+str(ModelVersion),eval) outputStr = subprocess.check_output([sys.executable, scriptPath,'-m','llmbenchmarking','-hv',hypervisor,'-i',instanceid,'-md',f'{mlmodels}-{modelSize}','-uc',usecasename+'_'+str(ModelVersion),'-e',eval]) return trainmodel(request) def trainresult(request): from appbe.aion_config import settings usecasetab = settings() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) usecasename = request.session['usecaseid'].replace(" ", "_") log = logging.getLogger('log_ux') computeinfrastructure = compute.readComputeConfig() trainmodel =request.POST.get('trainmodel') updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r+", encoding="utf-8") configSettingsData = f.read() configSettings = json.loads(configSettingsData) total_steps = calculate_total_activities(configSettings) request.session['total_steps'] = total_steps p = usecasedetails.objects.get(usecaseid=request.session['usecaseid']) usecaseindex = p.id #bugid:14163 if trainmodel == 'Train Model': try: if configSettings['basic']['analysisType']['survivalAnalysis'] != 'True' and configSettings['basic']['analysisType']['llmFineTuning'] != 'True': configSettings['advance']['testPercentage'] = int(request.POST.get('TrainTestPercentage',0)) #Unnati configSettings['advance']['categoryBalancingMethod'] = request.POST.get('BalancingMethod','NA') if configSettings['basic']['analysisType']['llmFineTuning'] == 'True': configSettings['basic']['vmRunning'] = request.POST.get('vmRunning','KeepRunning') if configSettings['basic']['analysisType']['similarityIdentification'] == 'True': dbs = configSettings['basic']['preprocessing']['similarityIdentification'] for dbt in dbs.keys(): configSettings['basic']['preprocessing']['similarityIdentification'][dbt]='False' configSettings['basic']['preprocessing']['similarityIdentification'][request.POST.get('contentdb')] = 'True' errorStatus = checkVectorDBPackage(request.POST.get('contentdb')) if errorStatus.lower() == 'true': return render(request, 'training.html', {'error': 'Error: Chromadb package not found.','selected_use_case': selected_use_case,'ModelVersion': ModelVersion,'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION,'contentdb':''}) if configSettings['basic']['analysisType']['contextualSearch'] == 'True': dbs = configSettings['basic']['preprocessing']['contextualSearch'] for dbt in dbs.keys(): configSettings['basic']['preprocessing']['contextualSearch'][dbt]='False' configSettings['basic']['preprocessing']['contextualSearch'][request.POST.get('contentdb')] = 'True' errorStatus = checkVectorDBPackage(request.POST.get('contentdb')) if errorStatus.lower() == 'true': return render(request, 'training.html', {'error': 'Error: Chromadb package not found.','selected_use_case': selected_use_case,'ModelVersion': ModelVersion,'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'version':AION_VERSION,'contentdb':''}) updatedConfigSettingsJson = json.dumps(configSettings) f.seek(0) f.write(updatedConfigSettingsJson) f.truncate() f.close() # output_json = aion_train_model(updatedConfigFile) request.session['noflines'] = 0 request.session['ModelStatus'] = 'Running' request.session.save() changeModelStatus(Existusecases,request.session['modelid'],'Running','NA','NA') #print(configSettings['basic']['distributedLearning']) #sys.exit() import timeit startTime = timeit.default_timer() process_killed = False if computeinfrastructure['computeInfrastructure'].lower() == 'aws' and configSettings['basic']['analysisType']['llmFineTuning'] != 'True': scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','aion.py')) #print(scriptPath,updatedConfigFile) outputStr = subprocess.check_output([sys.executable, scriptPath,'-m','awstraining','-c',updatedConfigFile]) elif computeinfrastructure['computeInfrastructure'].lower() in ['aws','gcp']: scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','aion.py')) outputStr = subprocess.check_output([sys.executable, scriptPath,'-m','llmtuning','-c',updatedConfigFile]) else: if configSettings['basic']['analysisType']['multiLabelPrediction'] == 'True' or configSettings['basic']['analysisType']['multiModalLearning'] == 'True': scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','bin','run_gluon.py')) outputStr = subprocess.check_output([sys.executable, scriptPath, updatedConfigFile]) elif configSettings['basic']['onlineLearning'] == 'True': scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','aion.py')) outputStr = subprocess.check_output([sys.executable, scriptPath,'-m','onlinetraining','-c',updatedConfigFile]) elif configSettings['basic']['distributedLearning'] == 'True': scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','aion.py')) outputStr = subprocess.check_output([sys.executable, scriptPath,'-m','distributedtraining','-c',updatedConfigFile]) else: scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','aion.py')) cmd = [sys.executable, scriptPath,'-m','training','-c',updatedConfigFile] # task 4343 abort training training_proc = subprocess.Popen( cmd) outputStr = '' while training_proc.poll() == None: if getModelStatus(Existusecases,request.session['modelid']) == 'Terminated': kill_child_proc_rec(training_proc.pid) training_proc.kill() process_killed = True time.sleep(1) if process_killed: outputStr = 'aion_learner_status:{"status":"Fail","message":"Terminated by user"}' else: outputStr = read_log_file( updatedConfigFile) usename = request.session['UseCaseName'].replace(" ", "_") outputfile = os.path.join(DEPLOY_LOCATION,usename,str(request.session['ModelVersion']),'etc','output.json') if os.path.isfile(outputfile): f1 = open(outputfile, "r+", encoding="utf-8") outputStr = f1.read() f1.close() else: if not isinstance( outputStr, str): outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_learner_status:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() resultJsonObj = json.loads(outputStr) #print(resultJsonObj) odataFile = request.session['outputfilepath'] with open(odataFile, 'w') as json_file: json.dump(resultJsonObj, json_file) json_file.close() model = Existusecases.objects.get(id=request.session['modelid']) request.session['ModelStatus'] = resultJsonObj['status'] ModelStatus = request.session['ModelStatus'] model.Status = resultJsonObj['status'] training_error = '' if resultJsonObj['status'] == 'SUCCESS': model.modelType = resultJsonObj['data']['ModelType'] model.DeployPath = str(resultJsonObj['data']['deployLocation']) if resultJsonObj['data']['ModelType'] in ['clustering','anomalydetection', 'timeSeriesAnomalyDetection']: #task 11997 model.ProblemType = 'unsupervised' else: model.ProblemType = 'supervised' else: training_error = resultJsonObj['message'] model.save() problemtypes = configSettings['basic']['analysisType'] #print(problemtypes.keys()) problem_typ = "" for k in problemtypes.keys(): if configSettings['basic']['analysisType'][k] == 'True': problem_typ = k break modeltyp = problem_typ listofmodels = '' problem_type,dproblem_type,sc,mlmodels,dlmodels,smodelsize = getMLModels(configSettings) if mlmodels != '': listofmodels += str(mlmodels) if dlmodels != '': listofmodels += listofmodels+' '+str(dlmodels) from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'Algorithms',listofmodels) # ----------------------------------------------------------------------------- # if (problem_type == 'classification' or problem_type == 'regression'): if len(mlmodels.split(',')) == 1: trainingTime = timeit.default_timer() - startTime trainingTime = round(trainingTime/60) # calculate the size of uploaded dataset filePath = configSettings['basic']['dataLocation'] sz = os.path.getsize(filePath) fileSizeMB = sz / (1024 * 1024) filesize = str(fileSizeMB) + " MB" featuresCount = str(len(configSettings['basic']['trainingFeatures'].split(','))) modelname = mlmodels.split(',')[0] fileSizeMBLimit = 0 configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','config','training.config') if(os.path.isfile(configfilepath)): file = open(configfilepath, "r", encoding="utf-8") read = file.read() file.close() for line in read.splitlines(): if 'fileSizeMBLimit=' in line: fileSizeMBLimit = int(line.split('=',1)[1]) # append the new entry into config only if size of uploaded dataset meets the threshold if fileSizeMB > fileSizeMBLimit: _val = updateRunConfig(trainingTime, filesize, featuresCount, modelname, problem_type) # ----------------------------------------------------------------------------- # if resultJsonObj['status'] == 'SUCCESS': #from appbe import telemetry request.session['deploypath'] = str(resultJsonObj['data']['deployLocation']) from appbe.trainresult import ParseResults result, survical_images = ParseResults(outputStr) model_perf = getPerformanceMatrix(request.session['deploypath'],outputStr) #telemetry.telemetry_data('Training Successfully Done',selected_use_case+'_'+str(ModelVersion),str(listofmodels)) UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'Operation','Success') request.session['currentstate'] = 3 request.session['finalstate'] = 4 request.session.save() file_path = request.session['logfilepath'] my_file = open(file_path, 'r',encoding="utf8") file_content = my_file.read() my_file.close() matched_lines = [line.replace('Status:-', '') for line in file_content.split('\n') if "Status:-" in line] matched_status_lines = matched_lines[::-1] matched_status_lines = matched_status_lines[0] matched_status_lines = matched_status_lines.split('...') matched_status_lines = matched_status_lines[1] no_lines = len(matched_lines) if 'noflines' not in request.session: request.session['noflines'] = 0 request.session['noflines'] = request.session['noflines'] + 1 if request.session['ModelStatus'] != 'SUCCESS': numberoflines = request.session['noflines'] if numberoflines > no_lines: numberoflines = no_lines request.session['noflines'] = no_lines matched_lines = matched_lines[0:numberoflines] shortlogs = getStatusCount(matched_lines,request.session['total_steps']) temp = {} temp['modelName'] = request.session['UseCaseName'] temp['modelVersion'] = request.session['ModelVersion'] config = {} config['modelName'] = request.session['UseCaseName'] config['modelVersion'] = request.session['ModelVersion'] config['datetimeFeatures'] = configSettings['basic']['dateTimeFeature'] config['sequenceFeatures'] = configSettings['basic']['indexFeature'] config['FeaturesList'] = configSettings['basic']['trainingFeatures'] config['unimportantFeatures'] = '' config['targetFeature'] = configSettings['basic']['targetFeature'] modelCondition = '' problemtypes = configSettings['basic']['analysisType'] problem_type = "" for k in problemtypes.keys(): if configSettings['basic']['analysisType'][k] == 'True': problem_type = k break problem_type,dproblemType,sc,mlmodels,dlmodels,smodelsize = getMLModels(configSettings) configSettings['basic']['problem_type'] = problem_type configSettings['basic']['dproblem_type'] = dproblemType if mlmodels != '': configSettings['basic']['mllearner'] = 'enable' if dlmodels != '': configSettings['basic']['dllearner'] = 'enable' if configSettings['basic']['analysisType']['multiLabelPrediction'] == 'True': configSettings['basic']['selected_ML_Models'] = 'AutoGluon' configSettings['basic']['mllearner'] = 'enable' else: configSettings['basic']['selected_ML_Models'] = mlmodels configSettings['basic']['selected_DL_Models'] = dlmodels configSettings['basic']['smodel_size'] = smodelsize if 'noOfRecords' in configSettings['basic']: records = configSettings['basic']['noOfRecords'] else: from appbe.train_output import getDataSetRecordsCount records = getDataSetRecordsCount(configSettings['basic']['dataLocation']) filesCount = 0 filesSize = 0 #print(configSettings['basic']['analysisType']['llmFineTuning'].lower()) #print(configSettings['basic']['folderSettings']['fileType'].lower()) if configSettings['basic']['analysisType']['llmFineTuning'].lower() == 'true' and configSettings['basic']['folderSettings']['fileType'].lower() in ['llm_document','llm_code']: filesCount,filesSize = getDataFileCountAndSize(configSettings['basic']) noofIteration = calculate_total_interations(configSettings) features = configSettings['basic']['trainingFeatures'].split(',') noOfTrainingFeatures = len(features) configSettings['basic']['problem_type']=problem_type featuretype = configSettings['advance']['profiler']['featureDict'] if ('Logistic Regression' not in mlmodels) or ('Linear Regression' not in mlmodels): selectedmodel = 'modelcomparision' else: selectedmodel = " " user_provided_data_type = {} text_type='' for feat_conf in featuretype: colm = feat_conf.get('feature', '') if feat_conf['type'] == "text": text_type="text" break contentdb = '' if problem_type.lower() in ['similarityidentification','contextualsearch']: if configSettings['basic']['preprocessing'][problem_type]['CSV'].lower() == 'true': contentdb = 'CSV' elif configSettings['basic']['preprocessing'][problem_type]['VectorDB'].lower() == 'true': contentdb = 'VectorDB' context = {'tab': 'trainresult','filesCount':filesCount,'filesSize':filesSize, 'result': result, 'selectedmodel': selectedmodel, 'advconfig': configSettings, 'shortlogs':shortlogs, 'selected_use_case': selected_use_case, 'noOfRecords': records,'noOfTrainingFeatures':noOfTrainingFeatures, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'usecaseid':usecaseindex,#bugid:14163 #BugID13336 'noofIteration':noofIteration,'log_file':file_content,'contentdb':contentdb, 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'], 'model_perf': model_perf,'logs':matched_status_lines, 'perf_images': survical_images, 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'usecasename':usecasename} context['version'] = AION_VERSION return render(request, 'training.html', context) else: UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'Operation','Error') request.session['currentstate'] = 3 request.session['finalstate'] = 4 #from appbe import telemetry if process_killed: errorMsg = 'Terminated by user' else: errorMsg = 'Model Training Error (check log file for more details)' contentdb = '' if problem_type.lower() in ['similarityidentification','contextualsearch']: if configSettings['basic']['preprocessing'][problem_type]['CSV'].lower() == 'true': contentdb = 'CSV' elif configSettings['basic']['preprocessing'][problem_type]['VectorDB'].lower() == 'true': contentdb = 'VectorDB' #telemetry.telemetry_data('Training Error',selected_use_case+'_'+str(ModelVersion),str(listofmodels)) context = {'tab': 'trainresult', 'error': errorMsg,'selected_use_case': selected_use_case,'contentdb':contentdb, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'usecaseid':usecaseindex,#bugid:14163 #BugID13336 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'], 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'usecasename':usecasename} context['version'] = AION_VERSION return render(request, 'training.html', context) except Exception as e: log.info('Training Fail:' + str(selected_use_case) + ':' + str(ModelVersion) + ':' + '0' + 'sec' + ':' + 'Training fail '+str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) print(e) return render(request, 'training.html', {'error': 'Model Training Error','selected_use_case': selected_use_case,'ModelVersion': ModelVersion,'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'usecasename':usecasename,'usecasetab':usecasetab,'version':AION_VERSION,'contentdb':''}) else: modelCondition = '' problemtypes = configSettings['basic']['analysisType'] problem_type = "" for k in problemtypes.keys(): if configSettings['basic']['analysisType'][k] == 'True': problem_type = k break problem_type,dproblem_type,sc,mlmodels,dlmodels,smodelsize = getMLModels(configSettings) configSettings['basic']['problem_type'] = problem_type configSettings['basic']['dproblem_type'] = dproblem_type if mlmodels != '': configSettings['basic']['mllearner'] = 'enable' if dlmodels != '': configSettings['basic']['dllearner'] = 'enable' if configSettings['basic']['analysisType']['multiLabelPrediction'] == 'True': configSettings['basic']['selected_ML_Models'] = 'AutoGluon' configSettings['basic']['mllearner'] = 'enable' else: configSettings['basic']['selected_ML_Models'] = mlmodels configSettings['basic']['selected_DL_Models'] = dlmodels if 'noofRecords' in configSettings['basic']: records = configSettings['basic']['noofRecords'] else: from appbe.train_output import getDataSetRecordsCount records = getDataSetRecordsCount(configSettings['basic']['dataLocation']) filesCount = 0 filesSize = 0 print(configSettings['basic']['analysisType']['llmFineTuning'].lower()) print(configSettings['basic']['folderSettings']['fileType'].lower()) if configSettings['basic']['analysisType']['llmFineTuning'].lower() == 'true' and \ configSettings['basic']['folderSettings']['fileType'].lower() in ['llm_document', 'llm_code']: filesCount, filesSize = getDataFileCountAndSize(configSettings['basic']) noofIteration = calculate_total_interations(configSettings) features = configSettings['basic']['trainingFeatures'].split(',') noOfTrainingFeatures = len(features) configSettings['basic']['problem_type']=problem_type context = { 'advconfig': configSettings,'filesCount':filesCount,'filesSize':filesSize, 'selected_use_case': selected_use_case, 'noOfRecords': records, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion,'noofIteration':noofIteration,'usecasename':usecasename, 'modelCondition':modelCondition, 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'noOfTrainingFeatures':noOfTrainingFeatures} context['version'] = AION_VERSION return render(request, 'training.html',context) def getTrainingTime(filePath, no_of_features): #filePath = 'C:\\MyFolder\AION\\AION Datasets\\Heavy Datasets\\class_1MRows_26Cols.csv' returnVal = '0_0' if(os.path.isfile(filePath)): trainingTime = 0 neartrainingTime = 0 # It's used to store the closest Training-Time nearsampleSize = 0 # It's used to store the closest Sample-Size leastSizeDifference = 0 # It's used to find the possible minimum difference between the dataset's actual size and Sample-Size in JSON file inRange = 0 # It's used to identify if Extrapolation is needed or not fileSizeMBLimit = 0 # It's used to check/limit the size of uploaded dataset acceptSizeVariance = 10 # It's used to cover the variance in sample-size featuresThreshold = 50 # It's used to set the boundary/binary-classification of records-typte # ------------------------------------------------------------------------------------------------------------ # configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','config','training.config') if(os.path.isfile(configfilepath)): file = open(configfilepath, "r") read = file.read() file.close() for line in read.splitlines(): if 'fileSizeMBLimit=' in line: fileSizeMBLimit = int(line.split('=',1)[1]) if 'acceptSizeVariance=' in line: acceptSizeVariance = int(line.split('=',1)[1]) if 'featuresThreshold=' in line: featuresThreshold = int(line.split('=',1)[1]) # get the size of uploaded dataset/file (in MB) sz = os.path.getsize(filePath) fileSizeMB = sz / (1024 * 1024) # check if uploaded dataset/file is bigger than defined threshold or not. If yes, only than go to calculate the tentative training-time if(fileSizeMB > fileSizeMBLimit): configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','config','training_runs.json') try: if(os.path.isfile(configfilepath)): # Opening JSON file f = open(configfilepath) # returns JSON object as a dictionary data = json.load(f) # Iterating through the json list for run in data['runs']: sampleSize = run['sampleSize'].replace(" MB","") sampleSize = int(float(sampleSize)) features = int(run['features']) # match records under 10% (+ or -) of variance sizeDifference = fileSizeMB - sampleSize if (sizeDifference < 0): sizeDifference = sizeDifference * -1 if (leastSizeDifference == 0): leastSizeDifference = sizeDifference # ------------------------------------------------------------------------------------------------ # if (no_of_features <= featuresThreshold): if ((sizeDifference * 100)/fileSizeMB < acceptSizeVariance and features <= featuresThreshold): acceptSizeVariance = (sizeDifference * 100)/fileSizeMB trainingTime = run['trainingTime'].replace(" Mins","") trainingTime = int(trainingTime) returnVal = str(trainingTime) + '_match' inRange = 1 # get the nearest value of sampleSize (which can be used for extrapolation) from the JSON file if (sizeDifference <= leastSizeDifference and features <= featuresThreshold): nearsampleSize = sampleSize leastSizeDifference = sizeDifference neartrainingTime = run['trainingTime'].replace(" Mins","") neartrainingTime = int(neartrainingTime) # ------------------------------------------------------------------------------------------------ # # ------------------------------------------------------------------------------------------------ # if (no_of_features > featuresThreshold): if ((sizeDifference * 100)/fileSizeMB < acceptSizeVariance and features > featuresThreshold): acceptSizeVariance = (sizeDifference * 100)/fileSizeMB trainingTime = run['trainingTime'].replace(" Mins","") trainingTime = int(trainingTime) returnVal = str(trainingTime) + '_match' inRange = 1 # get the nearest value of sampleSize (which can be used for extrapolation) from the JSON file if (sizeDifference <= leastSizeDifference and features > featuresThreshold): nearsampleSize = sampleSize leastSizeDifference = sizeDifference neartrainingTime = run['trainingTime'].replace(" Mins","") neartrainingTime = int(neartrainingTime) # ------------------------------------------------------------------------------------------------ # # When there is no record (sample-size) matched with 10% of variance then go for the extrapolation if (inRange == 0): sizeDifference = fileSizeMB - nearsampleSize ratio = (sizeDifference * 100)/nearsampleSize trainingTime = neartrainingTime + ((ratio * neartrainingTime)/100) trainingTime = int(trainingTime) returnVal = str(trainingTime) + '_extrapolation' # Closing file f.close() except Exception as inst: pass return returnVal def getllmmodelscore(usecaseid,model): DB_TABLE = 'llm_benchmarking' from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = str(Path(DATA_DIR)/'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') usecaseidcond = f'usecaseid="{usecaseid}"' helptxt ='' msg = '' #print(usecaseidcond) if sqlite_obj.table_exists(DB_TABLE): usecasemodelscore = sqlite_obj.read_data(DB_TABLE,usecaseidcond) status = '' finetunedscore = 'NA' foundationscore = 'NA' benchmarkdataset = 'NA' modelfunctionscore = {'CodeLLaMA-2-7B':'33%','CodeLLaMA-2-13B':'36%','LLaMA-2-7B':'16.8%','LLaMA-2-13B':'20.1%','LLaMA-2-70B':'31.0%','LLaMA-2-Chat-7B':'76%','LLaMA-2-Chat-13B':'79.2%','LLaMA-2-Chat-70B':'84.2%','Falcon-7B':'NA','Falcon-40B':'NA'} foundationscore = modelfunctionscore.get(model,'NA') scoretype='NA' for x in usecasemodelscore: #print(x) keys = sqlite_obj.column_names(DB_TABLE) #print(keys) status = x[keys.index('state')] if status.lower() in ['success','finished']: result_type = x[keys.index('result_type')] result = eval(x[keys.index('result')]) scoretype = list(result.keys())[0] if scoretype.lower() == 'hellaswag': benchmarkdataset = 'HellaSwag' helptxt = 'HellaSwag is a challenge dataset for evaluating commonsense Natural Language Inferencing. It consists of ~70k multiple choice questions with four answer choices about what might happen next. The correct answer is the (real) sentence for the next event; the three incorrect answers are adversarial generated and human verified.' else: benchmarkdataset = 'HumanEval' if result_type == 'dict': sub_result = list(result.values())[0] scoretype = list(sub_result.keys())[0] if scoretype == 'acc': scoretype = 'Accuracy' finetunedscore = str(round((float(list(sub_result.values())[0])*100),2)) finetunedscore = f'{finetunedscore}%' else: finetunedscore = str(round((float(list(result.values())[0])*100),2)) elif status.lower() == 'error': msg = x[keys.index('result')] evaluation = {'status':status,'msg':msg,'benchmarkdataset':benchmarkdataset,'scoreType':scoretype,'finetunedscore':str(finetunedscore),'foundationscore':foundationscore,'helptxt':helptxt} else: evaluation = {'status':'','scoreType':'','benchmarkdataset':'','finetunedscore':'','foundationscore':'','helptxt':''} #print(evaluation) return evaluation def trainmodel(request): from appbe.aion_config import settings usecasetab = settings() selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] usecasename = request.session['usecaseid'].replace(" ", "_") try: checkModelUnderTraining(request,usecasedetails,Existusecases) computeinfrastructure = compute.readComputeConfig() updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r+", encoding="utf-8") configSettingsData = f.read() configSettingsJson = json.loads(configSettingsData) total_steps = calculate_total_activities(configSettingsJson) warning = check_unsupported_col(configSettingsJson) time_series_warning = check_granularity(configSettingsJson) noofIteration = calculate_total_interations(configSettingsJson) request.session['total_steps'] = total_steps p = usecasedetails.objects.get(usecaseid=request.session['usecaseid']) usecaseid = p.id modelCondition = '' problem_type,dproblem_type,sc,mlmodels,dlmodels,smodelsize = getMLModels(configSettingsJson) configSettingsJson['basic']['problem_type'] = problem_type configSettingsJson['basic']['dproblem_type'] = dproblem_type if mlmodels != '': configSettingsJson['basic']['mllearner'] = 'enable' if dlmodels != '': configSettingsJson['basic']['dllearner'] = 'enable' if configSettingsJson['basic']['analysisType']['multiLabelPrediction'] == 'True' or configSettingsJson['basic']['analysisType']['multiModalLearning'] == 'True': configSettingsJson['basic']['selected_ML_Models'] = 'AutoGluon' configSettingsJson['basic']['mllearner'] = 'enable' else: configSettingsJson['basic']['selected_ML_Models'] = mlmodels configSettingsJson['basic']['selected_DL_Models'] = dlmodels configSettingsJson['basic']['smodel_size'] = smodelsize # ---------------------------------------------------------------------- # cal_trainingTime = 0. is_extrapolation = 'No' is_DataImbalance = 'No' if (request.session['ModelStatus'] == 'Not Trained' and (problem_type == 'classification' or problem_type == 'regression')): # <!-- ------------------------------ Data Imbalance Changes ------------------------------ --> if ( problem_type == 'classification' ): is_DataImbalance = 'Yes' # <!-- ------------------------------------------------------------------------------------- --> if len(mlmodels.split(',')) == 1: filePath = configSettingsJson['basic']['dataLocation'] no_of_features = len(configSettingsJson['basic']['trainingFeatures'].split(',')) returnVal = getTrainingTime(filePath, no_of_features) cal_trainingTime = int(returnVal.split('_')[0]) if (returnVal.split('_')[1] == 'extrapolation'): is_extrapolation = 'Yes' # ---------------------------------------------------------------------- # features = configSettingsJson['basic']['trainingFeatures'].split(',') if configSettingsJson['basic']['targetFeature'] in features: features.remove(configSettingsJson['basic']['targetFeature']) noOfTrainingFeatures = len(features) selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] if 'noOfRecords' in configSettingsJson['basic']: records = configSettingsJson['basic']['noOfRecords'] else: from appbe.train_output import getDataSetRecordsCount records = getDataSetRecordsCount(configSettingsJson['basic']['dataLocation']) filesCount = 0 filesSize = 0 try: if configSettingsJson['basic']['analysisType']['llmFineTuning'].lower() == 'true' and \ configSettingsJson['basic']['folderSettings']['fileType'].lower() in ['llm_document', 'llm_code']: filesCount, filesSize = getDataFileCountAndSize(configSettingsJson['basic']) except: pass if request.session['finalstate'] <= 3: request.session['finalstate'] = 3 request.session['currentstate'] = 3 if request.session['ModelStatus'].lower() == 'running': model = Existusecases.objects.get(ModelName=request.session['ModelName'], Version=request.session['ModelVersion']) status = checkversionrunningstatus(model.id,usecasedetails,Existusecases) request.session['ModelStatus'] = status request.session.save() if request.session['ModelStatus'] == 'SUCCESS': model = Existusecases.objects.get(ModelName=request.session['ModelName'], Version=request.session['ModelVersion']) output_train_json_filename = str(model.TrainOuputLocation) f = open(output_train_json_filename, "r+") training_output = f.read() f.close() model_perf = getPerformanceMatrix(request.session['deploypath'],training_output) from appbe.trainresult import ParseResults result, survical_images = ParseResults(training_output) file_path = request.session['logfilepath'] my_file = open(file_path, 'r',encoding="utf-8") file_content = my_file.read() my_file.close() matched_lines = [line.replace('Status:-', '') for line in file_content.split('\n') if "Status:-" in line] matched_status_lines = matched_lines[::-1] matched_status_lines = matched_status_lines[0] matched_status_lines = matched_status_lines.split('...') matched_status_lines = matched_status_lines[1] no_lines = len(matched_lines) if 'noflines' not in request.session: request.session['noflines'] = 0 request.session['noflines'] = request.session['noflines'] + 1 if request.session['ModelStatus'] != 'SUCCESS': numberoflines = request.session['noflines'] if numberoflines > no_lines: numberoflines = no_lines request.session['noflines'] = no_lines matched_lines = matched_lines[0:numberoflines] shortlogs = getStatusCount(matched_lines,request.session['total_steps']) featuretype = configSettingsJson['advance']['profiler']['featureDict'] user_provided_data_type = {} text_type='' for feat_conf in featuretype: colm = feat_conf.get('feature', '') if feat_conf['type'] == "text": text_type="text" break configSettingsJson['basic']['problem_type']= problem_type configSettingsJson['basic']['selected_ML_Models']= mlmodels if ('Logistic Regression' not in mlmodels) or ('Linear Regression' not in mlmodels): selectedmodel = 'modelcomparision' else: selectedmodel = " " contentdb = '' finetunedeval = {} if problem_type.lower() in ['similarityidentification','contextualsearch']: if configSettingsJson['basic']['preprocessing'][problem_type]['CSV'].lower() == 'true': contentdb = 'CSV' elif configSettingsJson['basic']['preprocessing'][problem_type]['VectorDB'].lower() == 'true': contentdb = 'VectorDB' if problem_type.lower() == 'llmfinetuning': modelSize = getModelSize(configSettingsJson,mlmodels) usecasename = request.session['usecaseid'].replace(" ", "_") finetunedeval = getllmmodelscore(f'{usecasename}_{ModelVersion}',f'{mlmodels}-{modelSize}') context = {'result': result,'filesCount':filesCount,'filesSize':filesSize, 'text_type':text_type,'selectedmodel':selectedmodel, 'advconfig': configSettingsJson,'usecaseid':usecaseid,'usecasename':usecasename, 'selected_use_case': selected_use_case, 'noOfRecords': records, 'ModelStatus': ModelStatus,'warning':warning,'time_series_warning':time_series_warning, 'modelCondition':modelCondition,'ModelVersion': ModelVersion,'shortlogs':shortlogs,'logs':matched_status_lines,'currentstate': request.session['currentstate'],'finalstate': request.session['finalstate'], 'model_perf': model_perf,'perf_images': survical_images, 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'noOfTrainingFeatures':noOfTrainingFeatures,'version':AION_VERSION,'noofIteration':noofIteration,'log_file':file_content,'contentdb':contentdb,'finetunedeval':finetunedeval} else: contentdb = '' if problem_type.lower() in ['similarityidentification','contextualsearch']: if configSettingsJson['basic']['preprocessing'][problem_type]['CSV'].lower() == 'true': contentdb = 'CSV' elif configSettingsJson['basic']['preprocessing'][problem_type]['VectorDB'].lower() == 'true': status = importlib.util.find_spec('chromadb') if not status: contentdb = 'CSV' else: contentdb = 'VectorDB' else: status = importlib.util.find_spec('chromadb') if not status: contentdb = 'CSV' else: contentdb = 'VectorDB' configSettingsJson['basic']['problem_type']=problem_type context = {'cal_trainingTime':cal_trainingTime,'filesCount':filesCount,'filesSize':filesSize, 'is_extrapolation': is_extrapolation,'advconfig': configSettingsJson,'usecaseid':usecaseid,'usecasename':usecasename, 'selected_use_case': selected_use_case, 'noOfRecords': records, 'ModelStatus': ModelStatus, 'warning':warning,'time_series_warning':time_series_warning,'is_DataImbalance' : is_DataImbalance, 'ModelVersion': ModelVersion, 'currentstate': request.session['currentstate'], 'modelCondition':modelCondition,'finalstate': request.session['finalstate'], 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'noOfTrainingFeatures':noOfTrainingFeatures,'version':AION_VERSION,'noofIteration':noofIteration,'contentdb':contentdb} return render(request, 'training.html', context) except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) context = { 'error': 'Model Training Error','selected_use_case': selected_use_case,'contentdb':'','usecasename':usecasename, 'ModelVersion': ModelVersion,'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'version':AION_VERSION} return render(request, 'training.html', context) from django.apps import AppConfig class ModelTrainingConfig(AppConfig): name = 'appfe.modelTraining' from django.test import TestCase # Create your tests here. from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect from appbe.pages import getusercasestatus from appbe.pages import getversion AION_VERSION = getversion() from appfe.modelTraining.models import usecasedetails from appfe.modelTraining.models import Existusecases import os from django.db.models import Max, F import pandas as pd from appbe.publish import check_input_data from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from appbe import installPackage import json from appbe import compute from appbe.training import checkModelUnderTraining import logging def opentraininglogs(request, id,currentVersion): from appbe.pages import usecases_page try: from appbe import installPackage modelID = installPackage.getMIDFromUseCaseVersion(id,currentVersion,usecasedetails,Existusecases) p = Existusecases.objects.get(id=modelID) configpath = str(p.ConfigPath) file_exists = os.path.exists(configpath) if not file_exists: request.session['IsRetraining'] = 'No' status,context,action = usecases_page(request,usecasedetails,Existusecases) context['errorMsg'] = 'Error in model launching: Some of the files are missing' return render(request,action,context) usecasename = p.ModelName.UsecaseName Version = p.Version request.session['ModelName'] = p.ModelName.id request.session['UseCaseName'] = usecasename request.session['usecaseid'] = p.ModelName.usecaseid request.session['ModelVersion'] = p.Version request.session['deploypath'] = str(p.DeployPath) request.session['config_json'] = configpath usename = request.session['usecaseid'].replace(" ", "_") request.session['logfilepath'] = os.path.join(DEPLOY_LOCATION,usename,str(request.session['ModelVersion']),'log','model_training_logs.log') request.session['finalstate'] = 3 request.session['ModelStatus'] = p.Status updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) problemtypes = configSettings['basic']['analysisType'] #print(problemtypes.keys()) problem_type = "" for k in problemtypes.keys(): if configSettings['basic']['analysisType'][k] == 'True': problem_type = k break if problem_type.lower() in ['videoforecasting','imageclassification','objectdetection','document','llmfinetuning']: request.session['datatype'] = configSettings['basic']['folderSettings']['fileType'] request.session['csvfullpath'] = configSettings['basic']['folderSettings']['labelDataFile'] request.session['datalocation'] = configSettings['basic']['dataLocation'] if problem_type.lower() == 'llmfinetuning': request.session['fileExtension'] = configSettings['basic']['folderSettings']['fileExtension'] else: request.session['datalocation'] = str(p.DataFilePath) request.session['datatype'] = 'Normal' if 'fileSettings' in configSettings['basic'].keys(): fileSettings = configSettings['basic']['fileSettings'] if 'delimiters' in fileSettings.keys(): delimiters = configSettings['basic']['fileSettings']['delimiters'] textqualifier = configSettings['basic']['fileSettings']['textqualifier'] request.session['delimiter'] = delimiters request.session['textqualifier'] = textqualifier else: request.session['delimiter'] = ',' request.session['textqualifier'] = '"' from appfe.modelTraining.views import uploaddata return uploaddata(request) except Exception as e: print(e) return render(request, 'usecases.html',{'error': 'Failed to launch model. Please train the model first before launching.','selected': 'prediction','version':AION_VERSION}) def retrain(request, id,currentVersion): from appbe.aion_config import eda_setting from appbe.pages import usecases_page from appbe.aion_config import settings usecasetab = settings() try: p = usecasedetails.objects.get(id=id) s1 = Existusecases.objects.filter(ModelName=id).annotate(maxver=Max('ModelName__existusecases__Version')) config_list = s1.filter(Version=F('maxver')) if config_list.count() > 0: Version = config_list[0].Version Version = Version + 1 else: Version = 1 usecasename = p.UsecaseName request.session['ModelName'] = p.id request.session['UseCaseName'] = usecasename request.session['usecaseid'] = p.usecaseid request.session['ModelVersion'] = Version request.session['ModelStatus'] = 'Not Trained' request.session['finalstate'] = 0 usecase = usecasedetails.objects.all().order_by('-id') # Retraing settings changes # -------- S T A R T -------- model = Existusecases.objects.filter(ModelName=p,Version=currentVersion) samplePercentage = 100 samplePercentval = 0 showRecommended = False if(model.count() > 0): indexVal = 0 configfile = str(model[indexVal].ConfigPath) f = open(configfile, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) dataFile = configSettings['basic']['dataLocation'] if configSettings['basic']['folderSettings']['fileType'] == 'Object': request.session['datatype'] = configSettings['basic']['folderSettings']['fileType'] request.session['objectLabelFileName'] = configSettings['basic']['folderSettings']['labelDataFile'] request.session['datalocation'] = configSettings['basic']['dataLocation'] return objectlabeldone(request) else: request.session['datalocation'] = str(configSettings['basic']['dataLocation']) request.session['datatype'] = 'Normal' if 'fileSettings' in configSettings['basic'].keys(): fileSettings = configSettings['basic']['fileSettings'] if 'delimiters' in fileSettings.keys(): delimiters = configSettings['basic']['fileSettings']['delimiters'] textqualifier = configSettings['basic']['fileSettings']['textqualifier'] request.session['delimiter'] = delimiters request.session['textqualifier'] = textqualifier else: request.session['delimiter'] = ',' request.session['textqualifier'] = '"' df = pd.read_csv(dataFile, encoding='utf8',nrows=10,encoding_errors= 'replace') records = df.shape[0] df1 = check_input_data(usecasename) if df1.shape[0] > 0: df = pd.read_csv(dataFile, encoding='utf8',encoding_errors= 'replace') df = df.append(df1, ignore_index=True) df = df.reset_index(drop=True) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') df.to_csv(dataFile, index=False) print(df.shape[0]) request.session['datalocation'] = str(dataFile) request.session['NoOfRecords'] = records request.session['IsRetraining'] = 'Yes' df_top = df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) # from AION import ux_eda # eda_obj = ux_eda(dataFile) # featuresList,datetimeFeatures,sequenceFeatures,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature = eda_obj.getFeatures() featuresList = df.columns.tolist() numberoffeatures = len(featuresList) from appfe.modelTraining.views import getimpfeatures imp_features = getimpfeatures(dataFile,numberoffeatures) check_df = pd.read_csv(dataFile,encoding='utf8',encoding_errors= 'replace') # EDA Performance change # ---------------------------- sample_size = int(eda_setting()) # dflength = len(eda_obj.getdata()) dflength = len(check_df) if dflength > sample_size: samplePercentage = int((sample_size/dflength) * 100) samplePercentval = samplePercentage / 100 showRecommended = True # ---------------------------- statusmsg = 'Data loaded Successfully for Retraining.' computeinfrastructure = compute.readComputeConfig() # ---------------------------- selected_use_case = request.session['UseCaseName'] ModelVersion = Version ModelStatus = 'Not Trained' if len(usecase) > 0: nouc = usecasedetails.objects.latest('id') nouc = (nouc.id)+1 else: nouc = 1 # Retraing settings changes # -------- S T A R T -------- # return render(request, 'usecases.html', {'usecasedetail': usecase,'nouc':nouc,'models': models, 'selectedusecase': usecasename, # 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, # 'ModelVersion': ModelVersion, 'selected': 'usecase'}) ps = Existusecases(DataFilePath=request.session['datalocation'], DeployPath='', Status='Not Trained',ConfigPath=configfile, Version=Version, ModelName=p,TrainOuputLocation='') ps.save() if(model.count() > 0): context = {'range':range(1,101),'samplePercentage':samplePercentage, 'samplePercentval':samplePercentval, 'showRecommended':showRecommended,'featuresList': featuresList, 'tab': 'tabconfigure','data': df_json,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning','exploratory':False, 'status_msg': statusmsg,'computeinfrastructure':computeinfrastructure,'IsRetrainingModel':True,'imp_features':imp_features,'numberoffeatures':numberoffeatures, 'dataSetPath': dataFile,'usecasetab':usecasetab,'finalstate':request.session['finalstate'],'version':AION_VERSION} else: context = {'tab': 'tabconfigure','selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab,'Modelretrain':request.session['ModelVersion'],'finalstate':request.session['finalstate'],'version':AION_VERSION} context['version'] = AION_VERSION return render(request, 'upload.html', context) except Exception as e: print(e) checkModelUnderTraining(request,usecasedetails,Existusecases) request.session['IsRetraining'] = 'No' status,context,action = usecases_page(request,usecasedetails,Existusecases) #print(context) context['version'] = AION_VERSION context['Status'] = 'Error' context['Msg'] = 'Error in retraining usecase. Check log file for more details' return render(request,action,context) def launchmodel(request, id,version): from appbe.pages import usecases_page try: modelID = installPackage.getMIDFromUseCaseVersion(id,version,usecasedetails,Existusecases) p = Existusecases.objects.get(id=modelID) configpath = str(p.ConfigPath) file_exists = os.path.exists(configpath) if not file_exists: request.session['IsRetraining'] = 'No' status,context,action = usecases_page(request,usecasedetails,Existusecases) context['errorMsg'] = 'Error in model launching: Some of the files are missing' return render(request,action,context) usecasename = p.ModelName.UsecaseName Version = p.Version request.session['ModelName'] = p.ModelName.id request.session['UseCaseName'] = usecasename request.session['usecaseid'] = p.ModelName.usecaseid request.session['ModelVersion'] = p.Version request.session['deploypath'] = str(p.DeployPath) request.session['config_json'] = configpath usename = request.session['usecaseid'].replace(" ", "_") request.session['logfilepath'] = os.path.join(DEPLOY_LOCATION,usename,str(request.session['ModelVersion']),'log','model_training_logs.log') request.session['finalstate'] = 3 request.session['ModelStatus'] = p.Status updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) problemtypes = configSettings['basic']['analysisType'] #print(problemtypes.keys()) problem_type = "" for k in problemtypes.keys(): if configSettings['basic']['analysisType'][k] == 'True': problem_type = k break if problem_type == 'videoForecasting' or problem_type == 'imageClassification' or problem_type == 'objectDetection': request.session['datatype'] = configSettings['basic']['folderSettings']['fileType'] request.session['csvfullpath'] = configSettings['basic']['folderSettings']['labelDataFile'] request.session['datalocation'] = configSettings['basic']['dataLocation'] elif configSettings['basic']['folderSettings']['fileType'] == 'Document': request.session['datatype'] = configSettings['basic']['folderSettings']['fileType'] request.session['csvfullpath'] = configSettings['basic']['folderSettings']['labelDataFile'] request.session['datalocation'] = configSettings['basic']['dataLocation'] else: request.session['datalocation'] = str(p.DataFilePath) request.session['datatype'] = 'Normal' if 'fileSettings' in configSettings['basic'].keys(): fileSettings = configSettings['basic']['fileSettings'] if 'delimiters' in fileSettings.keys(): delimiters = configSettings['basic']['fileSettings']['delimiters'] textqualifier = configSettings['basic']['fileSettings']['textqualifier'] request.session['delimiter'] = delimiters request.session['textqualifier'] = textqualifier else: request.session['delimiter'] = ',' request.session['textqualifier'] = '"' from appfe.modelTraining.prediction_views import Prediction return Prediction(request) except Exception as e: print(e) return render(request, 'prediction.html',{'error': 'Failed to launch model. Please train the model first before launching.','selected': 'prediction','version':AION_VERSION}) def modxplain(request, id,version): from appbe.pages import usecases_page log = logging.getLogger('log_ux') modelID = installPackage.getMIDFromUseCaseVersion(id,version,usecasedetails,Existusecases) p = Existusecases.objects.get(id=modelID) configpath = str(p.ConfigPath) usecasename = p.ModelName.UsecaseName Version = p.Version request.session['ModelName'] = p.ModelName.id request.session['UseCaseName'] = usecasename request.session['usecaseid'] = p.ModelName.usecaseid request.session['ModelVersion'] = p.Version request.session['deploypath'] = str(p.DeployPath) request.session['config_json'] = configpath usename = request.session['usecaseid'].replace(" ", "_") request.session['logfilepath'] = os.path.join(DEPLOY_LOCATION,usename,str(request.session['ModelVersion']),'log','model_training_logs.log') request.session['finalstate'] = 3 request.session['ModelStatus'] = p.Status file_exists = os.path.exists(configpath) if not file_exists: request.session['IsRetraining'] = 'No' status,context,action = usecases_page(request,usecasedetails,Existusecases) context['errorMsg'] = 'Error in model launching: Some of the files are missing' log.info('modxplain:' + str(selected_use_case) + ':' + str(ModelVersion) + ':' + '0 ' + 'sec' + ':' + 'Error:Error in model launching: Some of the files are missing') return render(request,action,context) usecasename = p.ModelName.UsecaseName Version = p.Version request.session['ModelName'] = p.ModelName.id request.session['UseCaseName'] = usecasename request.session['usecaseid'] = p.ModelName.usecaseid request.session['ModelVersion'] = p.Version request.session['deploypath'] = str(p.DeployPath) request.session['config_json'] = configpath usename = request.session['usecaseid'].replace(" ", "_") request.session['logfilepath'] = os.path.join(DEPLOY_LOCATION,usename,str(request.session['ModelVersion']),'log','model_training_logs.log') request.session['finalstate'] = 3 request.session['ModelStatus'] = p.Status from appfe.modelTraining import visualizer_views as v return v.xplain(request) def moddrift(request, id,version): from appbe.pages import usecases_page modelID = installPackage.getMIDFromUseCaseVersion(id,version,usecasedetails,Existusecases) p = Existusecases.objects.get(id=modelID) configpath = str(p.ConfigPath) file_exists = os.path.exists(configpath) if not file_exists: request.session['IsRetraining'] = 'No' status,context,action = usecases_page(request,usecasedetails,Existusecases) context['errorMsg'] = 'Error in model launching: Some of the files are missing' return render(request,action,context) usecasename = p.ModelName.UsecaseName Version = p.Version request.session['ModelName'] = p.ModelName.id request.session['UseCaseName'] = usecasename request.session['usecaseid'] = p.ModelName.usecaseid request.session['ModelVersion'] = p.Version request.session['deploypath'] = str(p.DeployPath) request.session['config_json'] = configpath usename = request.session['usecaseid'].replace(" ", "_") request.session['logfilepath'] = os.path.join(DEPLOY_LOCATION,usename,str(request.session['ModelVersion']),'log','model_training_logs.log') request.session['finalstate'] = 3 request.session['ModelStatus'] = p.Status f = open( configpath, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) trainingdataloc = configSettingsJson['basic']['dataLocation'] request.session['datalocation']= trainingdataloc return inputdrift(request) def inputdrift(request): log = logging.getLogger('log_ux') from appbe.aion_config import settings usecasetab = settings() from appbe import service_url try: selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) computeinfrastructure = compute.readComputeConfig() if ModelStatus != 'SUCCESS': context = {'error': 'Please train the model first or launch an existing trained model', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'monitoring','computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab} log.info('Error Please train the model first or launch an existing trained model') else: updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) problemtypes = configSettingsJson['basic']['analysisType'] problem_type = "" for k in problemtypes.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break problem = problem_type ser_url = service_url.read_monitoring_service_url_params(request) iterName = request.session['usecaseid'].replace(" ", "_") ModelVersion = request.session['ModelVersion'] ser_url = ser_url+'monitoring?usecaseid='+iterName+'&version='+str(ModelVersion) pser_url = service_url.read_performance_service_url_params(request) pser_url = pser_url+'performance?usecaseid='+iterName+'&version='+str(ModelVersion) if problem.lower() not in ['classification','regression']: context = {'error': 'Input drift only available for classification and regression problems', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'version':AION_VERSION, 'ModelVersion': ModelVersion, 'selected': 'monitoring','ser_url':ser_url,'pser_url':pser_url,'trainingDataLocation':request.session['datalocation'],'computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab} else: context = {'SUCCESS': 'Model is trained', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'version':AION_VERSION, 'ModelVersion': ModelVersion, 'selected': 'monitoring','ser_url':ser_url,'pser_url':pser_url,'trainingDataLocation':request.session['datalocation'],'computeinfrastructure':computeinfrastructure,'usecasetab':usecasetab} return render(request, 'inputdrif.html', context) except Exception as e: print(e) log.info('inputdrift; Error: Failed to perform drift analysis'+str(e)) return render(request, 'inputdrif.html', {'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion, 'selected': 'monitoring','computeinfrastructure':computeinfrastructure,'version':AION_VERSION,'error':'Fail to do inputdrift analysis','usecasetab':usecasetab}) # # AirflowLib.py # # It contains methods to consume rest API of Apache airflow instance # Apache Airflow exposed experimental API # One can achieve the API output just by using the methods implemented within this python file by importing the same # import requests import pandas as pd # base_url = 'http://localhost:8080/api/experimental' # It defines the API error which actually raised when error occured during API consumption from modelTraining.airflow_config import base_url class ApiError(Exception): """An API Error Exception""" def __init__(self, status): self.status = status def __str__(self): return "APIError: status={}".format(self.status) # This method takes dagId as parameter and return the list of Dag Run from apache airflow instance def GetDagRunList(dagId): resp = requests.get(base_url + '/dags/' + dagId + '/dag_runs') if resp.status_code != 200: raise ApiError('GetDagRunList {}'.format(resp)) dfData = ConvertJSONtoDF(resp.json()) return dfData # It is responsible to create/trigger dag of the Airflow instance # It takes 2 parameter dagId and paramJson def TriggerDag(dagId, paramJson): paramJson = {"conf": "{\"key\":\"value\"}"} resp = requests.post(base_url + '/dags/' + dagId + '/dag_runs', json=paramJson) print(resp) if resp.status_code != 200: raise ApiError('TriggerDag {}'.format(resp)) return resp.json() # This method toggle the Dag as off in the airflow instance def PauseDagRun(dagId): resp = requests.get(base_url + '/dags/' + dagId + '/paused/true') if resp.status_code != 200: raise ApiError('PauseDagRun {}'.format(resp)) return resp.json() # This method toggle the Dag as on in the airflow instance def UnPauseDagRun(dagId): resp = requests.get(base_url + '/dags/' + dagId + '/paused/false') if resp.status_code != 200: raise ApiError('UnPauseDagRun {}'.format(resp)) return resp.json() # It checks if Apache Airflow instance is up and running def TestAPI(): resp = requests.get(base_url + '/test') if resp.status_code != 200: raise ApiError('TestAPI {}'.format(resp)) return resp.json() # It return the latest dag run info for each available dag def GetLatestDagRun(): resp = requests.get(base_url + '/latest_runs') if resp.status_code != 200: raise ApiError('GetLatestDagRun {}'.format(resp)) dfData = ConvertJSONtoDF(resp.json()['items']) return dfData # It will return the list of available pools def GetPoolsList(): resp = requests.get(base_url + '/pools') if resp.status_code != 200: raise ApiError('GetPoolsList {}'.format(resp)) return resp.json() # It return the specific pool info by pool Name def GetPoolInfo(poolName): resp = requests.get(base_url + '/pools/' + poolName) if resp.status_code != 200: raise ApiError('GetPoolInfo {}'.format(resp)) return resp.json() # Return the task info created within the DAG def GetDagTaskInfo(dagId, taskId): resp = requests.get(base_url + '/dags/' + dagId + '/tasks/' + taskId) if resp.status_code != 200: raise ApiError('GetDagTaskInfo {}'.format(resp)) return resp.json() # Returns the Paused state of a DAG def GetDagPausedState(dagId): resp = requests.get(base_url + '/dags/' + dagId + '/paused') if resp.status_code != 200: raise ApiError('GetDagPausedState {}'.format(resp)) return resp.json() # It will create a pool into the Airflow instance def CreatePool(name, description, slots): paramJson = {"description": description, "name": name, "slots": slots} resp = requests.post(base_url + '/pools', json=paramJson) if resp.status_code != 200: raise ApiError('CreatePool {}'.format(resp)) return resp.json() # It is responsible to delete the specific pool by pool Name def DeletePool(name): resp = requests.delete(base_url + '/pools/' + name) if resp.status_code != 200: raise ApiError('DeletePool {}'.format(resp)) return resp.json() def ConvertJSONtoDF(jsonData): df = pd.json_normalize(jsonData) return df from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect from appbe.pages import getusercasestatus from appbe.pages import getversion AION_VERSION = getversion() from appfe.modelTraining.models import usecasedetails from appfe.modelTraining.models import Existusecases from appbe.aion_config import getrunningstatus import time def computetoGCPLLaMA13B(request): from appbe import compute from appbe.pages import get_usecase_page try: compute.updateToComputeSettings('GCP') time.sleep(2) request.session['IsRetraining'] = 'No' status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['version'] = AION_VERSION return render(request,action,context) except Exception as e: print(e) return render(request, 'usecases.html',{'error': 'Fail to update ComputeSettings','version':AION_VERSION}) def computetoLLaMMA7b(request): from appbe import compute from appbe.pages import get_usecase_page try: compute.updateToComputeSettings('AWS') time.sleep(2) #print(1) request.session['IsRetraining'] = 'No' status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['version'] = AION_VERSION return render(request,action,context) except Exception as e: print(e) return render(request, 'usecases.html',{'error': 'Fail to update ComputeSettings','version':AION_VERSION}) def computetoAWS(request): from appbe import compute from appbe.pages import get_usecase_page try: compute.updateToComputeSettings('AWS') time.sleep(2) #print(1) request.session['IsRetraining'] = 'No' status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['version'] = AION_VERSION return render(request,action,context) except Exception as e: print(e) return render(request, 'usecases.html',{'error': 'Fail to update ComputeSettings','version':AION_VERSION}) def setting_context(request): from appbe.aion_config import get_graviton_data from appbe.aion_config import get_edafeatures from appbe.aion_config import get_telemetryoptout from appbe.aion_config import get_llm_data from appbe.aion_config import running_setting from appbe import compute from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage from appbe.aion_config import settings usecasetab = settings() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) graviton_url, graviton_userid = get_graviton_data() No_of_Permissible_Features_EDA = get_edafeatures() telemetryoptout = get_telemetryoptout() llm_key,llm_url,api_type,api_version =get_llm_data() ruuningSetting = running_setting() computeinfrastructure = compute.readComputeConfig() try: context = {'computeinfrastructure':computeinfrastructure,'graviton_url':graviton_url,'graviton_userid':graviton_userid,'FeaturesEDA':No_of_Permissible_Features_EDA,'llm_key':llm_key,'llm_url':llm_url,'ruuningSetting':ruuningSetting,'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'api_type':api_type,'api_version':api_version,'telemetryoptout':telemetryoptout, 'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion':ModelVersion,'usecasetab':usecasetab,'azurestorage':get_azureStorage()} context['version'] = AION_VERSION return context except Exception as e: print(e) context = {'computeinfrastructure':computeinfrastructure,'error':'Error in Settings'} context['version'] = AION_VERSION return context def startKafka(request): try: nooftasks = getrunningstatus('AION_Consumer') if len(nooftasks): status = 'AION Kafka Consumer Already Running' else: import subprocess kafkapath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','sbin','AION_Consumer.bat')) #subprocess.Popen(kafkapath, shell=True) os.system('start cmd /c "'+kafkapath+'"') #addKafkaModel(request,request.session['datalocation']) status = 'Kafka Consumer Initiated Successfully' context = settings(request) context['status'] = status return render(request, 'settings_page.html', context) except: return render(request, 'settings_page.html', {'error':'Fail to start Kafka'}) def startPublishServices(request): from appbe.models import startServices startServices(request,usecasedetails,Existusecases) status = 'Publish services start successfully' context = setting_context(request) context['status'] = status return render(request, 'settings_page.html', context) def saveopenaiconfig(request): from appbe.aion_config import saveopenaisettings try: saveopenaisettings(request) context = setting_context(request) context['version'] = AION_VERSION context['success'] = True return render(request, 'settings_page.html', context) except: context = {'error': 'error', 'runtimeerror': 'runtimeerror'} return render(request, 'settings_page.html', context) def savegravitonconfig(request): from appbe.aion_config import savegravitonconfig try: savegravitonconfig(request) context = setting_context(request) context['version'] = AION_VERSION context['success'] = True return render(request, 'settings_page.html', context) except: context={'error':'error','runtimeerror':'runtimeerror'} return render(request, 'settings_page.html',context) def saveaionconfig(request): from appbe.aion_config import saveconfigfile try: saveconfigfile(request) context = setting_context(request) context['version'] = AION_VERSION context['success'] = True return render(request, 'settings_page.html', context) except: context={'error':'error','runtimeerror':'runtimeerror'} return render(request, 'settings_page.html',context) def settings_page(request): try: context = setting_context(request) context['version'] = AION_VERSION context['selected'] = 'Settings' return render(request, 'settings_page.html', context) except: return render(request, 'settings_page.html', {'error':'Please enter valid inputs','version':AION_VERSION}) def adds3bucket(request): try: if request.method == 'POST': from appbe.s3bucketsDB import add_new_s3bucket status = add_new_s3bucket(request) context = setting_context(request) context['version'] = AION_VERSION if status == 'error': from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage context = {'error':'Some values are missing','s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'azurestorage':get_azureStorage(),'version':AION_VERSION} if status == 'error1': from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage context = {'error':'Bucket with same name already exist','s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'azurestorage':get_azureStorage(),'version':AION_VERSION} return render(request,'settings_page.html',context) except: return render(request, 'settings_page.html',{'error': 'Fail to Add S3bucket'}) def GCSbucketAdd(request): try: if request.method == 'POST': from appbe.gcsbucketsDB import add_new_GCSBucket status = add_new_GCSBucket(request) context = setting_context(request) context['version'] = AION_VERSION if status == 'error': from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage context = {'error':'Some values are missing','gcsbuckets':get_gcs_bucket(),'s3buckets':get_s3_bucket(),'azurestorage':get_azureStorage(),'version':AION_VERSION} if status == 'error1': from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage context = {'error':'Bucket with same name already exist','s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'azurestorage':get_azureStorage(),'version':AION_VERSION} return render(request,'settings_page.html',context) except Exception as e: print(e) return render(request, 'settings_page.html',{'error': 'Fail to Add GCSbucket','version':AION_VERSION}) def azurestorageAdd(request): try: if request.method == 'POST': from appbe.azureStorageDB import add_new_azureStorage status = add_new_azureStorage(request) context = setting_context(request) context['version'] = AION_VERSION if status == 'error': from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage context = {'error':'Some values are missing','gcsbuckets':get_gcs_bucket(),'s3buckets':get_s3_bucket(),'azurestorage':get_azureStorage(),'version':AION_VERSION} if status == 'error1': from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage context = {'error':'Bucket with same name already exist','s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'azurestorage':get_azureStorage(),'version':AION_VERSION} return render(request,'settings_page.html',context) except: return render(request, 'settings_page.html',{'error': 'Fail to Add Azure Container'}) def removeazurebucket(request,name): try: if request.method == 'GET': from appbe.azureStorageDB import remove_azure_bucket status = remove_azure_bucket(name) context = setting_context(request) context['version'] = AION_VERSION if status == 'error': from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage context = {'error':'Failed to delete Azure Bucket','gcsbuckets':get_gcs_bucket(),'s3buckets':get_s3_bucket(),'azurestorage':get_azureStorage(),'version':AION_VERSION} return render(request,'settings_page.html',context) except: return render(request, 'settings_page.html',{'error': 'Failed to delete Azure Bucket'}) def removes3bucket(request,name): try: if request.method == 'GET': from appbe.s3bucketsDB import remove_s3_bucket status = remove_s3_bucket(name) context = setting_context(request) context['version'] = AION_VERSION if status == 'error': from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage context = {'error':'Failed to delete S3bucket','s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'azurestorage':get_azureStorage(),'version':AION_VERSION} return render(request,'settings_page.html',context) except: return render(request, 'settings_page.html',{'error': 'Failed to delete S3bucket'}) def removegcsbucket(request,name): try: if request.method == 'GET': from appbe.gcsbucketsDB import remove_gcs_bucket status = remove_gcs_bucket(name) context = setting_context(request) context['version'] = AION_VERSION if status == 'error': from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage context = {'error':'Failed to delete GCS Bucket','gcsbuckets':get_gcs_bucket(),'s3buckets':get_s3_bucket(),'azurestorage':get_azureStorage(),'version':AION_VERSION} return render(request,'settings_page.html',context) except: return render(request, 'settings_page.html',{'error': 'Failed to delete GCS Bucket'}) def gcpcomputesettings(request): try: from appbe import compute status = compute.updateGCPConfig(request) context = setting_context(request) if status == 'error': context['ErrorMsg'] = 'Some values are missing' context['version'] = AION_VERSION context['success'] = True return render(request, 'settings_page.html',context) except: return render(request, 'settings_page.html',{'error': 'Fail to Save GCP Settings','version':AION_VERSION}) def amazonec2settings(request): try: from appbe import compute status = compute.updateComputeConfig(request) context = setting_context(request) if status == 'error': context['ErrorMsg'] = 'Some values are missing' context['version'] = AION_VERSION context['success'] = True return render(request, 'settings_page.html',context) except: return render(request, 'settings_page.html',{'error': 'Fail to Save AWS Settings','version':AION_VERSION}) from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from appbe.pages import getversion AION_VERSION = getversion() def datagenrate(request): from appbe.aion_config import settings usecasetab = settings() context = {'selected':'DataOperations','usecasetab':usecasetab} context['version'] = AION_VERSION return render(request, "datagenrate.html",context) def generateconfig(request): from appbe import generate_json_config as gjc try: gjc.generate_json_config(request) return render(request, "datagenrate.html",context={'success':'success','selected':'DataOperations'}) except Exception as e: print(e) return render(request, "datagenrate.html",context={'error':str(e),'selected':'DataOperations'}) from django.contrib import admin # Register your models here. from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect from appbe.pages import getusercasestatus from appbe.pages import getversion AION_VERSION = getversion() from appfe.modelTraining.models import usecasedetails from appfe.modelTraining.models import Existusecases import os from django.db.models import Max, F import pandas as pd from appbe.publish import check_input_data from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION import json from appbe import compute import logging def get_instance_id(modelID): from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') if sqlite_obj.table_exists("LLMTuning"): data = sqlite_obj.get_data('LLMTuning','usecaseid',modelID) print(data) if len(data) > 0: return (data[3]+' instance '+data[2]) else: return 'Instance ID not available' else: return 'Instance ID not available' def PredictForSingleInstance(request): from appbe.trainresult import ParseResults submittype = request.POST.get('predictsubmit') from appbe.prediction import singleInstancePredict context = singleInstancePredict(request,Existusecases,usecasedetails) if submittype.lower() == 'predict': from appbe.train_output import get_train_model_details trainingStatus,modelType,bestmodel = get_train_model_details(DEPLOY_LOCATION,request) imagedf = '' model_count = Existusecases.objects.filter(ModelName=request.session['ModelName'],Version=request.session['ModelVersion'],Status='SUCCESS').count() model = Existusecases.objects.get(ModelName=request.session['ModelName'], Version=request.session['ModelVersion']) output_train_json_filename = str(model.TrainOuputLocation) f = open(output_train_json_filename, "r+", encoding="utf-8") training_output = f.read() f.close() result,survical_images = ParseResults(training_output) context.update({'result':result}) context['version'] = AION_VERSION context['modelType'] = modelType context['bestmodel'] = bestmodel return render(request, 'prediction.html', context) else: context['version'] = AION_VERSION return context def getTrainingStatus(request): model = Existusecases.objects.get(ModelName=request.session['ModelName'],Version=request.session['ModelVersion']) output_train_json_filename = str(model.TrainOuputLocation) f = open(output_train_json_filename, "r+", encoding="utf-8") training_output = f.read() f.close() from appbe.trainresult import FeaturesUsedForTraining return FeaturesUsedForTraining(training_output) def Prediction(request): log = logging.getLogger('log_ux') from appbe.trainresult import ParseResults from appbe.dataIngestion import delimitedsetting from appbe import service_url from appbe.aion_config import settings usecasetab = settings() try: selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) computeinfrastructure = compute.readComputeConfig() #print(computeinfrastructure) if ModelStatus != 'SUCCESS': log.info('Prediction:' + str(selected_use_case) + ':' + str(ModelVersion) + ':' + '0' + 'sec' + ':' + 'Error: Please train the model first or launch an existing trained model') return render(request, 'prediction.html', { 'error': 'Please train the model first or launch an existing trained model', 'selected': 'prediction','selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'usecasetab':usecasetab,'computeinfrastructure':computeinfrastructure,'version':AION_VERSION}) else: if 'ModelVersion' not in request.session: log.info('Prediction:' + str(selected_use_case) + ':' + str( ModelVersion) + ':' + '0' + 'sec' + ':' + 'Error: Please train the model first') return render(request, 'prediction.html', {'usecasetab':usecasetab,'error': 'Please train the model first', 'selected': 'prediction','version':AION_VERSION}) elif request.session['ModelVersion'] == 0: log.info('Prediction:' + str(selected_use_case) + ':' + str( ModelVersion) + ':' + '0' + 'sec' + ':' + 'Error: Please train the model first') return render(request,'prediction.html',{'usecasetab':usecasetab,'error':'Please train the model first','selected':'prediction','version':AION_VERSION}) else: from appbe.train_output import get_train_model_details trainingStatus,modelType,bestmodel = get_train_model_details(DEPLOY_LOCATION,request) imagedf = '' model_count = Existusecases.objects.filter(ModelName=request.session['ModelName'],Version=request.session['ModelVersion'],Status='SUCCESS').count() model = Existusecases.objects.get(ModelName=request.session['ModelName'], Version=request.session['ModelVersion']) output_train_json_filename = str(model.TrainOuputLocation) f = open(output_train_json_filename, "r+") training_output = f.read() f.close() result,survical_images = ParseResults(training_output) if model_count >= 1: updatedConfigFile = request.session['config_json'] #print(updatedConfigFile) f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) analysisType = configSettingsJson['basic']['analysisType'] problem_type = "" for k in analysisType.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break if problem_type.lower() == 'recommendersystem': modelName = "" recommender_models = configSettingsJson['basic']['algorithms']['recommenderSystem'] for k in recommender_models.keys(): if configSettingsJson['basic']['algorithms']['recommenderSystem'][k] == 'True': modelName = k break if modelName.lower() == 'associationrules-apriori': return render(request, 'prediction.html', { 'error': 'Prediction not supported for Association Rules (Apriori)', 'selected': 'prediction','selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'computeinfrastructure':computeinfrastructure,'version':AION_VERSION}) delimiters,textqualifier = delimitedsetting(configSettingsJson['basic']['fileSettings']['delimiters'],configSettingsJson['basic']['fileSettings']['textqualifier']) #problemtypes = configSettingsJson['basic']['analysisType'] #print(problemtypes.keys()) from appfe.modelTraining.train_views import getMLModels problem_type,dproblemtype,sc,mlmodels,dlmodels,smodelsize = getMLModels(configSettingsJson) iterName = request.session['usecaseid'].replace(" ", "_") selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] if problem_type == 'timeSeriesForecasting': #task 11997 inputFieldsDict = {'noofforecasts': 10} elif problem_type == 'recommenderSystem' and mlmodels=='ItemRating': inputFieldsDict = {"uid": 1, "numberOfRecommendation":10} #Task 11190 elif problem_type == 'stateTransition': inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] if inputFeatures != '': inputFeaturesList = inputFeatures.split(',') else: inputFeaturesList = [] inputFieldsDict = {inputFeatures:'session',targetFeature:'Activity'} else: inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] if inputFeatures != '': inputFeaturesList = inputFeatures.split(',') else: inputFeaturesList = [] if targetFeature in inputFeaturesList: inputFeaturesList.remove(targetFeature) if configSettingsJson['basic']['contextFeature'] != '': inputFeaturesList.append(configSettingsJson['basic']['contextFeature']) if problem_type == 'llmFineTuning': inputFeaturesList.append('Temperature') inputFeaturesList.append('Max Tokens') if problem_type in ['survivalAnalysis','anomalyDetection', 'timeSeriesAnomalyDetection']: #task 11997 if configSettingsJson['basic']['dateTimeFeature'] != '' and configSettingsJson['basic']['dateTimeFeature'] != 'na': inputFeaturesList.insert(0,configSettingsJson['basic']['dateTimeFeature']) dataFilePath = str(configSettingsJson['basic']['dataLocation']) if problem_type != 'llmFineTuning': if os.path.isfile(dataFilePath): df = pd.read_csv(dataFilePath,encoding='utf8',nrows=2,sep=delimiters,quotechar=textqualifier,skipinitialspace = True,encoding_errors= 'replace') try: inputFieldsDict = df.to_dict(orient='index')[0] except: inputFieldsDict = pd.Series(0, index =inputFeaturesList).to_dict() else: inputFieldsDict = {"File":"EnterFileContent"} else: inputFieldsDict = pd.Series('', index =inputFeaturesList).to_dict() inputFieldsDict['Temperature'] = '0.1' from appbe.prediction import get_instance hypervisor,instanceid,region,image = get_instance(iterName+'_'+str(ModelVersion)) if hypervisor.lower() == 'aws': inputFieldsDict['Max Tokens'] = '1024' else: inputFieldsDict['Max Tokens'] = '4096' inputFields = [] inputFields.append(inputFieldsDict) settings_url = '' if problem_type == 'llmFineTuning': ser_url = get_instance_id(iterName+'_'+str(ModelVersion)) settings_url = '' modelSize = '' if 'modelSize' in configSettingsJson['basic']: selectedModelSize = configSettingsJson['basic']['modelSize']['llmFineTuning'][mlmodels] for k in selectedModelSize.keys(): if configSettingsJson['basic']['modelSize']['llmFineTuning'][mlmodels][k] == 'True': modelSize = k break mlmodels = mlmodels+'-'+modelSize elif problem_type == 'stateTransition': ser_url = service_url.read_service_url_params(request) settings_url = service_url.read_service_url_params(request) ser_url = ser_url+'pattern_anomaly_predict?usecaseid='+iterName+'&version='+str(ModelVersion) settings_url = settings_url+'pattern_anomaly_settings?usecaseid='+iterName+'&version='+str(ModelVersion) else: ser_url = service_url.read_service_url_params(request) ser_url = ser_url+'predict?usecaseid='+iterName+'&version='+str(ModelVersion) onnx_runtime = False analyticsTypes = problem_type usecasename = request.session['usecaseid'].replace(" ", "_") return render(request, 'prediction.html', {'inputFields': inputFields,'usecasename':usecasename,'mlmodels':mlmodels,'configSettingsJson':configSettingsJson,'result':result,'imagedf':imagedf, 'selected_use_case': selected_use_case,'ser_url':ser_url,'analyticsType':analyticsTypes,'settings_url':settings_url, 'ModelStatus': ModelStatus,'onnx_edge':onnx_runtime,'ModelVersion': ModelVersion, 'selected': 'prediction','computeinfrastructure':computeinfrastructure,'version':AION_VERSION,'modelType':modelType,'bestmodel':bestmodel,'usecasetab':usecasetab}) else: log.info('Prediction; Error: Please train the model first') return render(request, 'prediction.html', {'usecasetab':usecasetab,'error': 'Please train the model first', 'selected': 'prediction','version':AION_VERSION}) except Exception as e: print(e) log.info('Prediction:' + str(selected_use_case) + ':' + str( ModelVersion) + ':' + '0' + 'sec' + ':' + 'Error:'+str(e)) return render(request, 'prediction.html',{'usecasetab':usecasetab,'error': 'Failed to perform prediction', 'selected': 'prediction','version':AION_VERSION}) from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect from appbe.pages import getusercasestatus from appbe.pages import getversion AION_VERSION = getversion() from appfe.modelTraining.models import usecasedetails from appfe.modelTraining.models import Existusecases import os from django.db.models import Max, F import pandas as pd from appbe.publish import check_input_data from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from appbe import installPackage import json from appbe import service_url from appbe import compute import sys import csv import time from appbe.training import checkModelUnderTraining import logging def Distribution(request): from appbe import exploratory_Analysis as ea log = logging.getLogger('log_ux') from appbe.aion_config import settings usecasetab = settings() computeinfrastructure = compute.readComputeConfig() try: from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'Drift','Yes') t1 = time.time() model = Existusecases.objects.get(ModelName=request.session['ModelName'], Version=request.session['ModelVersion']) output_train_json_filename = str(model.TrainOuputLocation) f = open(output_train_json_filename, "r+") training_output = f.read() f.close() training_output = json.loads(training_output) featuresused = training_output['data']['featuresused'] feature = eval(featuresused) dataFilePath = request.session['datalocation'] selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] ser_url = service_url.read_monitoring_service_url_params(request) iterName = request.session['usecaseid'].replace(" ", "_") ModelVersion = request.session['ModelVersion'] ser_url = ser_url+'monitoring?usecaseid='+iterName+'&version='+str(ModelVersion) pser_url = service_url.read_performance_service_url_params(request) pser_url = pser_url+'performanceusecaseid='+iterName+'&version='+str(ModelVersion) if request.POST.get('inputdriftsubmit') == 'trainingdatadrift': historicadata = request.session['datalocation'] trainingdf = pd.read_csv(historicadata) trainingDrift = ea.getDriftDistribution(feature, trainingdf) newDataDrift = '' concatDataDrift = '' drift_msg = '' driftdata = 'NA' else: historicadata = request.session['datalocation'] trainingdf = pd.read_csv(historicadata) trainingDrift = '' type = request.POST.get("optradio") if type == "url": try: url = request.POST.get('urlpathinput') newdatadf = pd.read_csv(url) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.csv') newdatadf.to_csv(dataFile, index=False) request.session['drift_datalocations']= dataFile driftdata = request.session['drift_datalocations'] except Exception as e: request.session['currentstate'] = 0 e = str(e) if e.find("tokenizing")!=-1: error = "This is not an open source URL to access data" elif e.find("connection")!=-1: error = "Can not access the URL through HCL network, please try with other network" else: error = 'Please provide a correct URL' context = {'error': error,'ModelVersion': ModelVersion,'computeinfrastructure':computeinfrastructure,'emptycsv':'emptycsv','s3buckets': get_s3_bucket(),'gcsbuckets':get_gcs_bucket(), 'kafkaSetting':'kafkaSetting','ruuningSetting':'ruuningSetting','usecasetab':usecasetab} log.info('Input Drift : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+error+', ' + e) return render(request, 'upload.html', context) else: if request.FILES: Datapath = request.FILES['DataFilePath'] from io import StringIO content = StringIO(Datapath.read().decode('utf-8')) reader = csv.reader(content) df = pd.DataFrame(reader) df.columns = df.iloc[0] df = df[1:] ext = str(Datapath).split('.')[-1] filetimestamp = str(int(time.time())) if ext.lower() in ['csv','tsv','tar','zip','avro','parquet']: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext) else: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp) with open(dataFile, 'wb+') as destination: for chunk in Datapath.chunks(): destination.write(chunk) destination.close() if(os.path.isfile(dataFile) == False): context = {'error': 'Data file does not exist', 'selected_use_case': selected_use_case, ' ModelStatus': ModelStatus, 'ModelVersion': ModelVersion} log.info('Input Drift : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Data file does not exist') return render(request, 'inputdrif.html', context) request.session['drift_datalocations'] = dataFile driftdata = request.session['drift_datalocations'] newdatadf = pd.read_csv(driftdata) newDataDrift = ea.getDriftDistribution(feature, trainingdf, newdatadf) condf = pd.concat([trainingdf, newdatadf], ignore_index=True, sort=True) concatDataDrift = ea.getDriftDistribution(feature,trainingdf,condf) drift_msg,htmlPath = Drift(request,historicadata, dataFile, feature) if htmlPath != 'NA': file = open(htmlPath, "r",errors='ignore') driftdata = file.read() file.close() else: driftdata = 'NA' t2 = time.time() log.info('Input Drift : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + str(round(t2 - t1)) + ' sec' + ' : ' + 'Success') return render(request, 'inputdrif.html', {'trainingDrift': trainingDrift, 'newDataDrift': newDataDrift, 'concatDataDrift': concatDataDrift,'usecasetab':usecasetab, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'version' :AION_VERSION, 'selected': 'monitoring', 'drift_msg': drift_msg,'htmlPath':driftdata,'ser_url':ser_url,'pser_url':pser_url,'trainingDataLocation':request.session['datalocation'],'computeinfrastructure':computeinfrastructure}) except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] ser_url = service_url.read_monitoring_service_url_params(request) iterName = request.session['usecaseid'].replace(" ", "_") ModelVersion = request.session['ModelVersion'] ser_url = ser_url+'monitoring?usecaseid='+iterName+'&version='+str(ModelVersion) pser_url = service_url.read_performance_service_url_params(request) pser_url = pser_url+'performanceusecaseid='+iterName+'&version='+str(ModelVersion) context = {'ser_url':ser_url,'pser_url':pser_url,'trainingDataLocation':request.session['datalocation'],'error': 'Failed to perform drift analysis', 'selected_use_case': selected_use_case,'usecasetab':usecasetab, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'computeinfrastructure':computeinfrastructure,'version' : AION_VERSION} log.info('Input Drift : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Failed to do drift analysis'+', '+str(inst)) log.info('Details : '+str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return render(request, 'inputdrif.html', context) def Drift(request,trainingdatalocation, newdatalocation, features): log = logging.getLogger('log_ux') selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) try: inputFieldsJson = {"trainingDataLocation":trainingdatalocation,"currentDataLocation":newdatalocation} inputFieldsJson = json.dumps(inputFieldsJson) iterName = request.session['usecaseid'].replace(" ", "_") ModelVersion = request.session['ModelVersion'] ser_url = service_url.read_monitoring_service_url_params(request) ser_url = ser_url+'monitoring?usecaseid='+iterName+'&version='+str(ModelVersion) import requests try: #print(inputFieldsJson) #print(ser_url) response = requests.post(ser_url,data=inputFieldsJson,headers={"Content-Type":"application/json",}) if response.status_code != 200: outputStr=response.content return outputStr except Exception as inst: print(inst) if 'Failed to establish a new connection' in str(inst): Msg = 'AION Service needs to be started' else: Msg = 'Error during Drift Analysis' log.info('Drift : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : ' + Msg+', '+str(inst)) return Msg outputStr=response.content outputStr = outputStr.decode('utf-8') outputStr = outputStr.strip() decoded_data = json.loads(outputStr) #print(decoded_data) htmlPath = 'NA' if decoded_data['status'] == 'SUCCESS': data = decoded_data['data'] htmlPath = decoded_data['htmlPath'] if 'Message' in data: Msg = [] Msg.append(data['Message']) else: Msg = data['Affected Columns'] log.info('Drift : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + 'sec' + ' : ' + 'Success') else: Msg = 'Error during Drift Analysis' htmlPath = 'NA' log.info('Drift : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : ' +str(Msg)) return Msg,htmlPath except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) log.info('Drift : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : ' + str(e)) log.info('Details : ' +str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def Evaluate(request): from appbe.aion_config import settings usecasetab = settings() log = logging.getLogger('log_ux') try: from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'Drift','Yes') t1 = time.time() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) computeinfrastructure = compute.readComputeConfig() type = request.POST.get("optradio") ser_url = service_url.read_monitoring_service_url_params(request) iterName = request.session['usecaseid'].replace(" ", "_") ModelVersion = request.session['ModelVersion'] ser_url = ser_url+'monitoring?usecaseid='+iterName+'_'+str(ModelVersion) pser_url = service_url.read_performance_service_url_params(request) pser_url = pser_url+'performance?usecaseid='+iterName+'&version='+str(ModelVersion) if type == "url": try: url = request.POST.get('urlpathinput') newdatadf = pd.read_csv(url) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.csv') newdatadf.to_csv(dataFile, index=False) except Exception as e: request.session['currentstate'] = 0 e = str(e) if e.find("tokenizing")!=-1: error = "This is not an open source URL to access data" log.info('Performance Drift : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+error+', '+str(e)) elif e.find("connection")!=-1: error = "Can not access the URL through HCL network, please try with other network" log.info('Performance Drift : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : ' + error +', '+e) else: error = 'Please provide a correct URL' log.info('Performance Drift : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error:' + error+', '+e) context = {'ser_url':ser_url,'pser_url':pser_url,'trainingDataLocation':request.session['datalocation'],'error': error,'ModelVersion': ModelVersion,'computeinfrastructure':computeinfrastructure,'emptycsv':'emptycsv','kafkaSetting':'kafkaSetting','ruuningSetting':'ruuningSetting','usecasetab':usecasetab,'version':AION_VERSION} return render(request, 'upload.html', context) else: if request.FILES: Datapath = request.FILES['DataFilePath'] from io import StringIO content = StringIO(Datapath.read().decode('utf-8')) reader = csv.reader(content) df = pd.DataFrame(reader) df.columns = df.iloc[0] df = df[1:] ext = str(Datapath).split('.')[-1] filetimestamp = str(int(time.time())) if ext.lower() in ['csv','tsv','tar','zip','avro','parquet']: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext) else: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp) with open(dataFile, 'wb+') as destination: for chunk in Datapath.chunks(): destination.write(chunk) destination.close() if(os.path.isfile(dataFile) == False): context = {'error': 'Data file does not exist', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'version':AION_VERSION} log.info('Performance Drift : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + ' Error : Data file does not exist') return render(request, 'inputdrif.html', context) trainingdatalocation = request.session['datalocation'] inputFieldsJson = {"trainingDataLocation":trainingdatalocation,"currentDataLocation":dataFile} inputFieldsJson = json.dumps(inputFieldsJson) import requests try: #response = requests.post(pser_url,auth=(aion_service_username,aion_service_password),data=inputFieldsJson,headers={"Content-Type":"application/json",}) response = requests.post(pser_url,data=inputFieldsJson,headers={"Content-Type":"application/json",}) if response.status_code != 200: outputStr=response.content log.info('Performance Drift:' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + 'sec' + ' : ' + 'Error: Status code != 200') return outputStr except Exception as inst: if 'Failed to establish a new connection' in str(inst): Msg = 'AION Service needs to be started' else: Msg = 'Error during Drift Analysis' log.info('Performance Drift : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' +'0 ' + 'sec' + ' : ' + 'Error : '+Msg+', ' + str(inst)) return Msg outputStr=response.content outputStr = outputStr.decode('utf-8') outputStr = outputStr.strip() decoded_data = json.loads(outputStr) #print(decoded_data) if decoded_data['status'] == 'SUCCESS': htmlPath = decoded_data['htmlPath'] #print(htmlPath) if htmlPath != 'NA': file = open(htmlPath, "r",errors='ignore') driftdata = file.read() file.close() else: driftdata = 'NA' print(htmlPath) context = {'status':'SUCCESS','ser_url':ser_url,'pser_url':pser_url,'trainingDataLocation':request.session['datalocation'],'htmlPath': driftdata,'selected_use_case': selected_use_case,'usecasetab':usecasetab, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'monitoring','computeinfrastructure':computeinfrastructure,'version':AION_VERSION} t2 = time.time() log.info('Performance Drift:' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + str(round(t2-t1)) + 'sec' + ' : ' + 'Success') return render(request, 'inputdrif.html', context=context) else: driftdata = 'Error' context = {'status':'ERROR','ser_url':ser_url,'pser_url':pser_url,'trainingDataLocation':request.session['datalocation'],'htmlPath': driftdata,'selected_use_case': selected_use_case,'usecasetab':usecasetab, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'monitoring','computeinfrastructure':computeinfrastructure,'version':AION_VERSION} log.info('Performance Drift : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : driftdata = Error') return render(request, 'inputdrif.html', context=context) except Exception as e: print(e) context = {'ser_url':ser_url,'pser_url':pser_url,'trainingDataLocation':request.session['datalocation'],'error': 'Fail to perform Drift Analysis', 'selected_use_case': selected_use_case,'usecasetab':usecasetab, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'monitoring','computeinfrastructure':computeinfrastructure,'version':AION_VERSION} log.info('Performance Drift : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Fail to perform Drift Analysis' + ', ' + str(e)) return render(request, 'inputdrif.html', context=context) from django.shortcuts import render from django.urls import reverse from django.http import HttpResponse from django.shortcuts import redirect import time from django.template import loader from django import template from appbe.aion_config import get_llm_data from django.views.decorators.csrf import csrf_exempt import os import json from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from utils.file_ops import read_df_compressed from appbe.dataPath import LOG_LOCATION from appbe.pages import getversion AION_VERSION = getversion() def QueryToOpenAI(text,tempPrompt): FragmentationAllowed="yes" #yes or no try: import openai key,url,api_type,api_version=get_llm_data() if (key == "") and (url == "") : print("No API Key") return("API Key and URL not provided") openai.api_key = key openai.api_base = url openai.api_type = 'azure' openai.api_version = '2023-05-15' deployment_name="Text-Datvinci-03" import tiktoken encoding = tiktoken.encoding_for_model("text-davinci-003") maxTokens=1024 #4096-1024 == 3072 lgt=0 if FragmentationAllowed=="yes" : words = text.split(".") chunk="" chunks=[] multipleChunk="no" partialData="no" for i in range(len(words)): chunk=chunk+words[i]+"." chunk_token_count = encoding.encode(chunk) length=len(chunk_token_count) partialData="yes" if length > 2800 : chunks.append(chunk) chunk="" #print("\n\n\n") partialData="no" multipleChunk="yes" if (multipleChunk =="no" ): chunks.append(chunk) chunk="" if ((partialData =="yes") and (multipleChunk =="yes")): chunks.append(chunk) chunk="" summaries = [] for chunk in chunks: response = openai.Completion.create(engine=deployment_name, prompt=f"{tempPrompt}: {chunk}",temperature=0.2, max_tokens=maxTokens,frequency_penalty=0,presence_penalty=0) summary = response['choices'][0]['text'].replace('\n', '').replace(' .', '.').strip() summaries.append(summary) wordsInSum = summary.split() summaries=' '.join(summaries) wordsInSum = summaries.split() return summaries else : return "ok" except openai.error.Timeout as e: return "exception : Timeout Error due to Network Connection" except Exception as e: return "exception : "+str(e) def azureOpenAiDavinciSumarization(request): inputDataType = str(request.GET.get('FileType')) import time t1=time.time() documentType="" if inputDataType == 'file': dataPath = str(request.GET.get('dataPath')) #print("Datapath--",dataPath) if dataPath.endswith(".pdf"): from appbe.dataIngestion import pdf2text originalText=pdf2text(dataPath) if dataPath.endswith(".txt"): data=[] with open(dataPath, "r",encoding="utf-8") as f: data.append(f.read()) str1 = "" for ele in data: str1 += ele originalText=str1 if dataPath.endswith(".docx"): import docx doc = docx.Document(dataPath) fullText = [] for para in doc.paragraphs: fullText.append(para.text) fullText= '\n'.join(fullText) originalText=fullText if inputDataType == 'rawText': originalText = str(request.GET.get('textDataProcessing')) dataPath="" if originalText== "None" or originalText== "": context = {'originalText': originalText,'returnedText': "No Input given"} print("returned due to None") return render(request, "textsummarization.html",context) KeyWords=str(request.GET.get('userUpdatedKeyword')) contextOfText=str(request.GET.get('userUpdatedContext')) doctype = str(request.GET.get('doctypeUserProvided')) docDomainType = ["medical","other"] Prompts = [ "Summarize the following article within 500 words with proper sub-heading so that summarization include all main points from topics like: study objective; study design;demographics of patients; devices used in study; duration of exposure to device; study outcomes; complications;adverse events;confounding factors; study limitations and weakness;usability of the device; misuse and off-label use of the device;conflict of interest;statistical analysis;conclusions;", "Summarize the following article with minimum 500 words so that summarization include all main points from topics like: " ] for i in range (len(docDomainType)) : if docDomainType[i] in doctype.lower() : docDomainPrompts=Prompts[i] if docDomainType[i]=="medical" : print("medical doc") documentType="medical" docDomainFinalPrompts=docDomainPrompts tempPrompt1="Summarize the following article so that summarization must include all main points from topics like: study objective; study design;demographics of patients; devices used in study; duration of exposure to device; study outcomes; complications;adverse events;confounding factors; study limitations and weakness;usability of the device; misuse and off-label use of the device;conflict of interest;statistical analysis;conclusions;" tempPrompt2="Summarize the following article within 500 words with proper sub-heading so that summarization include all main points from topics like: study objective; study design;demographics of patients; devices used in study; duration of exposure to device; study outcomes; complications;adverse events;confounding factors; study limitations and weakness;usability of the device; misuse and off-label use of the device;conflict of interest;statistical analysis;conclusions;" else : print("other doc-a-") docDomainFinalPrompts=docDomainPrompts+" "+contextOfText tempPrompt1="Summarize the following article with minimum 500 words so that summarization include all main points from topics like: "+contextOfText tempPrompt2=tempPrompt1 break if (i== len(docDomainType)-1) : print("other doc-b-") docDomainPrompts=Prompts[i] docDomainFinalPrompts=docDomainPrompts+" "+contextOfText tempPrompt1="Summarize the following article so that summarization include all main points from topics like: "+contextOfText tempPrompt2=tempPrompt1 try: pattern =['Summary','Study Objective','Study Design', 'Demographics of Patients', 'Devices Used in Study','Duration of Exposure to Device','Study Outcomes','Complications','Adverse Events','Confounding Factors','Study Limitations and Weakness','Usability of the Device','Misuse and Off-Label Use of the Device','Conflict of Interest','Statistical Analysis','Conclusions'] import tiktoken encoding = tiktoken.encoding_for_model("text-davinci-003") encodedData = encoding.encode(originalText) totalToken=len(encodedData) while totalToken > 2800: originalText=QueryToOpenAI(originalText,tempPrompt1) encodedData = encoding.encode(originalText) totalToken=len(encodedData) retText=QueryToOpenAI(originalText,tempPrompt2) import re summary1=retText summary2=retText if documentType=="medical" : for i in range(len(pattern)): summary1=summary1.replace(pattern[i]+':','<br>'+'<u>'+pattern[i]+'</u>'+'<br>') for i in range(len(pattern)): summary1=summary1.replace(pattern[i],'<br>'+'<u>'+pattern[i]+'</u>'+'<br>') for i in range(len(pattern)): summary2=summary2.replace(pattern[i]+':','') for i in range(len(pattern)): summary2=summary2.replace(pattern[i],'') #retText2="" #tempPrompt="Find some most highlighting points in the following article" #retText2=QueryToOpenAI(originalText,tempPrompt) #retText3="" #tempPrompt="Find only one or two risk factors that are mentioned in the following article" #retText3=QueryToOpenAI(originalText,tempPrompt) #retText4="" #tempPrompt="Find statistical informtation that are mentioned in the following article" #retText4=QueryToOpenAI(originalText,tempPrompt) #retText5="" #tempPrompt="Find name of the author only one time that are mentioned in the following article" #retText5=QueryToOpenAI(originalText,tempPrompt) #retText6="" #tempPrompt="Suggest the name of the title for the following article" #retText6=QueryToOpenAI(originalText,tempPrompt) t2=time.time() #print("\n time taken-->", t2-t1 ,"length of sum",str(length)) print("\n time taken-->", t2-t1 ) #print("\n summary from LLM-->\n",returnedText) #context = {'title': retText6, 'summary': summary1, 'summary2': summary2, 'AuthorName': "Author names :"+retText5,'BulletPoints': retText2,'Riskfactor': retText3,'StatInfo': retText4} context = {'title': "", 'summary': summary1, 'summary2': summary2, 'AuthorName': "",'BulletPoints': "",'Riskfactor': "",'StatInfo': ""} return HttpResponse(json.dumps(context), content_type="application/json") except: context = {'returnedText': "exception"} return HttpResponse(json.dumps(context), content_type="application/json") def azureOpenAiDavinci(request): key,url,api_type,api_version=get_llm_data() inputDataType = str(request.POST.get('FileType')) if inputDataType == 'file': Datapath = request.FILES['file'] #dataPath = str(request.GET.get('dataPath')) ext = str(Datapath).split('.')[-1] temp1=str(Datapath).split('.') filetimestamp = str(int(time.time())) if ext.lower() in ['pdf','txt','docx']: dataFile = os.path.join(DATA_FILE_PATH,'AION_' +temp1[0]+'_'+filetimestamp+'.'+ext) #dataFile = os.path.join(DATA_FILE_PATH,'AION_' +filetimestamp+'.'+ext) with open(dataFile, 'wb+') as destination: for chunk in Datapath.chunks(): destination.write(chunk) destination.close() dataPath = dataFile if dataPath.endswith(".pdf"): from appbe.dataIngestion import pdf2text originalText=pdf2text(dataPath) if dataPath.endswith(".txt"): data=[] with open(dataPath, "r",encoding="utf-8") as f: data.append(f.read()) str1 = "" for ele in data: str1 += ele originalText=str1 if dataPath.endswith(".docx"): import docx doc = docx.Document(dataPath) fullText = [] for para in doc.paragraphs: fullText.append(para.text) fullText= '\n'.join(fullText) originalText=fullText if inputDataType == 'rawText': originalText = str(request.POST.get('textDataProcessing')) dataPath="" doctype = str(request.POST.get('doctypeUserProvided')) if originalText== "None" or originalText== "": context = {'originalText': originalText,'returnedText': "No Input given"} print("returned due to None") return render(request, "textsummarization.html",context) length=len(originalText.split()) inputTextPromptForKeyWords="Create a list of keywords to summrizing the following document." inputTextPromptForKeyWords="Suggest only ten most important keywords from the following document." inputTextPromptForContext="Suggest ten most important context in the following article. " #inputTextPromptForDocType="Suggest on which domain or field or area the following article is or the article is on sports or politics or medical or music or technology or legal field. " try: tempPrompt=inputTextPromptForKeyWords retText=QueryToOpenAI(originalText,tempPrompt) KeyWords=retText tempPrompt=inputTextPromptForContext retText=QueryToOpenAI(originalText,tempPrompt) contextOfText=retText #tempPrompt=inputTextPromptForDocType #retText=QueryToOpenAI(originalText,tempPrompt) #doctype=retText context = {'originalText': originalText,'KeyWords': KeyWords,'contextOfText': contextOfText,'doctype': doctype,'dataPath' :dataPath} return HttpResponse(json.dumps(context), content_type="application/json") except Exception as e: print(e) context = {'originalText': originalText,'KeyWords': KeyWords,'contextOfText': contextOfText,'doctype': doctype,'dataPath' :dataPath} return HttpResponse(json.dumps(context), content_type="application/json") # Text Data Labelling using LLM related changes # -------------------------------------------------------- def uploadedTextData(request): from appbe.dataIngestion import ingestTextData context = ingestTextData(request,DATA_FILE_PATH) context['version'] = AION_VERSION return render(request, 'textdatalabelling.html', context) def getTextLabel(request): from appbe.llm_textdatalabelling import generateTextLabel context = generateTextLabel(request,DATA_FILE_PATH) context['version'] = AION_VERSION return render(request, 'textdatalabelling.html', context) def downloadTextLabelReport(request): file_path = request.session['texttopicdatapath'] if os.path.exists(file_path): with open(file_path, 'rb') as fh: response = HttpResponse(fh.read(), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') response['Content-Disposition'] = 'attachment; filename=' + os.path.basename(file_path) return response raise Http404 # QnA Generator using LLM related changes # -------------------------------------------------------- def genearateQA(request): from appbe.llm_generateQnA import ingestDataForQA context = ingestDataForQA(request,DATA_FILE_PATH) context['version'] = AION_VERSION context['selected'] = "llm_features" return render(request, 'QnA.html', context) def downloadQnAReport(request): file_path = request.session['QnAfilepath'] if os.path.exists(file_path): with open(file_path, 'rb') as fh: response = HttpResponse(fh.read(), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') response['Content-Disposition'] = 'attachment; filename=' + os.path.basename(file_path) return response raise Http404 # -------------------------------------------------------- # Generated by Django 3.0.8 on 2020-08-03 12:50 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('modelTraining', '0001_initial'), ] operations = [ migrations.AlterField( model_name='existusecases', name='ModelName', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='modelTraining.usecasedetails'), ), migrations.AlterField( model_name='existusecases', name='id', field=models.AutoField(primary_key=True, serialize=False), ), migrations.AlterField( model_name='usecasedetails', name='Description', field=models.CharField(max_length=200), ), migrations.AlterField( model_name='usecasedetails', name='UsecaseName', field=models.CharField(max_length=50), ), ] # Generated by Django 4.1.7 on 2023-05-17 10:46 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('modelTraining', '0010_existusecases_modeltype'), ] operations = [ migrations.AddField( model_name='existusecases', name='trainingPID', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='existusecases', name='ProblemType', field=models.CharField(blank=True, max_length=20, null=True), ), migrations.AlterField( model_name='existusecases', name='TrainOuputLocation', field=models.CharField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='existusecases', name='driftStatus', field=models.CharField(blank=True, max_length=20, null=True), ), migrations.AlterField( model_name='existusecases', name='modelType', field=models.CharField(blank=True, max_length=40, null=True), ), migrations.AlterField( model_name='existusecases', name='portNo', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='existusecases', name='publishPID', field=models.IntegerField(blank=True, null=True), ), ] # Generated by Django 3.2.8 on 2023-03-28 18:50 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('modelTraining', '0007_auto_20230328_1823'), ] operations = [ migrations.AddField( model_name='existusecases', name='publishtask', field=models.CharField(default='', max_length=500), ), ] # Generated by Django 3.2.8 on 2023-03-29 05:41 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('modelTraining', '0008_existusecases_publishtask'), ] operations = [ migrations.RemoveField( model_name='existusecases', name='publishtask', ), migrations.AddField( model_name='existusecases', name='publishPID', field=models.IntegerField(default=0), ), migrations.AlterField( model_name='existusecases', name='Version', field=models.IntegerField(default=0), ), migrations.AlterField( model_name='existusecases', name='portNo', field=models.IntegerField(default=0), ), ] # Generated by Django 3.2.8 on 2023-02-06 17:14 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('modelTraining', '0004_existusecases_problemtype'), ] operations = [ migrations.AddField( model_name='usecasedetails', name='UserDefinedName', field=models.CharField(default=models.CharField(max_length=50), max_length=50), ), ] # Generated by Django 3.0.8 on 2020-08-01 17:33 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Existusecases', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('ModelName', models.CharField(max_length=200)), ('Version', models.IntegerField()), ('DataFilePath', models.FileField(upload_to=None)), ('ConfigPath', models.FileField(upload_to=None)), ('DeployPath', models.FileField(upload_to=None)), ('Status', models.CharField(max_length=200)), ], options={ 'db_table': 'Existusecases', }, ), migrations.CreateModel( name='usecasedetails', fields=[ ('id', models.AutoField(primary_key=True, serialize=False)), ('UsecaseName', models.CharField(max_length=20)), ('Description', models.CharField(max_length=100)), ], options={ 'db_table': 'usecasedetails', }, ), ] # Generated by Django 3.0.8 on 2020-09-18 12:00 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('modelTraining', '0002_auto_20200803_1820'), ] operations = [ migrations.AddField( model_name='existusecases', name='TrainOuputLocation', field=models.CharField(default='', max_length=200), ), ] # Generated by Django 3.2.8 on 2023-03-29 18:37 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('modelTraining', '0009_auto_20230329_0541'), ] operations = [ migrations.AddField( model_name='existusecases', name='modelType', field=models.CharField(default='', max_length=40), ), ] # Generated by Django 3.2.8 on 2023-02-06 17:59 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('modelTraining', '0005_usecasedetails_userdefinedname'), ] operations = [ migrations.RemoveField( model_name='usecasedetails', name='UserDefinedName', ), migrations.AddField( model_name='usecasedetails', name='usecaseid', field=models.CharField(default=models.CharField(max_length=50), max_length=10), ), ] # Generated by Django 3.2.8 on 2023-03-28 18:23 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('modelTraining', '0006_auto_20230206_1759'), ] operations = [ migrations.AddField( model_name='existusecases', name='driftStatus', field=models.CharField(default='', max_length=20), ), migrations.AddField( model_name='existusecases', name='portNo', field=models.CharField(default='', max_length=5), ), migrations.AddField( model_name='existusecases', name='publishStatus', field=models.CharField(default='', max_length=20), ), migrations.AlterField( model_name='existusecases', name='ProblemType', field=models.CharField(default='', max_length=20), ), ] # Generated by Django 3.2.8 on 2022-10-28 09:07 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('modelTraining', '0003_existusecases_trainouputlocation'), ] operations = [ migrations.AddField( model_name='existusecases', name='ProblemType', field=models.CharField(default='', max_length=100), ), ] from django.contrib.staticfiles.management.commands.runserver import Command as RunServer class Command(RunServer): def check(self, *args, **kwargs): self.stdout.write(self.style.WARNING("SKIPPING SYSTEM CHECKS!\n")) def check_migrations(self, *args, **kwargs): self.stdout.write(self.style.WARNING("SKIPPING MIGRATION CHECKS!\n")) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ class input_drift(): def __init__(self, tab_size=4): self.tab = ' ' * tab_size self.codeText = '' def addInputDriftClass(self): text = "\ \nclass inputdrift():\ \n\ \n def __init__(self,base_config):\ \n self.usecase = base_config['modelName'] + '_' + base_config['modelVersion']\ \n self.currentDataLocation = base_config['currentDataLocation']\ \n home = Path.home()\ \n if platform.system() == 'Windows':\ \n from pathlib import WindowsPath\ \n output_data_dir = WindowsPath(home)/'AppData'/'Local'/'HCLT'/'AION'/'Data'\ \n output_model_dir = WindowsPath(home)/'AppData'/'Local'/'HCLT'/'AION'/'target'/self.usecase\ \n else:\ \n from pathlib import PosixPath\ \n output_data_dir = PosixPath(home)/'HCLT'/'AION'/'Data'\ \n output_model_dir = PosixPath(home)/'HCLT'/'AION'/'target'/self.usecase\ \n if not output_model_dir.exists():\ \n raise ValueError(f'Configuration file not found at {output_model_dir}')\ \n\ \n tracking_uri = 'file:///' + str(Path(output_model_dir)/'mlruns')\ \n registry_uri = 'sqlite:///' + str(Path(output_model_dir)/'mlruns.db')\ \n mlflow.set_tracking_uri(tracking_uri)\ \n mlflow.set_registry_uri(registry_uri)\ \n client = mlflow.tracking.MlflowClient(\ \n tracking_uri=tracking_uri,\ \n registry_uri=registry_uri,\ \n )\ \n model_version_uri = 'models:/{model_name}/production'.format(model_name=self.usecase)\ \n model = mlflow.pyfunc.load_model(model_version_uri)\ \n run = client.get_run(model.metadata.run_id)\ \n if run.info.artifact_uri.startswith('file:'):\ \n artifact_path = Path(run.info.artifact_uri[len('file:///') : ])\ \n else:\ \n artifact_path = Path(run.info.artifact_uri)\ \n self.trainingDataPath = artifact_path/(self.usecase + '_data.csv')\ \n\ \n def get_input_drift(self,current_data, historical_data):\ \n curr_num_feat = current_data.select_dtypes(include='number')\ \n hist_num_feat = historical_data.select_dtypes(include='number')\ \n num_features = [feat for feat in historical_data.columns if feat in curr_num_feat]\ \n alert_count = 0\ \n data = {\ \n 'current':{'data':current_data},\ \n 'hist': {'data': historical_data}\ \n }\ \n dist_changed_columns = []\ \n dist_change_message = []\ \n for feature in num_features:\ \n curr_static_value = st.ks_2samp( hist_num_feat[feature], curr_num_feat[feature]).pvalue\ \n if (curr_static_value < 0.05):\ \n distribution = {}\ \n distribution['hist'] = self.DistributionFinder( historical_data[feature])\ \n distribution['curr'] = self.DistributionFinder( current_data[feature])\ \n if(distribution['hist']['name'] == distribution['curr']['name']):\ \n pass\ \n else:\ \n alert_count = alert_count + 1\ \n dist_changed_columns.append(feature)\ \n changed_column = {}\ \n changed_column['Feature'] = feature\ \n changed_column['KS_Training'] = curr_static_value\ \n changed_column['Training_Distribution'] = distribution['hist']['name']\ \n changed_column['New_Distribution'] = distribution['curr']['name']\ \n dist_change_message.append(changed_column)\ \n if alert_count:\ \n resultStatus = dist_change_message\ \n else :\ \n resultStatus='Model is working as expected'\ \n return(alert_count, resultStatus)\ \n\ \n def DistributionFinder(self,data):\ \n best_distribution =''\ \n best_sse =0.0\ \n if(data.dtype in ['int','int64']):\ \n distributions= {'bernoulli':{'algo':st.bernoulli},\ \n 'binom':{'algo':st.binom},\ \n 'geom':{'algo':st.geom},\ \n 'nbinom':{'algo':st.nbinom},\ \n 'poisson':{'algo':st.poisson}\ \n }\ \n index, counts = np.unique(data.astype(int),return_counts=True)\ \n if(len(index)>=2):\ \n best_sse = np.inf\ \n y1=[]\ \n total=sum(counts)\ \n mean=float(sum(index*counts))/total\ \n variance=float((sum(index**2*counts) -total*mean**2))/(total-1)\ \n dispersion=mean/float(variance)\ \n theta=1/float(dispersion)\ \n r=mean*(float(theta)/1-theta)\ \n\ \n for j in counts:\ \n y1.append(float(j)/total)\ \n distributions['bernoulli']['pmf'] = distributions['bernoulli']['algo'].pmf(index,mean)\ \n distributions['binom']['pmf'] = distributions['binom']['algo'].pmf(index,len(index),p=mean/len(index))\ \n distributions['geom']['pmf'] = distributions['geom']['algo'].pmf(index,1/float(1+mean))\ \n distributions['nbinom']['pmf'] = distributions['nbinom']['algo'].pmf(index,mean,r)\ \n distributions['poisson']['pmf'] = distributions['poisson']['algo'].pmf(index,mean)\ \n\ \n sselist = []\ \n for dist in distributions.keys():\ \n distributions[dist]['sess'] = np.sum(np.power(y1 - distributions[dist]['pmf'], 2.0))\ \n if np.isnan(distributions[dist]['sess']):\ \n distributions[dist]['sess'] = float('inf')\ \n best_dist = min(distributions, key=lambda v: distributions[v]['sess'])\ \n best_distribution = best_dist\ \n best_sse = distributions[best_dist]['sess']\ \n\ \n elif (len(index) == 1):\ \n best_distribution = 'Constant Data-No Distribution'\ \n best_sse = 0.0\ \n elif(data.dtype in ['float64','float32']):\ \n distributions = [st.uniform,st.expon,st.weibull_max,st.weibull_min,st.chi,st.norm,st.lognorm,st.t,st.gamma,st.beta]\ \n best_distribution = st.norm.name\ \n best_sse = np.inf\ \n nrange = data.max() - data.min()\ \n\ \n y, x = np.histogram(data.astype(float), bins='auto', density=True)\ \n x = (x + np.roll(x, -1))[:-1] / 2.0\ \n\ \n for distribution in distributions:\ \n with warnings.catch_warnings():\ \n warnings.filterwarnings('ignore')\ \n params = distribution.fit(data.astype(float))\ \n arg = params[:-2]\ \n loc = params[-2]\ \n scale = params[-1]\ \n pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\ \n sse = np.sum(np.power(y - pdf, 2.0))\ \n if( sse < best_sse):\ \n best_distribution = distribution.name\ \n best_sse = sse\ \n\ \n return {'name':best_distribution, 'sse': best_sse}\ \n\ " return text def addSuffixCode(self, indent=1): text ="\n\ \ndef check_drift( config):\ \n inputdriftObj = inputdrift(config)\ \n historicaldataFrame=pd.read_csv(inputdriftObj.trainingDataPath)\ \n currentdataFrame=pd.read_csv(inputdriftObj.currentDataLocation)\ \n dataalertcount,message = inputdriftObj.get_input_drift(currentdataFrame,historicaldataFrame)\ \n if message == 'Model is working as expected':\ \n output_json = {'status':'SUCCESS','data':{'Message':'Model is working as expected'}}\ \n else:\ \n output_json = {'status':'SUCCESS','data':{'Affected Columns':message}}\ \n return(output_json)\ \n\ \nif __name__ == '__main__':\ \n try:\ \n if len(sys.argv) < 2:\ \n raise ValueError('config file not present')\ \n config = sys.argv[1]\ \n if Path(config).is_file() and Path(config).suffix == '.json':\ \n with open(config, 'r') as f:\ \n config = json.load(f)\ \n else:\ \n config = json.loads(config)\ \n output = check_drift(config)\ \n status = {'Status':'Success','Message':output}\ \n print('input_drift:'+json.dumps(status))\ \n except Exception as e:\ \n status = {'Status':'Failure','Message':str(e)}\ \n print('input_drift:'+json.dumps(status))" return text def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def generateCode(self): self.codeText += self.addInputDriftClass() self.codeText += self.addSuffixCode() def getCode(self): return self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ class output_drift(): def __init__(self, missing=False, word2num_features = None, cat_encoder=False, target_encoder=False, normalizer=False, text_profiler=False, feature_reducer=False, score_smaller_is_better=True, problem_type='classification', tab_size=4): self.tab = ' ' * tab_size self.codeText = '' self.missing = missing self.word2num_features = word2num_features self.cat_encoder = cat_encoder self.target_encoder = target_encoder self.normalizer = normalizer self.text_profiler = text_profiler self.feature_reducer = feature_reducer self.score_smaller_is_better = score_smaller_is_better self.problem_type = problem_type def addDatabaseClass(self, indent=0): text = "\ \nclass database():\ \n def __init__(self, config):\ \n self.host = config['host']\ \n self.port = config['port']\ \n self.user = config['user']\ \n self.password = config['password']\ \n self.database = config['database']\ \n self.measurement = config['measurement']\ \n self.tags = config['tags']\ \n self.client = self.get_client()\ \n\ \n def read_data(self, query)->pd.DataFrame:\ \n cursor = self.client.query(query)\ \n points = cursor.get_points()\ \n my_list=list(points)\ \n df=pd.DataFrame(my_list)\ \n return df\ \n\ \n def get_client(self):\ \n client = InfluxDBClient(self.host,self.port,self.user,self.password)\ \n databases = client.get_list_database()\ \n databases = [x['name'] for x in databases]\ \n if self.database not in databases:\ \n client.create_database(self.database)\ \n return InfluxDBClient(self.host,self.port,self.user,self.password, self.database)\ \n\ \n def write_data(self,data):\ \n if isinstance(data, pd.DataFrame):\ \n sorted_col = data.columns.tolist()\ \n sorted_col.sort()\ \n data = data[sorted_col]\ \n data = data.to_dict(orient='records')\ \n for row in data:\ \n if 'time' in row.keys():\ \n p = '%Y-%m-%dT%H:%M:%S.%fZ'\ \n time_str = datetime.strptime(row['time'], p)\ \n del row['time']\ \n else:\ \n time_str = None\ \n if 'model_ver' in row.keys():\ \n self.tags['model_ver']= row['model_ver']\ \n del row['model_ver']\ \n json_body = [{\ \n 'measurement': self.measurement,\ \n 'time': time_str,\ \n 'tags': self.tags,\ \n 'fields': row\ \n }]\ \n self.client.write_points(json_body)\ \n\ \n def close(self):\ \n self.client.close()\ \n" if indent: text = text.replace('\n', (self.tab * indent) + '\n') return text def addPredictClass(self, indent=0): text = "\ \nclass predict():\ \n\ \n def __init__(self, base_config):\ \n self.usecase = base_config['modelName'] + '_' + base_config['modelVersion']\ \n self.dataLocation = base_config['dataLocation']\ \n self.db_enabled = base_config.get('db_enabled', False)\ \n if self.db_enabled:\ \n self.db_config = base_config['db_config']\ \n home = Path.home()\ \n if platform.system() == 'Windows':\ \n from pathlib import WindowsPath\ \n output_data_dir = WindowsPath(home)/'AppData'/'Local'/'HCLT'/'AION'/'Data'\ \n output_model_dir = WindowsPath(home)/'AppData'/'Local'/'HCLT'/'AION'/'target'/self.usecase\ \n else:\ \n from pathlib import PosixPath\ \n output_data_dir = PosixPath(home)/'HCLT'/'AION'/'Data'\ \n output_model_dir = PosixPath(home)/'HCLT'/'AION'/'target'/self.usecase\ \n if not output_model_dir.exists():\ \n raise ValueError(f'Configuration file not found at {output_model_dir}')\ \n\ \n tracking_uri = 'file:///' + str(Path(output_model_dir)/'mlruns')\ \n registry_uri = 'sqlite:///' + str(Path(output_model_dir)/'mlruns.db')\ \n mlflow.set_tracking_uri(tracking_uri)\ \n mlflow.set_registry_uri(registry_uri)\ \n client = mlflow.tracking.MlflowClient(\ \n tracking_uri=tracking_uri,\ \n registry_uri=registry_uri,\ \n )\ \n self.model_version = client.get_latest_versions(self.usecase, stages=['production'] )[0].version\ \n model_version_uri = 'models:/{model_name}/production'.format(model_name=self.usecase)\ \n self.model = mlflow.pyfunc.load_model(model_version_uri)\ \n run = client.get_run(self.model.metadata.run_id)\ \n if run.info.artifact_uri.startswith('file:'): #remove file:///\ \n self.artifact_path = Path(run.info.artifact_uri[len('file:///') : ])\ \n else:\ \n self.artifact_path = Path(run.info.artifact_uri)\ \n with open(self.artifact_path/'deploy.json', 'r') as f:\ \n deployment_dict = json.load(f)\ \n with open(self.artifact_path/'features.txt', 'r') as f:\ \n self.train_features = f.readline().rstrip().split(',')\ \n\ \n self.dataLocation = base_config['dataLocation']\ \n self.selected_features = deployment_dict['load_data']['selected_features']\ \n self.target_feature = deployment_dict['load_data']['target_feature']\ \n self.output_model_dir = output_model_dir" if self.missing: text += "\n self.missing_values = deployment_dict['transformation']['fillna']" if self.word2num_features: text += "\n self.word2num_features = deployment_dict['transformation']['word2num_features']" if self.cat_encoder == 'labelencoding': text += "\n self.cat_encoder = deployment_dict['transformation']['cat_encoder']" elif (self.cat_encoder == 'targetencoding') or (self.cat_encoder == 'onehotencoding'): text += "\n self.cat_encoder = deployment_dict['transformation']['cat_encoder']['file']" text += "\n self.cat_encoder_cols = deployment_dict['transformation']['cat_encoder']['features']" if self.target_encoder: text += "\n self.target_encoder = joblib.load(self.artifact_path/deployment_dict['transformation']['target_encoder'])" if self.normalizer: text += "\n self.normalizer = joblib.load(self.artifact_path/deployment_dict['transformation']['normalizer']['file'])\ \n self.normalizer_col = deployment_dict['transformation']['normalizer']['features']" if self.text_profiler: text += "\n self.text_profiler = joblib.load(self.artifact_path/deployment_dict['transformation']['Status']['text_profiler']['file'])\ \n self.text_profiler_col = deployment_dict['transformation']['Status']['text_profiler']['features']" if self.feature_reducer: text += "\n self.feature_reducer = joblib.load(self.artifact_path/deployment_dict['featureengineering']['feature_reducer']['file'])\ \n self.feature_reducer_cols = deployment_dict['featureengineering']['feature_reducer']['features']" text += """ def read_data_from_db(self): if self.db_enabled: try: db = database(self.db_config) query = "SELECT * FROM {} WHERE model_ver = '{}' AND {} != ''".format(db.measurement, self.model_version, self.target_feature) if 'read_time' in self.db_config.keys() and self.db_config['read_time']: query += f" time > now() - {self.db_config['read_time']}" data = db.read_data(query) except: raise ValueError('Unable to read from the database') finally: if db: db.close() return data return None""" text += "\ \n def predict(self, data):\ \n df = pd.DataFrame()\ \n if Path(data).exists():\ \n if Path(data).suffix == '.tsv':\ \n df=read_data(data,encoding='utf-8',sep='\t')\ \n elif Path(data).suffix == '.csv':\ \n df=read_data(data,encoding='utf-8')\ \n else:\ \n if Path(data).suffix == '.json':\ \n jsonData = read_json(data)\ \n df = pd.json_normalize(jsonData)\ \n elif is_file_name_url(data):\ \n df = read_data(data,encoding='utf-8')\ \n else:\ \n jsonData = json.loads(data)\ \n df = pd.json_normalize(jsonData)\ \n if len(df) == 0:\ \n raise ValueError('No data record found')\ \n missing_features = [x for x in self.selected_features if x not in df.columns]\ \n if missing_features:\ \n raise ValueError(f'some feature/s is/are missing: {missing_features}')\ \n if self.target_feature not in df.columns:\ \n raise ValueError(f'Ground truth values/target column({self.target_feature}) not found in current data')\ \n df_copy = df.copy()\ \n df = df[self.selected_features]" if self.word2num_features: text += "\n for feat in self.word2num_features:" text += "\n df[ feat ] = df[feat].apply(lambda x: s2n(x))" if self.missing: text += "\n df.fillna(self.missing_values, inplace=True)" if self.cat_encoder == 'labelencoding': text += "\n df.replace(self.cat_encoder, inplace=True)" elif self.cat_encoder == 'targetencoding': text += "\n cat_enc = joblib.load(self.artifact_path/self.cat_encoder)" text += "\n df = cat_enc.transform(df)" elif self.cat_encoder == 'onehotencoding': text += "\n cat_enc = joblib.load(self.artifact_path/self.cat_encoder)" text += "\n transformed_data = cat_enc.transform(df[self.cat_encoder_cols]).toarray()" text += "\n df[cat_enc.get_feature_names()] = pd.DataFrame(transformed_data, columns=cat_enc.get_feature_names())[cat_enc.get_feature_names()]" if self.normalizer: text += "\n df[self.normalizer_col] = self.normalizer.transform(df[self.normalizer_col])" if self.text_profiler: text += "\n text_corpus = df[self.text_profiler_col].apply(lambda row: ' '.join(row.values.astype(str)), axis=1)\ \n df_vect=self.text_profiler.transform(text_corpus)\ \n if isinstance(df_vect, np.ndarray):\ \n df1 = pd.DataFrame(df_vect)\ \n else:\ \n df1 = pd.DataFrame(df_vect.toarray(),columns = self.text_profiler.named_steps['vectorizer'].get_feature_names())\ \n df1 = df1.add_suffix('_vect')\ \n df = pd.concat([df, df1],axis=1)" if self.feature_reducer: text += "\n df = self.feature_reducer.transform(df[self.feature_reducer_cols])" else: text += "\n df = df[self.train_features]" if self.target_encoder: text += "\n output = pd.DataFrame(self.model._model_impl.predict_proba(df), columns=self.target_encoder.classes_)\ \n df_copy['prediction'] = output.idxmax(axis=1)" else: text += "\n output = self.model.predict(df).reshape(1, -1)[0].round(2)\ \n df_copy['prediction'] = output" text += "\n return df_copy" if indent: text = text.replace('\n', (self.tab * indent) + '\n') return text def getClassificationMatrixCode(self, indent=0): text = "\ \ndef get_classification_metrices(actual_values, predicted_values):\ \n result = {}\ \n accuracy_score = sklearn.metrics.accuracy_score(actual_values, predicted_values)\ \n avg_precision = sklearn.metrics.precision_score(actual_values, predicted_values,\ \n average='macro')\ \n avg_recall = sklearn.metrics.recall_score(actual_values, predicted_values,\ \n average='macro')\ \n avg_f1 = sklearn.metrics.f1_score(actual_values, predicted_values,\ \n average='macro')\ \n\ \n result['accuracy'] = accuracy_score\ \n result['precision'] = avg_precision\ \n result['recall'] = avg_recall\ \n result['f1'] = avg_f1\ \n return result\ \n\ " if indent: text = text.replace('\n', (self.tab * indent) + '\n') return text def getRegrssionMatrixCode(self, indent=0): text = "\ \ndef get_regression_metrices( actual_values, predicted_values):\ \n result = {}\ \n\ \n me = np.mean(predicted_values - actual_values)\ \n sde = np.std(predicted_values - actual_values, ddof = 1)\ \n\ \n abs_err = np.abs(predicted_values - actual_values)\ \n mae = np.mean(abs_err)\ \n sdae = np.std(abs_err, ddof = 1)\ \n\ \n abs_perc_err = 100.*np.abs(predicted_values - actual_values) / actual_values\ \n mape = np.mean(abs_perc_err)\ \n sdape = np.std(abs_perc_err, ddof = 1)\ \n\ \n result['mean_error'] = me\ \n result['mean_abs_error'] = mae\ \n result['mean_abs_perc_error'] = mape\ \n result['error_std'] = sde\ \n result['abs_error_std'] = sdae\ \n result['abs_perc_error_std'] = sdape\ \n return result\ \n\ " if indent: text = text.replace('\n', (self.tab * indent) + '\n') return text def addSuffixCode(self, indent=1): text ="\n\ \ndef check_drift( config):\ \n prediction = predict(config)\ \n usecase = config['modelName'] + '_' + config['modelVersion']\ \n train_data_path = prediction.artifact_path/(usecase+'_data.csv')\ \n if not train_data_path.exists():\ \n raise ValueError(f'Training data not found at {train_data_path}')\ \n curr_with_pred = prediction.read_data_from_db()\ \n if prediction.target_feature not in curr_with_pred.columns:\ \n raise ValueError('Ground truth not updated for corresponding data in database')\ \n train_with_pred = prediction.predict(train_data_path)\ \n performance = {}" if self.problem_type == 'classification': text += "\n\ \n performance['train'] = get_classification_metrices(train_with_pred[prediction.target_feature], train_with_pred['prediction'])\ \n performance['current'] = get_classification_metrices(curr_with_pred[prediction.target_feature], curr_with_pred['prediction'])" else: text += "\n\ \n performance['train'] = get_regression_metrices(train_with_pred[prediction.target_feature], train_with_pred['prediction'])\ \n performance['current'] = get_regression_metrices(curr_with_pred[prediction.target_feature], curr_with_pred['prediction'])" text += "\n return performance" text += "\n\ \nif __name__ == '__main__':\ \n try:\ \n if len(sys.argv) < 2:\ \n raise ValueError('config file not present')\ \n config = sys.argv[1]\ \n if Path(config).is_file() and Path(config).suffix == '.json':\ \n with open(config, 'r') as f:\ \n config = json.load(f)\ \n else:\ \n config = json.loads(config)\ \n output = check_drift(config)\ \n status = {'Status':'Success','Message':json.loads(output)}\ \n print('output_drift:'+json.dumps(status))\ \n except Exception as e:\ \n status = {'Status':'Failure','Message':str(e)}\ \n print('output_drift:'+json.dumps(status))" if indent: text = text.replace('\n', (self.tab * indent) + '\n') return text def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def generateCode(self): self.codeText += self.addDatabaseClass() self.codeText += self.addPredictClass() if self.problem_type == 'classification': self.codeText += self.getClassificationMatrixCode() elif self.problem_type == 'regression': self.codeText += self.getRegrssionMatrixCode() else: raise ValueError(f"Unsupported problem type: {self.problem_type}") self.codeText += self.addSuffixCode() def getCode(self): return self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class deploy(): def __init__(self, target_encoder=False, feature_reducer=False, score_smaller_is_better=True, tab_size=4): self.tab = ' ' * tab_size self.codeText = "\n\n\ \nclass deploy():\ \n\ \n def __init__(self, base_config, log=None):\ \n self.targetPath = (Path('aion')/base_config['targetPath']).resolve()\ \n if log:\ \n self.logger = log\ \n else:\ \n log_file = self.targetPath/IOFiles['log']\ \n self.logger = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem)\ \n try:\ \n self.initialize(base_config)\ \n except Exception as e:\ \n self.logger.error(e, exc_info=True)\ \n\ \n def initialize(self, base_config):\ \n self.usecase = base_config['targetPath']\ \n monitoring_data = read_json(self.targetPath/IOFiles['monitor'])\ \n self.prod_db_type = monitoring_data['prod_db_type']\ \n self.db_config = monitoring_data['db_config']\ \n mlflow_default_config = {'artifacts_uri':'','tracking_uri_type':'','tracking_uri':'','registry_uri':''}\ \n tracking_uri, artifact_uri, registry_uri = get_mlflow_uris(monitoring_data.get('mlflow_config',mlflow_default_config), self.targetPath)\ \n mlflow.tracking.set_tracking_uri(tracking_uri)\ \n mlflow.tracking.set_registry_uri(registry_uri)\ \n client = mlflow.tracking.MlflowClient()\ \n self.model_version = client.get_latest_versions(self.usecase, stages=['production'] )\ \n model_version_uri = 'models:/{model_name}/production'.format(model_name=self.usecase)\ \n self.model = mlflow.pyfunc.load_model(model_version_uri)\ \n run = client.get_run(self.model.metadata.run_id)\ \n if run.info.artifact_uri.startswith('file:'): #remove file:///\ \n skip_name = 'file:'\ \n if run.info.artifact_uri.startswith('file:///'):\ \n skip_name = 'file:///'\ \n self.artifact_path = Path(run.info.artifact_uri[len(skip_name) : ])\ \n self.artifact_path_type = 'file'\ \n meta_data = read_json(self.artifact_path/IOFiles['metaData'])\ \n else:\ \n self.artifact_path = run.info.artifact_uri\ \n self.artifact_path_type = 'url'\ \n meta_data_file = mlflow.artifacts.download_artifacts(self.artifact_path+'/'+IOFiles['metaData'])\ \n meta_data = read_json(meta_data_file)\ \n self.selected_features = meta_data['load_data']['selected_features']\ \n self.train_features = meta_data['training']['features']" if target_encoder: self.codeText += "\ \n if self.artifact_path_type == 'url':\ \n preprocessor_file = mlflow.artifacts.download_artifacts(self.artifact_path+'/'+meta_data['transformation']['preprocessor'])\ \n target_encoder_file = mlflow.artifacts.download_artifacts(self.artifact_path+'/'+meta_data['transformation']['target_encoder'])\ \n else:\ \n preprocessor_file = self.artifact_path/meta_data['transformation']['preprocessor']\ \n target_encoder_file = self.artifact_path/meta_data['transformation']['target_encoder']\ \n self.target_encoder = joblib.load(target_encoder_file)" else: self.codeText += "\ \n if self.artifact_path_type == 'url':\ \n preprocessor_file = mlflow.artifacts.download_artifacts(self.artifact_path+'/'+meta_data['transformation']['preprocessor'])\ \n else:\ \n preprocessor_file = self.artifact_path/meta_data['transformation']['preprocessor']" self.codeText += "\ \n self.preprocessor = joblib.load(preprocessor_file)\ \n self.preprocess_out_columns = meta_data['transformation']['preprocess_out_columns']\ " if feature_reducer: self.codeText += "\ \n if self.artifact_path_type == 'url':\ \n feature_reducer_file = mlflow.artifacts.download_artifacts(self.artifact_path+'/'+meta_data['featureengineering']['feature_reducer']['file'])\ \n else:\ \n feature_reducer_file = self.artifact_path/meta_data['featureengineering']['feature_reducer']['file']\ \n self.feature_reducer = joblib.load(feature_reducer_file)\ \n self.feature_reducer_cols = meta_data['featureengineering']['feature_reducer']['features']" self.codeText +="\n\ \n def write_to_db(self, data):\ \n prod_file = IOFiles['prodData']\ \n writer = dataReader(reader_type=self.prod_db_type,target_path=self.targetPath, config=self.db_config )\ \n writer.write(data, prod_file)\ \n writer.close()\ \n\ \n def predict(self, data=None):\ \n try:\ \n return self.__predict(data)\ \n except Exception as e:\ \n if self.logger:\ \n self.logger.error(e, exc_info=True)\ \n raise ValueError(json.dumps({'Status':'Failure', 'Message': str(e)}))\ \n\ \n def __predict(self, data=None):\ \n df = pd.DataFrame()\ \n jsonData = json.loads(data)\ \n df = pd.json_normalize(jsonData)\ \n if len(df) == 0:\ \n raise ValueError('No data record found')\ \n missing_features = [x for x in self.selected_features if x not in df.columns]\ \n if missing_features:\ \n raise ValueError(f'some feature/s is/are missing: {missing_features}')\ \n df_copy = df.copy()\ \n df = df[self.selected_features]\ \n df = self.preprocessor.transform(df)\ \n if isinstance(df, scipy.sparse.spmatrix):\ \n df = df.toarray()\ \n df = pd.DataFrame(df, columns=self.preprocess_out_columns)" if feature_reducer: self.codeText += "\n df = self.feature_reducer.transform(df[self.feature_reducer_cols])" else: self.codeText += "\n df = df[self.train_features]" if target_encoder: self.codeText += "\n df = df.astype(np.float32)\ \n output = pd.DataFrame(self.model._model_impl.predict_proba(df), columns=self.target_encoder.classes_)\ \n df_copy['prediction'] = output.idxmax(axis=1)\ \n self.write_to_db(df_copy)\ \n df_copy['probability'] = output.max(axis=1).round(2)\ \n df_copy['remarks'] = output.apply(lambda x: x.to_json(), axis=1)\ \n output = df_copy.to_json(orient='records')" else: self.codeText += "\n output = self.model._model_impl.predict(df).reshape(1, -1)[0].round(2)\ \n df_copy['prediction'] = output\ \n self.write_to_db(df_copy)\ \n output = df_copy.to_json(orient='records')" self.codeText += "\n return output" self.input_files = {} self.output_files = {} self.addInputFiles({'inputData' : 'rawData.dat', 'metaData' : 'modelMetaData.json', 'performance' : 'performance.json','monitor':'monitoring.json','log':'predict.log','prodData':'prodData'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() text += '\n' text += self.getOutputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def addStatement(self, statement, indent=1): pass def getCode(self): return self.codeText def getGroundtruthCode(self): return """ import sys import math import json import sqlite3 import pandas as pd from datetime import datetime from pathlib import Path import platform from utility import * from data_reader import dataReader IOFiles = { "monitoring":"monitoring.json", "prodDataGT":"prodDataGT" } class groundtruth(): def __init__(self, base_config): self.targetPath = Path('aion')/base_config['targetPath'] data = read_json(self.targetPath/IOFiles['monitoring']) self.prod_db_type = data['prod_db_type'] self.db_config = data['db_config'] def actual(self, data=None): df = pd.DataFrame() jsonData = json.loads(data) df = pd.json_normalize(jsonData) if len(df) == 0: raise ValueError('No data record found') self.write_to_db(df) status = {'Status':'Success','Message':'uploaded'} return json.dumps(status) def write_to_db(self, data): prod_file = IOFiles['prodDataGT'] writer = dataReader(reader_type=self.prod_db_type, target_path=self.targetPath, config=self.db_config ) writer.write(data, prod_file) writer.close() """ def getServiceCode(self): return """ from http.server import BaseHTTPRequestHandler,HTTPServer from socketserver import ThreadingMixIn import os from os.path import expanduser import platform import threading import subprocess import argparse import re import cgi import json import shutil import logging import sys import time import seaborn as sns from pathlib import Path from predict import deploy from groundtruth import groundtruth import pandas as pd import scipy.stats as st import numpy as np import warnings from utility import * from data_reader import dataReader warnings.filterwarnings("ignore") config_input = None IOFiles = { "inputData": "rawData.dat", "metaData": "modelMetaData.json", "production": "production.json", "log": "aion.log", "monitoring":"monitoring.json", "prodData": "prodData", "prodDataGT":"prodDataGT" } def DistributionFinder(data): try: distributionName = "" sse = 0.0 KStestStatic = 0.0 dataType = "" if (data.dtype == "float64" or data.dtype == "float32"): dataType = "Continuous" elif (data.dtype == "int"): dataType = "Discrete" elif (data.dtype == "int64"): dataType = "Discrete" if (dataType == "Discrete"): distributions = [st.bernoulli, st.binom, st.geom, st.nbinom, st.poisson] index, counts = np.unique(data.astype(int), return_counts=True) if (len(index) >= 2): best_sse = np.inf y1 = [] total = sum(counts) mean = float(sum(index * counts)) / total variance = float((sum(index ** 2 * counts) - total * mean ** 2)) / (total - 1) dispersion = mean / float(variance) theta = 1 / float(dispersion) r = mean * (float(theta) / 1 - theta) for j in counts: y1.append(float(j) / total) pmf1 = st.bernoulli.pmf(index, mean) pmf2 = st.binom.pmf(index, len(index), p=mean / len(index)) pmf3 = st.geom.pmf(index, 1 / float(1 + mean)) pmf4 = st.nbinom.pmf(index, mean, r) pmf5 = st.poisson.pmf(index, mean) sse1 = np.sum(np.power(y1 - pmf1, 2.0)) sse2 = np.sum(np.power(y1 - pmf2, 2.0)) sse3 = np.sum(np.power(y1 - pmf3, 2.0)) sse4 = np.sum(np.power(y1 - pmf4, 2.0)) sse5 = np.sum(np.power(y1 - pmf5, 2.0)) sselist = [sse1, sse2, sse3, sse4, sse5] best_distribution = 'NA' for i in range(0, len(sselist)): if best_sse > sselist[i] > 0: best_distribution = distributions[i].name best_sse = sselist[i] elif (len(index) == 1): best_distribution = "Constant Data-No Distribution" best_sse = 0.0 distributionName = best_distribution sse = best_sse elif (dataType == "Continuous"): distributions = [st.uniform, st.expon, st.weibull_max, st.weibull_min, st.chi, st.norm, st.lognorm, st.t, st.gamma, st.beta] best_distribution = st.norm.name best_sse = np.inf datamin = data.min() datamax = data.max() nrange = datamax - datamin y, x = np.histogram(data.astype(float), bins='auto', density=True) x = (x + np.roll(x, -1))[:-1] / 2.0 for distribution in distributions: params = distribution.fit(data.astype(float)) arg = params[:-2] loc = params[-2] scale = params[-1] pdf = distribution.pdf(x, loc=loc, scale=scale, *arg) sse = np.sum(np.power(y - pdf, 2.0)) if (best_sse > sse > 0): best_distribution = distribution.name best_sse = sse distributionName = best_distribution sse = best_sse except: response = str(sys.exc_info()[0]) message = 'Job has Failed' + response exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) print(message) return distributionName, sse def getDriftDistribution(feature, dataframe, newdataframe=pd.DataFrame()): import matplotlib.pyplot as plt import math import io, base64, urllib np.seterr(divide='ignore', invalid='ignore') try: plt.clf() except: pass plt.rcParams.update({'figure.max_open_warning': 0}) sns.set(color_codes=True) pandasNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] if len(feature) > 4: numneroffeatures = len(feature) plt.figure(figsize=(10, numneroffeatures*2)) else: plt.figure(figsize=(10,5)) for i in enumerate(feature): dataType = dataframe[i[1]].dtypes if dataType not in pandasNumericDtypes: dataframe[i[1]] = pd.Categorical(dataframe[i[1]]) dataframe[i[1]] = dataframe[i[1]].cat.codes dataframe[i[1]] = dataframe[i[1]].astype(int) dataframe[i[1]] = dataframe[i[1]].fillna(dataframe[i[1]].mode()[0]) else: dataframe[i[1]] = dataframe[i[1]].fillna(dataframe[i[1]].mean()) plt.subplots_adjust(hspace=0.5, wspace=0.7, top=1) plt.subplot(math.ceil((len(feature) / 2)), 2, i[0] + 1) distname, sse = DistributionFinder(dataframe[i[1]]) print(distname) ax = sns.distplot(dataframe[i[1]], label=distname) ax.legend(loc='best') if newdataframe.empty == False: dataType = newdataframe[i[1]].dtypes if dataType not in pandasNumericDtypes: newdataframe[i[1]] = pd.Categorical(newdataframe[i[1]]) newdataframe[i[1]] = newdataframe[i[1]].cat.codes newdataframe[i[1]] = newdataframe[i[1]].astype(int) newdataframe[i[1]] = newdataframe[i[1]].fillna(newdataframe[i[1]].mode()[0]) else: newdataframe[i[1]] = newdataframe[i[1]].fillna(newdataframe[i[1]].mean()) distname, sse = DistributionFinder(newdataframe[i[1]]) print(distname) ax = sns.distplot(newdataframe[i[1]],label=distname) ax.legend(loc='best') buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) string = base64.b64encode(buf.read()) uri = urllib.parse.quote(string) return uri def read_json(file_path): data = None with open(file_path,'r') as f: data = json.load(f) return data class HTTPRequestHandler(BaseHTTPRequestHandler): def do_POST(self): print('PYTHON ######## REQUEST ####### STARTED') if None != re.search('/AION/', self.path) or None != re.search('/aion/', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) data = self.rfile.read(length) usecase = self.path.split('/')[-2] if usecase.lower() == config_input['targetPath'].lower(): operation = self.path.split('/')[-1] data = json.loads(data) dataStr = json.dumps(data) if operation.lower() == 'predict': output=deployobj.predict(dataStr) resp = output elif operation.lower() == 'groundtruth': gtObj = groundtruth(config_input) output = gtObj.actual(dataStr) resp = output elif operation.lower() == 'delete': targetPath = Path('aion')/config_input['targetPath'] for file in data: x = targetPath/file if x.exists(): os.remove(x) resp = json.dumps({'Status':'Success'}) else: outputStr = json.dumps({'Status':'Error','Msg':'Operation not supported'}) resp = outputStr else: outputStr = json.dumps({'Status':'Error','Msg':'Wrong URL'}) resp = outputStr else: outputStr = json.dumps({'Status':'ERROR','Msg':'Content-Type Not Present'}) resp = outputStr resp=resp+'\\n' resp=resp.encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(resp) else: print('python ==> else1') self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() print('PYTHON ######## REQUEST ####### ENDED') return def do_GET(self): print('PYTHON ######## REQUEST ####### STARTED') if None != re.search('/AION/', self.path) or None != re.search('/aion/', self.path): usecase = self.path.split('/')[-2] self.send_response(200) self.targetPath = Path('aion')/config_input['targetPath'] meta_data_file = self.targetPath/IOFiles['metaData'] if meta_data_file.exists(): meta_data = read_json(meta_data_file) else: raise ValueError(f'Configuration file not found: {meta_data_file}') production_file = self.targetPath/IOFiles['production'] if production_file.exists(): production_data = read_json(production_file) else: raise ValueError(f'Production Details not found: {production_file}') operation = self.path.split('/')[-1] if (usecase.lower() == config_input['targetPath'].lower()) and (operation.lower() == 'metrices'): self.send_header('Content-Type', 'text/html') self.end_headers() ModelString = production_data['Model'] ModelPerformance = ModelString+'_performance.json' performance_file = self.targetPath/ModelPerformance if performance_file.exists(): performance_data = read_json(performance_file) else: raise ValueError(f'Production Details not found: {performance_data}') Scoring_Creteria = performance_data['scoring_criteria'] train_score = round(performance_data['metrices']['train_score'],2) test_score = round(performance_data['metrices']['test_score'],2) current_score = 'NA' monitoring = read_json(self.targetPath/IOFiles['monitoring']) reader = dataReader(reader_type=monitoring['prod_db_type'],target_path=self.targetPath, config=monitoring['db_config']) inputDatafile = self.targetPath/IOFiles['inputData'] NoOfPrediction = 0 NoOfGroundTruth = 0 inputdistribution = '' if reader.file_exists(IOFiles['prodData']): dfPredict = reader.read(IOFiles['prodData']) dfinput = pd.read_csv(inputDatafile) features = meta_data['training']['features'] inputdistribution = getDriftDistribution(features,dfinput,dfPredict) NoOfPrediction = len(dfPredict) if reader.file_exists(IOFiles['prodDataGT']): dfGroundTruth = reader.read(IOFiles['prodDataGT']) NoOfGroundTruth = len(dfGroundTruth) common_col = [k for k in dfPredict.columns.tolist() if k in dfGroundTruth.columns.tolist()] proddataDF = pd.merge(dfPredict, dfGroundTruth, on =common_col,how = 'inner') if Scoring_Creteria.lower() == 'accuracy': from sklearn.metrics import accuracy_score current_score = accuracy_score(proddataDF[config_input['target_feature']], proddataDF['prediction']) current_score = round((current_score*100),2) elif Scoring_Creteria.lower() == 'recall': from sklearn.metrics import accuracy_score current_score = recall_score(proddataDF[config_input['target_feature']], proddataDF['prediction'],average='macro') current_score = round((current_score*100),2) msg = \"""<html> <head> <title>Performance Details</title> </head> <style> table, th, td {border} </style> <body> <h2><b>Deployed Model:</b>{ModelString}</h2> <br/> <table style="width:50%"> <tr> <td>No of Prediction</td> <td>{NoOfPrediction}</td> </tr> <tr> <td>No of GroundTruth</td> <td>{NoOfGroundTruth}</td> </tr> </table> <br/> <table style="width:100%"> <tr> <th>Score Type</th> <th>Train Score</th> <th>Test Score</th> <th>Production Score</th> </tr> <tr> <td>{Scoring_Creteria}</td> <td>{train_score}</td> <td>{test_score}</td> <td>{current_score}</td> </tr> </table> <br/> <br/> <img src="data:image/png;base64,{newDataDrift}" alt="" > </body> </html> \""".format(border='{border: 1px solid black;}',ModelString=ModelString,Scoring_Creteria=Scoring_Creteria,NoOfPrediction=NoOfPrediction,NoOfGroundTruth=NoOfGroundTruth,train_score=train_score,test_score=test_score,current_score=current_score,newDataDrift=inputdistribution) elif (usecase.lower() == config_input['targetPath'].lower()) and (operation.lower() == 'logs'): self.send_header('Content-Type', 'text/plain') self.end_headers() log_file = self.targetPath/IOFiles['log'] if log_file.exists(): with open(log_file) as f: msg = f.read() f.close() else: raise ValueError(f'Log Details not found: {log_file}') else: self.send_header('Content-Type', 'application/json') self.end_headers() features = meta_data['load_data']['selected_features'] bodydes='[' for x in features: if bodydes != '[': bodydes = bodydes+',' bodydes = bodydes+'{"'+x+'":"value"}' bodydes+=']' urltext = '/AION/'+config_input['targetPath']+'/predict' urltextgth='/AION/'+config_input['targetPath']+'/groundtruth' urltextproduction='/AION/'+config_input['targetPath']+'/metrices' msg=\""" Version:{modelversion} RunNo: {runNo} URL for Prediction ================== URL:{url} RequestType: POST Content-Type=application/json Body: {displaymsg} Output: prediction,probability(if Applicable),remarks corresponding to each row. URL for GroundTruth =================== URL:{urltextgth} RequestType: POST Content-Type=application/json Note: Make Sure that one feature (ID) should be unique in both predict and groundtruth. Otherwise outputdrift will not work URL for Model In Production Analysis ==================================== URL:{urltextproduction} RequestType: GET Content-Type=application/json \""".format(modelversion=config_input['modelVersion'],runNo=config_input['deployedRunNo'],url=urltext,urltextgth=urltextgth,urltextproduction=urltextproduction,displaymsg=bodydes) self.wfile.write(msg.encode()) else: self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() return class ThreadedHTTPServer(ThreadingMixIn, HTTPServer): allow_reuse_address = True def shutdown(self): self.socket.close() HTTPServer.shutdown(self) class file_status(): def __init__(self, reload_function, params, file, logger): self.files_status = {} self.initializeFileStatus(file) self.reload_function = reload_function self.params = params self.logger = logger def initializeFileStatus(self, file): self.files_status = {'path': file, 'time':file.stat().st_mtime} def is_file_changed(self): if self.files_status['path'].stat().st_mtime > self.files_status['time']: self.files_status['time'] = self.files_status['path'].stat().st_mtime return True return False def run(self): global config_input while( True): time.sleep(30) if self.is_file_changed(): production_details = targetPath/IOFiles['production'] if not production_details.exists(): raise ValueError(f'Model in production details does not exist') productionmodel = read_json(production_details) config_file = Path(__file__).parent/'config.json' if not Path(config_file).exists(): raise ValueError(f'Config file is missing: {config_file}') config_input = read_json(config_file) config_input['deployedModel'] = productionmodel['Model'] config_input['deployedRunNo'] = productionmodel['runNo'] self.logger.info('Model changed Reloading.....') self.logger.info(f'Model: {config_input["deployedModel"]}') self.logger.info(f'Version: {str(config_input["modelVersion"])}') self.logger.info(f'runNo: {str(config_input["deployedRunNo"])}') self.reload_function(config_input) class SimpleHttpServer(): def __init__(self, ip, port, model_file_path,reload_function,params, logger): self.server = ThreadedHTTPServer((ip,port), HTTPRequestHandler) self.status_checker = file_status( reload_function, params, model_file_path, logger) def start(self): self.server_thread = threading.Thread(target=self.server.serve_forever) self.server_thread.daemon = True self.server_thread.start() self.status_thread = threading.Thread(target=self.status_checker.run) self.status_thread.start() def waitForThread(self): self.server_thread.join() self.status_thread.join() def stop(self): self.server.shutdown() self.waitForThread() if __name__=='__main__': parser = argparse.ArgumentParser(description='HTTP Server') parser.add_argument('-ip','--ipAddress', help='HTTP Server IP') parser.add_argument('-pn','--portNo', type=int, help='Listening port for HTTP Server') args = parser.parse_args() config_file = Path(__file__).parent/'config.json' if not Path(config_file).exists(): raise ValueError(f'Config file is missing: {config_file}') config = read_json(config_file) if args.ipAddress: config['ipAddress'] = args.ipAddress if args.portNo: config['portNo'] = args.portNo targetPath = Path('aion')/config['targetPath'] if not targetPath.exists(): raise ValueError(f'targetPath does not exist') production_details = targetPath/IOFiles['production'] if not production_details.exists(): raise ValueError(f'Model in production details does not exist') productionmodel = read_json(production_details) config['deployedModel'] = productionmodel['Model'] config['deployedRunNo'] = productionmodel['runNo'] #server = SimpleHttpServer(config['ipAddress'],int(config['portNo'])) config_input = config logging.basicConfig(filename= Path(targetPath)/IOFiles['log'], filemode='a', format='%(asctime)s %(name)s- %(message)s', level=logging.INFO, datefmt='%d-%b-%y %H:%M:%S') logger = logging.getLogger(Path(__file__).parent.name) deployobj = deploy(config_input, logger) server = SimpleHttpServer(config['ipAddress'],int(config['portNo']),targetPath/IOFiles['production'],deployobj.initialize,config_input, logger) logger.info('HTTP Server Running...........') logger.info(f"IP Address: {config['ipAddress']}") logger.info(f"Port No.: {config['portNo']}") print('HTTP Server Running...........') print('For Prediction') print('================') print('Request Type: Post') print('Content-Type: application/json') print('URL: /AION/'+config['targetPath']+'/predict') print('\\nFor GroundTruth') print('================') print('Request Type: Post') print('Content-Type: application/json') print('URL: /AION/'+config['targetPath']+'/groundtruth') print('\\nFor Help') print('================') print('Request Type: Get') print('Content-Type: application/json') print('URL: /AION/'+config['targetPath']+'/help') print('\\nFor Model In Production Analysis') print('================') print('Request Type: Get') print('Content-Type: application/json') print('URL: /AION/'+config['targetPath']+'/metrices') server.start() server.waitForThread() """ """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class learner(): def __init__(self, problem_type="classification", target_feature="", sample_method=None,indent=0, tab_size=4): self.tab = " "*tab_size self.df_name = 'df' self.problem_type = problem_type self.target_feature = target_feature self.search_space = [] self.codeText = f"\ndef train(log):" self.input_files = {} self.output_files = {} self.function_code = '' self.addInputFiles({'inputData' : 'featureEngineeredData.dat','testData' : 'test.dat', 'metaData' : 'modelMetaData.json','monitor':'monitoring.json','log' : 'aion.log'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def __addValidateConfigCode(self): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = read_json(config_file)\ \n return config" return text def __addSaveModelCode(self): text = "\n\ \ndef save_model( experiment_id, estimator, features, metrices, params,tags, scoring):\ \n # mlflow log model, metrices and parameters\ \n with mlflow.start_run(experiment_id = experiment_id, run_name = model_name):\ \n return logMlflow(params, metrices, estimator, tags, model_name.split('_')[0])" return text def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def getCode(self): return self.function_code + '\n' + self.codeText def addLocalFunctionsCode(self): self.function_code += self.__addValidateConfigCode() self.function_code += self.__addSaveModelCode() def getPrefixModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'pandas', 'mod_as':'pd'} ] return modules def addPrefixCode(self, indent=1): self.codeText += "\ \n config = validateConfig()\ \n targetPath = Path('aion')/config['targetPath']\ \n if not targetPath.exists():\ \n raise ValueError(f'targetPath does not exist')\ \n meta_data_file = targetPath/IOFiles['metaData']\ \n if meta_data_file.exists():\ \n meta_data = read_json(meta_data_file)\ \n else:\ \n raise ValueError(f'Configuration file not found: {meta_data_file}')\ \n log_file = targetPath/IOFiles['log']\ \n log = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem)\ \n dataLoc = targetPath/IOFiles['inputData']\ \n if not dataLoc.exists():\ \n return {'Status':'Failure','Message':'Data location does not exists.'}\ \n\ \n status = dict()\ \n usecase = config['targetPath']\ \n df = pd.read_csv(dataLoc)\ \n prev_step_output = meta_data['featureengineering']['Status']" def getSuffixModules(self): modules = [{'module':'platform'} ,{'module':'time'} ,{'module':'mlflow'} ] return modules def add_100_trainsize_code(self): self.codeText +="\n\ \n else:\ \n test_score = train_score\ \n metrices = {}" def addSuffixCode(self, indent=1): self.codeText += "\n\ \n meta_data['training'] = {}\ \n meta_data['training']['features'] = features\ \n scoring = config['scoring_criteria']\ \n tags = {'estimator_name': model_name}\ \n monitoring_data = read_json(targetPath/IOFiles['monitor'])\ \n mlflow_default_config = {'artifacts_uri':'','tracking_uri_type':'','tracking_uri':'','registry_uri':''}\ \n mlflow_client, experiment_id = mlflow_create_experiment(monitoring_data.get('mlflow_config',mlflow_default_config), targetPath, usecase)\ \n run_id = save_model(experiment_id, estimator,features, metrices,best_params,tags,scoring)\ \n write_json(meta_data, targetPath/IOFiles['metaDataOutput'])\ \n write_json({'scoring_criteria': scoring, 'metrices':metrices, 'param':best_params}, targetPath/IOFiles['performance'])\ \n\ \n # return status\ \n status = {'Status':'Success','mlflow_run_id':run_id,'FeaturesUsed':features,'test_score':metrices['test_score'],'train_score':metrices['train_score']}\ \n log.info(f'Test score: {test_score}')\ \n log.info(f'Train score: {train_score}')\ \n log.info(f'MLflow run id: {run_id}')\ \n log.info(f'output: {status}')\ \n return json.dumps(status)" def getMainCodeModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'sys'} ,{'module':'json'} ,{'module':'logging'} ] return modules def addMainCode(self, indent=1): self.codeText += "\n\ \nif __name__ == '__main__':\ \n log = None\ \n try:\ \n print(train(log))\ \n except Exception as e:\ \n if log:\ \n log.error(e, exc_info=True)\ \n status = {'Status':'Failure','Message':str(e)}\ \n print(json.dumps(status))\ " def add_variable(self, name, value, indent=1): if isinstance(value, str): self.codeText += f"\n{self.tab * indent}{name} = '{value}'" else: self.codeText += f"\n{self.tab * indent}{name} = {value}" def addStatement(self, statement, indent=1): self.codeText += f"\n{self.tab * indent}{statement}" def add_search_space_w(self, algoritms): for model, params in algoritms.items(): d = {'clf': f"[{model}()]"} for k,v in params.items(): if isinstance(v, str): d[f'clf__{k}']=f"'{v}'" else: d[f'clf__{k}']= f"{v}" self.search_space.append(d) def add_search_space(self, indent=1): self.codeText += f"\n{self.tab}search_space = config['search_space']" def add_train_test_split(self, train_feature, target_feature,test_ratio, indent=1): self.codeText += "\n\n # split the data for training\ \n selected_features = prev_step_output['selected_features']\ \n target_feature = config['target_feature']\ \n train_features = prev_step_output['total_features'].copy()\ \n train_features.remove(target_feature)\ \n X_train = df[train_features]\ \n y_train = df[target_feature]\ \n if config['test_ratio'] > 0.0:\ \n test_data = read_data(targetPath/IOFiles['testData'])\ \n X_test = test_data[train_features]\ \n y_test = test_data[target_feature]\ \n else:\ \n X_test = pd.DataFrame()\ \n y_test = pd.DataFrame()" def add_model_fit(self, estimator, optimizer, selector_method, importer, indent=1): # need to adjust the indent importer.addModule('importlib') importer.addModule('operator') text = f"\n features = selected_features['{selector_method}']\ \n estimator = {estimator}()\ \n param = config['algorithms']['{estimator}']" if optimizer == 'GridSearchCV': text += "\n grid = GridSearchCV(estimator, param,cv=config['optimization_param']['trainTestCVSplit'])\ \n grid.fit(X_train[features], y_train)\ \n train_score = grid.best_score_ * 100\ \n best_params = grid.best_params_\ \n estimator = grid.best_estimator_" elif optimizer == 'GeneticSelectionCV': text += "\n grid = GeneticSelectionCV(estimator, scoring=scorer, n_generations=config['optimization_param']['iterations'],cv=config['optimization_param']['trainTestCVSplit'],n_population=config['optimization_param']['geneticparams']['n_population'],crossover_proba=config['optimization_param']['geneticparams']['crossover_proba'],mutation_proba=config['optimization_param']['geneticparams']['mutation_proba'],crossover_independent_proba=config['optimization_param']['geneticparams']['crossover_independent_proba'],mutation_independent_proba=config['optimization_param']['geneticparams']['mutation_independent_proba'],tournament_size=config['optimization_param']['geneticparams']['tournament_size'],n_gen_no_change=config['optimization_param']['geneticparams']['n_gen_no_change'])\ \n grid.fit(X_train[features], y_train)\ \n train_score = grid.score(X_train[features], y_train)\ \n best_params = grid.estimator_.get_params()\ \n estimator = grid.estimator_" else: text += f"\n grid = {optimizer}(estimator, param, scoring=scorer, n_iter=config['optimization_param']['iterations'],cv=config['optimization_param']['trainTestCVSplit'])\ \n grid.fit(X_train[features], y_train)\ \n train_score = grid.best_score_ * 100\ \n best_params = grid.best_params_\ \n estimator = grid.best_estimator_" self.codeText += text def addLearner(self, model_name, params, importer, indent=1): importer.addModule('Pipeline', mod_from='sklearn.pipeline') importer.addModule('ColumnTransformer', mod_from='sklearn.compose') importer.addModule('confusion_matrix', mod_from='sklearn.metrics') model_params = [] for k,v in params.items(): if isinstance(v, str): model_params.append(f"{k}='{v}'") else: model_params.append(f"{k}={v}") model_params = ",".join(model_params) self.codeText += self.getTransformer() text = f"\n{self.tab * indent}pipeline = Pipeline(steps = [('preprocessor', preprocessor),('learner',{model_name}({model_params}))])" self.codeText += text self.codeText += self.splitTargetFeature(importer) if self.balancing: self.codeText += self.balancingCode(importer) self.codeText += self.fitModelCode(importer) def splitTargetFeature(self, importer, indent=1): importer.addModule('train_test_split', mod_from='sklearn.model_selection') return f"\n{self.tab * indent}target = df['{self.target_feature}']\ \n{self.tab * indent}df = df.drop(['{self.target_feature}'], axis=1)\ \n{self.tab * indent}X_train, X_test, y_train, y_test = train_test_split(df,target, train_size = percentage/100.0)" def getCode_remove(self, model_name=None, indent=1): return self.codeText def getDFName(self): return self.df_name def copyCode(self, learner): self.codeText = learner.getCode() """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class selector(): def __init__(self, indent=0, tab_size=4): self.tab = " "*tab_size self.codeText = f"\n\ndef featureSelector(log):" self.pipe = 'pipe' self.code_generated = False self.input_files = {} self.output_files = {} self.function_code = '' self.addInputFiles({'inputData' : 'transformedData.dat', 'metaData' : 'modelMetaData.json','log' : 'aion.log','outputData' : 'featureEngineeredData.dat'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def __addValidateConfigCode(self): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = read_json(config_file)\ \n return config" return text def addMainCode(self): self.codeText += "\n\ \nif __name__ == '__main__':\ \n log = None\ \n try:\ \n print(featureSelector(log))\ \n except Exception as e:\ \n if log:\ \n log.error(e, exc_info=True)\ \n status = {'Status':'Failure','Message':str(e)}\ \n print(json.dumps(status))\ " def addValidateConfigCode(self, indent=1): self.function_code += self.__addValidateConfigCode() def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def getCode(self): return self.function_code + '\n' + self.codeText def addLocalFunctionsCode(self): self.addValidateConfigCode() def getPrefixModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'pandas', 'mod_as':'pd'} ] return modules def addPrefixCode(self, indent=1): self.codeText += "\ \n config = validateConfig()\ \n targetPath = Path('aion')/config['targetPath']\ \n if not targetPath.exists():\ \n raise ValueError(f'targetPath does not exist')\ \n meta_data_file = targetPath/IOFiles['metaData']\ \n if meta_data_file.exists():\ \n meta_data = read_json(meta_data_file)\ \n else:\ \n raise ValueError(f'Configuration file not found: {meta_data_file}')\ \n log_file = targetPath/IOFiles['log']\ \n log = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem)\ \n dataLoc = targetPath/IOFiles['inputData']\ \n if not dataLoc.exists():\ \n return {'Status':'Failure','Message':'Data location does not exists.'}\ \n\ \n status = dict()\ \n df = pd.read_csv(dataLoc)\ \n prev_step_output = meta_data['transformation']" def getSuffixModules(self): modules = [{'module':'platform'} ,{'module':'time'} ] return modules def addSuffixCode(self, indent=1): self.codeText += "\n\ \n csv_path = str(targetPath/IOFiles['outputData'])\ \n write_data(df, csv_path,index=False)\ \n status = {'Status':'Success','DataFilePath':IOFiles['outputData'],'total_features':total_features, 'selected_features':selected_features}\ \n log.info(f'Selected data saved at {csv_path}')\ \n meta_data['featureengineering']['Status'] = status\ \n write_json(meta_data, str(targetPath/IOFiles['metaData']))\ \n log.info(f'output: {status}')\ \n return json.dumps(status)" def getMainCodeModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'sys'} ,{'module':'json'} ,{'module':'logging'} ,{'module':'argparse'} ] return modules def add_variable(self, name, value, indent=1): if isinstance(value, str): self.codeText += f"\n{self.tab * indent}{name} = '{value}'" else: self.codeText += f"\n{self.tab * indent}{name} = {value}" def addStatement(self, statement, indent=1): self.codeText += f"\n{self.tab * indent}{statement}" def modelBased(self, problem_type, indent=1): if problem_type == 'classification': self.codeText += f"\n{self.tab * indent}selector = SelectFromModel(ExtraTreesClassifier())" self.codeText += f"\n{self.tab * indent}selector()" if problem_type == 'regression': self.codeText += f"\n{self.tab * indent}pipe = Pipeline([('selector', SelectFromModel(Lasso()))])" self.codeText += f"\n{self.tab * indent}selector.fit(df[train_features],df[target_feature])" self.codeText += f"\n{self.tab * indent}selected_features = [x for x,y in zip(train_features, selector.get_support()) if y]" self.codeText += f"\n{self.tab * indent}df = df[selected_features + [target_feature]]" def featureReductionBased(self, reducer, n_components, indent=1): if reducer == 'pca': if n_components == 0: self.codeText += f"\n{self.tab * indent}pipe = Pipeline([('selector', PCA(n_components='mle',svd_solver = 'full'))])" elif n_components < 1: self.codeText += f"\n{self.tab * indent}pipe = Pipeline([('selector', PCA(n_components={n_components},svd_solver = 'full'))])" else: self.codeText += f"\n{self.tab * indent}pipe = Pipeline([('selector', PCA(n_components=int({n_components})))])" self.codeText += "pipe.fit_transform(df)" def getPipe(self): return self.pipe """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from .imports import importModule utility_functions = { 'load_data': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'transformer': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'selector': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'train': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'register': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'Prediction': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'drift': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], } #TODO convert read and write functions in to class functions functions_code = { 'read_json':{'imports':[{'mod':'json'}],'code':"\n\ \ndef read_json(file_path):\ \n data = None\ \n with open(file_path,'r') as f:\ \n data = json.load(f)\ \n return data\ \n"}, 'write_json':{'imports':[{'mod':'json'}],'code':"\n\ \ndef write_json(data, file_path):\ \n with open(file_path,'w') as f:\ \n json.dump(data, f)\ \n"}, 'read_data':{'imports':[{'mod':'pandas','mod_as':'pd'}],'code':"\n\ \ndef read_data(file_path, encoding='utf-8', sep=','):\ \n return pd.read_csv(file_path, encoding=encoding, sep=sep)\ \n"}, 'write_data':{'imports':[{'mod':'pandas','mod_as':'pd'}],'code':"\n\ \ndef write_data(data, file_path, index=False):\ \n return data.to_csv(file_path, index=index)\ \n\ \n#Uncomment and change below code for google storage\ \n#from google.cloud import storage\ \n#def write_data(data, file_path, index=False):\ \n# file_name= file_path.name\ \n# data.to_csv('output_data.csv')\ \n# storage_client = storage.Client()\ \n# bucket = storage_client.bucket('aion_data')\ \n# bucket.blob('prediction/'+file_name).upload_from_filename('output_data.csv', content_type='text/csv')\ \n# return data\ \n"}, 'is_file_name_url':{'imports':[],'code':"\n\ \ndef is_file_name_url(file_name):\ \n supported_urls_starts_with = ('gs://','https://','http://')\ \n return file_name.startswith(supported_urls_starts_with)\ \n"}, 'logger_class':{'imports':[{'mod':'logging'}, {'mod':'io'}],'code':"\n\ \nclass logger():\ \n #setup the logger\ \n def __init__(self, log_file, mode='w', logger_name=None):\ \n logging.basicConfig(filename=log_file, filemode=mode, format='%(asctime)s %(name)s- %(message)s', level=logging.INFO, datefmt='%d-%b-%y %H:%M:%S')\ \n self.log = logging.getLogger(logger_name)\ \n\ \n #get logger\ \n def getLogger(self):\ \n return self.log\ \n\ \n def info(self, msg):\ \n self.log.info(msg)\ \n\ \n def error(self, msg, exc_info=False):\ \n self.log.error(msg,exc_info)\ \n\ \n # format and log dataframe\ \n def log_dataframe(self, df, rows=2, msg=None):\ \n buffer = io.StringIO()\ \n df.info(buf=buffer)\ \n log_text = 'Data frame{}'.format(' after ' + msg + ':' if msg else ':')\ \n log_text += '\\n\\t'+str(df.head(rows)).replace('\\n','\\n\\t')\ \n log_text += ('\\n\\t' + buffer.getvalue().replace('\\n','\\n\\t'))\ \n self.log.info(log_text)\ \n"}, } class utility_function(): def __init__(self, module): if module in utility_functions.keys(): self.module_name = module else: self.module_name = None self.importer = importModule() self.codeText = "" def get_code(self): code = "" if self.module_name: functions = utility_functions[self.module_name] for function in functions: self.codeText += self.get_function_code(function) code = self.importer.getCode() code += self.codeText return code def get_function_code(self, name): code = "" if name in functions_code.keys(): code += functions_code[name]['code'] if self.importer: if 'imports' in functions_code[name].keys(): for module in functions_code[name]['imports']: mod_name = module['mod'] mod_from = module.get('mod_from', None) mod_as = module.get('mod_as', None) self.importer.addModule(mod_name, mod_from=mod_from, mod_as=mod_as) return code def get_importer(self): return self.importer if __name__ == '__main__': obj = utility_function('load_data') p = obj.get_utility_code() print(p) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class drift(): def __init__(self, tab_size=4): self.tab = ' ' * tab_size self.codeText = '' def getInputFiles(self): IOFiles = { "log": "aion.log", "trainingData":"rawData.dat", "production": "production.json", "monitoring":"monitoring.json", "prodData": "prodData", "prodDataGT":"prodDataGT" } text = 'IOFiles = ' if not IOFiles: text += '{ }' else: text += json.dumps(IOFiles, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def getCode(self): return self.codeText # temporary code def get_input_drift_import_modules(self): return [ {'module': 'sys', 'mod_from': None, 'mod_as': None}, {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'mlflow', 'mod_from': None, 'mod_as': None}, {'module': 'stats', 'mod_from': 'scipy', 'mod_as': 'st'}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'warnings', 'mod_from': None, 'mod_as': None}, {'module': 'platform', 'mod_from': None, 'mod_as': None } ] def get_input_drift_code(self): return """ class inputdrift(): def __init__(self,base_config): if 'mlflowURL' in base_config: self.usecase = base_config['modelName'] + '_' + base_config['modelVersion'] self.currentDataLocation = base_config['currentDataLocation'] home = Path.home() if platform.system() == 'Windows': from pathlib import WindowsPath output_data_dir = WindowsPath(home)/'AppData'/'Local'/'HCLT'/'AION'/'Data' output_model_dir = WindowsPath(home)/'AppData'/'Local'/'HCLT'/'AION'/'target'/self.usecase else: from pathlib import PosixPath output_data_dir = PosixPath(home)/'HCLT'/'AION'/'Data' output_model_dir = PosixPath(home)/'HCLT'/'AION'/'target'/self.usecase if not output_model_dir.exists(): raise ValueError(f'Configuration file not found at {output_model_dir}') tracking_uri = 'file:///' + str(Path(output_model_dir)/'mlruns') registry_uri = 'sqlite:///' + str(Path(output_model_dir)/'mlruns.db') mlflow.set_tracking_uri(tracking_uri) mlflow.set_registry_uri(registry_uri) client = mlflow.tracking.MlflowClient( tracking_uri=tracking_uri, registry_uri=registry_uri, ) model_version_uri = 'models:/{model_name}/production'.format(model_name=self.usecase) model = mlflow.pyfunc.load_model(model_version_uri) run = client.get_run(model.metadata.run_id) if run.info.artifact_uri.startswith('file:'): artifact_path = Path(run.info.artifact_uri[len('file:///') : ]) else: artifact_path = Path(run.info.artifact_uri) self.trainingDataPath = artifact_path/(self.usecase + '_data.csv') def get_input_drift(self,current_data, historical_data): curr_num_feat = current_data.select_dtypes(include='number') hist_num_feat = historical_data.select_dtypes(include='number') num_features = [feat for feat in historical_data.columns if feat in curr_num_feat] alert_count = 0 data = { 'current':{'data':current_data}, 'hist': {'data': historical_data} } dist_changed_columns = [] dist_change_message = [] for feature in num_features: curr_static_value = round(st.ks_2samp( hist_num_feat[feature], curr_num_feat[feature]).pvalue,3) if (curr_static_value < 0.05): try: distribution = {} distribution['hist'] = self.DistributionFinder( historical_data[feature]) distribution['curr'] = self.DistributionFinder( current_data[feature]) if(distribution['hist']['name'] == distribution['curr']['name']): pass else: alert_count = alert_count + 1 dist_changed_columns.append(feature) changed_column = {} changed_column['Feature'] = feature changed_column['KS_Training'] = curr_static_value changed_column['Training_Distribution'] = distribution['hist']['name'] changed_column['New_Distribution'] = distribution['curr']['name'] dist_change_message.append(changed_column) except: pass if alert_count: resultStatus = dist_change_message else : resultStatus='Model is working as expected' return(alert_count, resultStatus) def DistributionFinder(self,data): best_distribution ='' best_sse =0.0 if(data.dtype in ['int','int64']): distributions= {'bernoulli':{'algo':st.bernoulli}, 'binom':{'algo':st.binom}, 'geom':{'algo':st.geom}, 'nbinom':{'algo':st.nbinom}, 'poisson':{'algo':st.poisson} } index, counts = np.unique(data.astype(int),return_counts=True) if(len(index)>=2): best_sse = np.inf y1=[] total=sum(counts) mean=float(sum(index*counts))/total variance=float((sum(index**2*counts) -total*mean**2))/(total-1) dispersion=mean/float(variance) theta=1/float(dispersion) r=mean*(float(theta)/1-theta) for j in counts: y1.append(float(j)/total) distributions['bernoulli']['pmf'] = distributions['bernoulli']['algo'].pmf(index,mean) distributions['binom']['pmf'] = distributions['binom']['algo'].pmf(index,len(index),p=mean/len(index)) distributions['geom']['pmf'] = distributions['geom']['algo'].pmf(index,1/float(1+mean)) distributions['nbinom']['pmf'] = distributions['nbinom']['algo'].pmf(index,mean,r) distributions['poisson']['pmf'] = distributions['poisson']['algo'].pmf(index,mean) sselist = [] for dist in distributions.keys(): distributions[dist]['sess'] = np.sum(np.power(y1 - distributions[dist]['pmf'], 2.0)) if np.isnan(distributions[dist]['sess']): distributions[dist]['sess'] = float('inf') best_dist = min(distributions, key=lambda v: distributions[v]['sess']) best_distribution = best_dist best_sse = distributions[best_dist]['sess'] elif (len(index) == 1): best_distribution = 'Constant Data-No Distribution' best_sse = 0.0 elif(data.dtype in ['float64','float32']): distributions = [st.uniform,st.expon,st.weibull_max,st.weibull_min,st.chi,st.norm,st.lognorm,st.t,st.gamma,st.beta] best_distribution = st.norm.name best_sse = np.inf nrange = data.max() - data.min() y, x = np.histogram(data.astype(float), bins='auto', density=True) x = (x + np.roll(x, -1))[:-1] / 2.0 for distribution in distributions: with warnings.catch_warnings(): warnings.filterwarnings('ignore') params = distribution.fit(data.astype(float)) arg = params[:-2] loc = params[-2] scale = params[-1] pdf = distribution.pdf(x, loc=loc, scale=scale, *arg) sse = np.sum(np.power(y - pdf, 2.0)) if( sse < best_sse): best_distribution = distribution.name best_sse = sse return {'name':best_distribution, 'sse': best_sse} def check_drift( config): inputdriftObj = inputdrift(config) historicaldataFrame=pd.read_csv(inputdriftObj.trainingDataPath,skipinitialspace = True,na_values=['-','?']) currentdataFrame=pd.read_csv(inputdriftObj.currentDataLocation,skipinitialspace = True,na_values=['-','?']) historicaldataFrame.columns = historicaldataFrame.columns.str.strip() currentdataFrame.columns = currentdataFrame.columns.str.strip() dataalertcount,message = inputdriftObj.get_input_drift(currentdataFrame,historicaldataFrame) if message == 'Model is working as expected': output_json = {'status':'SUCCESS','data':{'Message':'Model is working as expected'}} else: output_json = {'status':'SUCCESS','data':{'Affected Columns':message}} return(output_json) """ def get_main_drift_code(self, problem_type, smaller_is_better=True): text = '' if problem_type == 'classification': text += """ def is_drift_within_limits(production, current_matrices,scoring_criteria,threshold = 5): testscore = production['score'] current_score = current_matrices[scoring_criteria] threshold_value = testscore * threshold / 100.0 if current_score > (testscore - threshold_value) : return True else: return False def get_metrices(actual_values, predicted_values): from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score result = {} accuracy_score = accuracy_score(actual_values, predicted_values) avg_precision = precision_score(actual_values, predicted_values, average='macro') avg_recall = recall_score(actual_values, predicted_values, average='macro') avg_f1 = f1_score(actual_values, predicted_values, average='macro') result['accuracy'] = round((accuracy_score*100),2) result['precision'] = round((avg_precision*100),2) result['recall'] = round((avg_recall*100),2) result['f1'] = round((avg_f1*100),2) return result """ else: text += """ def is_drift_within_limits(production, current_matrices,scoring_criteria,threshold = 5): testscore = production['score'] current_score = current_matrices[scoring_criteria] threshold_value = testscore * threshold / 100.0 """ if smaller_is_better: text += """ if current_score < (testscore + threshold_value) :""" else: text += """ if current_score > (testscore - threshold_value) :""" text += """ return True else: return False def get_metrices(actual_values, predicted_values): import numpy as np result = {} me = np.mean(predicted_values - actual_values) sde = np.std(predicted_values - actual_values, ddof = 1) abs_err = np.abs(predicted_values - actual_values) mae = np.mean(abs_err) sdae = np.std(abs_err, ddof = 1) abs_perc_err = 100.0 * np.abs(predicted_values - actual_values) / actual_values mape = np.mean(abs_perc_err) sdape = np.std(abs_perc_err, ddof = 1) result['mean_error'] = me result['mean_abs_error'] = mae result['mean_abs_perc_error'] = mape result['error_std'] = sde result['abs_error_std'] = sdae result['abs_perc_error_std'] = sdape return result """ text += """ def monitoring(config, log=None): targetPath = Path('aion')/config['targetPath'] targetPath.mkdir(parents=True, exist_ok=True) log_file = targetPath/IOFiles['log'] log = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem) output_json = {} trainingDataLocation = targetPath/IOFiles['trainingData'] monitoring = targetPath/IOFiles['monitoring'] log.info(f'Input Location External: {config["inputUriExternal"]}') trainingStatus = 'False' dataFileLocation = '' driftStatus = 'No Drift' if monitoring.exists(): monitoring_data = read_json(monitoring) if monitoring_data.get('runNo', False): reader = dataReader(reader_type=monitoring_data.get('prod_db_type','sqlite'),target_path=targetPath, config=config.get('db_config',None)) production= targetPath/IOFiles['production'] proddataDF = pd.DataFrame() predicted_data = pd.DataFrame() if production.exists(): production = read_json(production) if reader.file_exists(IOFiles['prodData']) and reader.file_exists(IOFiles['prodDataGT']): predicted_data = reader.read(IOFiles['prodData']) actual_data = reader.read(IOFiles['prodDataGT']) common_col = [k for k in predicted_data.columns.tolist() if k in actual_data.columns.tolist()] proddataDF = pd.merge(actual_data, predicted_data, on =common_col,how = 'inner') currentPerformance = {} currentPerformance = get_metrices(proddataDF[config['target_feature']], proddataDF['prediction']) if is_drift_within_limits(production, currentPerformance,config['scoring_criteria']): log.info(f'OutputDrift: No output drift found') output_json.update({'outputDrift':'Model score is with in limits'}) else: log.info(f'OutputDrift: Found Output Drift') log.info(f'Original Test Score: {production["score"]}') log.info(f'Current Score: {currentPerformance[config["scoring_criteria"]]}') output_json.update({'outputDrift':{'Meassage': 'Model output is drifted','trainedScore':production["score"], 'currentScore':currentPerformance[config["scoring_criteria"]]}}) trainingStatus = 'True' driftStatus = 'Output Drift' else: if reader.file_exists(IOFiles['prodData']): predicted_data = reader.read(IOFiles['prodData']) log.info(f'OutputDrift: Prod Data not found') output_json.update({'outputDrift':'Prod Data not found'}) else: log.info(f'Last Time pipeline not executed completely') output_json.update({'Msg':'Pipeline is not executed completely'}) trainingStatus = 'True' if config['inputUriExternal']: dataFileLocation = config['inputUriExternal'] elif 's3' in config.keys(): dataFileLocation = 'cloud' else: dataFileLocation = config['inputUri'] if trainingStatus == 'False': historicaldataFrame=pd.read_csv(trainingDataLocation) if config['inputUriExternal']: currentdataFrame=pd.read_csv(config['inputUriExternal']) elif not predicted_data.empty: currentdataFrame = predicted_data.copy() elif 's3' in config.keys(): reader = dataReader(reader_type='s3',target_path=config['targetPath'], config=config['s3']) currentdataFrame = reader.read(config['s3']['file_name']) else: currentdataFrame=pd.read_csv(config['inputUri']) inputdriftObj = inputdrift(config) dataalertcount,inputdrift_message = inputdriftObj.get_input_drift(currentdataFrame,historicaldataFrame) if inputdrift_message == 'Model is working as expected': log.info(f'InputDrift: No input drift found') output_json.update({'Status':'SUCCESS','inputDrift':'Model is working as expected'}) else: log.info(f'InputDrift: Input drift found') log.info(f'Affected Columns {inputdrift_message}') output_json.update({'inputDrift':{'Affected Columns':inputdrift_message}}) trainingStatus = 'True' driftStatus = 'Input Drift' if config['inputUriExternal']: dataFileLocation = config['inputUriExternal'] elif actual_data_path.exists() and predict_data_path.exists(): dataFileLocation = '' elif 's3' in config.keys(): dataFileLocation = 'cloud' else: dataFileLocation = config['inputUri'] else: log.info(f'Pipeline Executing first Time') output_json.update({'Msg':'Pipeline executing first time'}) trainingStatus = 'True' if config['inputUriExternal']: dataFileLocation = config['inputUriExternal'] elif 's3' in config.keys(): dataFileLocation = 'cloud' else: dataFileLocation = config['inputUri'] else: log.info(f'Pipeline Executing first Time') output_json.update({'Msg':'Pipeline executing first time'}) trainingStatus = 'True' if config['inputUriExternal']: dataFileLocation = config['inputUriExternal'] elif 's3' in config.keys(): dataFileLocation = 'cloud' else: dataFileLocation = config['inputUri'] if monitoring.exists(): monitoring_data['runNo'] = int(monitoring_data.get('runNo', '0')) + 1 else: monitoring_data = {} monitoring_data['runNo'] = 1 monitoring_data['prod_db_type'] = config.get('prod_db_type', 'sqlite') monitoring_data['db_config'] = config.get('db_config', {}) monitoring_data['mlflow_config'] = config.get('mlflow_config', None) if 's3' in config.keys(): monitoring_data['s3'] = config['s3'] monitoring_data['dataLocation'] = dataFileLocation monitoring_data['driftStatus'] = driftStatus write_json(monitoring_data,targetPath/IOFiles['monitoring']) output = {'Status':'SUCCESS'} output.update(output_json) return(json.dumps(output)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-i', '--inputUri', help='Training Data Location') args = parser.parse_args() config_file = Path(__file__).parent/'config.json' if not Path(config_file).exists(): raise ValueError(f'Config file is missing: {config_file}') config = read_json(config_file) config['inputUriExternal'] = None if args.inputUri: if args.inputUri != '': config['inputUriExternal'] = args.inputUri log = None try: print(monitoring(config, log)) except Exception as e: if log: log.error(e, exc_info=True) status = {'Status':'Failure','Message':str(e)} print(json.dumps(status)) raise Exception(str(e)) """ return text """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from .imports import importModule supported_reader = ['sqlite', 'influx','s3'] functions_code = { 'dataReader':{'imports':[{'mod':'json'},{'mod': 'Path', 'mod_from': 'pathlib', 'mod_as': None},{'mod': 'pandas', 'mod_from': None, 'mod_as': 'pd'}],'code':""" class dataReader(): def get_reader(self, reader_type, target_path=None, config=None): if reader_type == 'sqlite': return sqlite_writer(target_path=target_path) elif reader_type == 'influx': return Influx_writer(config=config) elif reader_type == 'gcs': return gcs(config=config) elif reader_type == 'azure': return azure(config=config) elif reader_type == 's3': return s3bucket(config=config) else: raise ValueError(reader_type) """ }, 'sqlite':{'imports':[{'mod':'sqlite3'},{'mod': 'pandas', 'mod_from': None, 'mod_as': 'pd'},{'mod': 'Path', 'mod_from': 'pathlib', 'mod_as': None}],'code':"""\n\ class sqlite_writer(): def __init__(self, target_path): self.target_path = Path(target_path) database_file = self.target_path.stem + '.db' self.db = sqlite_db(self.target_path, database_file) def file_exists(self, file): if file: return self.db.table_exists(file) else: return False def read(self, file): return self.db.read(file) def write(self, data, file): self.db.write(data, file) def close(self): self.db.close() class sqlite_db(): def __init__(self, location, database_file=None): if not isinstance(location, Path): location = Path(location) if database_file: self.database_name = database_file else: self.database_name = location.stem + '.db' db_file = str(location/self.database_name) self.conn = sqlite3.connect(db_file) self.cursor = self.conn.cursor() self.tables = [] def table_exists(self, name): if name in self.tables: return True elif name: query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';" listOfTables = self.cursor.execute(query).fetchall() if len(listOfTables) > 0 : self.tables.append(name) return True return False def read(self, table_name): return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn) def create_table(self,name, columns, dtypes): query = f'CREATE TABLE IF NOT EXISTS {name} (' for column, data_type in zip(columns, dtypes): query += f"'{column}' TEXT," query = query[:-1] query += ');' self.conn.execute(query) return True def write(self,data, table_name): if not self.table_exists(table_name): self.create_table(table_name, data.columns, data.dtypes) tuple_data = list(data.itertuples(index=False, name=None)) insert_query = f'INSERT INTO {table_name} VALUES(' for i in range(len(data.columns)): insert_query += '?,' insert_query = insert_query[:-1] + ')' self.cursor.executemany(insert_query, tuple_data) self.conn.commit() return True def delete(self, name): pass def close(self): self.conn.close() """ }, 'influx':{'imports':[{'mod':'InfluxDBClient','mod_from':'influxdb'},{'mod': 'Path', 'mod_from': 'pathlib', 'mod_as': None},{'mod': 'pandas', 'mod_from': None, 'mod_as': 'pd'}],'code':"""\n\ class Influx_writer(): def __init__(self, config): self.db = influx_db(config) def file_exists(self, file): if file: return self.db.table_exists(file) else: return False def read(self, file): query = "SELECT * FROM {}".format(file) if 'read_time' in self.db_config.keys() and self.db_config['read_time']: query += f" time > now() - {self.db_config['read_time']}" return self.db.read(query) def write(self, data, file): self.db.write(data, file) def close(self): pass class influx_db(): def __init__(self, config): self.host = config['host'] self.port = config['port'] self.user = config.get('user', None) self.password = config.get('password', None) self.token = config.get('token', None) self.database = config['database'] self.measurement = config['measurement'] self.tags = config['tags'] self.client = self.get_client() def table_exists(self, name): query = f"SHOW MEASUREMENTS ON {self.database}" result = self.client(query) for measurement in result['measurements']: if measurement['name'] == name: return True return False def read(self, query)->pd.DataFrame: cursor = self.client.query(query) points = cursor.get_points() my_list=list(points) df=pd.DataFrame(my_list) return df def get_client(self): headers = None if self.token: headers={"Authorization": self.token} client = InfluxDBClient(self.host,self.port,self.user, self.password,headers=headers) databases = client.get_list_database() databases = [x['name'] for x in databases] if self.database not in databases: client.create_database(self.database) return InfluxDBClient(self.host,self.port,self.user,self.password,self.database,headers=headers) def write(self,data, measurement=None): if isinstance(data, pd.DataFrame): sorted_col = data.columns.tolist() sorted_col.sort() data = data[sorted_col] data = data.to_dict(orient='records') if not measurement: measurement = self.measurement for row in data: if 'time' in row.keys(): p = '%Y-%m-%dT%H:%M:%S.%fZ' time_str = datetime.strptime(row['time'], p) del row['time'] else: time_str = None if 'model_ver' in row.keys(): self.tags['model_ver']= row['model_ver'] del row['model_ver'] json_body = [{ 'measurement': measurement, 'time': time_str, 'tags': self.tags, 'fields': row }] self.client.write_points(json_body) def delete(self, name): pass def close(self): self.client.close() """ }, 's3':{'imports':[{'mod':'boto3'},{'mod': 'ClientError', 'mod_from': 'botocore.exceptions'},{'mod': 'Path', 'mod_from': 'pathlib'},{'mod': 'pandas', 'mod_as': 'pd'}],'code':"""\n\ class s3bucket(): def __init__(self, config={}): if 's3' in config.keys(): config = config['s3'] aws_access_key_id = config.get('aws_access_key_id','') aws_secret_access_key = config.get('aws_secret_access_key','') bucket_name = config.get('bucket_name','') if not aws_access_key_id: raise ValueError('aws_access_key_id can not be empty') if not aws_secret_access_key: raise ValueError('aws_secret_access_key can not be empty') self.client = boto3.client('s3', aws_access_key_id=aws_access_key_id, aws_secret_access_key=str(aws_secret_access_key)) self.bucket_name = bucket_name def read(self, file_name): try: response = self.client.get_object(Bucket=self.bucket_name, Key=file_name) return pd.read_csv(response['Body']) except ClientError as ex: if ex.response['Error']['Code'] == 'NoSuchBucket': raise ValueError(f"Bucket '{self.bucket_name}' not found in aws s3 storage") elif ex.response['Error']['Code'] == 'NoSuchKey': raise ValueError(f"File '{file_name}' not found in s3 bucket '{self.bucket_name}'") else: raise """ }, 'azure':{'imports':[{'mod':'DataLakeServiceClient', 'mod_from':'azure.storage.filedatalake'},{'mod':'detect', 'mod_from':'detect_delimiter'},{'mod':'pandavro', 'mod_as':'pdx'},{'mod':'io'},{'mod': 'Path', 'mod_from': 'pathlib'},{'mod': 'pandas', 'mod_as': 'pd'}],'code':"""\n\ def azure(): def __init__(self,config={}): if 'azure' in config.keys(): config = config['azure'] account_name = config.get('account_name','') account_key = config.get('account_key','') container_name = config.get('container_name','') if not account_name: raise ValueError('Account name can not be empty') if not account_key: raise ValueError('Account key can not be empty') if not container_name: raise ValueError('Container name can not be empty') service_client = DataLakeServiceClient(account_url="{}://{}.dfs.core.windows.net".format("https", account_name), credential=account_key) self.file_system_client = service_client.get_file_system_client(container_name) def read(self, directory_name): root_dir = str(directory_name) file_paths = self.file_system_client.get_paths(path=root_dir) main_df = pd.DataFrame() for path in file_paths: if not path.is_directory: file_client = file_system_client.get_file_client(path.name) file_ext = Path(path.name).suffix if file_ext in [".csv", ".tsv"]: with open(csv_local, "wb") as my_file: file_client.download_file().readinto(my_file) with open(csv_local, 'r') as file: data = file.read() row_delimiter = detect(text=data, default=None, whitelist=[',', ';', ':', '|', '\t']) processed_df = pd.read_csv(csv_local, sep=row_delimiter) elif file_ext == ".parquet": stream = io.BytesIO() file_client.download_file().readinto(stream) processed_df = pd.read_parquet(stream, engine='pyarrow') elif file_ext == ".avro": with open(avro_local, "wb") as my_file: file_client.download_file().readinto(my_file) processed_df = pdx.read_avro(avro_local) if main_df.empty: main_df = pd.DataFrame(processed_df) else: main_df = main_df.append(processed_df, ignore_index=True) return main_df """ }, 'gcs':{'imports':[{'mod':'storage','mod_from':'google.cloud'},{'mod': 'Path', 'mod_from': 'pathlib'},{'mod': 'pandas', 'mod_as': 'pd'}],'code':"""\n\ class gcs(): def __init__(self, config={}): if 'gcs' in config.keys(): config = config['gcs'] account_key = config.get('account_key','') bucket_name = config.get('bucket_name','') if not account_key: raise ValueError('Account key can not be empty') if not bucket_name: raise ValueError('bucket name can not be empty') storage_client = storage.Client.from_service_account_json(account_key) self.bucket = storage_client.get_bucket(bucket_name) def read(self, bucket_name, file_name): data = self.bucket.blob(file_name).download_as_text() return pd.read_csv(data, encoding = 'utf-8', sep = ',') """ } } class data_reader(): def __init__(self, reader_type=[]): self.supported_readers = supported_reader if isinstance(reader_type, str): self.readers = [reader_type] elif not reader_type: self.readers = self.supported_readers else: self.readers = reader_type unsupported_reader = [ x for x in self.readers if x not in self.supported_readers] if unsupported_reader: raise ValueError(f"reader type '{unsupported_reader}' is not supported\nSupported readers are {self.supported_readers}") self.codeText = "" self.importer = importModule() def get_reader_code(self, readers): reader_code = { 'sqlite': 'return sqlite_writer(target_path=target_path)', 'influx': 'return Influx_writer(config=config)', 'gcs': 'return gcs(config=config)', 'azure': 'return azure(config=config)', 's3': 'return s3bucket(config=config)' } code = "\n\ndef dataReader(reader_type, target_path=None, config=None):\n" for i, reader in enumerate(readers): if not i: code += f" if reader_type == '{reader}':\n" else: code += f" elif reader_type == '{reader}':\n" code += f" {reader_code[reader]}\n" if readers: code += " else:\n" code += f""" raise ValueError("'{{reader_type}}' not added during code generation")\n""" else: code += f""" raise ValueError("'{{reader_type}}' not added during code generation")\n""" return code def get_code(self): code = self.get_reader_code(self.readers) functions = [] for reader in self.readers: functions.append(reader) for function in functions: code += self.get_function_code(function) self.codeText += self.importer.getCode() self.codeText += code return self.codeText def get_function_code(self, name): code = "" if name in functions_code.keys(): code += functions_code[name]['code'] if self.importer: if 'imports' in functions_code[name].keys(): for module in functions_code[name]['imports']: mod_name = module['mod'] mod_from = module.get('mod_from', None) mod_as = module.get('mod_as', None) self.importer.addModule(mod_name, mod_from=mod_from, mod_as=mod_as) return code def get_importer(self): return self.importer """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from importlib.metadata import version import sys class importModule(): def __init__(self): self.importModule = {} self.stdlibModule = [] self.localModule = {} def addLocalModule(self,module, mod_from=None, mod_as=None): if module == '*': if module not in self.localModule.keys(): self.localModule[module]= [mod_from] else: self.localModule[module].append(mod_from) elif module not in self.localModule.keys(): self.localModule[module] = {'from':mod_from, 'as':mod_as} def addModule(self, module, mod_from=None, mod_as=None): if module not in self.importModule.keys(): self.importModule[module] = {'from':mod_from, 'as':mod_as} if module in sys.stdlib_module_names: self.stdlibModule.append(module) elif isinstance(self.importModule[module], list): if mod_as not in [x['as'] for x in self.importModule[module]]: self.importModule[module].append({'from':mod_from, 'as':mod_as}) elif mod_as not in [x['from'] for x in self.importModule[module]]: self.importModule[module].append({'from':mod_from, 'as':mod_as}) elif mod_as != self.importModule[module]['as']: as_list = [self.importModule[module]] as_list.append({'from':mod_from, 'as':mod_as}) self.importModule[module] = as_list elif mod_from != self.importModule[module]['from']: as_list = [self.importModule[module]] as_list.append({'from':mod_from, 'as':mod_as}) self.importModule[module] = as_list def getModules(self): return (self.importModule, self.stdlibModule) def getBaseModule(self, extra_importers=[]): modules_alias = { 'sklearn':'scikit-learn', 'genetic_selection':'sklearn-genetic', 'google': 'google-cloud-storage', 'azure':'azure-storage-file-datalake'} local_modules = {'AIX':'/app/AIX-0.1-py3-none-any.whl'} modules = [] require = "" if extra_importers: extra_importers = [importer.importModule for importer in extra_importers if isinstance(importer, importModule)] importers_module = [self.importModule] + extra_importers for importer_module in importers_module: for k,v in importer_module.items(): if v['from']: mod = v['from'].split('.')[0] else: mod = k if mod in modules_alias.keys(): mod = modules_alias[mod] modules.append(mod) modules = list(set(modules)) for mod in modules: try: if mod in local_modules.keys(): require += f"{local_modules[mod]}\n" else: require += f"{mod}=={version(mod)}\n" except : if mod not in sys.stdlib_module_names: raise return require def getCode(self): def to_string(k, v): mod = '' if v['from']: mod += 'from {} '.format(v['from']) mod += 'import {}'.format(k) if v['as']: mod += ' as {} '.format(v['as']) return mod modules = "" local_modules = "" std_lib_modules = "" third_party_modules = "" for k,v in self.importModule.items(): if k in self.stdlibModule: std_lib_modules = std_lib_modules + '\n' + to_string(k, v) elif isinstance(v, dict): third_party_modules = third_party_modules + '\n' + to_string(k, v) elif isinstance(v, list): for alias in v: third_party_modules = third_party_modules + '\n' + to_string(k, alias) for k,v in self.localModule.items(): if k != '*': local_modules = local_modules + '\n' + to_string(k, v) else: for mod_from in v: local_modules = local_modules + '\n' + f'from {mod_from} import {k}' if std_lib_modules: modules = modules + "\n#Standard Library modules" + std_lib_modules if third_party_modules: modules = modules + "\n\n#Third Party modules" + third_party_modules if local_modules: modules = modules + "\n\n#local modules" + local_modules + '\n' return modules def copyCode(self, importer): self.importModule, self.stdlibModule = importer.getModules() """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class transformer(): def __init__(self, indent=0, tab_size=4): self.df_name = 'df' self.tab = ' ' * tab_size self.codeText = "" self.transformers = [] self.TxCols = [] self.imputers = {} self.input_files = {} self.output_files = {} self.function_code = '' self.addInputFiles({'inputData' : 'rawData.dat', 'metaData' : 'modelMetaData.json','log' : 'aion.log','trainData' : 'transformedData.dat','testData' : 'test.dat','preprocessor' : 'preprocessor.pkl'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def __addValidateConfigCode(self): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = read_json(config_file)\ \n return config" return text def getPrefixModules(self): modules = [ {'module':'Path', 'mod_from':'pathlib'} ,{'module':'pandas', 'mod_as':'pd'} ,{'module':'numpy', 'mod_as':'np'} ,{'module':'scipy'} ] return modules def addPrefixCode(self, indent=1): self.codeText += """ def transformation(log): config = validateConfig() targetPath = Path('aion')/config['targetPath'] if not targetPath.exists(): raise ValueError(f'targetPath does not exist') meta_data_file = targetPath/IOFiles['metaData'] if meta_data_file.exists(): meta_data = read_json(meta_data_file) else: raise ValueError(f'Configuration file not found: {meta_data_file}') log_file = targetPath/IOFiles['log'] log = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem) dataLoc = targetPath/IOFiles['inputData'] if not dataLoc.exists(): return {'Status':'Failure','Message':'Data location does not exists.'} status = dict() df = read_data(dataLoc) log.log_dataframe(df) target_feature = config['target_feature'] if config['test_ratio'] == 0.0: train_data = df test_data = pd.DataFrame() else: """ def getSuffixModules(self): modules = [{'module':'pandas','mod_as':'pd'} ,{'module':'json'} ,{'module':'joblib'} ] return modules def addSuffixCode(self,encoder=False, indent=1): self.codeText += """ train_data, preprocess_pipe, label_encoder = profilerObj.transform() if not preprocess_pipe: raise ValueError('Pipeline not created') joblib.dump(preprocess_pipe, targetPath/IOFiles['preprocessor']) test_data.reset_index(inplace=True) """ if encoder: self.codeText += """ joblib.dump(label_encoder, targetPath/IOFiles['targetEncoder']) if not test_data.empty: ytest = label_encoder.transform(test_data[target_feature]) """ else: self.codeText += """ if not test_data.empty: ytest = test_data[target_feature] """ self.codeText += """ test_data.astype(profilerObj.train_features_type) test_data = preprocess_pipe.transform(test_data) if isinstance(test_data, scipy.sparse.spmatrix): test_data = test_data.toarray() preprocess_out_columns = train_data.columns.tolist() preprocess_out_columns.remove(target_feature) write_data(train_data,targetPath/IOFiles['trainData'],index=False) if isinstance( test_data, np.ndarray): test_data = pd.DataFrame(test_data, columns=preprocess_out_columns) test_data[target_feature] = ytest write_data(test_data,targetPath/IOFiles['testData'],index=False) log.log_dataframe(train_data) status = {'Status':'Success','trainData':IOFiles['trainData'],'testData':IOFiles['testData']} meta_data['transformation'] = {} meta_data['transformation']['cat_features'] = train_data.select_dtypes('category').columns.tolist() meta_data['transformation']['preprocessor'] = IOFiles['preprocessor'] meta_data['transformation']['preprocess_out_columns'] = preprocess_out_columns """ if encoder: self.codeText += """ meta_data['transformation']['target_encoder'] = IOFiles['targetEncoder'] """ self.codeText += """ meta_data['transformation']['Status'] = status write_json(meta_data, str(targetPath/IOFiles['metaData'])) log.info(f"Transformed data saved at {targetPath/IOFiles['trainData']}") log.info(f'output: {status}') return json.dumps(status) """ def getMainCodeModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'sys'} ,{'module':'json'} ,{'module':'logging'} ,{'module':'argparse'} ] return modules def addMainCode(self, indent=1): self.codeText += "\n\ \nif __name__ == '__main__':\ \n log = None\ \n try:\ \n print(transformation(log))\ \n except Exception as e:\ \n if log:\ \n log.error(e, exc_info=True)\ \n status = {'Status':'Failure','Message':str(e)}\ \n print(json.dumps(status))" def addValidateConfigCode(self, indent=1): self.function_code += self.__addValidateConfigCode() def addLocalFunctionsCode(self): self.addValidateConfigCode() def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def getCode(self, indent=1): return self.function_code + '\n' + self.codeText def getDFName(self): return self.df_name class data_profiler(): def __init__(self, importer, text_features=False): self.importer = importer self.codeText = "" self.text_features = text_features def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def get_module_import_statement(self, mod): text = "" if not mod.get('module', None): return text if mod.get('mod_from', None): text += f"from {mod['mod_from']} " text += f"import {mod['module']} " if mod.get('mod_as', None): text += f"as {mod['mod_as']}" text += "\n" return text def get_import_modules(self): profiler_importes = [ {'module': 'scipy', 'mod_from': None, 'mod_as': None}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'w2n', 'mod_from': 'word2number', 'mod_as': None}, {'module': 'LabelEncoder', 'mod_from': 'sklearn.preprocessing', 'mod_as': None }, {'module': 'OrdinalEncoder', 'mod_from': 'sklearn.preprocessing', 'mod_as': None }, {'module': 'OneHotEncoder', 'mod_from': 'sklearn.preprocessing', 'mod_as': None }, {'module': 'SimpleImputer', 'mod_from': 'sklearn.impute', 'mod_as': None }, {'module': 'KNNImputer', 'mod_from': 'sklearn.impute', 'mod_as': None }, {'module': 'Pipeline', 'mod_from': 'sklearn.pipeline', 'mod_as': None }, {'module': 'FeatureUnion', 'mod_from': 'sklearn.pipeline', 'mod_as': None }, {'module': 'MinMaxScaler', 'mod_from': 'sklearn.preprocessing', 'mod_as': None }, {'module': 'StandardScaler', 'mod_from': 'sklearn.preprocessing', 'mod_as': None }, {'module': 'PowerTransformer', 'mod_from': 'sklearn.preprocessing', 'mod_as': None }, {'module': 'ColumnTransformer', 'mod_from': 'sklearn.compose', 'mod_as': None }, {'module': 'TransformerMixin', 'mod_from': 'sklearn.base', 'mod_as': None }, {'module': 'IsolationForest', 'mod_from': 'sklearn.ensemble', 'mod_as': None }, {'module': 'TargetEncoder', 'mod_from': 'category_encoders', 'mod_as': None } ] if self.text_features: profiler_importes.append({'module': 'textProfiler', 'mod_from': 'text.textProfiler', 'mod_as': None }) profiler_importes.append({'module': 'textCombine', 'mod_from': 'text.textProfiler', 'mod_as': None }) return profiler_importes def get_importer(self): return self.importer def get_code(self): common_importes = self.get_import_modules() for module in common_importes: mod_name = module['module'] mod_from = module.get('mod_from', None) mod_as = module.get('mod_as', None) if module['module'] in ['textProfiler','textCombine']: self.importer.addLocalModule(mod_name, mod_from=mod_from, mod_as=mod_as) else: self.importer.addModule(mod_name, mod_from=mod_from, mod_as=mod_as) self.codeText += """ STR_TO_CAT_CONVERSION_LEN_MAX = 10 log_suffix = f'[{Path(__file__).stem}] ' target_encoding_method_change = {'targetencoding': 'labelencoding'} supported_method = { 'fillNa': { 'categorical' : ['mode','zero','na'], 'numeric' : ['median','mean','knnimputer','zero','drop','na'], }, 'categoryEncoding': ['labelencoding','targetencoding','onehotencoding','na','none'], 'normalization': ['standardscaler','minmax','lognormal', 'na','none'], 'outlier_column_wise': ['iqr','zscore', 'disable'], 'outlierOperation': ['dropdata', 'average', 'nochange'] } def findiqrOutlier(df): Q1 = df.quantile(0.25) Q3 = df.quantile(0.75) IQR = Q3 - Q1 index = ~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))) return index def findzscoreOutlier(df): z = np.abs(scipy.stats.zscore(df)) index = (z < 3) return index def findiforestOutlier(df): isolation_forest = IsolationForest(n_estimators=100) isolation_forest.fit(df) y_pred_train = isolation_forest.predict(df) return y_pred_train == 1 def get_one_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def get_boolean(value): if (isinstance(value, str) and value.lower() == 'true') or (isinstance(value, bool) and value == True): return True else: return False class profiler(): def __init__(self, xtrain, ytrain=None, target=None, encode_target = True, config={}, keep_unprocessed=[], log=None): if not isinstance(xtrain, pd.DataFrame): raise ValueError(f'{log_suffix}supported data type is pandas.DataFrame but provide data is of {type(xtrain)} type') if xtrain.empty: raise ValueError(f'{log_suffix}Data frame is empty') if target and target in xtrain.columns: self.target = xtrain[target] xtrain.drop(target, axis=1, inplace=True) self.target_name = target elif ytrain: self.target = ytrain self.target_name = 'target' else: self.target = pd.Series() self.target_name = None self.encode_target = encode_target self.label_encoder = None keep_unprocessed = [x for x in keep_unprocessed if x in xtrain.columns] if keep_unprocessed: self.unprocessed = xtrain[keep_unprocessed] self.data = xtrain.drop(keep_unprocessed, axis=1) else: self.data = xtrain self.unprocessed = pd.DataFrame() self.colm_type = {} for colm, infer_type in zip(self.data.columns, self.data.dtypes): self.colm_type[colm] = infer_type self.numeric_feature = [] self.cat_feature = [] self.text_feature = [] self.wordToNumericFeatures = [] self.added_features = [] self.pipeline = [] self.dropped_features = {} self.train_features_type={} self.__update_type() self.config = config self.featureDict = config.get('featureDict', []) self.output_columns = [] self.feature_expender = [] self.text_to_num = {} if log: self.log = log else: self.log = logging.getLogger('eion') self.type_conversion = {} def log_dataframe(self, msg=None): import io buffer = io.StringIO() self.data.info(buf=buffer) if msg: log_text = f'Data frame after {msg}:' else: log_text = 'Data frame:' log_text += '\\n\\t'+str(self.data.head(2)).replace('\\n','\\n\\t') log_text += ('\\n\\t' + buffer.getvalue().replace('\\n','\\n\\t')) self.log.info(log_text) def transform(self): if self.is_target_available(): if self.target_name: self.log.info(f"Target feature name: '{self.target_name}'") self.log.info(f"Target feature size: {len(self.target)}") else: self.log.info(f"Target feature not present") self.log_dataframe() try: self.process() except Exception as e: self.log.error(e, exc_info=True) raise pipe = FeatureUnion(self.pipeline) self.log.info(pipe) process_data = pipe.fit_transform(self.data, y=self.target) self.update_output_features_names(pipe) if isinstance(process_data, scipy.sparse.spmatrix): process_data = process_data.toarray() df = pd.DataFrame(process_data, columns=self.output_columns) if self.is_target_available() and self.target_name: df[self.target_name] = self.target if not self.unprocessed.empty: df[self.unprocessed.columns] = self.unprocessed self.log_numerical_fill() self.log_categorical_fill() self.log_normalization() return df, pipe, self.label_encoder def log_type_conversion(self): if self.log: self.log.info('----------- Inspecting Features -----------') self.log.info('----------- Type Conversion -----------') count = 0 for k, v in self.type_conversion.items(): if v[0] != v[1]: self.log.info(f'{k} -> from {v[0]} to {v[1]} : {v[2]}') self.log.info('Status:- |... Feature inspection done') def check_config(self): removeDuplicate = self.config.get('removeDuplicate', False) self.config['removeDuplicate'] = get_boolean(removeDuplicate) self.config['misValueRatio'] = float(self.config.get('misValueRatio', '1.0')) self.config['numericFeatureRatio'] = float(self.config.get('numericFeatureRatio', '1.0')) self.config['categoryMaxLabel'] = int(self.config.get('categoryMaxLabel', '20')) featureDict = self.config.get('featureDict', []) if isinstance(featureDict, dict): self.config['featureDict'] = [] if isinstance(featureDict, str): self.config['featureDict'] = [] def process(self): #remove duplicate not required at the time of prediction self.check_config() self.remove_constant_feature() self.remove_empty_feature(self.config['misValueRatio']) self.remove_index_features() self.drop_na_target() if self.config['removeDuplicate']: self.drop_duplicate() self.check_categorical_features() self.string_to_numeric() self.process_target() self.train_features_type = dict(zip(self.data.columns, self.data.dtypes)) self.parse_process_step_config() self.process_drop_fillna() #self.log_type_conversion() self.update_num_fill_dict() #print(self.num_fill_method_dict) self.update_cat_fill_dict() self.create_pipeline() self.text_pipeline(self.config) self.apply_outlier() self.log.info(self.process_method) self.log.info(self.train_features_type) def is_target_available(self): return (isinstance(self.target, pd.Series) and not self.target.empty) or len(self.target) def process_target(self, operation='encode', arg=None): if self.encode_target: if self.is_target_available(): self.label_encoder = LabelEncoder() self.target = self.label_encoder.fit_transform(self.target) return self.label_encoder return None def is_target_column(self, column): return column == self.target_name def fill_default_steps(self): num_fill_method = get_one_true_option(self.config.get('numericalFillMethod',None)) normalization_method = get_one_true_option(self.config.get('normalization',None)) for colm in self.numeric_feature: if num_fill_method: self.fill_missing_value_method(colm, num_fill_method.lower()) if normalization_method: self.fill_normalizer_method(colm, normalization_method.lower()) cat_fill_method = get_one_true_option(self.config.get('categoricalFillMethod',None)) cat_encode_method = get_one_true_option(self.config.get('categoryEncoding',None)) for colm in self.cat_feature: if cat_fill_method: self.fill_missing_value_method(colm, cat_fill_method.lower()) if cat_encode_method: self.fill_encoder_value_method(colm, cat_encode_method.lower(), default=True) def parse_process_step_config(self): self.process_method = {} user_provided_data_type = {} for feat_conf in self.featureDict: colm = feat_conf.get('feature', '') if not self.is_target_column(colm): if colm in self.data.columns: user_provided_data_type[colm] = feat_conf['type'] if user_provided_data_type: self.update_user_provided_type(user_provided_data_type) self.fill_default_steps() for feat_conf in self.featureDict: colm = feat_conf.get('feature', '') if not self.is_target_column(colm): if colm in self.data.columns: if feat_conf.get('fillMethod', None): self.fill_missing_value_method(colm, feat_conf['fillMethod'].lower()) if feat_conf.get('categoryEncoding', None): self.fill_encoder_value_method(colm, feat_conf['categoryEncoding'].lower()) if feat_conf.get('normalization', None): self.fill_normalizer_method(colm, feat_conf['normalization'].lower()) if feat_conf.get('outlier', None): self.fill_outlier_method(colm, feat_conf['outlier'].lower()) if feat_conf.get('outlierOperation', None): self.fill_outlier_process(colm, feat_conf['outlierOperation'].lower()) def update_output_features_names(self, pipe): columns = self.output_columns start_index = {} for feat_expender in self.feature_expender: if feat_expender: step_name = list(feat_expender.keys())[0] index = list(feat_expender.values())[0] for transformer_step in pipe.transformer_list: if transformer_step[1].steps[-1][0] in step_name: start_index[index] = {transformer_step[1].steps[-1][0]: transformer_step[1].steps[-1][1].get_feature_names()} if start_index: index_shifter = 0 for key,value in start_index.items(): for k,v in value.items(): if k == 'vectorizer': v = [f'{x}_vect' for x in v] key = key + index_shifter self.output_columns[key:key] = v index_shifter += len(v) self.added_features = [*self.added_features, *v] def text_pipeline(self, conf_json): if self.text_feature: pipeList = [] max_features = 2000 text_pipe = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", self.text_feature) ], remainder="drop")), ("text_fillNa",SimpleImputer(strategy='constant', fill_value='')), ("merge_text_feature", textCombine())]) obj = textProfiler() pipeList = obj.textProfiler(conf_json, pipeList, max_features) last_step = "merge_text_feature" for pipe_elem in pipeList: text_pipe.steps.append((pipe_elem[0], pipe_elem[1])) last_step = pipe_elem[0] text_transformer = ('text_process', text_pipe) self.pipeline.append(text_transformer) self.feature_expender.append({last_step:len(self.output_columns)}) def create_pipeline(self): num_pipe = {} for k,v in self.num_fill_method_dict.items(): for k1,v1 in v.items(): if k1 and k1 != 'none': num_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_num_imputer(k)), (k1, self.get_num_scaler(k1)) ]) else: num_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_num_imputer(k)) ]) self.output_columns.extend(v1) cat_pipe = {} for k,v in self.cat_fill_method_dict.items(): for k1,v1 in v.items(): cat_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_cat_imputer(k)), (k1, self.get_cat_encoder(k1)) ]) if k1 not in ['onehotencoding']: self.output_columns.extend(v1) else: self.feature_expender.append({k1:len(self.output_columns)}) for key, pipe in num_pipe.items(): self.pipeline.append((key, pipe)) for key, pipe in cat_pipe.items(): self.pipeline.append((key, pipe)) if not self.unprocessed.empty: self.pipeline.append(Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", self.unprocessed.columns) ], remainder="drop"))])) "Drop: feature during training but replace with zero during prediction " def process_drop_fillna(self): drop_column = [] if 'numFill' in self.process_method.keys(): for col, method in self.process_method['numFill'].items(): if method == 'drop': self.process_method['numFill'][col] = 'zero' drop_column.append(col) if 'catFill' in self.process_method.keys(): for col, method in self.process_method['catFill'].items(): if method == 'drop': self.process_method['catFill'][col] = 'zero' drop_column.append(col) if drop_column: self.data.dropna(subset=drop_column, inplace=True) def update_num_fill_dict(self): self.num_fill_method_dict = {} if 'numFill' in self.process_method.keys(): for f in supported_method['fillNa']['numeric']: self.num_fill_method_dict[f] = {} for en in supported_method['normalization']: self.num_fill_method_dict[f][en] = [] for col in self.numeric_feature: numFillDict = self.process_method.get('numFill',{}) normalizationDict = self.process_method.get('normalization',{}) if f == numFillDict.get(col, '') and en == normalizationDict.get(col,''): self.num_fill_method_dict[f][en].append(col) if not self.num_fill_method_dict[f][en] : del self.num_fill_method_dict[f][en] if not self.num_fill_method_dict[f]: del self.num_fill_method_dict[f] def update_cat_fill_dict(self): self.cat_fill_method_dict = {} if 'catFill' in self.process_method.keys(): for f in supported_method['fillNa']['categorical']: self.cat_fill_method_dict[f] = {} for en in supported_method['categoryEncoding']: self.cat_fill_method_dict[f][en] = [] for col in self.cat_feature: catFillDict = self.process_method.get('catFill',{}) catEncoderDict = self.process_method.get('catEncoder',{}) if f == catFillDict.get(col, '') and en == catEncoderDict.get(col,''): self.cat_fill_method_dict[f][en].append(col) if not self.cat_fill_method_dict[f][en] : del self.cat_fill_method_dict[f][en] if not self.cat_fill_method_dict[f]: del self.cat_fill_method_dict[f] def __update_type(self): self.numeric_feature = self.data.select_dtypes(include='number').columns.tolist() self.cat_feature = self.data.select_dtypes(include='category').columns.tolist() self.date_time = self.data.select_dtypes(include='datetime').columns.tolist() self.text_feature = self.data.select_dtypes(include='object').columns.tolist() def update_user_provided_type(self, data_types): allowed_types = ['numerical','categorical', 'text','date','index'] type_mapping = {'numerical': np.dtype('float'), 'float': np.dtype('float'),'categorical': 'category', 'text':np.dtype('object'),'date':'datetime64[ns]','index': np.dtype('int64'),} mapped_type = {k:type_mapping[v] for k,v in data_types.items()} #self.log.info(mapped_type) self.update_type(mapped_type, 'user provided data type') def get_type(self, as_list=False): if as_list: return [self.colm_type.values()] else: return self.colm_type def update_type(self, data_types={}, reason=''): invalid_features = [x for x in data_types.keys() if x not in self.data.columns] if invalid_features: valid_feat = list(set(data_types.keys()) - set(invalid_features)) valid_feat_type = {k:v for k,v in data_types if k in valid_feat} else: valid_feat_type = data_types for k,v in valid_feat_type.items(): if v != self.colm_type[k].name: try: self.data.astype({k:v}) self.colm_type.update({k:self.data[k].dtype}) self.type_conversion[k] = (self.colm_type[k] , v, 'Done', reason) except: self.type_conversion[k] = (self.colm_type[k] , v, 'Fail', reason) self.data = self.data.astype(valid_feat_type) self.__update_type() def string_to_numeric(self): def to_number(x): try: return w2n.word_to_num(x) except: return np.nan for col in self.text_feature: col_values = self.data[col].copy() col_values = pd.to_numeric(col_values, errors='coerce') if col_values.count() >= (self.config['numericFeatureRatio'] * len(col_values)): self.text_to_num[col] = 'float64' self.wordToNumericFeatures.append(col) if self.text_to_num: columns = list(self.text_to_num.keys()) self.data[columns] = self.data[columns].apply(lambda x: to_number(x)) self.update_type(self.text_to_num) self.log.info('----------- Inspecting Features -----------') for col in self.text_feature: self.log.info(f'-------> Feature : {col}') if col in self.text_to_num: self.log.info('----------> Numeric Status :Yes') self.log.info('----------> Data Type Converting to numeric :Yes') else: self.log.info('----------> Numeric Status :No') self.log.info(f'\\nStatus:- |... Feature inspection done for numeric data: {len(self.text_to_num)} feature(s) converted to numeric') self.log.info(f'\\nStatus:- |... Feature word to numeric treatment done: {self.text_to_num}') self.log.info('----------- Inspecting Features End -----------') def check_categorical_features(self): num_data = self.data.select_dtypes(include='number') num_data_unique = num_data.nunique() num_to_cat_col = {} for i, value in enumerate(num_data_unique): if value < self.config['categoryMaxLabel']: num_to_cat_col[num_data_unique.index[i]] = 'category' if num_to_cat_col: self.update_type(num_to_cat_col, 'numerical to categorical') str_to_cat_col = {} str_data = self.data.select_dtypes(include='object') str_data_unique = str_data.nunique() for i, value in enumerate(str_data_unique): if value < self.config['categoryMaxLabel']: str_to_cat_col[str_data_unique.index[i]] = 'category' for colm in str_data.columns: if self.data[colm].str.len().max() < STR_TO_CAT_CONVERSION_LEN_MAX: str_to_cat_col[colm] = 'category' if str_to_cat_col: self.update_type(str_to_cat_col, 'text to categorical') def drop_features(self, features=[], reason='unspecified'): if isinstance(features, str): features = [features] feat_to_remove = [x for x in features if x in self.data.columns] if feat_to_remove: self.data.drop(feat_to_remove, axis=1, inplace=True) for feat in feat_to_remove: self.dropped_features[feat] = reason self.log_drop_feature(feat_to_remove, reason) self.__update_type() def drop_duplicate(self): index = self.data.duplicated(keep='first') if index.sum(): self.remove_rows(index, 'duplicate rows') def drop_na_target(self): if self.is_target_available(): self.remove_rows(self.target.isna(), 'null target values') def log_drop_feature(self, columns, reason): self.log.info(f'---------- Dropping {reason} features ----------') self.log.info(f'\\nStatus:- |... {reason} feature treatment done: {len(columns)} {reason} feature(s) found') self.log.info(f'-------> Drop Features: {columns}') self.log.info(f'Data Frame Shape After Dropping (Rows,Columns): {self.data.shape}') def log_normalization(self): if self.process_method.get('normalization', None): self.log.info(f'\\nStatus:- !... Normalization treatment done') for method in supported_method['normalization']: cols = [] for col, m in self.process_method['normalization'].items(): if m == method: cols.append(col) if cols and method != 'none': self.log.info(f'Running {method} on features: {cols}') def log_numerical_fill(self): if self.process_method.get('numFill', None): self.log.info(f'\\nStatus:- !... Fillna for numeric feature done') for method in supported_method['fillNa']['numeric']: cols = [] for col, m in self.process_method['numFill'].items(): if m == method: cols.append(col) if cols: self.log.info(f'-------> Running {method} on features: {cols}') def log_categorical_fill(self): if self.process_method.get('catFill', None): self.log.info(f'\\nStatus:-!... FillNa for categorical feature done') for method in supported_method['fillNa']['categorical']: cols = [] for col, m in self.process_method['catFill'].items(): if m == method: cols.append(col) if cols: self.log.info(f'-------> Running {method} on features: {cols}') def remove_constant_feature(self): unique_values = self.data.nunique() constant_features = [] for i, value in enumerate(unique_values): if value == 1: constant_features.append(unique_values.index[i]) if constant_features: self.drop_features(constant_features, "constant") for i in constant_features: try: self.numeric_feature.remove(i) except ValueError: pass try: self.cat_feature.remove(i) except ValueError: pass def remove_empty_feature(self, misval_ratio=1.0): missing_ratio = self.data.isnull().sum() / len(self.data) missing_ratio = {k:v for k,v in zip(self.data.columns, missing_ratio)} empty_features = [k for k,v in missing_ratio.items() if v > misval_ratio] if empty_features: self.drop_features(empty_features, "empty") for i in empty_features: try: self.numeric_feature.remove(i) except ValueError: pass try: self.cat_feature.remove(i) except: pass def remove_index_features(self): index_feature = [] for feat in self.numeric_feature: if self.data[feat].nunique() == len(self.data): if (self.data[feat].sum()- sum(self.data.index) == (self.data.iloc[0][feat]-self.data.index[0])*len(self.data)): index_feature.append(feat) self.drop_features(index_feature, "index") for i in index_feature: try: self.numeric_feature.remove(i) except ValueError: pass try: self.cat_feature.remove(i) except: pass def fill_missing_value_method(self, colm, method): if colm in self.numeric_feature: if method in supported_method['fillNa']['numeric']: if 'numFill' not in self.process_method.keys(): self.process_method['numFill'] = {} if method == 'na' and self.process_method['numFill'].get(colm, None): pass # don't overwrite else: self.process_method['numFill'][colm] = method if colm in self.cat_feature: if method in supported_method['fillNa']['categorical']: if 'catFill' not in self.process_method.keys(): self.process_method['catFill'] = {} if method == 'na' and self.process_method['catFill'].get(colm, None): pass else: self.process_method['catFill'][colm] = method def check_encoding_method(self, method, colm,default=False): if not self.is_target_available() and (method.lower() == list(target_encoding_method_change.keys())[0]): method = target_encoding_method_change[method.lower()] if default: self.log.info(f"Applying Label encoding instead of Target encoding on feature '{colm}' as target feature is not present") return method def fill_encoder_value_method(self,colm, method, default=False): if colm in self.cat_feature: if method.lower() in supported_method['categoryEncoding']: if 'catEncoder' not in self.process_method.keys(): self.process_method['catEncoder'] = {} if method == 'na' and self.process_method['catEncoder'].get(colm, None): pass else: self.process_method['catEncoder'][colm] = self.check_encoding_method(method, colm,default) else: self.log.info(f"-------> categorical encoding method '{method}' is not supported. supported methods are {supported_method['categoryEncoding']}") def fill_normalizer_method(self,colm, method): if colm in self.numeric_feature: if method in supported_method['normalization']: if 'normalization' not in self.process_method.keys(): self.process_method['normalization'] = {} if (method == 'na' or method == 'none') and self.process_method['normalization'].get(colm, None): pass else: self.process_method['normalization'][colm] = method else: self.log.info(f"-------> Normalization method '{method}' is not supported. supported methods are {supported_method['normalization']}") def apply_outlier(self): inlier_indices = np.array([True] * len(self.data)) if self.process_method.get('outlier', None): self.log.info('-------> Feature wise outlier detection:') for k,v in self.process_method['outlier'].items(): if k in self.numeric_feature: if v == 'iqr': index = findiqrOutlier(self.data[k]) elif v == 'zscore': index = findzscoreOutlier(self.data[k]) elif v == 'disable': index = None if k in self.process_method['outlierOperation'].keys(): if self.process_method['outlierOperation'][k] == 'dropdata': inlier_indices = np.logical_and(inlier_indices, index) elif self.process_method['outlierOperation'][k] == 'average': mean = self.data[k].mean() index = ~index self.data.loc[index,[k]] = mean self.log.info(f'-------> {k}: Replaced by Mean {mean}: total replacement {index.sum()}') elif self.process_method['outlierOperation'][k] == 'nochange' and v != 'disable': self.log.info(f'-------> Total outliers in "{k}": {(~index).sum()}') if self.config.get('outlierDetection',None): if self.config['outlierDetection'].get('IsolationForest','False') == 'True': index = findiforestOutlier(self.data[self.numeric_feature]) inlier_indices = np.logical_and(inlier_indices, index) self.log.info(f'-------> Numeric feature based Outlier detection(IsolationForest):') if inlier_indices.sum() != len(self.data): self.remove_rows( inlier_indices == False, 'outlier detection') self.log.info('Status:- |... Outlier treatment done') self.log.info(f'-------> Data Frame Shape After Outlier treatment (Rows,Columns): {self.data.shape}') def remove_rows(self, indices, msg=''): if indices.sum(): indices = ~indices if len(indices) != len(self.data): raise ValueError('Data Frame length mismatch') self.data = self.data[indices] self.data.reset_index(drop=True, inplace=True) if self.is_target_available(): self.target = self.target[indices] if isinstance(self.target, pd.Series): self.target.reset_index(drop=True, inplace=True) if not self.unprocessed.empty: self.unprocessed = self.unprocessed[indices] self.unprocessed.reset_index(drop=True, inplace=True) self.log.info(f'-------> {msg} dropped rows count: {(indices == False).sum()}') def fill_outlier_method(self,colm, method): if colm in self.numeric_feature: if method in supported_method['outlier_column_wise']: if 'outlier' not in self.process_method.keys(): self.process_method['outlier'] = {} if method != 'Disable': self.process_method['outlier'][colm] = method else: self.log.info(f"-------> outlier detection method '{method}' is not supported for column wise. supported methods are {supported_method['outlier_column_wise']}") def fill_outlier_process(self,colm, method): if colm in self.numeric_feature: if method in supported_method['outlierOperation']: if 'outlierOperation' not in self.process_method.keys(): self.process_method['outlierOperation'] = {} self.process_method['outlierOperation'][colm] = method else: self.log.info(f"-------> outlier process method '{method}' is not supported for column wise. supported methods are {supported_method['outlieroperation']}") def get_cat_imputer(self,method): if method == 'mode': return SimpleImputer(strategy='most_frequent') elif method == 'zero': return SimpleImputer(strategy='constant', fill_value=0) def get_cat_encoder(self,method): if method == 'labelencoding': return OrdinalEncoder(handle_unknown="error") elif method == 'onehotencoding': return OneHotEncoder(sparse=False,handle_unknown="error") elif method == 'targetencoding': if not self.is_target_available(): raise ValueError('Can not apply Target Encoding when target feature is not present') return TargetEncoder(handle_unknown='error') def get_num_imputer(self,method): if method == 'mode': return SimpleImputer(strategy='most_frequent') elif method == 'mean': return SimpleImputer(strategy='mean') elif method == 'median': return SimpleImputer(strategy='median') elif method == 'knnimputer': return KNNImputer() elif method == 'zero': return SimpleImputer(strategy='constant', fill_value=0) def get_num_scaler(self,method): if method == 'minmax': return MinMaxScaler() elif method == 'standardscaler': return StandardScaler() elif method == 'lognormal': return PowerTransformer(method='yeo-johnson', standardize=False) """ return self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ class global_function(): def __init__(self, tab_size=4): self.tab = ' ' * tab_size self.codeText = "" self.available_functions = { 'iqr':{'name':'iqrOutlier','code':f"\n\ndef iqrOutlier(df):\ \n{self.tab}Q1 = df.quantile(0.25)\ \n{self.tab}Q3 = df.quantile(0.75)\ \n{self.tab}IQR = Q3 - Q1\ \n{self.tab}index = ~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)\ \n{self.tab}return index"}, 'zscore':{'name':'zscoreOutlier','imports':[{'mod':'stats','mod_from':'scipy'},{'mod':'numpy'}],'code':f"\n\ndef zscoreOutlier(df):\ \n{self.tab}z = numpy.abs(stats.zscore(df))\ \n{self.tab}index = (z < 3).all(axis=1)\ \n{self.tab}return index"}, 'iforest':{'name':'iforestOutlier','imports':[{'mod':'IsolationForest','mod_from':'sklearn.ensemble'}],'code':f"\n\ndef iforestOutlier(df):\ \n{self.tab}from sklearn.ensemble import IsolationForest\ \n{self.tab}isolation_forest = IsolationForest(n_estimators=100)\ \n{self.tab}isolation_forest.fit(df)\ \n{self.tab}y_pred_train = isolation_forest.predict(df)\ \n{self.tab}return y_pred_train == 1"}, 'minMaxImputer':{'name':'minMaxImputer','code':f"\n\nclass minMaxImputer(TransformerMixin):\ \n{self.tab}def __init__(self, strategy='max'):\ \n{self.tab}{self.tab}self.strategy = strategy\ \n{self.tab}def fit(self, X, y=None):\ \n{self.tab}{self.tab}self.feature_names_in_ = X.columns\ \n{self.tab}{self.tab}if self.strategy == 'min':\ \n{self.tab}{self.tab}{self.tab}self.statistics_ = X.min()\ \n{self.tab}{self.tab}else:\ \n{self.tab}{self.tab}{self.tab}self.statistics_ = X.max()\ \n{self.tab}{self.tab}return self\ \n{self.tab}def transform(self, X):\ \n{self.tab}{self.tab}import numpy\ \n{self.tab}{self.tab}return numpy.where(X.isna(), self.statistics_, X)"}, 'DummyEstimator':{'name':'DummyEstimator','code':f"\n\nclass DummyEstimator(BaseEstimator):\ \n{self.tab}def fit(self): pass\ \n{self.tab}def score(self): pass"}, 'start_reducer':{'name':'start_reducer','imports':[{'mod':'itertools'},{'mod':'numpy','mod_as':'np'},{'mod':'pandas','mod_as':'pd'},{'mod':'VarianceThreshold','mod_from':'sklearn.feature_selection'}], 'code':""" def start_reducer(df,target_feature,corr_threshold=0.85,var_threshold=0.05): qconstantColumns = [] train_features = df.columns.tolist() train_features.remove(target_feature) df = df.loc[:, (df != df.iloc[0]).any()] #remove constant feature numeric_features = df.select_dtypes(include='number').columns.tolist() non_numeric_features = df.select_dtypes(exclude='number').columns.tolist() if numeric_features and var_threshold: qconstantFilter = VarianceThreshold(threshold=var_threshold) tempDf=df[numeric_features] qconstantFilter.fit(tempDf) qconstantColumns = [column for column in numeric_features if column not in tempDf.columns[qconstantFilter.get_support()]] if target_feature in qconstantColumns: qconstantColumns.remove(target_feature) numeric_features = list(set(numeric_features) - set(qconstantColumns)) if numeric_features: numColPairs = list(itertools.product(numeric_features, numeric_features)) for item in numColPairs: if(item[0] == item[1]): numColPairs.remove(item) tempArray = [] for item in numColPairs: tempCorr = np.abs(df[item[0]].corr(df[item[1]])) if(tempCorr > corr_threshold): tempArray.append(item[0]) tempArray = np.unique(tempArray).tolist() nonsimilarNumericalCols = list(set(numeric_features) - set(tempArray)) groupedFeatures = [] if tempArray: corrDic = {} for feature in tempArray: temp = [] for col in tempArray: tempCorr = np.abs(df[feature].corr(df[col])) temp.append(tempCorr) corrDic[feature] = temp #Similar correlation df corrDF = pd.DataFrame(corrDic,index = tempArray) corrDF.loc[:,:] = np.tril(corrDF, k=-1) alreadyIn = set() similarFeatures = [] for col in corrDF: perfectCorr = corrDF[col][corrDF[col] > corr_threshold].index.tolist() if perfectCorr and col not in alreadyIn: alreadyIn.update(set(perfectCorr)) perfectCorr.append(col) similarFeatures.append(perfectCorr) updatedSimFeatures = [] for items in similarFeatures: if(target_feature != '' and target_feature in items): for p in items: updatedSimFeatures.append(p) else: updatedSimFeatures.append(items[0]) newTempFeatures = list(set(updatedSimFeatures + nonsimilarNumericalCols)) updatedFeatures = list(set(newTempFeatures + non_numeric_features)) else: updatedFeatures = list(set(df.columns) -set(qconstantColumns)) else: updatedFeatures = list(set(df.columns) -set(qconstantColumns)) return updatedFeatures """}, 'feature_importance_class':{'name':'feature_importance_class','code':"\n\ \ndef feature_importance_class(df, numeric_features, cat_features,target_feature,pValTh,corrTh):\ \n import pandas as pd\ \n from sklearn.feature_selection import chi2\ \n from sklearn.feature_selection import f_classif\ \n from sklearn.feature_selection import mutual_info_classif\ \n \ \n impFeatures = []\ \n if cat_features:\ \n categoricalData=df[cat_features]\ \n chiSqCategorical=chi2(categoricalData,df[target_feature])[1]\ \n corrSeries=pd.Series(chiSqCategorical, index=cat_features)\ \n impFeatures.append(corrSeries[corrSeries<pValTh].index.tolist())\ \n if numeric_features:\ \n quantData=df[numeric_features]\ \n fclassScore=f_classif(quantData,df[target_feature])[1]\ \n miClassScore=mutual_info_classif(quantData,df[target_feature])\ \n fClassSeries=pd.Series(fclassScore,index=numeric_features)\ \n miClassSeries=pd.Series(miClassScore,index=numeric_features)\ \n impFeatures.append(fClassSeries[fClassSeries<pValTh].index.tolist())\ \n impFeatures.append(miClassSeries[miClassSeries>corrTh].index.tolist())\ \n pearsonScore=df.corr() \ \n targetPScore=abs(pearsonScore[target_feature])\ \n impFeatures.append(targetPScore[targetPScore<pValTh].index.tolist())\ \n return list(set(sum(impFeatures, [])))"}, 'feature_importance_reg':{'name':'feature_importance_reg','code':"\n\ \ndef feature_importance_reg(df, numeric_features, target_feature,pValTh,corrTh):\ \n import pandas as pd\ \n from sklearn.feature_selection import f_regression\ \n from sklearn.feature_selection import mutual_info_regression\ \n \ \n impFeatures = []\ \n if numeric_features:\ \n quantData =df[numeric_features]\ \n fregScore=f_regression(quantData,df[target_feature])[1]\ \n miregScore=mutual_info_regression(quantData,df[target_feature])\ \n fregSeries=pd.Series(fregScore,index=numeric_features)\ \n miregSeries=pd.Series(miregScore,index=numeric_features)\ \n impFeatures.append(fregSeries[fregSeries<pValTh].index.tolist())\ \n impFeatures.append(miregSeries[miregSeries>corrTh].index.tolist())\ \n pearsonScore=df.corr()\ \n targetPScore=abs(pearsonScore[target_feature])\ \n impFeatures.append(targetPScore[targetPScore<pValTh].index.tolist())\ \n return list(set(sum(impFeatures, [])))"}, 'scoring_criteria':{'name':'scoring_criteria','imports':[{'mod':'make_scorer','mod_from':'sklearn.metrics'},{'mod':'roc_auc_score','mod_from':'sklearn.metrics'}], 'code':"\n\ \ndef scoring_criteria(score_param, problem_type, class_count):\ \n if problem_type == 'classification':\ \n scorer_mapping = {\ \n 'recall':{'binary_class': 'recall', 'multi_class': 'recall_weighted'},\ \n 'precision':{'binary_class': 'precision', 'multi_class': 'precision_weighted'},\ \n 'f1_score':{'binary_class': 'f1', 'multi_class': 'f1_weighted'},\ \n 'roc_auc':{'binary_class': 'roc_auc', 'multi_class': 'roc_auc_ovr_weighted'}\ \n }\ \n if (score_param.lower() == 'roc_auc') and (class_count > 2):\ \n score_param = make_scorer(roc_auc_score, needs_proba=True,multi_class='ovr',average='weighted')\ \n else:\ \n class_type = 'binary_class' if class_count == 2 else 'multi_class'\ \n if score_param in scorer_mapping.keys():\ \n score_param = scorer_mapping[score_param][class_type]\ \n else:\ \n score_param = 'accuracy'\ \n return score_param"}, 'log_dataframe':{'name':'log_dataframe','code':f"\n\ \ndef log_dataframe(df, msg=None):\ \n import io\ \n buffer = io.StringIO()\ \n df.info(buf=buffer)\ \n if msg:\ \n log_text = f'Data frame after {{msg}}:'\ \n else:\ \n log_text = 'Data frame:'\ \n log_text += '\\n\\t'+str(df.head(2)).replace('\\n','\\n\\t')\ \n log_text += ('\\n\\t' + buffer.getvalue().replace('\\n','\\n\\t'))\ \n get_logger().info(log_text)"}, 'BayesSearchCV':{'name':'BayesSearchCV','imports':[{'mod':'cross_val_score','mod_from':'sklearn.model_selection'},{'mod':'fmin','mod_from':'hyperopt'},{'mod':'tpe','mod_from':'hyperopt'},{'mod':'hp','mod_from':'hyperopt'},{'mod':'STATUS_OK','mod_from':'hyperopt'},{'mod':'Trials','mod_from':'hyperopt'},{'mod':'numpy','mod_as':'np'}],'code':"\n\ \nclass BayesSearchCV():\ \n\ \n def __init__(self, estimator, params, scoring, n_iter, cv):\ \n self.estimator = estimator\ \n self.params = params\ \n self.scoring = scoring\ \n self.iteration = n_iter\ \n self.cv = cv\ \n self.best_estimator_ = None\ \n self.best_score_ = None\ \n self.best_params_ = None\ \n\ \n def __min_fun(self, params):\ \n score=cross_val_score(self.estimator, self.X, self.y,scoring=self.scoring,cv=self.cv)\ \n acc = score.mean()\ \n return {'loss':-acc,'score': acc, 'status': STATUS_OK,'model' :self.estimator,'params': params}\ \n\ \n def fit(self, X, y):\ \n trials = Trials()\ \n self.X = X\ \n self.y = y\ \n best = fmin(self.__min_fun,self.params,algo=tpe.suggest, max_evals=self.iteration, trials=trials)\ \n result = sorted(trials.results, key = lambda x: x['loss'])[0]\ \n self.best_estimator_ = result['model']\ \n self.best_score_ = result['score']\ \n self.best_params_ = result['params']\ \n self.best_estimator_.fit(X, y)\ \n\ \n def hyperOptParamConversion( paramSpace):\ \n paramDict = {}\ \n for j in list(paramSpace.keys()):\ \n inp = paramSpace[j]\ \n isLog = False\ \n isLin = False\ \n isRan = False\ \n isList = False\ \n isString = False\ \n try:\ \n # check if functions are given as input and reassign paramspace\ \n v = paramSpace[j]\ \n if 'logspace' in paramSpace[j]:\ \n paramSpace[j] = v[v.find('(') + 1:v.find(')')].replace(' ', '')\ \n isLog = True\ \n elif 'linspace' in paramSpace[j]:\ \n paramSpace[j] = v[v.find('(') + 1:v.find(')')].replace(' ', '')\ \n isLin = True\ \n elif 'range' in paramSpace[j]:\ \n paramSpace[j] = v[v.find('(') + 1:v.find(')')].replace(' ', '')\ \n isRan = True\ \n elif 'list' in paramSpace[j]:\ \n paramSpace[j] = v[v.find('(') + 1:v.find(')')].replace(' ', '')\ \n isList = True\ \n elif '[' and ']' in paramSpace[j]:\ \n paramSpace[j] = v.split('[')[1].split(']')[0].replace(' ', '')\ \n isList = True\ \n x = paramSpace[j].split(',')\ \n except:\ \n x = paramSpace[j]\ \n str_arg = paramSpace[j]\ \n\ \n # check if arguments are string\ \n try:\ \n test = eval(x[0])\ \n except:\ \n isString = True\ \n\ \n if isString:\ \n paramDict.update({j: hp.choice(j, x)})\ \n else:\ \n res = eval(str_arg)\ \n if isLin:\ \n y = eval('np.linspace' + str(res))\ \n paramDict.update({j: hp.uniform(j, eval(x[0]), eval(x[1]))})\ \n elif isLog:\ \n y = eval('np.logspace' + str(res))\ \n paramDict.update(\ \n {j: hp.uniform(j, 10 ** eval(x[0]), 10 ** eval(x[1]))})\ \n elif isRan:\ \n y = eval('np.arange' + str(res))\ \n paramDict.update({j: hp.choice(j, y)})\ \n # check datatype of argument\ \n elif isinstance(eval(x[0]), bool):\ \n y = list(map(lambda i: eval(i), x))\ \n paramDict.update({j: hp.choice(j, eval(str(y)))})\ \n elif isinstance(eval(x[0]), float):\ \n res = eval(str_arg)\ \n if len(str_arg.split(',')) == 3 and not isList:\ \n y = eval('np.linspace' + str(res))\ \n #print(y)\ \n paramDict.update({j: hp.uniform(j, eval(x[0]), eval(x[1]))})\ \n else:\ \n y = list(res) if isinstance(res, tuple) else [res]\ \n paramDict.update({j: hp.choice(j, y)})\ \n else:\ \n res = eval(str_arg)\ \n if len(str_arg.split(',')) == 3 and not isList:\ \n y = eval('np.linspace' +str(res)) if eval(x[2]) >= eval(x[1]) else eval('np.arange'+str(res))\ \n else:\ \n y = list(res) if isinstance(res, tuple) else [res]\ \n paramDict.update({j: hp.choice(j, y)})\ \n return paramDict"}, 's2n':{'name':'s2n','imports':[{'mod':'word2number','mod_as':'w2n'},{'mod':'numpy','mod_as':'np'}],'code':"\n\ \ndef s2n(value):\ \n try:\ \n x=eval(value)\ \n return x\ \n except:\ \n try:\ \n return w2n.word_to_num(value)\ \n except:\ \n return np.nan"}, 'readWrite':{'name':'readWrite','imports':[{'mod':'json'},{'mod':'pandas','mod_as':'pd'}],'code':"\n\ \ndef read_json(file_path):\ \n data = None\ \n with open(file_path,'r') as f:\ \n data = json.load(f)\ \n return data\ \n\ \ndef write_json(data, file_path):\ \n with open(file_path,'w') as f:\ \n json.dump(data, f)\ \n\ \ndef read_data(file_path, encoding='utf-8', sep=','):\ \n return pd.read_csv(file_path, encoding=encoding, sep=sep)\ \n\ \ndef write_data(data, file_path, index=False):\ \n return data.to_csv(file_path, index=index)\ \n\ \n#Uncomment and change below code for google storage\ \n#def write_data(data, file_path, index=False):\ \n# file_name= file_path.name\ \n# data.to_csv('output_data.csv')\ \n# storage_client = storage.Client()\ \n# bucket = storage_client.bucket('aion_data')\ \n# bucket.blob('prediction/'+file_name).upload_from_filename('output_data.csv', content_type='text/csv')\ \n# return data\ \n\ \ndef is_file_name_url(file_name):\ \n supported_urls_starts_with = ('gs://','https://','http://')\ \n return file_name.startswith(supported_urls_starts_with)\ \n"}, 'logger':{'name':'set_logger','imports':[{'mod':'logging'}],'code':f"\n\ \nlog = None\ \ndef set_logger(log_file, mode='a'):\ \n global log\ \n logging.basicConfig(filename=log_file, filemode=mode, format='%(asctime)s %(name)s- %(message)s', level=logging.INFO, datefmt='%d-%b-%y %H:%M:%S')\ \n log = logging.getLogger(Path(__file__).parent.name)\ \n return log\ \n\ \ndef get_logger():\ \n return log\n"}, 'mlflowSetPath':{'name':'mlflowSetPath','code':f"\n\ndef mlflowSetPath(path, name):\ \n{self.tab}db_name = str(Path(path)/'mlruns')\ \n{self.tab}mlflow.set_tracking_uri('file:///' + db_name)\ \n{self.tab}mlflow.set_experiment(str(Path(path).name))\ \n"}, 'mlflow_create_experiment':{'name':'mlflow_create_experiment','code':f"\n\ndef mlflow_create_experiment(config, path, name):\ \n{self.tab}tracking_uri, artifact_uri, registry_uri = get_mlflow_uris(config, path)\ \n{self.tab}mlflow.tracking.set_tracking_uri(tracking_uri)\ \n{self.tab}mlflow.tracking.set_registry_uri(registry_uri)\ \n{self.tab}client = mlflow.tracking.MlflowClient()\ \n{self.tab}experiment = client.get_experiment_by_name(name)\ \n{self.tab}if experiment:\ \n{self.tab}{self.tab}experiment_id = experiment.experiment_id\ \n{self.tab}else:\ \n{self.tab}{self.tab}experiment_id = client.create_experiment(name, artifact_uri)\ \n{self.tab}return client, experiment_id\ \n"}, 'get_mlflow_uris':{'name':'get_mlflow_uris','code':f"\n\ndef get_mlflow_uris(config, path):\ \n artifact_uri = None\ \n tracking_uri_type = config.get('tracking_uri_type',None)\ \n if tracking_uri_type == 'localDB':\ \n tracking_uri = 'sqlite:///' + str(path.resolve()/'mlruns.db')\ \n elif tracking_uri_type == 'server' and config.get('tracking_uri', None):\ \n tracking_uri = config['tracking_uri']\ \n if config.get('artifacts_uri', None):\ \n if Path(config['artifacts_uri']).exists():\ \n artifact_uri = 'file:' + config['artifacts_uri']\ \n else:\ \n artifact_uri = config['artifacts_uri']\ \n else:\ \n artifact_uri = 'file:' + str(path.resolve()/'mlruns')\ \n else:\ \n tracking_uri = 'file:' + str(path.resolve()/'mlruns')\ \n artifact_uri = None\ \n if config.get('registry_uri', None):\ \n registry_uri = config['registry_uri']\ \n else:\ \n registry_uri = 'sqlite:///' + str(path.resolve()/'registry.db')\ \n return tracking_uri, artifact_uri, registry_uri\ \n"}, 'logMlflow':{'name':'logMlflow','code':f"\n\ndef logMlflow( params, metrices, estimator,tags={{}}, algoName=None):\ \n{self.tab}run_id = None\ \n{self.tab}for k,v in params.items():\ \n{self.tab}{self.tab}mlflow.log_param(k, v)\ \n{self.tab}for k,v in metrices.items():\ \n{self.tab}{self.tab}mlflow.log_metric(k, v)\ \n{self.tab}if 'CatBoost' in algoName:\ \n{self.tab}{self.tab}model_info = mlflow.catboost.log_model(estimator, 'model')\ \n{self.tab}else:\ \n{self.tab}{self.tab}model_info = mlflow.sklearn.log_model(sk_model=estimator, artifact_path='model')\ \n{self.tab}tags['processed'] = 'no'\ \n{self.tab}tags['registered'] = 'no'\ \n{self.tab}mlflow.set_tags(tags)\ \n{self.tab}if model_info:\ \n{self.tab}{self.tab}run_id = model_info.run_id\ \n{self.tab}return run_id\ \n"}, 'classification_metrices':{'name':'classification_metrices','imports':[{'mod':'sklearn'},{'mod':'math'}],'code':"\ndef get_classification_metrices( actual_values, predicted_values):\ \n result = {}\ \n accuracy_score = sklearn.metrics.accuracy_score(actual_values, predicted_values)\ \n avg_precision = sklearn.metrics.precision_score(actual_values, predicted_values,\ \n average='macro')\ \n avg_recall = sklearn.metrics.recall_score(actual_values, predicted_values,\ \n average='macro')\ \n avg_f1 = sklearn.metrics.f1_score(actual_values, predicted_values,\ \n average='macro')\ \n\ \n result['accuracy'] = math.floor(accuracy_score*10000)/100\ \n result['precision'] = math.floor(avg_precision*10000)/100\ \n result['recall'] = math.floor(avg_recall*10000)/100\ \n result['f1'] = math.floor(avg_f1*10000)/100\ \n return result\ \n"}, 'regression_metrices':{'name':'regression_metrices','imports':[{'mod':'numpy', 'mod_as':'np'}],'code':"\ndef get_regression_metrices( actual_values, predicted_values):\ \n result = {}\ \n\ \n me = np.mean(predicted_values - actual_values)\ \n sde = np.std(predicted_values - actual_values, ddof = 1)\ \n\ \n abs_err = np.abs(predicted_values - actual_values)\ \n mae = np.mean(abs_err)\ \n sdae = np.std(abs_err, ddof = 1)\ \n\ \n abs_perc_err = 100.*np.abs(predicted_values - actual_values) / actual_values\ \n mape = np.mean(abs_perc_err)\ \n sdape = np.std(abs_perc_err, ddof = 1)\ \n\ \n result['mean_error'] = me\ \n result['mean_abs_error'] = mae\ \n result['mean_abs_perc_error'] = mape\ \n result['error_std'] = sde\ \n result['abs_error_std'] = sdae\ \n result['abs_perc_error_std'] = sdape\ \n return result\ \n"} } def add_function(self, name, importer=None): if name in self.available_functions.keys(): self.codeText += self.available_functions[name]['code'] if importer: if 'imports' in self.available_functions[name].keys(): for module in self.available_functions[name]['imports']: mod_name = module['mod'] mod_from = module.get('mod_from', None) mod_as = module.get('mod_as', None) importer.addModule(mod_name, mod_from=mod_from, mod_as=mod_as) def get_function_name(self, name): if name in self.available_functions.keys(): return self.available_functions[name]['name'] return None def getCode(self): return self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class register(): def __init__(self, importer, indent=0, tab_size=4): self.tab = " "*tab_size self.codeText = "" self.function_code = "" self.importer = importer self.input_files = {} self.output_files = {} self.addInputFiles({'log' : 'aion.log', 'metaData' : 'modelMetaData.json','model' : 'model.pkl', 'performance': 'performance.json','production':'production.json','monitor':'monitoring.json'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def code_imports(self): modules = [{'module':'sys'} ,{'module':'json'} ,{'module':'time'} ,{'module':'platform'} ,{'module':'tempfile'} ,{'module':'sqlite3'} ,{'module':'mlflow'} ,{'module':'Path', 'mod_from':'pathlib'} ,{'module':'ViewType', 'mod_from':'mlflow.entities'} ,{'module':'MlflowClient', 'mod_from':'mlflow.tracking'} ,{'module':'ModelVersionStatus', 'mod_from':'mlflow.entities.model_registry.model_version_status'} ] self.import_modules(modules) def import_module(self, module, mod_from=None, mod_as=None): self.importer.addModule(module, mod_from=mod_from, mod_as=mod_as) def import_modules(self, modules): if isinstance(modules, list): for mod in modules: if isinstance(mod, dict): self.importer.addModule(mod['module'], mod_from= mod.get('mod_from', None), mod_as=mod.get('mod_as', None)) def getImportCode(self): return self.importer.getCode() def __addValidateConfigCode(self, models=None): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = read_json(config_file)\ \n return config\ " return text def addLocalFunctionsCode(self, models): self.function_code += self.__addValidateConfigCode(models) def addPrefixCode(self, indent=1): self.code_imports() self.codeText += "\n\ \ndef __merge_logs(log_file_sequence,path, files):\ \n if log_file_sequence['first'] in files:\ \n with open(path/log_file_sequence['first'], 'r') as f:\ \n main_log = f.read()\ \n files.remove(log_file_sequence['first'])\ \n for file in files:\ \n with open(path/file, 'r') as f:\ \n main_log = main_log + f.read()\ \n (path/file).unlink()\ \n with open(path/log_file_sequence['merged'], 'w') as f:\ \n f.write(main_log)\ \n\ \ndef merge_log_files(folder, models):\ \n log_file_sequence = {\ \n 'first': 'aion.log',\ \n 'merged': 'aion.log'\ \n }\ \n log_file_suffix = '_aion.log'\ \n log_files = [x+log_file_suffix for x in models if (folder/(x+log_file_suffix)).exists()]\ \n log_files.append(log_file_sequence['first'])\ \n __merge_logs(log_file_sequence, folder, log_files)\ \n\ \ndef register_model(targetPath,models,usecasename, meta_data):\ \n register = mlflow_register(targetPath, usecasename, meta_data)\ \n register.setup_registration()\ \n\ \n runs_with_score = register.get_unprocessed_runs(models)\ \n best_run = register.get_best_run(runs_with_score)\ \n register.update_unprocessed(runs_with_score)\ \n return register.register_model(models, best_run)\ \n\ \ndef register(log):\ \n config = validateConfig()\ \n targetPath = Path('aion')/config['targetPath']\ \n models = config['models']\ \n merge_log_files(targetPath, models)\ \n meta_data_file = targetPath/IOFiles['metaData']\ \n if meta_data_file.exists():\ \n meta_data = read_json(meta_data_file)\ \n else:\ \n raise ValueError(f'Configuration file not found: {meta_data_file}')\ \n usecase = config['targetPath']\ \n # enable logging\ \n log_file = targetPath/IOFiles['log']\ \n log = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem)\ \n register_model_name = register_model(targetPath,models,usecase, meta_data)\ \n status = {'Status':'Success','Message':f'Model Registered: {register_model_name}'}\ \n log.info(f'output: {status}')\ \n return json.dumps(status)" def getMainCodeModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'sys'} ,{'module':'os'} ,{'module':'json'} ,{'module':'logging'} ,{'module':'shutil'} ,{'module':'argparse'} ] return modules def addMainCode(self, models, indent=1): self.codeText += "\n\ \nif __name__ == '__main__':\ \n log = None\ \n try:\ \n print(register(log))\ \n except Exception as e:\ \n if log:\ \n log.error(e, exc_info=True)\ \n status = {'Status':'Failure','Message':str(e)}\ \n print(json.dumps(status))" def addStatement(self, statement, indent=1): self.codeText += f"\n{self.tab * indent}{statement}" def query_with_quetes_code(self, decs=True, indent=1): return """\n{first_indentation}def __get_unprocessed_runs_sorted(self): {indentation}query = "tags.processed = 'no'" {indentation}runs = self.client.search_runs( {indentation} experiment_ids=self.experiment_id, {indentation} filter_string=query, {indentation} run_view_type=ViewType.ACTIVE_ONLY, {indentation} order_by=['metrics.test_score {0}'] {indentation}) {indentation}return runs\n""".format('DESC' if decs else 'ASC', first_indentation=indent*self.tab, indentation=(1+indent)*self.tab) def addClassCode(self, smaller_is_better=False): self.codeText += "\ \nclass mlflow_register():\ \n\ \n def __init__(self, input_path, model_name, meta_data):\ \n self.input_path = Path(input_path).resolve()\ \n self.model_name = model_name\ \n self.meta_data = meta_data\ \n self.logger = logging.getLogger('ModelRegister')\ \n self.client = None\ \n self.monitoring_data = read_json(self.input_path/IOFiles['monitor'])\ \n mlflow_default_config = {'artifacts_uri':'','tracking_uri_type':'','tracking_uri':'','registry_uri':''}\ \n if not self.monitoring_data.get('mlflow_config',False):\ \n self.monitoring_data['mlflow_config'] = mlflow_default_config\ \n\ \n def setup_registration(self):\ \n tracking_uri, artifact_uri, registry_uri = get_mlflow_uris(self.monitoring_data['mlflow_config'],self.input_path)\ \n self.logger.info(f'MLflow tracking uri: {tracking_uri}')\ \n self.logger.info(f'MLflow registry uri: {registry_uri}')\ \n mlflow.set_tracking_uri(tracking_uri)\ \n mlflow.set_registry_uri(registry_uri)\ \n self.client = mlflow.tracking.MlflowClient(\ \n tracking_uri=tracking_uri,\ \n registry_uri=registry_uri,\ \n )\ \n self.experiment_id = self.client.get_experiment_by_name(self.model_name).experiment_id\ \n" self.codeText += self.query_with_quetes_code(smaller_is_better == False) self.codeText += "\ \n def __log_unprocessed_runs(self, runs):\ \n self.logger.info('Unprocessed runs:')\ \n for run in runs:\ \n self.logger.info(' {}: {}'.format(run.info.run_id,run.data.metrics['test_score']))\ \n\ \n def get_unprocessed_runs(self, model_path):\ \n unprocessed_runs = self.__get_unprocessed_runs_sorted()\ \n if not unprocessed_runs:\ \n raise ValueError('Registering fail: No new trained model')\ \n self.__log_unprocessed_runs( unprocessed_runs)\ \n return unprocessed_runs\ \n\ \n def __wait_until_ready(self, model_name, model_version):\ \n client = MlflowClient()\ \n for _ in range(10):\ \n model_version_details = self.client.get_model_version(\ \n name=model_name,\ \n version=model_version,\ \n )\ \n status = ModelVersionStatus.from_string(model_version_details.status)\ \n if status == ModelVersionStatus.READY:\ \n break\ \n time.sleep(1)\ \n\ \n def __create_model(self, run):\ \n artifact_path = 'model'\ \n model_uri = 'runs:/{run_id}/{artifact_path}'.format(run_id=run.info.run_id, artifact_path=artifact_path)\ \n self.logger.info(f'Registering model (run id): {run.info.run_id}')\ \n model_details = mlflow.register_model(model_uri=model_uri, name=self.model_name)\ \n self.__wait_until_ready(model_details.name, model_details.version)\ \n self.client.set_tag(run.info.run_id, 'registered', 'yes' )\ \n state_transition = self.client.transition_model_version_stage(\ \n name=model_details.name,\ \n version=model_details.version,\ \n stage='Production',\ \n )\ \n self.logger.info(state_transition)\ \n return model_details\ \n\ \n def get_best_run(self, models):\ \n return models[0]\ \n\ \n def __validate_config(self):\ \n try:\ \n load_data_loc = self.meta_data['load_data']['Status']['DataFilePath']\ \n except KeyError:\ \n raise ValueError('DataIngestion step output is corrupted')\ \n\ \n def __mlflow_log_transformer_steps(self, best_run):\ \n run_id = best_run.info.run_id\ \n meta_data = read_json(self.input_path/(best_run.data.tags['mlflow.runName']+'_'+IOFiles['metaData']))\ \n self.__validate_config()\ \n with mlflow.start_run(run_id):\ \n if 'transformation' in meta_data.keys():\ \n if 'target_encoder' in meta_data['transformation'].keys():\ \n source_loc = meta_data['transformation']['target_encoder']\ \n mlflow.log_artifact(str(self.input_path/source_loc))\ \n meta_data['transformation']['target_encoder'] = Path(source_loc).name\ \n if 'preprocessor' in meta_data['transformation'].keys():\ \n source_loc = meta_data['transformation']['preprocessor']\ \n mlflow.log_artifact(str(self.input_path/source_loc))\ \n meta_data['transformation']['preprocessor'] = Path(source_loc).name\ \n\ \n write_json(meta_data, self.input_path/IOFiles['metaData'])\ \n mlflow.log_artifact(str(self.input_path/IOFiles['metaData']))\ \n\ \n def __update_processing_tag(self, processed_runs):\ \n self.logger.info('Changing status to processed:')\ \n for run in processed_runs:\ \n self.client.set_tag(run.info.run_id, 'processed', 'yes')\ \n self.logger.info(f' run id: {run.info.run_id}')\ \n\ \n def update_unprocessed(self, runs):\ \n return self.__update_processing_tag( runs)\ \n\ \n def __force_register(self, best_run):\ \n self.__create_model( best_run)\ \n self.__mlflow_log_transformer_steps( best_run)\ \n production_json = self.input_path/IOFiles['production']\ \n production_model = {'Model':best_run.data.tags['mlflow.runName'],'runNo':self.monitoring_data['runNo'],'score':best_run.data.metrics['test_score']}\ \n write_json(production_model, production_json)\ \n database_path = self.input_path/(self.input_path.stem + '.db')\ \n if database_path.exists():\ \n database_path.unlink()\ \n return best_run.data.tags['mlflow.runName']\ \n\ \n def __get_register_model_score(self):\ \n reg = self.client.list_registered_models()\ \n if not reg:\ \n return '', 0\ \n run_id = reg[0].latest_versions[0].run_id\ \n run = self.client.get_run(run_id)\ \n score = run.data.metrics['test_score']\ \n return run_id, score\ \n\ \n def register_model(self, models, best_run):\ \n return self.__force_register(best_run)" def local_functions_code(self, smaller_is_better=True, indent=1): if smaller_is_better: min_max = 'min' else: min_max = 'max' self.codeText += "\ndef validate_config(deploy_dict):\ \n try:\ \n load_data_loc = deploy_dict['load_data']['Status']['DataFilePath']\ \n except KeyError:\ \n raise ValueError('DataIngestion step output is corrupted')\ \n\ \ndef get_digest(fname):\ \n import hashlib\ \n hash_algo = hashlib.sha256()\ \n with open(fname, 'rb') as f:\ \n for chunk in iter(lambda: f.read(2 ** 20), b''):\ \n hash_algo.update(chunk)\ \n return hash_algo.hexdigest()\ \n" def getCode(self, indent=1): return self.function_code + '\n' + self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from .imports import importModule from .load_data import tabularDataReader from .transformer import transformer as profiler from .transformer import data_profiler from .selector import selector from .trainer import learner from .register import register from .deploy import deploy from .drift_analysis import drift from .functions import global_function from .data_reader import data_reader from .utility import utility_function """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class tabularDataReader(): def __init__(self, tab_size=4): self.tab = ' ' * tab_size self.function_code = '' self.codeText = '' self.code_generated = False def getInputFiles(self): IOFiles = { "rawData": "rawData.dat", "metaData" : "modelMetaData.json", "log" : "aion.log", "outputData" : "rawData.dat", "monitoring":"monitoring.json", "prodData": "prodData", "prodDataGT":"prodDataGT" } text = 'IOFiles = ' if not IOFiles: text += '{ }' else: text += json.dumps(IOFiles, indent=4) return text def getOutputFiles(self): output_files = { 'metaData' : 'modelMetaData.json', 'log' : 'aion.log', 'outputData' : 'rawData.dat' } text = 'output_file = ' if not output_files: text += '{ }' else: text += json.dumps(output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def __addValidateConfigCode(self): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = read_json(config_file)\ \n if not config['targetPath']:\ \n raise ValueError(f'Target Path is not configured')\ \n return config" return text def addMainCode(self): self.codeText += "\n\ \nif __name__ == '__main__':\ \n log = None\ \n try:\ \n print(load_data(log))\ \n except Exception as e:\ \n if log:\ \n log.getLogger().error(e, exc_info=True)\ \n status = {'Status':'Failure','Message':str(e)}\ \n print(json.dumps(status))\ \n raise Exception(str(e))\ " def addLoadDataCode(self): self.codeText += """ #This function will read the data and save the data on persistent storage def load_data(log): config = validateConfig() targetPath = Path('aion')/config['targetPath'] targetPath.mkdir(parents=True, exist_ok=True) log_file = targetPath/IOFiles['log'] log = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem) monitoring = targetPath/IOFiles['monitoring'] if monitoring.exists(): monitoringStatus = read_json(monitoring) if monitoringStatus['dataLocation'] == '' and monitoringStatus['driftStatus'] != 'No Drift': reader = dataReader(reader_type=monitoring_data.get('prod_db_type','sqlite'),target_path=targetPath, config=config.get('db_config',None)) raw_data_location = targetPath/IOFiles['rawData'] if reader.file_exists(IOFiles['prodData']) and reader.file_exists(IOFiles['prodDataGT']): predicted_data = reader.read(IOFiles['prodData']) actual_data = reader.read(IOFiles['prodDataGT']) common_col = [k for k in predicted_data.columns.tolist() if k in actual_data.columns.tolist()] mergedRes = pd.merge(actual_data, predicted_data, on =common_col,how = 'inner') raw_data_path = pd.read_csv(raw_data_location) df = pd.concat([raw_data_path,mergedRes]) else: raise ValueError(f'Prod Data not found') elif monitoringStatus['dataLocation'] == '': raise ValueError(f'Data Location does not exist') else: if 's3' in monitoringStatus.keys(): input_reader = dataReader(reader_type='s3',target_path=None, config=monitoringStatus['s3']) log.info(f"Downloading '{monitoringStatus['s3']['file_name']}' from s3 bucket '{monitoringStatus['s3']['bucket_name']}'") df = input_reader.read(monitoringStatus['s3']['file_name']) else: location = monitoringStatus['dataLocation'] log.info(f'Dataset path: {location}') df = read_data(location) else: raise ValueError(f'Monitoring.json does not exist') status = {} output_data_path = targetPath/IOFiles['outputData'] log.log_dataframe(df) required_features = list(set(config['selected_features'] + [config['target_feature']])) log.info('Dataset features required: ' + ','.join(required_features)) missing_features = [x for x in required_features if x not in df.columns.tolist()] if missing_features: raise ValueError(f'Some feature/s is/are missing: {missing_features}') log.info('Removing unused features: '+','.join(list(set(df.columns) - set(required_features)))) df = df[required_features] log.info(f'Required features: {required_features}') try: log.info(f'Saving Dataset: {str(output_data_path)}') write_data(df, output_data_path, index=False) status = {'Status':'Success','DataFilePath':IOFiles['outputData'],'Records':len(df)} except: raise ValueError('Unable to create data file') meta_data_file = targetPath/IOFiles['metaData'] meta_data = dict() meta_data['load_data'] = {} meta_data['load_data']['selected_features'] = [x for x in config['selected_features'] if x != config['target_feature']] meta_data['load_data']['Status'] = status write_json(meta_data, meta_data_file) output = json.dumps(status) log.info(output) return output """ def addValidateConfigCode(self, indent=1): self.function_code += self.__addValidateConfigCode() def generateCode(self): self.addValidateConfigCode() self.addLoadDataCode() self.addMainCode() self.code_generated = True def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def getCode(self): if not self.code_generated: self.generateCode() return self.function_code + '\n' + self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json from mlac.ml.core import * from .utility import * def run_input_drift(config): importer = importModule() drifter = input_drift() importer.addModule('sys') importer.addModule('json') importer.addModule('mlflow') importer.addModule('platform') importer.addModule('warnings') importer.addModule('numpy', mod_as='np') importer.addModule('pandas', mod_as='pd') importer.addModule('stats', mod_from='scipy', mod_as='st') importer.addModule('Path', mod_from='pathlib') code = file_header(config['modelName']+'_'+config['modelVersion']) code += importer.getCode() drifter.generateCode() code += drifter.getCode() deploy_path = Path(config["deploy_path"])/'MLaC'/'InputDrift' deploy_path.mkdir(parents=True, exist_ok=True) py_file = deploy_path/"input_drift.py" with open(py_file, "w") as f: f.write(code) req_file = deploy_path/"requirements.txt" with open(req_file, "w") as f: f.write(importer.getBaseModule()) create_docker_file('input_drift', deploy_path) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json from mlac.ml.core import * from .utility import * def run_output_drift(config): importer = importModule() drifter = output_drift(missing = get_variable('fillna', False), word2num_features= get_variable('word2num_features', False), cat_encoder = get_variable('cat_encoder', False),target_encoder = get_variable('target_encoder', False),normalizer = get_variable('normalizer', False),text_profiler = get_variable('text_features', False),feature_reducer = get_variable('feature_reducer', False),score_smaller_is_better = get_variable('smaller_is_better', False),problem_type=config['problem_type']) function = global_function() importer.addModule('sys') importer.addModule('math') importer.addModule('json') importer.addModule('platform') importer.addModule('joblib') importer.addModule('mlflow') importer.addModule('sklearn') importer.addModule('numpy', mod_as='np') importer.addModule('pandas', mod_as='pd') importer.addModule('Path', mod_from='pathlib') importer.addModule('InfluxDBClient', mod_from='influxdb') function.add_function('readWrite') code = file_header(config['modelName']+'_'+config['modelVersion']) code += importer.getCode() code += function.getCode() drifter.generateCode() code += drifter.getCode() deploy_path = Path(config["deploy_path"])/'MLaC'/'OutputDrift' deploy_path.mkdir(parents=True, exist_ok=True) py_file = deploy_path/"output_drift.py" with open(py_file, "w") as f: f.write(code) req_file = deploy_path/"requirements.txt" with open(req_file, "w") as f: f.write(importer.getBaseModule()) create_docker_file('output_drift', deploy_path) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import shutil from pathlib import Path import json from mlac.ml.core import * from .utility import * import tarfile def add_text_dependency(): return """nltk==3.6.3 textblob==0.15.3 spacy==3.1.3 demoji==1.1.0 bs4==0.0.1 text_unidecode==1.3 contractions==0.1.73 """ def get_deploy_params(config): param_keys = ["modelVersion","problem_type","target_feature"] data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] data['ipAddress'] = '127.0.0.1' data['portNo'] = '8094' return data def import_trainer_module(importer): non_sklearn_modules = get_variable('non_sklearn_modules') if non_sklearn_modules: for mod in non_sklearn_modules: module = get_module_mapping(mod) mod_from = module.get('mod_from',None) mod_as = module.get('mod_as',None) importer.addModule(module['module'], mod_from=mod_from, mod_as=mod_as) imported_modules = [ {'module': 'sys', 'mod_from': None, 'mod_as': None}, {'module': 'math', 'mod_from': None, 'mod_as': None}, {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'scipy', 'mod_from': None, 'mod_as': None}, {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'shutil', 'mod_from': None, 'mod_as': None}, {'module': 'mlflow', 'mod_from': None, 'mod_as': None}, {'module': 'sklearn', 'mod_from': None, 'mod_as': None}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'argparse', 'mod_from': None, 'mod_as': None}, {'module': 'platform', 'mod_from': None, 'mod_as': None} ] def run_deploy(config): generated_files = [] importer = importModule() deployer = deploy(target_encoder = get_variable('target_encoder', False),feature_reducer = get_variable('feature_reducer', False),score_smaller_is_better = get_variable('smaller_is_better', False)) function = global_function() importModules(importer, imported_modules) if get_variable('cat_encoder', False): importer.addModule('category_encoders') import_trainer_module(importer) if get_variable('word2num_features'): function.add_function('s2n', importer) if get_variable('text_features'): importer.addLocalModule('textProfiler', mod_from='text.textProfiler') usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'ModelServing' deploy_path.mkdir(parents=True, exist_ok=True) # create the utility file importer.addLocalModule('*', mod_from='utility') utility_obj = utility_function('Prediction') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create the production data reader file importer.addLocalModule('*', mod_from='data_reader') reader_obj = data_reader(['sqlite','influx']) with open(deploy_path/"data_reader.py", 'w') as f: f.write(file_header(usecase) + reader_obj.get_code()) generated_files.append("data_reader.py") # need to copy data profiler from AION code as code is splitted and merging code amnnually # can add bugs aion_utilities = Path(__file__).parent.parent.parent.parent / 'utilities' with tarfile.open(aion_utilities / 'text.tar') as file: file.extractall(deploy_path) if (deploy_path / 'utils').exists(): shutil.rmtree(deploy_path / 'utils') with tarfile.open(aion_utilities / 'utils.tar') as file: file.extractall(deploy_path ) generated_files.append("text") generated_files.append("utils") # create empty init file required for creating a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") function.add_function('get_mlflow_uris') code = file_header(usecase) code += importer.getCode() code += deployer.getInputOutputFiles() code += function.getCode() code += deployer.getCode() # create prediction file with open(deploy_path/"predict.py", 'w') as f: f.write(code) generated_files.append("predict.py") # create groundtruth file with open(deploy_path/"groundtruth.py", 'w') as f: f.write(file_header(usecase) + deployer.getGroundtruthCode()) generated_files.append("groundtruth.py") # create create service file with open(deploy_path/"aionCode.py", 'w') as f: f.write(file_header(usecase) + deployer.getServiceCode()) generated_files.append("aionCode.py") importer.addModule('seaborn') # create requirements file req_file = deploy_path/"requirements.txt" with open(req_file, "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer(), reader_obj.get_importer()]) if config["text_features"]: req += add_text_dependency() f.write(req) generated_files.append("requirements.txt") # create config file config_file = deploy_path/"config.json" config_data = get_deploy_params(config) with open (config_file, "w") as f: json.dump(config_data, f, indent=4) generated_files.append("config.json") # create docker file create_docker_file('Prediction', deploy_path,config['modelName'], generated_files, True if config["text_features"] else False) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json from mlac.ml.core import * from .utility import * def get_model_name(algo, method): if method == 'modelBased': return algo + '_' + 'MLBased' if method == 'statisticalBased': return algo + '_' + 'StatisticsBased' else: return algo def get_training_params(config, algo): param_keys = ["modelVersion","problem_type","target_feature","train_features","scoring_criteria","test_ratio","optimization_param"] data = {key:value for (key,value) in config.items() if key in param_keys} data['algorithms'] = {algo: config['algorithms'][algo]} data['targetPath'] = config['modelName'] return data def addImporterLearner(model, importer): module = get_module_mapping(model) mod_from = module.get('mod_from',None) mod_as = module.get('mod_as',None) importer.addModule(module['module'], mod_from=mod_from, mod_as=mod_as) if not get_variable('non_sklearn_modules'): update_variable('non_sklearn_modules', []) if 'sklearn' not in mod_from: modules = get_variable('non_sklearn_modules') modules.append(model) update_variable('non_sklearn_modules', modules) def addEvaluator(scorer_type, optimizer,trainer, importer): trainer.addStatement("if not X_test.empty:") if optimizer == 'genetic': trainer.addStatement('features = [x for i,x in enumerate(features) if grid.support_[i]]',indent=2) trainer.addStatement('y_pred = estimator.predict(X_test[features])',indent=2) if scorer_type == 'accuracy': importer.addModule('accuracy_score', mod_from='sklearn.metrics') trainer.addStatement(f"test_score = round(accuracy_score(y_test,y_pred),2) * 100",indent=2) importer.addModule('confusion_matrix', mod_from='sklearn.metrics') trainer.addStatement("log.info('Confusion Matrix:')",indent=2) trainer.addStatement("log.info('\\n' + pd.DataFrame(confusion_matrix(y_test,y_pred)).to_string())",indent=2) elif scorer_type == 'recall': importer.addModule('recall_score', mod_from='sklearn.metrics') trainer.addStatement(f"test_score = round(recall_score(y_test,y_pred,average='macro'),2) * 100",indent=2) importer.addModule('confusion_matrix', mod_from='sklearn.metrics') trainer.addStatement(f"log.info('Confusion Matrix:\\n')",indent=2) trainer.addStatement(f'log.info(pd.DataFrame(confusion_matrix(y_test,y_pred)))',indent=2) elif scorer_type == 'precision': importer.addModule('precision_score', mod_from='sklearn.metrics') trainer.addStatement(f"test_score = round(precision_score(y_test,y_pred,average='macro'),2) * 100",indent=2) importer.addModule('confusion_matrix', mod_from='sklearn.metrics') trainer.addStatement(f"log.info('Confusion Matrix:\\n')",indent=2) trainer.addStatement(f'log.info(pd.DataFrame(confusion_matrix(y_test,y_pred)))',indent=2) elif scorer_type == 'f1_score': importer.addModule('f1_score', mod_from='sklearn.metrics') trainer.addStatement(f"test_score = round(f1_score(y_test,y_pred,average='macro'),2) * 100",indent=2) importer.addModule('confusion_matrix', mod_from='sklearn.metrics') trainer.addStatement(f"log.info('Confusion Matrix:\\n')",indent=2) trainer.addStatement(f'log.info(pd.DataFrame(confusion_matrix(y_test,y_pred)))',indent=2) elif scorer_type == 'roc_auc': importer.addModule('roc_auc_score', mod_from='sklearn.metrics') trainer.addStatement("try:") trainer.addStatement(f"test_score = round(roc_auc_score(y_test,y_pred),2) * 100", indent=3) importer.addModule('confusion_matrix', mod_from='sklearn.metrics') trainer.addStatement(f"log.info('Confusion Matrix:\\n')",indent=3) trainer.addStatement(f'log.info(pd.DataFrame(confusion_matrix(y_test,y_pred)))',indent=3) trainer.addStatement("except:") trainer.addStatement("try:",indent=3) trainer.addStatement("actual = pd.get_dummies(y_test)",indent=4) trainer.addStatement("y_pred = pd.get_dummies(y_pred)",indent=4) trainer.addStatement(f"test_score = round(roc_auc_score(y_test,y_pred,average='weighted', multi_class='ovr'),2) * 100", indent=3) trainer.addStatement(f"log.info('Confusion Matrix:\\n')",indent=4) trainer.addStatement(f'log.info(pd.DataFrame(confusion_matrix(y_test,y_pred)))',indent=4) trainer.addStatement("except:",indent=3) trainer.addStatement(f"test_score = 0.0", indent=4) elif scorer_type == 'neg_mean_squared_error' or scorer_type == 'mse': importer.addModule('mean_squared_error', mod_from='sklearn.metrics') trainer.addStatement(f'test_score = round(mean_squared_error(y_test,y_pred),2)',indent=2) update_variable('smaller_is_better', True) elif scorer_type == 'neg_root_mean_squared_error' or scorer_type == 'rmse': importer.addModule('mean_squared_error', mod_from='sklearn.metrics') trainer.addStatement(f'test_score = round(mean_squared_error(y_test,y_pred,squared=False),2)',indent=2) update_variable('smaller_is_better', True) elif scorer_type == 'neg_mean_absolute_error' or scorer_type == 'mae': importer.addModule('mean_absolute_error', mod_from='sklearn.metrics') trainer.addStatement(f'test_score = round(mean_absolute_error(y_test,y_pred),2)',indent=2) update_variable('smaller_is_better', True) elif scorer_type == 'r2': importer.addModule('r2_score', mod_from='sklearn.metrics') trainer.addStatement(f'test_score = round(r2_score(y_test,y_pred),2)',indent=2) def update_search_space(algo, config): search_space = [] algoritms = config["algorithms"] model = algo params = algoritms[model] model_dict = {model:get_module_mapping(model)['mod_from']} d = {'algo': model_dict} d['param'] = params search_space.append(d) config['search_space'] = search_space def get_optimization(optimization, importer, function=None): if optimization == 'grid': importer.addModule('GridSearchCV', mod_from='sklearn.model_selection') optimization = 'GridSearchCV' elif optimization == 'random': importer.addModule('RandomizedSearchCV', mod_from='sklearn.model_selection') optimization = 'RandomizedSearchCV' elif optimization == 'genetic': importer.addModule('GeneticSelectionCV', mod_from='genetic_selection') optimization = 'GeneticSelectionCV' elif optimization == 'bayesopt': optimization = 'BayesSearchCV' function.add_function(optimization,importer) return optimization def scoring_criteria_reg(score_param): scorer_mapping = { 'mse':'neg_mean_squared_error', 'rmse':'neg_root_mean_squared_error', 'mae':'neg_mean_absolute_error', 'r2':'r2' } return scorer_mapping.get(score_param, 'neg_mean_squared_error') def addBalancing(balancingMethod, importer, code): if balancingMethod == 'oversample': importer.addModule('SMOTE', mod_from='imblearn.over_sampling') code.addStatement("\n # data balancing") code.addStatement("X_train, y_train = SMOTE(sampling_strategy='auto', k_neighbors=1, random_state=100).fit_resample(X_train, y_train)") if balancingMethod == 'undersample': importer.addModule('TomekLinks', mod_from='imblearn.under_sampling') code.addStatement("\n # data balancing") code.addStatement("X_train, y_train = TomekLinks().fit_resample(X_train, y_train)") def run_trainer(base_config): base_trainer = learner() base_importer = importModule() function = global_function() base_importer.addModule('joblib') base_importer.addModule('warnings') base_importer.addModule('argparse') base_importer.addModule('pandas', mod_as='pd') base_importer.addModule('Path', mod_from='pathlib') function.add_function('get_mlflow_uris') function.add_function('mlflow_create_experiment') importModules(base_importer,base_trainer.getPrefixModules()) base_trainer.addPrefixCode() if base_config["algorithms"]: base_trainer.add_train_test_split('train_features', 'target_feature', "config['test_ratio']") if base_config["problem_type"] == 'classification': if base_config["balancingMethod"]: addBalancing(base_config["balancingMethod"],base_importer,base_trainer) base_trainer.addStatement(f"log.info('Data balancing done')") base_trainer.addStatement("\n #select scorer") if base_config["problem_type"] == 'classification': function.add_function('scoring_criteria', base_importer) base_trainer.addStatement("scorer = scoring_criteria(config['scoring_criteria'],config['problem_type'], df[target_feature].nunique())") else: base_config['scoring_criteria'] = scoring_criteria_reg(base_config['scoring_criteria']) base_trainer.addStatement(f"scorer = config['scoring_criteria']") base_trainer.addStatement(f"log.info('Scoring criteria: {base_config['scoring_criteria']}')") feature_selector = [] if base_config['feature_reducer']: feature_selector.append(base_config['feature_reducer']) elif base_config['feature_selector']: feature_selector = base_config['feature_selector'] for algo in base_config["algorithms"].keys(): for method in feature_selector: trainer = learner() importer = importModule() trainer.copyCode(base_trainer) importer.copyCode(base_importer) config = base_config usecase = config['modelName']+'_'+config['modelVersion'] addImporterLearner(algo, importer) trainer.addStatement("\n #Training model") trainer.addStatement(f"log.info('Training {algo} for {method}')") trainer.add_model_fit(algo, get_optimization(config["optimization"], importer, function), method, importer) trainer.addStatement("\n #model evaluation") addEvaluator(config['scoring_criteria'],config["optimization"], trainer, importer) function.add_function('mlflowSetPath') function.add_function('logMlflow') importModules(importer, trainer.getSuffixModules()) importModules(importer, trainer.getMainCodeModules()) if base_config["problem_type"] == 'classification': function.add_function('classification_metrices', importer) trainer.addStatement("metrices = get_classification_metrices(y_test,y_pred)",indent=2) trainer.add_100_trainsize_code() trainer.addStatement("metrices.update({'train_score': train_score, 'test_score':test_score})") else: function.add_function('regression_metrices', importer) trainer.addStatement("metrices = get_regression_metrices(y_test,y_pred)",indent=2) trainer.add_100_trainsize_code() trainer.addStatement("metrices.update({'train_score': train_score, 'test_score':test_score})") trainer.addSuffixCode() trainer.addMainCode() model_name = get_model_name(algo,method) deploy_path = Path(config["deploy_path"])/'MLaC'/('ModelTraining'+'_' + model_name) deploy_path.mkdir(parents=True, exist_ok=True) generated_files = [] # create the utility file importer.addLocalModule('*', mod_from='utility') utility_obj = utility_function('train') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create empty init file to make a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") code = importer.getCode() code += 'warnings.filterwarnings("ignore")\n' code += f"\nmodel_name = '{model_name}'\n" append_variable('models_name',model_name) out_files = {'log':f'{model_name}_aion.log','model':f'{model_name}_model.pkl','performance':f'{model_name}_performance.json','metaDataOutput':f'{model_name}_modelMetaData.json'} trainer.addOutputFiles(out_files) code += trainer.getInputOutputFiles() code += function.getCode() trainer.addLocalFunctionsCode() code += trainer.getCode() with open(deploy_path/"aionCode.py", "w") as f: f.write(code) generated_files.append("aionCode.py") with open(deploy_path/"requirements.txt", "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") with open (deploy_path/"config.json", "w") as f: json.dump(get_training_params(config, algo), f, indent=4) generated_files.append("config.json") create_docker_file('train', deploy_path,config['modelName'], generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json import platform from mlac.ml.core import * from .utility import * output_file_map = { 'feature_reducer' : {'feature_reducer' : 'feature_reducer.pkl'} } def get_selector_params(config): param_keys = ["modelVersion","problem_type","target_feature","train_features","cat_features","n_components"] data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] return data def run_selector(config): select = selector() importer = importModule() function = global_function() importModules(importer,select.getPrefixModules()) select.addPrefixCode() if config["target_feature"] in config["train_features"]: config["train_features"].remove(config["target_feature"]) select.addStatement("train_features = df.columns.tolist()") select.addStatement("target_feature = config['target_feature']") select.addStatement("train_features.remove(target_feature)") select.addStatement("cat_features = prev_step_output['cat_features']") select.add_variable('total_features',[]) select.addStatement("log.log_dataframe(df)") methods = config.get("feature_selector", None) feature_reducer = config.get("feature_reducer", None) select.addStatement("selected_features = {}") select.addStatement("meta_data['featureengineering']= {}") if feature_reducer: update_variable('feature_reducer', True) select.addStatement(f"log.info('Running dimensionality reduction technique( {feature_reducer})')") if feature_reducer == 'pca': importer.addModule('PCA', mod_from='sklearn.decomposition') if int(config["n_components"]) == 0: select.addStatement("dimension_reducer = PCA(n_components='mle',svd_solver = 'full')") elif int(config["n_components"]) < 1: select.addStatement("dimension_reducer = PCA(n_components=config['n_components'],svd_solver = 'full')") else: select.addStatement("dimension_reducer = PCA(n_components=config['n_components'])") elif feature_reducer == 'svd': importer.addModule('TruncatedSVD', mod_from='sklearn.decomposition') if config["n_components"] < 2: config["n_components"] = 2 select.addStatement("dimension_reducer = TruncatedSVD(n_components=config['n_components'], n_iter=7, random_state=42)") elif feature_reducer == 'factoranalysis': importer.addModule('FactorAnalysis', mod_from='sklearn.decomposition') if config["n_components"] == 0: select.addStatement("dimension_reducer = FactorAnalysis()") else: select.addStatement("dimension_reducer = FactorAnalysis(n_components=config['n_components'])") elif feature_reducer == 'ica': importer.addModule('FastICA', mod_from='sklearn.decomposition') if config["n_components"] == 0: select.addStatement("dimension_reducer = FastICA()") else: select.addStatement("dimension_reducer = FastICA(n_components=config['n_components'])") select.addStatement("pca_array = dimension_reducer.fit_transform(df[train_features])") select.addStatement("pca_columns = ['pca_'+str(e) for e in list(range(pca_array.shape[1]))]") select.addStatement("scaledDF = pd.DataFrame(pca_array, columns=pca_columns)") select.addStatement("scaledDF[target_feature] = df[target_feature]") select.addStatement("df = scaledDF") select.addStatement(f"selected_features['{feature_reducer}'] = pca_columns") select.addStatement("total_features = df.columns.tolist()") select.addStatement("meta_data['featureengineering']['feature_reducer']= {}") select.addStatement("reducer_file_name = str(targetPath/IOFiles['feature_reducer'])") importer.addModule('joblib') select.addStatement("joblib.dump(dimension_reducer, reducer_file_name)") select.addStatement("meta_data['featureengineering']['feature_reducer']['file']= IOFiles['feature_reducer']") select.addStatement("meta_data['featureengineering']['feature_reducer']['features']= train_features") select.addOutputFiles(output_file_map['feature_reducer']) elif methods: if 'allFeatures' in methods: addDropFeature('target_feature', 'train_features', select) select.addStatement("selected_features['allFeatures'] = train_features") if 'modelBased' in methods: select.addStatement(f"log.info('Model Based Correlation Analysis Start')") select.addStatement("model_based_feat = []") importer.addModule('numpy', mod_as='np') importer.addModule('RFE', mod_from='sklearn.feature_selection') importer.addModule('MinMaxScaler', mod_from='sklearn.preprocessing') if config["problem_type"] == 'classification': importer.addModule('ExtraTreesClassifier', mod_from='sklearn.ensemble') select.addStatement("estimator = ExtraTreesClassifier(n_estimators=100)") else: importer.addModule('Lasso', mod_from='sklearn.linear_model') select.addStatement("estimator = Lasso()") select.addStatement("estimator.fit(df[train_features],df[target_feature])") select.addStatement("rfe = RFE(estimator, n_features_to_select=1, verbose =0 )") select.addStatement("rfe.fit(df[train_features],df[target_feature])") select.addStatement("ranks = MinMaxScaler().fit_transform(-1*np.array([list(map(float, rfe.ranking_))]).T).T[0]") select.addStatement("ranks = list(map(lambda x: round(x,2), ranks))") select.addStatement("for item, rank in zip(df.columns,ranks):") select.addStatement("if rank > 0.30:", indent=2) select.addStatement("model_based_feat.append(item)", indent=3) addDropFeature('target_feature', 'model_based_feat', select) select.addStatement("selected_features['modelBased'] = model_based_feat") select.addStatement(f"log.info(f'Highly Correlated Features : {{model_based_feat}}')") if 'statisticalBased' in methods: select.addStatement(f"log.info('Statistical Based Correlation Analysis Start')") function.add_function('start_reducer',importer) select.addStatement(f"features = start_reducer(df, target_feature, {config['corr_threshold']},{config['var_threshold']})") select.addStatement("train_features = [x for x in features if x in train_features]") select.addStatement("cat_features = [x for x in cat_features if x in features]") select.addStatement("numeric_features = df[features].select_dtypes('number').columns.tolist()") if config["problem_type"] == 'classification': function.add_function('feature_importance_class') select.addStatement(f"statistics_based_feat = feature_importance_class(df[features], numeric_features, cat_features, target_feature, {config['pValueThreshold']},{config['corr_threshold']})") else: function.add_function('feature_importance_reg') select.addStatement(f"statistics_based_feat = feature_importance_reg(df[features], numeric_features, target_feature, {config['pValueThreshold']},{config['corr_threshold']})") addDropFeature('target_feature', 'statistics_based_feat', select) select.addStatement("selected_features['statisticalBased'] = statistics_based_feat") select.addStatement(f"log.info('Highly Correlated Features : {{statistics_based_feat}}')") select.addStatement("total_features = list(set([x for y in selected_features.values() for x in y] + [target_feature]))") select.addStatement(f"df = df[total_features]") select.addStatement("log.log_dataframe(df)") select.addSuffixCode() importModules(importer, select.getSuffixModules()) importModules(importer, select.getMainCodeModules()) select.addMainCode() generated_files = [] usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'FeatureEngineering' deploy_path.mkdir(parents=True, exist_ok=True) # create the utility file importer.addLocalModule('*', mod_from='utility') utility_obj = utility_function('selector') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create empty init file to make a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") code = file_header(usecase) code += importer.getCode() code += select.getInputOutputFiles() code += function.getCode() select.addLocalFunctionsCode() code += select.getCode() with open(deploy_path/"aionCode.py", "w") as f: f.write(code) generated_files.append("aionCode.py") with open(deploy_path/"requirements.txt", "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") config_file = deploy_path/"config.json" config_data = get_selector_params(config) with open (config_file, "w") as f: json.dump(config_data, f, indent=4) generated_files.append("config.json") create_docker_file('selector', deploy_path,config['modelName'], generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import datetime from pathlib import Path variables = {} def init_variables(): global variables variables = {} def update_variable(name, value): variables[name] = value def get_variable(name, default=None): return variables.get(name, default) def append_variable(name, value): data = get_variable(name) if not data: update_variable(name, [value]) elif not isinstance(data, list): update_variable(name, [data, value]) else: data.append(value) update_variable(name, data) def addDropFeature(feature, features_list, coder, indent=1): coder.addStatement(f'if {feature} in {features_list}:', indent=indent) coder.addStatement(f'{features_list}.remove({feature})', indent=indent+1) def importModules(importer, modules_list): for module in modules_list: mod_from = module.get('mod_from',None) mod_as = module.get('mod_as',None) importer.addModule(module['module'], mod_from=mod_from, mod_as=mod_as) def file_header(use_case, module_name=None): time_str = datetime.datetime.now().isoformat(timespec='seconds', sep=' ') text = "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n" return text + f"'''\nThis file is automatically generated by AION for {use_case} usecase.\nFile generation time: {time_str}\n'''" def get_module_mapping(module): mapping = { "LogisticRegression": {'module':'LogisticRegression', 'mod_from':'sklearn.linear_model'} ,"GaussianNB": {'module':'GaussianNB', 'mod_from':'sklearn.naive_bayes'} ,"DecisionTreeClassifier": {'module':'DecisionTreeClassifier', 'mod_from':'sklearn.tree'} ,"SVC": {'module':'SVC', 'mod_from':'sklearn.svm'} ,"KNeighborsClassifier": {'module':'KNeighborsClassifier', 'mod_from':'sklearn.neighbors'} ,"GradientBoostingClassifier": {'module':'GradientBoostingClassifier', 'mod_from':'sklearn.ensemble'} ,'RandomForestClassifier':{'module':'RandomForestClassifier','mod_from':'sklearn.ensemble'} ,'XGBClassifier':{'module':'XGBClassifier','mod_from':'xgboost'} ,'LGBMClassifier':{'module':'LGBMClassifier','mod_from':'lightgbm'} ,'CatBoostClassifier':{'module':'CatBoostClassifier','mod_from':'catboost'} ,"LinearRegression": {'module':'LinearRegression', 'mod_from':'sklearn.linear_model'} ,"Lasso": {'module':'Lasso', 'mod_from':'sklearn.linear_model'} ,"Ridge": {'module':'Ridge', 'mod_from':'sklearn.linear_model'} ,"DecisionTreeRegressor": {'module':'DecisionTreeRegressor', 'mod_from':'sklearn.tree'} ,'RandomForestRegressor':{'module':'RandomForestRegressor','mod_from':'sklearn.ensemble'} ,'XGBRegressor':{'module':'XGBRegressor','mod_from':'xgboost'} ,'LGBMRegressor':{'module':'LGBMRegressor','mod_from':'lightgbm'} ,'CatBoostRegressor':{'module':'CatBoostRegressor','mod_from':'catboost'} } return mapping.get(module, None) def create_docker_file(name, path,usecasename,files=[],text_feature=False): text = "" if name == 'load_data': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='RUN pip install --no-cache-dir -r requirements.txt' elif name == 'transformer': text='FROM python:3.8-slim-buster\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='''RUN \ ''' text+=''' pip install --no-cache-dir -r requirements.txt\ ''' if text_feature: text += ''' && python -m nltk.downloader stopwords && python -m nltk.downloader punkt && python -m nltk.downloader wordnet && python -m nltk.downloader averaged_perceptron_tagger\ ''' text+='\n' elif name == 'selector': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='RUN pip install --no-cache-dir -r requirements.txt' elif name == 'train': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' text+='COPY requirements.txt requirements.txt' text+='\n' text+='COPY config.json config.json' text+='\n' text+='COPY aionCode.py aionCode.py' text+='\n' text+='COPY utility.py utility.py' text+='\n' text+='RUN pip install --no-cache-dir -r requirements.txt' elif name == 'register': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='RUN pip install --no-cache-dir -r requirements.txt' elif name == 'Prediction': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='''RUN \ ''' text+='''pip install --no-cache-dir -r requirements.txt\ ''' if text_feature: text += ''' && python -m nltk.downloader stopwords && python -m nltk.downloader punkt && python -m nltk.downloader wordnet && python -m nltk.downloader averaged_perceptron_tagger\ ''' text+='\n' text+='ENTRYPOINT ["python", "aionCode.py","-ip","0.0.0.0","-pn","8094"]\n' elif name == 'input_drift': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='RUN pip install --no-cache-dir -r requirements.txt' file_name = Path(path)/'Dockerfile' with open(file_name, 'w') as f: f.write(text) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json from mlac.ml.core import * from .utility import * imported_modules = [ {'module': 'sys', 'mod_from': None, 'mod_as': None}, {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'math', 'mod_from': None, 'mod_as': None}, {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'mlflow', 'mod_from': None, 'mod_as': None}, {'module': 'sklearn', 'mod_from': None, 'mod_as': None}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'argparse', 'mod_from': None, 'mod_as': None}, {'module': 'stats', 'mod_from': 'scipy', 'mod_as': 'st'}, {'module': 'platform', 'mod_from': None, 'mod_as': None } ] def get_drift_params(config): param_keys = ["modelVersion","problem_type","target_feature","selected_features","scoring_criteria","s3"] data = {key:value for (key,value) in config.items() if key in param_keys} usecase = config['modelName'] data['targetPath'] = usecase if config['dataLocation'] != '': data['inputUri'] = config['dataLocation'] else: data['inputUri'] = '<input datalocation>' data['prod_db_type'] = config.get('prod_db_type', 'sqlite') data['db_config'] = config.get('db_config', {}) data['mlflow_config'] = config.get('mlflow_config', {'artifacts_uri':'','tracking_uri_type':'','tracking_uri':'','registry_uri':''}) return data def run_drift_analysis(config): init_variables() importer = importModule() function = global_function() drifter = drift() importModules(importer, imported_modules) usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'ModelMonitoring' deploy_path.mkdir(parents=True, exist_ok=True) generated_files = [] # create the utility file importer.addLocalModule('*', mod_from='utility') utility_obj = utility_function('drift') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create the production data reader file importer.addLocalModule('dataReader', mod_from='data_reader') readers = ['sqlite','influx'] if 's3' in config.keys(): readers.append('s3') reader_obj = data_reader(readers) with open(deploy_path/"data_reader.py", 'w') as f: f.write(file_header(usecase) + reader_obj.get_code()) generated_files.append("data_reader.py") # create empty init file to make a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") importer.addLocalModule('inputdrift', mod_from='input_drift') code = file_header(usecase) code += importer.getCode() code += drifter.getInputOutputFiles() code += function.getCode() code += drifter.get_main_drift_code(config['problem_type'], get_variable('smaller_is_better', False)) with open(deploy_path/"aionCode.py", "w") as f: f.write(code) generated_files.append("aionCode.py") input_drift_importer = importModule() importModules(input_drift_importer, drifter.get_input_drift_import_modules()) code = file_header(usecase) code += input_drift_importer.getCode() code += drifter.get_input_drift_code() with open(deploy_path/"input_drift.py", "w") as f: f.write(code) generated_files.append("input_drift.py") with open (deploy_path/"config.json", "w") as f: json.dump(get_drift_params(config), f, indent=4) generated_files.append("config.json") req_file = deploy_path/"requirements.txt" with open(req_file, "w") as f: f.write(importer.getBaseModule(extra_importers=[utility_obj.get_importer(), reader_obj.get_importer(), input_drift_importer])) generated_files.append("requirements.txt") create_docker_file('input_drift', deploy_path,config['modelName'], generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import shutil from pathlib import Path import json from mlac.ml.core import * from .utility import * import tarfile output_file_map = { 'text' : {'text' : 'text_profiler.pkl'}, 'targetEncoder' : {'targetEncoder' : 'targetEncoder.pkl'}, 'featureEncoder' : {'featureEncoder' : 'inputEncoder.pkl'}, 'normalizer' : {'normalizer' : 'normalizer.pkl'} } def add_common_imports(importer): common_importes = [ {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'argparse', 'mod_from': None, 'mod_as': None}, {'module': 'platform', 'mod_from': None, 'mod_as': None } ] for mod in common_importes: importer.addModule(mod['module'], mod_from=mod['mod_from'], mod_as=mod['mod_as']) def add_text_dependency(): return """nltk==3.6.3 textblob==0.15.3 spacy==3.1.3 demoji==1.1.0 bs4==0.0.1 text_unidecode==1.3 contractions==0.1.73 """ def get_transformer_params(config): param_keys = ["modelVersion","problem_type","target_feature","train_features","text_features","profiler","test_ratio"] #Bugid 13217 data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] return data def run_transformer(config): transformer = profiler() importer = importModule() function = global_function() importModules(importer, transformer.getPrefixModules()) importer.addModule('warnings') transformer.addPrefixCode() importer.addModule('train_test_split', mod_from='sklearn.model_selection') if config["problem_type"] == 'classification': importer.addModule('LabelEncoder', mod_from='sklearn.preprocessing') transformer.addInputFiles({'targetEncoder':'targetEncoder.pkl'}) update_variable('target_encoder', True) transformer.addStatement("train_data, test_data = train_test_split(df,stratify=df[target_feature],test_size=config['test_ratio'])",indent=2) #Bugid 13217 transformer.addStatement("profilerObj = profiler(xtrain=train_data, target=target_feature, encode_target=True, config=config['profiler'],log=log)") #Bugid 13217 else: transformer.addStatement("train_data, test_data = train_test_split(df,test_size=config['test_ratio'])",indent=2) transformer.addStatement("profilerObj = profiler(xtrain=train_data, target=target_feature, config=config['profiler'],log=log)") importModules(importer, transformer.getSuffixModules()) importModules(importer, transformer.getMainCodeModules()) transformer.addSuffixCode( config["problem_type"] == 'classification') transformer.addMainCode() usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'DataTransformation' deploy_path.mkdir(parents=True, exist_ok=True) generated_files = [] # create the utility file importer.addLocalModule('*', mod_from='utility') utility_obj = utility_function('transformer') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create empty init file to make a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") # create the dataProfiler file profiler_importer = importModule() importer.addLocalModule('profiler', mod_from='dataProfiler') profiler_obj = data_profiler(profiler_importer, True if config["text_features"] else False) code_text = profiler_obj.get_code() # import statement will be generated when profiler_obj.get_code is called. # need to copy data profiler from AION code as code is splitted and merging code amnnually # can add bugs. need a better way to find the imported module #aion_transformer = Path(__file__).parent.parent.parent.parent/'transformations' aion_utilities = Path(__file__).parent.parent.parent.parent/'utilities' #added for non encryption --Usnish (deploy_path/'transformations').mkdir(parents=True, exist_ok=True) if not (aion_utilities/'transformations'/'dataProfiler.py').exists(): raise ValueError('Data profiler file removed from AION') shutil.copy(aion_utilities/'transformations'/'dataProfiler.py',deploy_path/"dataProfiler.py") shutil.copy(aion_utilities/'transformations'/'data_profiler_functions.py',deploy_path/"transformations"/"data_profiler_functions.py") if (deploy_path/'text').exists(): shutil.rmtree(deploy_path/'text') with tarfile.open(aion_utilities/'text.tar') as file: file.extractall(deploy_path) if (deploy_path/'utils').exists(): shutil.rmtree(deploy_path/'utils') with tarfile.open(aion_utilities / 'utils.tar') as file: file.extractall(deploy_path) generated_files.append("dataProfiler.py") generated_files.append("transformations") generated_files.append("text") generated_files.append("utils") code = file_header(usecase) code += "\nimport os\nos.path.abspath(os.path.join(__file__, os.pardir))\n" #chdir to import from current dir code += importer.getCode() code += '\nwarnings.filterwarnings("ignore")\n' code += transformer.getInputOutputFiles() code += function.getCode() transformer.addLocalFunctionsCode() code += transformer.getCode() with open(deploy_path/"aionCode.py", "w") as f: f.write(code) generated_files.append("aionCode.py") with open(deploy_path/"requirements.txt", "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer(), profiler_importer]) if config["text_features"]: req += add_text_dependency() f.write(req) generated_files.append("requirements.txt") config_file = deploy_path/"config.json" config_data = get_transformer_params(config) with open (config_file, "w") as f: json.dump(config_data, f, indent=4) generated_files.append("config.json") create_docker_file('transformer', deploy_path,config['modelName'], generated_files,True if config["text_features"] else False) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json from mlac.ml.core import * from .utility import * def get_register_params(config, models): param_keys = ["modelVersion","problem_type"] data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] data['models'] = models return data def run_register(config): importer = importModule() function = global_function() registration = register(importer) function.add_function('get_mlflow_uris') models = get_variable('models_name') smaller_is_better = get_variable('smaller_is_better', False) registration.addClassCode(smaller_is_better) registration.addLocalFunctionsCode(models) registration.addPrefixCode() registration.addMainCode(models) importModules(importer, registration.getMainCodeModules()) importer.addModule('warnings') generated_files = [] usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'ModelRegistry' deploy_path.mkdir(parents=True, exist_ok=True) # create the utility file importer.addLocalModule('*', mod_from='utility') utility_obj = utility_function('register') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create empty init file required for creating a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") code = registration.getImportCode() code += '\nwarnings.filterwarnings("ignore")\n' code += registration.getInputOutputFiles() code += function.getCode() code += registration.getCode() # create serving file with open(deploy_path/"aionCode.py", 'w') as f: f.write(file_header(usecase) + code) generated_files.append("aionCode.py") # create requirements file req_file = deploy_path/"requirements.txt" with open(req_file, "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") # create config file with open (deploy_path/"config.json", "w") as f: json.dump(get_register_params(config, models), f, indent=4) generated_files.append("config.json") # create docker file create_docker_file('register', deploy_path,config['modelName'], generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from .load_data import run_loader from .transformer import run_transformer from .selector import run_selector from .trainer import run_trainer from .register import run_register from .deploy import run_deploy from .drift_analysis import run_drift_analysis """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json import platform from mlac.ml.core import * from .utility import * imported_modules = [ {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'argparse', 'mod_from': None, 'mod_as': None}, {'module': 'platform', 'mod_from': None, 'mod_as': None } ] def get_load_data_params(config): param_keys = ["modelVersion","problem_type","target_feature","selected_features"] data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] return data def run_loader(config): generated_files = [] importer = importModule() loader = tabularDataReader() importModules(importer, imported_modules) usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'DataIngestion' deploy_path.mkdir(parents=True, exist_ok=True) # create the utility file importer.addLocalModule('*', mod_from='utility') utility_obj = utility_function('load_data') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create the production data reader file importer.addLocalModule('dataReader', mod_from='data_reader') readers = ['sqlite','influx'] if 's3' in config.keys(): readers.append('s3') reader_obj = data_reader(readers) with open(deploy_path/"data_reader.py", 'w') as f: f.write(file_header(usecase) + reader_obj.get_code()) generated_files.append("data_reader.py") # create empty init file to make a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") code = file_header(usecase) code += importer.getCode() code += loader.getInputOutputFiles() code += loader.getCode() with open(deploy_path/"aionCode.py", "w") as f: f.write(code) generated_files.append("aionCode.py") with open(deploy_path/"requirements.txt", "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer(), reader_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") config_file = deploy_path/"config.json" config_data = get_load_data_params(config) with open (config_file, "w") as f: json.dump(config_data, f, indent=4) generated_files.append("config.json") create_docker_file('load_data', deploy_path,config['modelName'],generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ class input_drift(): def __init__(self, tab_size=4): self.tab = ' ' * tab_size self.codeText = '' def addInputDriftClass(self): text = "\ \nclass inputdrift():\ \n\ \n def __init__(self,base_config):\ \n self.usecase = base_config['modelName'] + '_' + base_config['modelVersion']\ \n self.currentDataLocation = base_config['currentDataLocation']\ \n home = Path.home()\ \n if platform.system() == 'Windows':\ \n from pathlib import WindowsPath\ \n output_data_dir = WindowsPath(home)/'AppData'/'Local'/'HCLT'/'AION'/'Data'\ \n output_model_dir = WindowsPath(home)/'AppData'/'Local'/'HCLT'/'AION'/'target'/self.usecase\ \n else:\ \n from pathlib import PosixPath\ \n output_data_dir = PosixPath(home)/'HCLT'/'AION'/'Data'\ \n output_model_dir = PosixPath(home)/'HCLT'/'AION'/'target'/self.usecase\ \n if not output_model_dir.exists():\ \n raise ValueError(f'Configuration file not found at {output_model_dir}')\ \n\ \n tracking_uri = 'file:///' + str(Path(output_model_dir)/'mlruns')\ \n registry_uri = 'sqlite:///' + str(Path(output_model_dir)/'mlruns.db')\ \n mlflow.set_tracking_uri(tracking_uri)\ \n mlflow.set_registry_uri(registry_uri)\ \n client = mlflow.tracking.MlflowClient(\ \n tracking_uri=tracking_uri,\ \n registry_uri=registry_uri,\ \n )\ \n model_version_uri = 'models:/{model_name}/production'.format(model_name=self.usecase)\ \n model = mlflow.pyfunc.load_model(model_version_uri)\ \n run = client.get_run(model.metadata.run_id)\ \n if run.info.artifact_uri.startswith('file:'):\ \n artifact_path = Path(run.info.artifact_uri[len('file:///') : ])\ \n else:\ \n artifact_path = Path(run.info.artifact_uri)\ \n self.trainingDataPath = artifact_path/(self.usecase + '_data.csv')\ \n\ \n def get_input_drift(self,current_data, historical_data):\ \n curr_num_feat = current_data.select_dtypes(include='number')\ \n hist_num_feat = historical_data.select_dtypes(include='number')\ \n num_features = [feat for feat in historical_data.columns if feat in curr_num_feat]\ \n alert_count = 0\ \n data = {\ \n 'current':{'data':current_data},\ \n 'hist': {'data': historical_data}\ \n }\ \n dist_changed_columns = []\ \n dist_change_message = []\ \n for feature in num_features:\ \n curr_static_value = st.ks_2samp( hist_num_feat[feature], curr_num_feat[feature]).pvalue\ \n if (curr_static_value < 0.05):\ \n distribution = {}\ \n distribution['hist'] = self.DistributionFinder( historical_data[feature])\ \n distribution['curr'] = self.DistributionFinder( current_data[feature])\ \n if(distribution['hist']['name'] == distribution['curr']['name']):\ \n pass\ \n else:\ \n alert_count = alert_count + 1\ \n dist_changed_columns.append(feature)\ \n changed_column = {}\ \n changed_column['Feature'] = feature\ \n changed_column['KS_Training'] = curr_static_value\ \n changed_column['Training_Distribution'] = distribution['hist']['name']\ \n changed_column['New_Distribution'] = distribution['curr']['name']\ \n dist_change_message.append(changed_column)\ \n if alert_count:\ \n resultStatus = dist_change_message\ \n else :\ \n resultStatus='Model is working as expected'\ \n return(alert_count, resultStatus)\ \n\ \n def DistributionFinder(self,data):\ \n best_distribution =''\ \n best_sse =0.0\ \n if(data.dtype in ['int','int64']):\ \n distributions= {'bernoulli':{'algo':st.bernoulli},\ \n 'binom':{'algo':st.binom},\ \n 'geom':{'algo':st.geom},\ \n 'nbinom':{'algo':st.nbinom},\ \n 'poisson':{'algo':st.poisson}\ \n }\ \n index, counts = np.unique(data.astype(int),return_counts=True)\ \n if(len(index)>=2):\ \n best_sse = np.inf\ \n y1=[]\ \n total=sum(counts)\ \n mean=float(sum(index*counts))/total\ \n variance=float((sum(index**2*counts) -total*mean**2))/(total-1)\ \n dispersion=mean/float(variance)\ \n theta=1/float(dispersion)\ \n r=mean*(float(theta)/1-theta)\ \n\ \n for j in counts:\ \n y1.append(float(j)/total)\ \n distributions['bernoulli']['pmf'] = distributions['bernoulli']['algo'].pmf(index,mean)\ \n distributions['binom']['pmf'] = distributions['binom']['algo'].pmf(index,len(index),p=mean/len(index))\ \n distributions['geom']['pmf'] = distributions['geom']['algo'].pmf(index,1/float(1+mean))\ \n distributions['nbinom']['pmf'] = distributions['nbinom']['algo'].pmf(index,mean,r)\ \n distributions['poisson']['pmf'] = distributions['poisson']['algo'].pmf(index,mean)\ \n\ \n sselist = []\ \n for dist in distributions.keys():\ \n distributions[dist]['sess'] = np.sum(np.power(y1 - distributions[dist]['pmf'], 2.0))\ \n if np.isnan(distributions[dist]['sess']):\ \n distributions[dist]['sess'] = float('inf')\ \n best_dist = min(distributions, key=lambda v: distributions[v]['sess'])\ \n best_distribution = best_dist\ \n best_sse = distributions[best_dist]['sess']\ \n\ \n elif (len(index) == 1):\ \n best_distribution = 'Constant Data-No Distribution'\ \n best_sse = 0.0\ \n elif(data.dtype in ['float64','float32']):\ \n distributions = [st.uniform,st.expon,st.weibull_max,st.weibull_min,st.chi,st.norm,st.lognorm,st.t,st.gamma,st.beta]\ \n best_distribution = st.norm.name\ \n best_sse = np.inf\ \n nrange = data.max() - data.min()\ \n\ \n y, x = np.histogram(data.astype(float), bins='auto', density=True)\ \n x = (x + np.roll(x, -1))[:-1] / 2.0\ \n\ \n for distribution in distributions:\ \n with warnings.catch_warnings():\ \n warnings.filterwarnings('ignore')\ \n params = distribution.fit(data.astype(float))\ \n arg = params[:-2]\ \n loc = params[-2]\ \n scale = params[-1]\ \n pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\ \n sse = np.sum(np.power(y - pdf, 2.0))\ \n if( sse < best_sse):\ \n best_distribution = distribution.name\ \n best_sse = sse\ \n\ \n return {'name':best_distribution, 'sse': best_sse}\ \n\ " return text def addSuffixCode(self, indent=1): text ="\n\ \ndef check_drift( config):\ \n inputdriftObj = inputdrift(config)\ \n historicaldataFrame=pd.read_csv(inputdriftObj.trainingDataPath)\ \n currentdataFrame=pd.read_csv(inputdriftObj.currentDataLocation)\ \n dataalertcount,message = inputdriftObj.get_input_drift(currentdataFrame,historicaldataFrame)\ \n if message == 'Model is working as expected':\ \n output_json = {'status':'SUCCESS','data':{'Message':'Model is working as expected'}}\ \n else:\ \n output_json = {'status':'SUCCESS','data':{'Affected Columns':message}}\ \n return(output_json)\ \n\ \nif __name__ == '__main__':\ \n try:\ \n if len(sys.argv) < 2:\ \n raise ValueError('config file not present')\ \n config = sys.argv[1]\ \n if Path(config).is_file() and Path(config).suffix == '.json':\ \n with open(config, 'r') as f:\ \n config = json.load(f)\ \n else:\ \n config = json.loads(config)\ \n output = check_drift(config)\ \n status = {'Status':'Success','Message':output}\ \n print('input_drift:'+json.dumps(status))\ \n except Exception as e:\ \n status = {'Status':'Failure','Message':str(e)}\ \n print('input_drift:'+json.dumps(status))" return text def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def generateCode(self): self.codeText += self.addInputDriftClass() self.codeText += self.addSuffixCode() def getCode(self): return self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ class output_drift(): def __init__(self, missing=False, word2num_features = None, cat_encoder=False, target_encoder=False, normalizer=False, text_profiler=False, feature_reducer=False, score_smaller_is_better=True, problem_type='classification', tab_size=4): self.tab = ' ' * tab_size self.codeText = '' self.missing = missing self.word2num_features = word2num_features self.cat_encoder = cat_encoder self.target_encoder = target_encoder self.normalizer = normalizer self.text_profiler = text_profiler self.feature_reducer = feature_reducer self.score_smaller_is_better = score_smaller_is_better self.problem_type = problem_type def addDatabaseClass(self, indent=0): text = "\ \nclass database():\ \n def __init__(self, config):\ \n self.host = config['host']\ \n self.port = config['port']\ \n self.user = config['user']\ \n self.password = config['password']\ \n self.database = config['database']\ \n self.measurement = config['measurement']\ \n self.tags = config['tags']\ \n self.client = self.get_client()\ \n\ \n def read_data(self, query)->pd.DataFrame:\ \n cursor = self.client.query(query)\ \n points = cursor.get_points()\ \n my_list=list(points)\ \n df=pd.DataFrame(my_list)\ \n return df\ \n\ \n def get_client(self):\ \n client = InfluxDBClient(self.host,self.port,self.user,self.password)\ \n databases = client.get_list_database()\ \n databases = [x['name'] for x in databases]\ \n if self.database not in databases:\ \n client.create_database(self.database)\ \n return InfluxDBClient(self.host,self.port,self.user,self.password, self.database)\ \n\ \n def write_data(self,data):\ \n if isinstance(data, pd.DataFrame):\ \n sorted_col = data.columns.tolist()\ \n sorted_col.sort()\ \n data = data[sorted_col]\ \n data = data.to_dict(orient='records')\ \n for row in data:\ \n if 'time' in row.keys():\ \n p = '%Y-%m-%dT%H:%M:%S.%fZ'\ \n time_str = datetime.strptime(row['time'], p)\ \n del row['time']\ \n else:\ \n time_str = None\ \n if 'model_ver' in row.keys():\ \n self.tags['model_ver']= row['model_ver']\ \n del row['model_ver']\ \n json_body = [{\ \n 'measurement': self.measurement,\ \n 'time': time_str,\ \n 'tags': self.tags,\ \n 'fields': row\ \n }]\ \n self.client.write_points(json_body)\ \n\ \n def close(self):\ \n self.client.close()\ \n" if indent: text = text.replace('\n', (self.tab * indent) + '\n') return text def addPredictClass(self, indent=0): text = "\ \nclass predict():\ \n\ \n def __init__(self, base_config):\ \n self.usecase = base_config['modelName'] + '_' + base_config['modelVersion']\ \n self.dataLocation = base_config['dataLocation']\ \n self.db_enabled = base_config.get('db_enabled', False)\ \n if self.db_enabled:\ \n self.db_config = base_config['db_config']\ \n home = Path.home()\ \n if platform.system() == 'Windows':\ \n from pathlib import WindowsPath\ \n output_data_dir = WindowsPath(home)/'AppData'/'Local'/'HCLT'/'AION'/'Data'\ \n output_model_dir = WindowsPath(home)/'AppData'/'Local'/'HCLT'/'AION'/'target'/self.usecase\ \n else:\ \n from pathlib import PosixPath\ \n output_data_dir = PosixPath(home)/'HCLT'/'AION'/'Data'\ \n output_model_dir = PosixPath(home)/'HCLT'/'AION'/'target'/self.usecase\ \n if not output_model_dir.exists():\ \n raise ValueError(f'Configuration file not found at {output_model_dir}')\ \n\ \n tracking_uri = 'file:///' + str(Path(output_model_dir)/'mlruns')\ \n registry_uri = 'sqlite:///' + str(Path(output_model_dir)/'mlruns.db')\ \n mlflow.set_tracking_uri(tracking_uri)\ \n mlflow.set_registry_uri(registry_uri)\ \n client = mlflow.tracking.MlflowClient(\ \n tracking_uri=tracking_uri,\ \n registry_uri=registry_uri,\ \n )\ \n self.model_version = client.get_latest_versions(self.usecase, stages=['production'] )[0].version\ \n model_version_uri = 'models:/{model_name}/production'.format(model_name=self.usecase)\ \n self.model = mlflow.pyfunc.load_model(model_version_uri)\ \n run = client.get_run(self.model.metadata.run_id)\ \n if run.info.artifact_uri.startswith('file:'): #remove file:///\ \n self.artifact_path = Path(run.info.artifact_uri[len('file:///') : ])\ \n else:\ \n self.artifact_path = Path(run.info.artifact_uri)\ \n with open(self.artifact_path/'deploy.json', 'r') as f:\ \n deployment_dict = json.load(f)\ \n with open(self.artifact_path/'features.txt', 'r') as f:\ \n self.train_features = f.readline().rstrip().split(',')\ \n\ \n self.dataLocation = base_config['dataLocation']\ \n self.selected_features = deployment_dict['load_data']['selected_features']\ \n self.target_feature = deployment_dict['load_data']['target_feature']\ \n self.output_model_dir = output_model_dir" if self.missing: text += "\n self.missing_values = deployment_dict['transformation']['fillna']" if self.word2num_features: text += "\n self.word2num_features = deployment_dict['transformation']['word2num_features']" if self.cat_encoder == 'labelencoding': text += "\n self.cat_encoder = deployment_dict['transformation']['cat_encoder']" elif (self.cat_encoder == 'targetencoding') or (self.cat_encoder == 'onehotencoding'): text += "\n self.cat_encoder = deployment_dict['transformation']['cat_encoder']['file']" text += "\n self.cat_encoder_cols = deployment_dict['transformation']['cat_encoder']['features']" if self.target_encoder: text += "\n self.target_encoder = joblib.load(self.artifact_path/deployment_dict['transformation']['target_encoder'])" if self.normalizer: text += "\n self.normalizer = joblib.load(self.artifact_path/deployment_dict['transformation']['normalizer']['file'])\ \n self.normalizer_col = deployment_dict['transformation']['normalizer']['features']" if self.text_profiler: text += "\n self.text_profiler = joblib.load(self.artifact_path/deployment_dict['transformation']['Status']['text_profiler']['file'])\ \n self.text_profiler_col = deployment_dict['transformation']['Status']['text_profiler']['features']" if self.feature_reducer: text += "\n self.feature_reducer = joblib.load(self.artifact_path/deployment_dict['featureengineering']['feature_reducer']['file'])\ \n self.feature_reducer_cols = deployment_dict['featureengineering']['feature_reducer']['features']" text += """ def read_data_from_db(self): if self.db_enabled: try: db = database(self.db_config) query = "SELECT * FROM {} WHERE model_ver = '{}' AND {} != ''".format(db.measurement, self.model_version, self.target_feature) if 'read_time' in self.db_config.keys() and self.db_config['read_time']: query += f" time > now() - {self.db_config['read_time']}" data = db.read_data(query) except: raise ValueError('Unable to read from the database') finally: if db: db.close() return data return None""" text += "\ \n def predict(self, data):\ \n df = pd.DataFrame()\ \n if Path(data).exists():\ \n if Path(data).suffix == '.tsv':\ \n df=read_data(data,encoding='utf-8',sep='\t')\ \n elif Path(data).suffix == '.csv':\ \n df=read_data(data,encoding='utf-8')\ \n else:\ \n if Path(data).suffix == '.json':\ \n jsonData = read_json(data)\ \n df = pd.json_normalize(jsonData)\ \n elif is_file_name_url(data):\ \n df = read_data(data,encoding='utf-8')\ \n else:\ \n jsonData = json.loads(data)\ \n df = pd.json_normalize(jsonData)\ \n if len(df) == 0:\ \n raise ValueError('No data record found')\ \n missing_features = [x for x in self.selected_features if x not in df.columns]\ \n if missing_features:\ \n raise ValueError(f'some feature/s is/are missing: {missing_features}')\ \n if self.target_feature not in df.columns:\ \n raise ValueError(f'Ground truth values/target column({self.target_feature}) not found in current data')\ \n df_copy = df.copy()\ \n df = df[self.selected_features]" if self.word2num_features: text += "\n for feat in self.word2num_features:" text += "\n df[ feat ] = df[feat].apply(lambda x: s2n(x))" if self.missing: text += "\n df.fillna(self.missing_values, inplace=True)" if self.cat_encoder == 'labelencoding': text += "\n df.replace(self.cat_encoder, inplace=True)" elif self.cat_encoder == 'targetencoding': text += "\n cat_enc = joblib.load(self.artifact_path/self.cat_encoder)" text += "\n df = cat_enc.transform(df)" elif self.cat_encoder == 'onehotencoding': text += "\n cat_enc = joblib.load(self.artifact_path/self.cat_encoder)" text += "\n transformed_data = cat_enc.transform(df[self.cat_encoder_cols]).toarray()" text += "\n df[cat_enc.get_feature_names()] = pd.DataFrame(transformed_data, columns=cat_enc.get_feature_names())[cat_enc.get_feature_names()]" if self.normalizer: text += "\n df[self.normalizer_col] = self.normalizer.transform(df[self.normalizer_col])" if self.text_profiler: text += "\n text_corpus = df[self.text_profiler_col].apply(lambda row: ' '.join(row.values.astype(str)), axis=1)\ \n df_vect=self.text_profiler.transform(text_corpus)\ \n if isinstance(df_vect, np.ndarray):\ \n df1 = pd.DataFrame(df_vect)\ \n else:\ \n df1 = pd.DataFrame(df_vect.toarray(),columns = self.text_profiler.named_steps['vectorizer'].get_feature_names())\ \n df1 = df1.add_suffix('_vect')\ \n df = pd.concat([df, df1],axis=1)" if self.feature_reducer: text += "\n df = self.feature_reducer.transform(df[self.feature_reducer_cols])" else: text += "\n df = df[self.train_features]" if self.target_encoder: text += "\n output = pd.DataFrame(self.model._model_impl.predict_proba(df), columns=self.target_encoder.classes_)\ \n df_copy['prediction'] = output.idxmax(axis=1)" else: text += "\n output = self.model.predict(df).reshape(1, -1)[0].round(2)\ \n df_copy['prediction'] = output" text += "\n return df_copy" if indent: text = text.replace('\n', (self.tab * indent) + '\n') return text def getClassificationMatrixCode(self, indent=0): text = "\ \ndef get_classification_metrices(actual_values, predicted_values):\ \n result = {}\ \n accuracy_score = sklearn.metrics.accuracy_score(actual_values, predicted_values)\ \n avg_precision = sklearn.metrics.precision_score(actual_values, predicted_values,\ \n average='macro')\ \n avg_recall = sklearn.metrics.recall_score(actual_values, predicted_values,\ \n average='macro')\ \n avg_f1 = sklearn.metrics.f1_score(actual_values, predicted_values,\ \n average='macro')\ \n\ \n result['accuracy'] = accuracy_score\ \n result['precision'] = avg_precision\ \n result['recall'] = avg_recall\ \n result['f1'] = avg_f1\ \n return result\ \n\ " if indent: text = text.replace('\n', (self.tab * indent) + '\n') return text def getRegrssionMatrixCode(self, indent=0): text = "\ \ndef get_regression_metrices( actual_values, predicted_values):\ \n result = {}\ \n\ \n me = np.mean(predicted_values - actual_values)\ \n sde = np.std(predicted_values - actual_values, ddof = 1)\ \n\ \n abs_err = np.abs(predicted_values - actual_values)\ \n mae = np.mean(abs_err)\ \n sdae = np.std(abs_err, ddof = 1)\ \n\ \n abs_perc_err = 100.*np.abs(predicted_values - actual_values) / actual_values\ \n mape = np.mean(abs_perc_err)\ \n sdape = np.std(abs_perc_err, ddof = 1)\ \n\ \n result['mean_error'] = me\ \n result['mean_abs_error'] = mae\ \n result['mean_abs_perc_error'] = mape\ \n result['error_std'] = sde\ \n result['abs_error_std'] = sdae\ \n result['abs_perc_error_std'] = sdape\ \n return result\ \n\ " if indent: text = text.replace('\n', (self.tab * indent) + '\n') return text def addSuffixCode(self, indent=1): text ="\n\ \ndef check_drift( config):\ \n prediction = predict(config)\ \n usecase = config['modelName'] + '_' + config['modelVersion']\ \n train_data_path = prediction.artifact_path/(usecase+'_data.csv')\ \n if not train_data_path.exists():\ \n raise ValueError(f'Training data not found at {train_data_path}')\ \n curr_with_pred = prediction.read_data_from_db()\ \n if prediction.target_feature not in curr_with_pred.columns:\ \n raise ValueError('Ground truth not updated for corresponding data in database')\ \n train_with_pred = prediction.predict(train_data_path)\ \n performance = {}" if self.problem_type == 'classification': text += "\n\ \n performance['train'] = get_classification_metrices(train_with_pred[prediction.target_feature], train_with_pred['prediction'])\ \n performance['current'] = get_classification_metrices(curr_with_pred[prediction.target_feature], curr_with_pred['prediction'])" else: text += "\n\ \n performance['train'] = get_regression_metrices(train_with_pred[prediction.target_feature], train_with_pred['prediction'])\ \n performance['current'] = get_regression_metrices(curr_with_pred[prediction.target_feature], curr_with_pred['prediction'])" text += "\n return performance" text += "\n\ \nif __name__ == '__main__':\ \n try:\ \n if len(sys.argv) < 2:\ \n raise ValueError('config file not present')\ \n config = sys.argv[1]\ \n if Path(config).is_file() and Path(config).suffix == '.json':\ \n with open(config, 'r') as f:\ \n config = json.load(f)\ \n else:\ \n config = json.loads(config)\ \n output = check_drift(config)\ \n status = {'Status':'Success','Message':json.loads(output)}\ \n print('output_drift:'+json.dumps(status))\ \n except Exception as e:\ \n status = {'Status':'Failure','Message':str(e)}\ \n print('output_drift:'+json.dumps(status))" if indent: text = text.replace('\n', (self.tab * indent) + '\n') return text def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def generateCode(self): self.codeText += self.addDatabaseClass() self.codeText += self.addPredictClass() if self.problem_type == 'classification': self.codeText += self.getClassificationMatrixCode() elif self.problem_type == 'regression': self.codeText += self.getRegrssionMatrixCode() else: raise ValueError(f"Unsupported problem type: {self.problem_type}") self.codeText += self.addSuffixCode() def getCode(self): return self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class deploy(): def __init__(self, tab_size=4): self.tab = ' ' * tab_size self.codeText = "" self.input_files = {} self.output_files = {} self.addInputFiles({'metaData' : 'modelMetaData.json','log':'predict.log'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() text += '\n' text += self.getOutputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def addStatement(self, statement, indent=1): pass def getPredictionCodeModules(self): modules = [{'module':'json'} ,{'module':'joblib'} ,{'module':'pandas', 'mod_as':'pd'} ,{'module':'numpy', 'mod_as':'np'} ,{'module':'Path', 'mod_from':'pathlib'} ,{'module':'json_normalize', 'mod_from':'pandas'} ,{'module':'load_model', 'mod_from':'tensorflow.keras.models'} ] return modules def addPredictionCode(self): self.codeText += """ class deploy(): def __init__(self, base_config, log=None): self.targetPath = (Path('aion') / base_config['targetPath']).resolve() if log: self.logger = log else: log_file = self.targetPath / IOFiles['log'] self.logger = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem) try: self.initialize(base_config) except Exception as e: self.logger.error(e, exc_info=True) def initialize(self, base_config): targetPath = Path('aion') / base_config['targetPath'] meta_data_file = targetPath / IOFiles['metaData'] if meta_data_file.exists(): meta_data = utils.read_json(meta_data_file) self.dateTimeFeature = meta_data['training']['dateTimeFeature'] self.targetFeature = meta_data['training']['target_feature'] normalization_file = meta_data['transformation']['Status']['Normalization_file'] self.normalizer = joblib.load(normalization_file) self.lag_order = base_config['lag_order'] self.noofforecasts = base_config['noofforecasts'] run_id = str(meta_data['register']['runId']) model_path = str(targetPath/'runs'/str(meta_data['register']['runId'])/meta_data['register']['model']/'model') self.model = load_model(model_path) self.model_name = meta_data['register']['model'] def predict(self, data=None): try: return self.__predict(data) except Exception as e: if self.logger: self.logger.error(e, exc_info=True) raise ValueError(json.dumps({'Status': 'Failure', 'Message': str(e)})) def __predict(self, data=None): jsonData = json.loads(data) dataFrame = json_normalize(jsonData) xtrain = dataFrame if len(dataFrame) == 0: raise ValueError('No data record found') df_l = len(dataFrame) pred_threshold = 0.1 max_pred_by_user = round((df_l) * pred_threshold) # prediction for 24 steps or next 24 hours if self.noofforecasts == -1: self.noofforecasts = max_pred_by_user no_of_prediction = self.noofforecasts if (str(no_of_prediction) > str(max_pred_by_user)): no_of_prediction = max_pred_by_user noofforecasts = no_of_prediction # self.sfeatures.remove(self.datetimeFeature) features = self.targetFeature if len(features) == 1: xt = xtrain[features].values else: xt = xtrain[features].values xt = xt.astype('float32') xt = self.normalizer.transform(xt) pred_data = xt y_future = [] self.lag_order = int(self.lag_order) for i in range(int(no_of_prediction)): pdata = pred_data[-self.lag_order:] if len(features) == 1: pdata = pdata.reshape((1, self.lag_order)) else: pdata = pdata.reshape((1, self.lag_order, len(features))) if (len(features) > 1): pred = self.model.predict(pdata) predout = self.normalizer.inverse_transform(pred) y_future.append(predout) pred_data = np.append(pred_data, pred, axis=0) else: pred = self.model.predict(pdata) predout = self.normalizer.inverse_transform(pred) y_future.append(predout.flatten()[-1]) pred_data = np.append(pred_data, pred) pred = pd.DataFrame(index=range(0, len(y_future)), columns=self.targetFeature) for i in range(0, len(y_future)): pred.iloc[i] = y_future[i] predictions = pred forecast_output = predictions.to_json(orient='records') return forecast_output """ def getCode(self): return self.codeText def getServiceCode(self): return """ from http.server import BaseHTTPRequestHandler,HTTPServer from socketserver import ThreadingMixIn import os from os.path import expanduser import platform import threading import subprocess import argparse import re import cgi import json import shutil import logging import sys import time import seaborn as sns from pathlib import Path from predict import deploy import pandas as pd import scipy.stats as st import numpy as np import warnings from utility import * warnings.filterwarnings("ignore") config_input = None IOFiles = { "inputData": "rawData.dat", "metaData": "modelMetaData.json", "production": "production.json", "log": "aion.log", "monitoring":"monitoring.json", "prodData": "prodData", "prodDataGT":"prodDataGT" } def DistributionFinder(data): try: distributionName = "" sse = 0.0 KStestStatic = 0.0 dataType = "" if (data.dtype == "float64" or data.dtype == "float32"): dataType = "Continuous" elif (data.dtype == "int"): dataType = "Discrete" elif (data.dtype == "int64"): dataType = "Discrete" if (dataType == "Discrete"): distributions = [st.bernoulli, st.binom, st.geom, st.nbinom, st.poisson] index, counts = np.unique(data.astype(int), return_counts=True) if (len(index) >= 2): best_sse = np.inf y1 = [] total = sum(counts) mean = float(sum(index * counts)) / total variance = float((sum(index ** 2 * counts) - total * mean ** 2)) / (total - 1) dispersion = mean / float(variance) theta = 1 / float(dispersion) r = mean * (float(theta) / 1 - theta) for j in counts: y1.append(float(j) / total) pmf1 = st.bernoulli.pmf(index, mean) pmf2 = st.binom.pmf(index, len(index), p=mean / len(index)) pmf3 = st.geom.pmf(index, 1 / float(1 + mean)) pmf4 = st.nbinom.pmf(index, mean, r) pmf5 = st.poisson.pmf(index, mean) sse1 = np.sum(np.power(y1 - pmf1, 2.0)) sse2 = np.sum(np.power(y1 - pmf2, 2.0)) sse3 = np.sum(np.power(y1 - pmf3, 2.0)) sse4 = np.sum(np.power(y1 - pmf4, 2.0)) sse5 = np.sum(np.power(y1 - pmf5, 2.0)) sselist = [sse1, sse2, sse3, sse4, sse5] best_distribution = 'NA' for i in range(0, len(sselist)): if best_sse > sselist[i] > 0: best_distribution = distributions[i].name best_sse = sselist[i] elif (len(index) == 1): best_distribution = "Constant Data-No Distribution" best_sse = 0.0 distributionName = best_distribution sse = best_sse elif (dataType == "Continuous"): distributions = [st.uniform, st.expon, st.weibull_max, st.weibull_min, st.chi, st.norm, st.lognorm, st.t, st.gamma, st.beta] best_distribution = st.norm.name best_sse = np.inf datamin = data.min() datamax = data.max() nrange = datamax - datamin y, x = np.histogram(data.astype(float), bins='auto', density=True) x = (x + np.roll(x, -1))[:-1] / 2.0 for distribution in distributions: params = distribution.fit(data.astype(float)) arg = params[:-2] loc = params[-2] scale = params[-1] pdf = distribution.pdf(x, loc=loc, scale=scale, *arg) sse = np.sum(np.power(y - pdf, 2.0)) if (best_sse > sse > 0): best_distribution = distribution.name best_sse = sse distributionName = best_distribution sse = best_sse except: response = str(sys.exc_info()[0]) message = 'Job has Failed' + response exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) print(message) return distributionName, sse def getDriftDistribution(feature, dataframe, newdataframe=pd.DataFrame()): import matplotlib.pyplot as plt import math import io, base64, urllib np.seterr(divide='ignore', invalid='ignore') try: plt.clf() except: pass plt.rcParams.update({'figure.max_open_warning': 0}) sns.set(color_codes=True) pandasNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] if len(feature) > 4: numneroffeatures = len(feature) plt.figure(figsize=(10, numneroffeatures*2)) else: plt.figure(figsize=(10,5)) for i in enumerate(feature): dataType = dataframe[i[1]].dtypes if dataType not in pandasNumericDtypes: dataframe[i[1]] = pd.Categorical(dataframe[i[1]]) dataframe[i[1]] = dataframe[i[1]].cat.codes dataframe[i[1]] = dataframe[i[1]].astype(int) dataframe[i[1]] = dataframe[i[1]].fillna(dataframe[i[1]].mode()[0]) else: dataframe[i[1]] = dataframe[i[1]].fillna(dataframe[i[1]].mean()) plt.subplots_adjust(hspace=0.5, wspace=0.7, top=1) plt.subplot(math.ceil((len(feature) / 2)), 2, i[0] + 1) distname, sse = DistributionFinder(dataframe[i[1]]) print(distname) ax = sns.distplot(dataframe[i[1]], label=distname) ax.legend(loc='best') if newdataframe.empty == False: dataType = newdataframe[i[1]].dtypes if dataType not in pandasNumericDtypes: newdataframe[i[1]] = pd.Categorical(newdataframe[i[1]]) newdataframe[i[1]] = newdataframe[i[1]].cat.codes newdataframe[i[1]] = newdataframe[i[1]].astype(int) newdataframe[i[1]] = newdataframe[i[1]].fillna(newdataframe[i[1]].mode()[0]) else: newdataframe[i[1]] = newdataframe[i[1]].fillna(newdataframe[i[1]].mean()) distname, sse = DistributionFinder(newdataframe[i[1]]) print(distname) ax = sns.distplot(newdataframe[i[1]],label=distname) ax.legend(loc='best') buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) string = base64.b64encode(buf.read()) uri = urllib.parse.quote(string) return uri def read_json(file_path): data = None with open(file_path,'r') as f: data = json.load(f) return data class HTTPRequestHandler(BaseHTTPRequestHandler): def do_POST(self): print('PYTHON ######## REQUEST ####### STARTED') if None != re.search('/AION/', self.path) or None != re.search('/aion/', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) data = self.rfile.read(length) usecase = self.path.split('/')[-2] if usecase.lower() == config_input['targetPath'].lower(): operation = self.path.split('/')[-1] data = json.loads(data) dataStr = json.dumps(data) if operation.lower() == 'predict': output=deployobj.predict(dataStr) resp = output elif operation.lower() == 'groundtruth': gtObj = groundtruth(config_input) output = gtObj.actual(dataStr) resp = output elif operation.lower() == 'delete': targetPath = Path('aion')/config_input['targetPath'] for file in data: x = targetPath/file if x.exists(): os.remove(x) resp = json.dumps({'Status':'Success'}) else: outputStr = json.dumps({'Status':'Error','Msg':'Operation not supported'}) resp = outputStr else: outputStr = json.dumps({'Status':'Error','Msg':'Wrong URL'}) resp = outputStr else: outputStr = json.dumps({'Status':'ERROR','Msg':'Content-Type Not Present'}) resp = outputStr resp=resp+'\\n' resp=resp.encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(resp) else: print('python ==> else1') self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() print('PYTHON ######## REQUEST ####### ENDED') return def do_GET(self): print('PYTHON ######## REQUEST ####### STARTED') if None != re.search('/AION/', self.path) or None != re.search('/aion/', self.path): usecase = self.path.split('/')[-2] self.send_response(200) self.targetPath = Path('aion')/config_input['targetPath'] meta_data_file = self.targetPath/IOFiles['metaData'] if meta_data_file.exists(): meta_data = read_json(meta_data_file) else: raise ValueError(f'Configuration file not found: {meta_data_file}') production_file = self.targetPath/IOFiles['production'] if production_file.exists(): production_data = read_json(production_file) else: raise ValueError(f'Production Details not found: {production_file}') operation = self.path.split('/')[-1] if (usecase.lower() == config_input['targetPath'].lower()) and (operation.lower() == 'metrices'): self.send_header('Content-Type', 'text/html') self.end_headers() ModelString = production_data['Model'] ModelPerformance = ModelString+'_performance.json' performance_file = self.targetPath/ModelPerformance if performance_file.exists(): performance_data = read_json(performance_file) else: raise ValueError(f'Production Details not found: {performance_data}') Scoring_Creteria = performance_data['scoring_criteria'] train_score = round(performance_data['metrices']['train_score'],2) test_score = round(performance_data['metrices']['test_score'],2) current_score = 'NA' monitoring = read_json(self.targetPath/IOFiles['monitoring']) reader = dataReader(reader_type=monitoring['prod_db_type'],target_path=self.targetPath, config=monitoring['db_config']) inputDatafile = self.targetPath/IOFiles['inputData'] NoOfPrediction = 0 NoOfGroundTruth = 0 inputdistribution = '' if reader.file_exists(IOFiles['prodData']): dfPredict = reader.read(IOFiles['prodData']) dfinput = pd.read_csv(inputDatafile) features = meta_data['training']['features'] inputdistribution = getDriftDistribution(features,dfinput,dfPredict) NoOfPrediction = len(dfPredict) if reader.file_exists(IOFiles['prodDataGT']): dfGroundTruth = reader.read(IOFiles['prodDataGT']) NoOfGroundTruth = len(dfGroundTruth) common_col = [k for k in dfPredict.columns.tolist() if k in dfGroundTruth.columns.tolist()] proddataDF = pd.merge(dfPredict, dfGroundTruth, on =common_col,how = 'inner') if Scoring_Creteria.lower() == 'accuracy': from sklearn.metrics import accuracy_score current_score = accuracy_score(proddataDF[config_input['target_feature']], proddataDF['prediction']) current_score = round((current_score*100),2) elif Scoring_Creteria.lower() == 'recall': from sklearn.metrics import accuracy_score current_score = recall_score(proddataDF[config_input['target_feature']], proddataDF['prediction'],average='macro') current_score = round((current_score*100),2) msg = \"""<html> <head> <title>Performance Details</title> </head> <style> table, th, td {border} </style> <body> <h2><b>Deployed Model:</b>{ModelString}</h2> <br/> <table style="width:50%"> <tr> <td>No of Prediction</td> <td>{NoOfPrediction}</td> </tr> <tr> <td>No of GroundTruth</td> <td>{NoOfGroundTruth}</td> </tr> </table> <br/> <table style="width:100%"> <tr> <th>Score Type</th> <th>Train Score</th> <th>Test Score</th> <th>Production Score</th> </tr> <tr> <td>{Scoring_Creteria}</td> <td>{train_score}</td> <td>{test_score}</td> <td>{current_score}</td> </tr> </table> <br/> <br/> <img src="data:image/png;base64,{newDataDrift}" alt="" > </body> </html> \""".format(border='{border: 1px solid black;}',ModelString=ModelString,Scoring_Creteria=Scoring_Creteria,NoOfPrediction=NoOfPrediction,NoOfGroundTruth=NoOfGroundTruth,train_score=train_score,test_score=test_score,current_score=current_score,newDataDrift=inputdistribution) elif (usecase.lower() == config_input['targetPath'].lower()) and (operation.lower() == 'logs'): self.send_header('Content-Type', 'text/plain') self.end_headers() log_file = self.targetPath/IOFiles['log'] if log_file.exists(): with open(log_file) as f: msg = f.read() f.close() else: raise ValueError(f'Log Details not found: {log_file}') else: self.send_header('Content-Type', 'application/json') self.end_headers() features = meta_data['load_data']['selected_features'] bodydes='[' for x in features: if bodydes != '[': bodydes = bodydes+',' bodydes = bodydes+'{"'+x+'":"value"}' bodydes+=']' urltext = '/AION/'+config_input['targetPath']+'/predict' urltextgth='/AION/'+config_input['targetPath']+'/groundtruth' urltextproduction='/AION/'+config_input['targetPath']+'/metrices' msg=\""" Version:{modelversion} RunNo: {runNo} URL for Prediction ================== URL:{url} RequestType: POST Content-Type=application/json Body: {displaymsg} Output: prediction,probability(if Applicable),remarks corresponding to each row. URL for GroundTruth =================== URL:{urltextgth} RequestType: POST Content-Type=application/json Note: Make Sure that one feature (ID) should be unique in both predict and groundtruth. Otherwise outputdrift will not work URL for Model In Production Analysis ==================================== URL:{urltextproduction} RequestType: GET Content-Type=application/json \""".format(modelversion=config_input['modelVersion'],runNo=config_input['deployedRunNo'],url=urltext,urltextgth=urltextgth,urltextproduction=urltextproduction,displaymsg=bodydes) self.wfile.write(msg.encode()) else: self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() return class ThreadedHTTPServer(ThreadingMixIn, HTTPServer): allow_reuse_address = True def shutdown(self): self.socket.close() HTTPServer.shutdown(self) class file_status(): def __init__(self, reload_function, params, file, logger): self.files_status = {} self.initializeFileStatus(file) self.reload_function = reload_function self.params = params self.logger = logger def initializeFileStatus(self, file): self.files_status = {'path': file, 'time':file.stat().st_mtime} def is_file_changed(self): if self.files_status['path'].stat().st_mtime > self.files_status['time']: self.files_status['time'] = self.files_status['path'].stat().st_mtime return True return False def run(self): global config_input while( True): time.sleep(30) if self.is_file_changed(): production_details = targetPath/IOFiles['production'] if not production_details.exists(): raise ValueError(f'Model in production details does not exist') productionmodel = read_json(production_details) config_file = Path(__file__).parent/'config.json' if not Path(config_file).exists(): raise ValueError(f'Config file is missing: {config_file}') config_input = read_json(config_file) config_input['deployedModel'] = productionmodel['Model'] config_input['deployedRunNo'] = productionmodel['runNo'] self.logger.info('Model changed Reloading.....') self.logger.info(f'Model: {config_input["deployedModel"]}') self.logger.info(f'Version: {str(config_input["modelVersion"])}') self.logger.info(f'runNo: {str(config_input["deployedRunNo"])}') self.reload_function(config_input) class SimpleHttpServer(): def __init__(self, ip, port, model_file_path,reload_function,params, logger): self.server = ThreadedHTTPServer((ip,port), HTTPRequestHandler) self.status_checker = file_status( reload_function, params, model_file_path, logger) def start(self): self.server_thread = threading.Thread(target=self.server.serve_forever) self.server_thread.daemon = True self.server_thread.start() self.status_thread = threading.Thread(target=self.status_checker.run) self.status_thread.start() def waitForThread(self): self.server_thread.join() self.status_thread.join() def stop(self): self.server.shutdown() self.waitForThread() if __name__=='__main__': parser = argparse.ArgumentParser(description='HTTP Server') parser.add_argument('-ip','--ipAddress', help='HTTP Server IP') parser.add_argument('-pn','--portNo', type=int, help='Listening port for HTTP Server') args = parser.parse_args() config_file = Path(__file__).parent/'config.json' if not Path(config_file).exists(): raise ValueError(f'Config file is missing: {config_file}') config = read_json(config_file) if args.ipAddress: config['ipAddress'] = args.ipAddress if args.portNo: config['portNo'] = args.portNo targetPath = Path('aion')/config['targetPath'] if not targetPath.exists(): raise ValueError(f'targetPath does not exist') production_details = targetPath/IOFiles['production'] if not production_details.exists(): raise ValueError(f'Model in production details does not exist') productionmodel = read_json(production_details) config['deployedModel'] = productionmodel['Model'] config['deployedRunNo'] = productionmodel['runNo'] #server = SimpleHttpServer(config['ipAddress'],int(config['portNo'])) config_input = config logging.basicConfig(filename= Path(targetPath)/IOFiles['log'], filemode='a', format='%(asctime)s %(name)s- %(message)s', level=logging.INFO, datefmt='%d-%b-%y %H:%M:%S') logger = logging.getLogger(Path(__file__).parent.name) deployobj = deploy(config_input, logger) server = SimpleHttpServer(config['ipAddress'],int(config['portNo']),targetPath/IOFiles['production'],deployobj.initialize,config_input, logger) logger.info('HTTP Server Running...........') logger.info(f"IP Address: {config['ipAddress']}") logger.info(f"Port No.: {config['portNo']}") print('HTTP Server Running...........') print('For Prediction') print('================') print('Request Type: Post') print('Content-Type: application/json') print('URL: /AION/'+config['targetPath']+'/predict') print('\\nFor GroundTruth') print('================') print('Request Type: Post') print('Content-Type: application/json') print('URL: /AION/'+config['targetPath']+'/groundtruth') print('\\nFor Help') print('================') print('Request Type: Get') print('Content-Type: application/json') print('URL: /AION/'+config['targetPath']+'/help') print('\\nFor Model In Production Analysis') print('================') print('Request Type: Get') print('Content-Type: application/json') print('URL: /AION/'+config['targetPath']+'/metrices') server.start() server.waitForThread() """ """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class learner(): def __init__(self, problem_type="classification", target_feature="", sample_method=None,indent=0, tab_size=4): self.tab = " "*tab_size self.df_name = 'df' self.problem_type = problem_type self.target_feature = target_feature self.search_space = [] self.codeText = f"\ndef train(log):" self.input_files = {} self.output_files = {} self.function_code = '' self.addInputFiles({'inputData' : 'featureEngineeredData.dat', 'metaData' : 'modelMetaData.json','monitor':'monitoring.json'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def __addValidateConfigCode(self): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = utils.read_json(config_file)\ \n return config" return text def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def getCode(self): return self.function_code + '\n' + self.codeText def addLocalFunctionsCode(self): self.function_code += self.__addValidateConfigCode() def getPrefixModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'pandas', 'mod_as':'pd'} ] return modules def addPrefixCode(self, indent=1): self.codeText += "\ " def getSuffixModules(self): modules = [] return modules def addSuffixCode(self, indent=1): self.codeText += "\n\ " def getMainCodeModules(self): modules = [{'module':'logging'} ] return modules def getMlpCodeModules(self): modules = [{'module':'math'} ,{'module':'json'} ,{'module':'joblib'} ,{'module':'keras_tuner'} ,{'module':'pandas', 'mod_as':'pd'} ,{'module':'numpy', 'mod_as':'np'} ,{'module':'Path', 'mod_from':'pathlib'} ,{'module':'r2_score', 'mod_from':'sklearn.metrics'} ,{'module':'mean_squared_error', 'mod_from':'sklearn.metrics'} ,{'module':'mean_absolute_error', 'mod_from':'sklearn.metrics'} ,{'module':'Dense', 'mod_from':'tensorflow.keras.layers'} ,{'module':'Sequential', 'mod_from':'tensorflow.keras'} ,{'module':'Dropout', 'mod_from':'tensorflow.keras.layers'} ] return modules def addMlpCode(self): self.codeText = """ def getdlparams(config): for k, v in config.items(): if (k == "activation"): activation_fn = str(v) elif (k == "optimizer"): optimizer = str(v) elif (k == "loss"): loss_fn = str(v) elif (k == "first_layer"): if not isinstance(k, list): first_layer = str(v).split(',') else: first_layer = k elif (k == "lag_order"): lag_order = int(v) elif (k == "hidden_layers"): hidden_layers = int(v) elif (k == "dropout"): if not isinstance(k, list): dropout = str(v).split(',') else: dropout = k elif (k == "batch_size"): batch_size = int(v) elif (k == "epochs"): epochs = int(v) elif (k == "model_name"): model_name = str(v) return activation_fn, optimizer, loss_fn, first_layer, lag_order, hidden_layers, dropout, batch_size, epochs, model_name def numpydf(dataset, look_back): dataX, dataY = [], [] for i in range(len(dataset) - look_back - 1): subset = dataset[i:(i + look_back), 0] dataX.append(subset) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) def startTraining(dataset,train_size,mlpConfig,filename_scaler,target_feature,scoreParam,log): log.info('Training started') activation_fn, optimizer, loss_fn, first_layer, hidden_layers, look_back, dropout, batch_size, epochs, model_name = getdlparams(mlpConfig) hp = keras_tuner.HyperParameters() first_layer_min = round(int(first_layer[0])) first_layer_max = round(int(first_layer[1])) dropout_min = float(dropout[0]) dropout_max = float(dropout[1]) dataset = dataset.values train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :] trainX, trainY = numpydf(train, look_back) testX, testY = numpydf(test, look_back) # create and fit Multilayer Perceptron model model = Sequential() model.add(Dense(units=hp.Int('units',min_value=first_layer_min,max_value=first_layer_max,step=16), input_dim=look_back, activation=activation_fn)) #BUGID 13484 model.add(Dropout(hp.Float('Dropout_rate',min_value=dropout_min,max_value=dropout_max,step=0.1))) #BUGID 13484 model.add(Dense(1, activation='sigmoid')) model.compile(loss=loss_fn, optimizer=optimizer) model_fit = model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2) # Estimate model performance trainScore = model.evaluate(trainX, trainY, verbose=0) testScore = model.evaluate(testX, testY, verbose=0) # Scoring values for the model mse_eval = testScore rmse_eval = math.sqrt(testScore) # generate predictions for training trainPredict = model.predict(trainX) testPredict = model.predict(testX) scaler = joblib.load(filename_scaler) trainY = scaler.inverse_transform([trainY]) trainPredict = scaler.inverse_transform(trainPredict) ## For test data testY = scaler.inverse_transform([testY]) testPredict = scaler.inverse_transform(testPredict) mse_mlp = mean_squared_error(testY.T, testPredict) scores = {} r2 = round(r2_score(testY.T, testPredict), 2) scores['R2'] = r2 mae = round(mean_absolute_error(testY.T, testPredict), 2) scores['MAE'] = mae scores['MSE'] = round(mse_mlp, 2) rmse = round(math.sqrt(mse_mlp), 2) scores['RMSE'] = rmse scores[scoreParam] = scores.get(scoreParam.upper(), scores['MSE']) log.info("mlp rmse: "+str(rmse)) log.info("mlp mse: "+str(round(mse_mlp, 2))) log.info("mlp r2: "+str(r2)) log.info("mlp mae: "+str(mae)) return model, look_back, scaler,testScore,trainScore,scores def train(config, targetPath, log): dataLoc = targetPath / IOFiles['inputData'] if not dataLoc.exists(): return {'Status': 'Failure', 'Message': 'Data location does not exists.'} status = dict() usecase = config['targetPath'] df = utils.read_data(dataLoc) target_feature = config['target_feature'] dateTimeFeature= config['dateTimeFeature'] df.set_index(dateTimeFeature, inplace=True) train_size = int(len(df) * (1-config['test_ratio'])) #BugID:13217 mlpConfig = config['algorithms']['MLP'] filename = meta_data['transformation']['Status']['Normalization_file'] scoreParam = config['scoring_criteria'] log.info('Training MLP for TimeSeries') mlp_model, look_back, scaler,testScore,trainScore, error_matrix = startTraining(df,train_size,mlpConfig,filename,target_feature,scoreParam,log) score = error_matrix[scoreParam] # Training model model_path = targetPath/'runs'/str(meta_data['monitoring']['runId'])/model_name model_file_name = str(model_path/'model') mlp_model.save(model_file_name) meta_data['training'] = {} meta_data['training']['model_filename'] = model_file_name meta_data['training']['dateTimeFeature'] = dateTimeFeature meta_data['training']['target_feature'] = target_feature utils.write_json(meta_data, targetPath / IOFiles['metaData']) utils.write_json({'scoring_criteria': scoreParam, 'metrices': error_matrix,'score':error_matrix[scoreParam]}, model_path / IOFiles['metrics']) # return status status = {'Status': 'Success', 'errorMatrix': error_matrix, 'test_score':testScore, 'train_score': trainScore,'score':error_matrix[scoreParam]} log.info(f'Test score: {testScore}') log.info(f'Train score: {trainScore}') log.info(f'output: {status}') return json.dumps(status) """ def getLstmCodeModules(self): modules = [{'module':'math'} ,{'module':'json'} ,{'module':'joblib'} ,{'module':'keras_tuner'} ,{'module':'pandas', 'mod_as':'pd'} ,{'module':'numpy', 'mod_as':'np'} ,{'module':'Path', 'mod_from':'pathlib'} ,{'module':'r2_score', 'mod_from':'sklearn.metrics'} ,{'module':'mean_squared_error', 'mod_from':'sklearn.metrics'} ,{'module':'mean_absolute_error', 'mod_from':'sklearn.metrics'} ,{'module':'Dense', 'mod_from':'tensorflow.keras.layers'} ,{'module':'Sequential', 'mod_from':'tensorflow.keras'} ,{'module':'Dropout', 'mod_from':'tensorflow.keras.layers'} ,{'module':'LSTM', 'mod_from':'tensorflow.keras.layers'} ,{'module':'TimeseriesGenerator', 'mod_from':'tensorflow.keras.preprocessing.sequence'} ,{'module':'train_test_split', 'mod_from':'sklearn.model_selection'} ] return modules def addLstmCode(self): self.codeText = """ def getdlparams(config): for k, v in config.items(): if (k == "activation"): activation_fn = str(v) elif (k == "optimizer"): optimizer = str(v) elif (k == "loss"): loss_fn = str(v) elif (k == "first_layer"): if not isinstance(k, list): first_layer = str(v).split(',') else: first_layer = k elif (k == "lag_order"): lag_order = int(v) elif (k == "hidden_layers"): hidden_layers = int(v) elif (k == "dropout"): if not isinstance(k, list): dropout = str(v).split(',') else: dropout = k elif (k == "batch_size"): batch_size = int(v) elif (k == "epochs"): epochs = int(v) return activation_fn, optimizer, loss_fn, first_layer, lag_order, hidden_layers, dropout, batch_size, epochs def numpydf(dataset, look_back): dataX, dataY = [], [] for i in range(len(dataset) - look_back - 1): subset = dataset[i:(i + look_back), 0] dataX.append(subset) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) def startTraining(dataset,test_size,mlpConfig,filename_scaler,target_feature,scoreParam,log): log.info('Training started') activation_fn, optimizer, loss_fn, first_layer, look_back,hidden_layers, dropout, batch_size, epochs= getdlparams(mlpConfig) n_features = len(target_feature) n_input = look_back hp = keras_tuner.HyperParameters() first_layer_min = round(int(first_layer[0])) first_layer_max = round(int(first_layer[1])) dropout_min = float(dropout[0]) dropout_max = float(dropout[1]) dataset = dataset[target_feature] dataset_np = dataset.values train, test = train_test_split(dataset_np, test_size=test_size, shuffle=False) generatorTrain = TimeseriesGenerator(train, train, length=n_input, batch_size=8) generatorTest = TimeseriesGenerator(test, test, length=n_input, batch_size=8) batch_0 = generatorTrain[0] x, y = batch_0 epochs = int(epochs) ##Multivariate LSTM model model = Sequential() model.add(LSTM(units=hp.Int('units',min_value=first_layer_min,max_value=first_layer_max,step=16), activation=activation_fn, input_shape=(n_input, n_features))) model.add(Dropout(hp.Float('Dropout_rate',min_value=dropout_min,max_value=dropout_max,step=0.1))) model.add(Dense(n_features)) model.compile(optimizer=optimizer, loss=loss_fn) # model.fit(generatorTrain,epochs=epochs,batch_size=self.batch_size,shuffle=False) model.fit_generator(generatorTrain, steps_per_epoch=1, epochs=epochs, shuffle=False, verbose=0) # lstm_mv_testScore_mse = model.evaluate(x, y, verbose=0) predictions = [] future_pred_len = n_input # To get values for prediction,taking look_back steps of rows first_batch = train[-future_pred_len:] c_batch = first_batch.reshape((1, future_pred_len, n_features)) current_pred = None for i in range(len(test)): # get pred for firstbatch current_pred = model.predict(c_batch)[0] predictions.append(current_pred) # remove first val c_batch_rmv_first = c_batch[:, 1:, :] # update c_batch = np.append(c_batch_rmv_first, [[current_pred]], axis=1) ## Prediction, inverse the minmax transform scaler = joblib.load(filename_scaler) prediction_actual = scaler.inverse_transform(predictions) test_data_actual = scaler.inverse_transform(test) mse = None rmse = None ## Creating dataframe for actual,predictions pred_cols = list() for i in range(len(target_feature)): pred_cols.append(target_feature[i] + '_pred') predictions = pd.DataFrame(prediction_actual, columns=pred_cols) actual = pd.DataFrame(test_data_actual, columns=target_feature) actual.columns = [str(col) + '_actual' for col in dataset.columns] df_predicted = pd.concat([actual, predictions], axis=1) print("LSTM Multivariate prediction dataframe: \\n" + str(df_predicted)) # df_predicted.to_csv('mlp_prediction.csv') from math import sqrt from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error target = target_feature mse_dict = {} rmse_dict = {} mae_dict = {} r2_dict = {} lstm_var = 0 for name in target: index = dataset.columns.get_loc(name) mse = mean_squared_error(test_data_actual[:, index], prediction_actual[:, index]) mse_dict[name] = mse rmse = sqrt(mse) rmse_dict[name] = rmse lstm_var = lstm_var + rmse print("Name of the target feature: " + str(name)) print("RMSE of the target feature: " + str(rmse)) r2 = r2_score(test_data_actual[:, index], prediction_actual[:, index]) r2_dict[name] = r2 mae = mean_absolute_error(test_data_actual[:, index], prediction_actual[:, index]) mae_dict[name] = mae ## For VAR comparison, send last target mse and rmse from above dict lstm_var = lstm_var / len(target) select_msekey = list(mse_dict.keys())[-1] l_mse = list(mse_dict.values())[-1] select_rmsekey = list(rmse_dict.keys())[-1] l_rmse = list(rmse_dict.values())[-1] select_r2key = list(r2_dict.keys())[-1] l_r2 = list(r2_dict.values())[-1] select_maekey = list(mae_dict.keys())[-1] l_mae = list(mae_dict.values())[-1] log.info('Selected target feature of LSTM for best model selection: ' + str(select_rmsekey)) scores = {} scores['R2'] = l_r2 scores['MAE'] = l_mae scores['MSE'] = l_mse scores['RMSE'] = l_rmse scores[scoreParam] = scores.get(scoreParam.upper(), scores['MSE']) log.info("lstm rmse: "+str(l_rmse)) log.info("lstm mse: "+str(l_mse)) log.info("lstm r2: "+str(l_r2)) log.info("lstm mae: "+str(l_mae)) return model,look_back,scaler, scores def train(config, targetPath, log): dataLoc = targetPath / IOFiles['inputData'] if not dataLoc.exists(): return {'Status': 'Failure', 'Message': 'Data location does not exists.'} status = dict() usecase = config['targetPath'] df = utils.read_data(dataLoc) target_feature = config['target_feature'] dateTimeFeature= config['dateTimeFeature'] scoreParam = config['scoring_criteria'] testSize = config['test_ratio'] lstmConfig = config['algorithms']['LSTM'] filename = meta_data['transformation']['Status']['Normalization_file'] if (type(target_feature) is list): pass else: target_feature = list(target_feature.split(",")) df.set_index(dateTimeFeature, inplace=True) log.info('Training LSTM for TimeSeries') mlp_model, look_back, scaler, error_matrix = startTraining(df,testSize,lstmConfig,filename,target_feature,scoreParam,log) score = error_matrix[scoreParam] log.info("LSTM Multivariant all scoring param results: "+str(error_matrix)) # Training model model_path = targetPath/'runs'/str(meta_data['monitoring']['runId'])/model_name model_file_name = str(model_path/'model') mlp_model.save(model_file_name) meta_data['training'] = {} meta_data['training']['model_filename'] = model_file_name meta_data['training']['dateTimeFeature'] = dateTimeFeature meta_data['training']['target_feature'] = target_feature utils.write_json(meta_data, targetPath / IOFiles['metaData']) utils.write_json({'scoring_criteria': scoreParam, 'metrices': error_matrix,'score':error_matrix[scoreParam]}, model_path / IOFiles['metrics']) # return status status = {'Status': 'Success', 'errorMatrix': error_matrix,'score':error_matrix[scoreParam]} log.info(f'score: {error_matrix[scoreParam]}') log.info(f'output: {status}') return json.dumps(status) """ def addMainCode(self, indent=1): self.codeText += """ if __name__ == '__main__': config = validateConfig() targetPath = Path('aion') / config['targetPath'] if not targetPath.exists(): raise ValueError(f'targetPath does not exist') meta_data_file = targetPath / IOFiles['metaData'] if meta_data_file.exists(): meta_data = utils.read_json(meta_data_file) else: raise ValueError(f'Configuration file not found: {meta_data_file}') log_file = targetPath / IOFiles['log'] log = utils.logger(log_file, mode='a', logger_name=Path(__file__).parent.stem) try: print(train(config, targetPath, log)) except Exception as e: status = {'Status': 'Failure', 'Message': str(e)} print(json.dumps(status)) """ def add_variable(self, name, value, indent=1): if isinstance(value, str): self.codeText += f"\n{self.tab * indent}{name} = '{value}'" else: self.codeText += f"\n{self.tab * indent}{name} = {value}" def addStatement(self, statement, indent=1): self.codeText += f"\n{self.tab * indent}{statement}" """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class selector(): def __init__(self, indent=0, tab_size=4): self.tab = " "*tab_size self.codeText = "" self.pipe = 'pipe' self.code_generated = False self.input_files = {} self.output_files = {} self.function_code = '' self.addInputFiles({'inputData' : 'transformedData.dat', 'metaData' : 'modelMetaData.json','log' : 'aion.log','outputData' : 'featureEngineeredData.dat'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def __addValidateConfigCode(self): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = read_json(config_file)\ \n return config" return text def addMainCode(self): self.codeText += """ if __name__ == '__main__': config = validateConfig() targetPath = Path('aion') / config['targetPath'] if not targetPath.exists(): raise ValueError(f'targetPath does not exist') meta_data_file = targetPath / IOFiles['metaData'] if meta_data_file.exists(): meta_data = read_json(meta_data_file) else: raise ValueError(f'Configuration file not found: {meta_data_file}') log_file = targetPath / IOFiles['log'] log = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem) try: print(featureSelector(config,targetPath, log)) except Exception as e: status = {'Status': 'Failure', 'Message': str(e)} print(json.dumps(status)) """ def addValidateConfigCode(self, indent=1): self.function_code += self.__addValidateConfigCode() def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def getCode(self): return self.function_code + '\n' + self.codeText def addLocalFunctionsCode(self): self.addValidateConfigCode() def getPrefixModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'pandas', 'mod_as':'pd'} ] return modules def addPrefixCode(self, indent=1): self.codeText += """ def featureSelector(config, targetPath, log): dataLoc = targetPath / IOFiles['inputData'] if not dataLoc.exists(): return {'Status': 'Failure', 'Message': 'Data location does not exists.'} status = dict() df = pd.read_csv(dataLoc) log.log_dataframe(df) csv_path = str(targetPath / IOFiles['outputData']) write_data(df, csv_path, index=False) status = {'Status': 'Success', 'dataFilePath': IOFiles['outputData']} log.info(f'Selected data saved at {csv_path}') meta_data['featureengineering'] = {} meta_data['featureengineering']['Status'] = status write_json(meta_data, str(targetPath / IOFiles['metaData'])) log.info(f'output: {status}') return json.dumps(status) """ def getSuffixModules(self): modules = [] return modules def addSuffixCode(self, indent=1): self.codeText += "" def getMainCodeModules(self): modules = [ {'module':'json'} ,{'module':'logging'} ] return modules def addStatement(self, statement, indent=1): self.codeText += f"\n{self.tab * indent}{statement}" def getPipe(self): return self.pipe """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from .imports import importModule utility_functions = { 'load_data': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'transformer': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'selector': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'train': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'register': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'Prediction': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], 'drift': ['read_json','write_json','read_data','write_data','is_file_name_url','logger_class'], } #TODO convert read and write functions in to class functions functions_code = { 'read_json':{'imports':[{'mod':'json'}],'code':"\n\ \ndef read_json(file_path):\ \n data = None\ \n with open(file_path,'r') as f:\ \n data = json.load(f)\ \n return data\ \n"}, 'write_json':{'imports':[{'mod':'json'}],'code':"\n\ \ndef write_json(data, file_path):\ \n with open(file_path,'w') as f:\ \n json.dump(data, f)\ \n"}, 'read_data':{'imports':[{'mod':'pandas','mod_as':'pd'}],'code':"\n\ \ndef read_data(file_path, encoding='utf-8', sep=','):\ \n return pd.read_csv(file_path, encoding=encoding, sep=sep)\ \n"}, 'write_data':{'imports':[{'mod':'pandas','mod_as':'pd'}],'code':"\n\ \ndef write_data(data, file_path, index=False):\ \n return data.to_csv(file_path, index=index)\ \n\ \n#Uncomment and change below code for google storage\ \n#from google.cloud import storage\ \n#def write_data(data, file_path, index=False):\ \n# file_name= file_path.name\ \n# data.to_csv('output_data.csv')\ \n# storage_client = storage.Client()\ \n# bucket = storage_client.bucket('aion_data')\ \n# bucket.blob('prediction/'+file_name).upload_from_filename('output_data.csv', content_type='text/csv')\ \n# return data\ \n"}, 'is_file_name_url':{'imports':[],'code':"\n\ \ndef is_file_name_url(file_name):\ \n supported_urls_starts_with = ('gs://','https://','http://')\ \n return file_name.startswith(supported_urls_starts_with)\ \n"}, 'logger_class':{'imports':[{'mod':'logging'}, {'mod':'io'}],'code':"\n\ \nclass logger():\ \n #setup the logger\ \n def __init__(self, log_file, mode='w', logger_name=None):\ \n logging.basicConfig(filename=log_file, filemode=mode, format='%(asctime)s %(name)s- %(message)s', level=logging.INFO, datefmt='%d-%b-%y %H:%M:%S')\ \n self.log = logging.getLogger(logger_name)\ \n\ \n #get logger\ \n def getLogger(self):\ \n return self.log\ \n\ \n def info(self, msg):\ \n self.log.info(msg)\ \n\ \n def error(self, msg, exc_info=False):\ \n self.log.error(msg,exc_info)\ \n\ \n # format and log dataframe\ \n def log_dataframe(self, df, rows=2, msg=None):\ \n buffer = io.StringIO()\ \n df.info(buf=buffer)\ \n log_text = 'Data frame{}'.format(' after ' + msg + ':' if msg else ':')\ \n log_text += '\\n\\t'+str(df.head(rows)).replace('\\n','\\n\\t')\ \n log_text += ('\\n\\t' + buffer.getvalue().replace('\\n','\\n\\t'))\ \n self.log.info(log_text)\ \n"}, } class utility_function(): def __init__(self, module): if module in utility_functions.keys(): self.module_name = module else: self.module_name = None self.importer = importModule() self.codeText = "" def get_code(self): code = "" if self.module_name: functions = utility_functions[self.module_name] for function in functions: self.codeText += self.get_function_code(function) code = self.importer.getCode() code += self.codeText return code def get_function_code(self, name): code = "" if name in functions_code.keys(): code += functions_code[name]['code'] if self.importer: if 'imports' in functions_code[name].keys(): for module in functions_code[name]['imports']: mod_name = module['mod'] mod_from = module.get('mod_from', None) mod_as = module.get('mod_as', None) self.importer.addModule(mod_name, mod_from=mod_from, mod_as=mod_as) return code def get_importer(self): return self.importer if __name__ == '__main__': obj = utility_function('load_data') p = obj.get_utility_code() print(p) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class drift(): def __init__(self, indent=0, tab_size=4): self.tab = " "*tab_size self.codeText = "" self.function_code = "" self.input_files = {} self.output_files = {} self.addInputFiles({'log' : 'aion.log', 'metaData' : 'modelMetaData.json'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def __addValidateConfigCode(self): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = utils.read_json(config_file)\ \n return config\ " return text def addLocalFunctionsCode(self): self.function_code += self.__addValidateConfigCode() def addPrefixCode(self, smaller_is_better=False, indent=1): self.codeText += """ def monitoring(config, targetPath, log): retrain = False last_run_id = 0 retrain_threshold = config.get('retrainThreshold', 100) meta_data_file = targetPath / IOFiles['metaData'] if meta_data_file.exists(): meta_data = utils.read_json(meta_data_file) if not meta_data.get('register', None): log.info('Last time Pipeline not executed properly') retrain = True else: last_run_id = meta_data['register']['runId'] df = utils.read_data(config['dataLocation']) df_len = len(df) if not meta_data['monitoring'].get('endIndex', None): meta_data['monitoring']['endIndex'] = int(meta_data['load_data']['Status']['Records']) meta_data['monitoring']['endIndexTemp'] = meta_data['monitoring']['endIndex'] if meta_data['register'].get('registered', False): meta_data['monitoring']['endIndex'] = meta_data['monitoring']['endIndexTemp'] meta_data['register']['registered'] = False #ack registery if (meta_data['monitoring']['endIndex'] + retrain_threshold) < df_len: meta_data['monitoring']['endIndexTemp'] = df_len retrain = True else: log.info('Pipeline running first time') meta_data = {} meta_data['monitoring'] = {} retrain = True if retrain: meta_data['monitoring']['runId'] = last_run_id + 1 meta_data['monitoring']['retrain'] = retrain utils.write_json(meta_data, targetPath/IOFiles['metaData']) status = {'Status':'Success','retrain': retrain, 'runId':meta_data['monitoring']['runId']} log.info(f'output: {status}') return json.dumps(status) """ def getMainCodeModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'pandas','mod_as':'pd'} ,{'module':'json'} ] return modules def addMainCode(self, indent=1): self.codeText += """ if __name__ == '__main__': config = validateConfig() targetPath = Path('aion') / config['targetPath'] targetPath.mkdir(parents=True, exist_ok=True) log_file = targetPath / IOFiles['log'] log = utils.logger(log_file, mode='a', logger_name=Path(__file__).parent.stem) try: print(monitoring(config, targetPath, log)) except Exception as e: status = {'Status': 'Failure', 'Message': str(e)} print(json.dumps(status)) """ def addStatement(self, statement, indent=1): self.codeText += f"\n{self.tab * indent}{statement}" def getCode(self, indent=1): return self.function_code + '\n' + self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from .imports import importModule supported_reader = ['sqlite', 'influx','s3'] functions_code = { 'dataReader':{'imports':[{'mod':'json'},{'mod': 'Path', 'mod_from': 'pathlib', 'mod_as': None},{'mod': 'pandas', 'mod_from': None, 'mod_as': 'pd'}],'code':""" class dataReader(): def get_reader(self, reader_type, target_path=None, config=None): if reader_type == 'sqlite': return sqlite_writer(target_path=target_path) elif reader_type == 'influx': return Influx_writer(config=config) elif reader_type == 'gcs': return gcs(config=config) elif reader_type == 'azure': return azure(config=config) elif reader_type == 's3': return s3bucket(config=config) else: raise ValueError(reader_type) """ }, 'sqlite':{'imports':[{'mod':'sqlite3'},{'mod': 'pandas', 'mod_from': None, 'mod_as': 'pd'},{'mod': 'Path', 'mod_from': 'pathlib', 'mod_as': None}],'code':"""\n\ class sqlite_writer(): def __init__(self, target_path): self.target_path = Path(target_path) database_file = self.target_path.stem + '.db' self.db = sqlite_db(self.target_path, database_file) def file_exists(self, file): if file: return self.db.table_exists(file) else: return False def read(self, file): return self.db.read(file) def write(self, data, file): self.db.write(data, file) def close(self): self.db.close() class sqlite_db(): def __init__(self, location, database_file=None): if not isinstance(location, Path): location = Path(location) if database_file: self.database_name = database_file else: self.database_name = location.stem + '.db' db_file = str(location/self.database_name) self.conn = sqlite3.connect(db_file) self.cursor = self.conn.cursor() self.tables = [] def table_exists(self, name): if name in self.tables: return True elif name: query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';" listOfTables = self.cursor.execute(query).fetchall() if len(listOfTables) > 0 : self.tables.append(name) return True return False def read(self, table_name): return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn) def create_table(self,name, columns, dtypes): query = f'CREATE TABLE IF NOT EXISTS {name} (' for column, data_type in zip(columns, dtypes): query += f"'{column}' TEXT," query = query[:-1] query += ');' self.conn.execute(query) return True def write(self,data, table_name): if not self.table_exists(table_name): self.create_table(table_name, data.columns, data.dtypes) tuple_data = list(data.itertuples(index=False, name=None)) insert_query = f'INSERT INTO {table_name} VALUES(' for i in range(len(data.columns)): insert_query += '?,' insert_query = insert_query[:-1] + ')' self.cursor.executemany(insert_query, tuple_data) self.conn.commit() return True def delete(self, name): pass def close(self): self.conn.close() """ }, 'influx':{'imports':[{'mod':'InfluxDBClient','mod_from':'influxdb'},{'mod': 'Path', 'mod_from': 'pathlib', 'mod_as': None},{'mod': 'pandas', 'mod_from': None, 'mod_as': 'pd'}],'code':"""\n\ class Influx_writer(): def __init__(self, config): self.db = influx_db(config) def file_exists(self, file): if file: return self.db.table_exists(file) else: return False def read(self, file): query = "SELECT * FROM {}".format(file) if 'read_time' in self.db_config.keys() and self.db_config['read_time']: query += f" time > now() - {self.db_config['read_time']}" return self.db.read(query) def write(self, data, file): self.db.write(data, file) def close(self): pass class influx_db(): def __init__(self, config): self.host = config['host'] self.port = config['port'] self.user = config.get('user', None) self.password = config.get('password', None) self.token = config.get('token', None) self.database = config['database'] self.measurement = config['measurement'] self.tags = config['tags'] self.client = self.get_client() def table_exists(self, name): query = f"SHOW MEASUREMENTS ON {self.database}" result = self.client(query) for measurement in result['measurements']: if measurement['name'] == name: return True return False def read(self, query)->pd.DataFrame: cursor = self.client.query(query) points = cursor.get_points() my_list=list(points) df=pd.DataFrame(my_list) return df def get_client(self): headers = None if self.token: headers={"Authorization": self.token} client = InfluxDBClient(self.host,self.port,self.user, self.password,headers=headers) databases = client.get_list_database() databases = [x['name'] for x in databases] if self.database not in databases: client.create_database(self.database) return InfluxDBClient(self.host,self.port,self.user,self.password,self.database,headers=headers) def write(self,data, measurement=None): if isinstance(data, pd.DataFrame): sorted_col = data.columns.tolist() sorted_col.sort() data = data[sorted_col] data = data.to_dict(orient='records') if not measurement: measurement = self.measurement for row in data: if 'time' in row.keys(): p = '%Y-%m-%dT%H:%M:%S.%fZ' time_str = datetime.strptime(row['time'], p) del row['time'] else: time_str = None if 'model_ver' in row.keys(): self.tags['model_ver']= row['model_ver'] del row['model_ver'] json_body = [{ 'measurement': measurement, 'time': time_str, 'tags': self.tags, 'fields': row }] self.client.write_points(json_body) def delete(self, name): pass def close(self): self.client.close() """ }, 's3':{'imports':[{'mod':'boto3'},{'mod': 'ClientError', 'mod_from': 'botocore.exceptions'},{'mod': 'Path', 'mod_from': 'pathlib'},{'mod': 'pandas', 'mod_as': 'pd'}],'code':"""\n\ class s3bucket(): def __init__(self, config={}): if 's3' in config.keys(): config = config['s3'] aws_access_key_id = config.get('aws_access_key_id','') aws_secret_access_key = config.get('aws_secret_access_key','') bucket_name = config.get('bucket_name','') if not aws_access_key_id: raise ValueError('aws_access_key_id can not be empty') if not aws_secret_access_key: raise ValueError('aws_secret_access_key can not be empty') self.client = boto3.client('s3', aws_access_key_id=aws_access_key_id, aws_secret_access_key=str(aws_secret_access_key)) self.bucket_name = bucket_name def read(self, file_name): try: response = self.client.get_object(Bucket=self.bucket_name, Key=file_name) return pd.read_csv(response['Body']) except ClientError as ex: if ex.response['Error']['Code'] == 'NoSuchBucket': raise ValueError(f"Bucket '{self.bucket_name}' not found in aws s3 storage") elif ex.response['Error']['Code'] == 'NoSuchKey': raise ValueError(f"File '{file_name}' not found in s3 bucket '{self.bucket_name}'") else: raise """ }, 'azure':{'imports':[{'mod':'DataLakeServiceClient', 'mod_from':'azure.storage.filedatalake'},{'mod':'detect', 'mod_from':'detect_delimiter'},{'mod':'pandavro', 'mod_as':'pdx'},{'mod':'io'},{'mod': 'Path', 'mod_from': 'pathlib'},{'mod': 'pandas', 'mod_as': 'pd'}],'code':"""\n\ def azure(): def __init__(self,config={}): if 'azure' in config.keys(): config = config['azure'] account_name = config.get('account_name','') account_key = config.get('account_key','') container_name = config.get('container_name','') if not account_name: raise ValueError('Account name can not be empty') if not account_key: raise ValueError('Account key can not be empty') if not container_name: raise ValueError('Container name can not be empty') service_client = DataLakeServiceClient(account_url="{}://{}.dfs.core.windows.net".format("https", account_name), credential=account_key) self.file_system_client = service_client.get_file_system_client(container_name) def read(self, directory_name): root_dir = str(directory_name) file_paths = self.file_system_client.get_paths(path=root_dir) main_df = pd.DataFrame() for path in file_paths: if not path.is_directory: file_client = file_system_client.get_file_client(path.name) file_ext = Path(path.name).suffix if file_ext in [".csv", ".tsv"]: with open(csv_local, "wb") as my_file: file_client.download_file().readinto(my_file) with open(csv_local, 'r') as file: data = file.read() row_delimiter = detect(text=data, default=None, whitelist=[',', ';', ':', '|', '\t']) processed_df = pd.read_csv(csv_local, sep=row_delimiter) elif file_ext == ".parquet": stream = io.BytesIO() file_client.download_file().readinto(stream) processed_df = pd.read_parquet(stream, engine='pyarrow') elif file_ext == ".avro": with open(avro_local, "wb") as my_file: file_client.download_file().readinto(my_file) processed_df = pdx.read_avro(avro_local) if main_df.empty: main_df = pd.DataFrame(processed_df) else: main_df = main_df.append(processed_df, ignore_index=True) return main_df """ }, 'gcs':{'imports':[{'mod':'storage','mod_from':'google.cloud'},{'mod': 'Path', 'mod_from': 'pathlib'},{'mod': 'pandas', 'mod_as': 'pd'}],'code':"""\n\ class gcs(): def __init__(self, config={}): if 'gcs' in config.keys(): config = config['gcs'] account_key = config.get('account_key','') bucket_name = config.get('bucket_name','') if not account_key: raise ValueError('Account key can not be empty') if not bucket_name: raise ValueError('bucket name can not be empty') storage_client = storage.Client.from_service_account_json(account_key) self.bucket = storage_client.get_bucket(bucket_name) def read(self, bucket_name, file_name): data = self.bucket.blob(file_name).download_as_text() return pd.read_csv(data, encoding = 'utf-8', sep = ',') """ } } class data_reader(): def __init__(self, reader_type=[]): self.supported_readers = supported_reader if isinstance(reader_type, str): self.readers = [reader_type] elif not reader_type: self.readers = self.supported_readers else: self.readers = reader_type unsupported_reader = [ x for x in self.readers if x not in self.supported_readers] if unsupported_reader: raise ValueError(f"reader type '{unsupported_reader}' is not supported\nSupported readers are {self.supported_readers}") self.codeText = "" self.importer = importModule() def get_reader_code(self, readers): reader_code = { 'sqlite': 'return sqlite_writer(target_path=target_path)', 'influx': 'return Influx_writer(config=config)', 'gcs': 'return gcs(config=config)', 'azure': 'return azure(config=config)', 's3': 'return s3bucket(config=config)' } code = "\n\ndef dataReader(reader_type, target_path=None, config=None):\n" for i, reader in enumerate(readers): if not i: code += f" if reader_type == '{reader}':\n" else: code += f" elif reader_type == '{reader}':\n" code += f" {reader_code[reader]}\n" if readers: code += " else:\n" code += f""" raise ValueError("'{{reader_type}}' not added during code generation")\n""" else: code += f""" raise ValueError("'{{reader_type}}' not added during code generation")\n""" return code def get_code(self): code = self.get_reader_code(self.readers) functions = [] for reader in self.readers: functions.append(reader) for function in functions: code += self.get_function_code(function) self.codeText += self.importer.getCode() self.codeText += code return self.codeText def get_function_code(self, name): code = "" if name in functions_code.keys(): code += functions_code[name]['code'] if self.importer: if 'imports' in functions_code[name].keys(): for module in functions_code[name]['imports']: mod_name = module['mod'] mod_from = module.get('mod_from', None) mod_as = module.get('mod_as', None) self.importer.addModule(mod_name, mod_from=mod_from, mod_as=mod_as) return code def get_importer(self): return self.importer """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from importlib.metadata import version import sys class importModule(): def __init__(self): self.importModule = {} self.stdlibModule = [] self.localModule = {} def addLocalModule(self,module, mod_from=None, mod_as=None): if module == '*': if module not in self.localModule.keys(): self.localModule[module]= [mod_from] else: self.localModule[module].append(mod_from) elif module not in self.localModule.keys(): self.localModule[module] = {'from':mod_from, 'as':mod_as} def addModule(self, module, mod_from=None, mod_as=None): if module not in self.importModule.keys(): self.importModule[module] = {'from':mod_from, 'as':mod_as} if module in sys.stdlib_module_names: self.stdlibModule.append(module) elif isinstance(self.importModule[module], list): if mod_as not in [x['as'] for x in self.importModule[module]]: self.importModule[module].append({'from':mod_from, 'as':mod_as}) elif mod_as not in [x['from'] for x in self.importModule[module]]: self.importModule[module].append({'from':mod_from, 'as':mod_as}) elif mod_as != self.importModule[module]['as']: as_list = [self.importModule[module]] as_list.append({'from':mod_from, 'as':mod_as}) self.importModule[module] = as_list elif mod_from != self.importModule[module]['from']: as_list = [self.importModule[module]] as_list.append({'from':mod_from, 'as':mod_as}) self.importModule[module] = as_list def getModules(self): return (self.importModule, self.stdlibModule) def getBaseModule(self, extra_importers=[]): modules_alias = { 'sklearn':'scikit-learn', 'genetic_selection':'sklearn-genetic', 'google': 'google-cloud-storage', 'azure':'azure-storage-file-datalake'} local_modules = {'AIX':'/app/AIX-0.1-py3-none-any.whl'} modules = [] require = "" if extra_importers: extra_importers = [importer.importModule for importer in extra_importers if isinstance(importer, importModule)] importers_module = [self.importModule] + extra_importers for importer_module in importers_module: for k,v in importer_module.items(): if v['from']: mod = v['from'].split('.')[0] else: mod = k if mod in modules_alias.keys(): mod = modules_alias[mod] modules.append(mod) modules = list(set(modules)) for mod in modules: try: if mod in local_modules.keys(): require += f"{local_modules[mod]}\n" else: require += f"{mod}=={version(mod)}\n" except : if mod not in sys.stdlib_module_names: raise return require def getCode(self): def to_string(k, v): mod = '' if v['from']: mod += 'from {} '.format(v['from']) mod += 'import {}'.format(k) if v['as']: mod += ' as {} '.format(v['as']) return mod modules = "" local_modules = "" std_lib_modules = "" third_party_modules = "" for k,v in self.importModule.items(): if k in self.stdlibModule: std_lib_modules = std_lib_modules + '\n' + to_string(k, v) elif isinstance(v, dict): third_party_modules = third_party_modules + '\n' + to_string(k, v) elif isinstance(v, list): for alias in v: third_party_modules = third_party_modules + '\n' + to_string(k, alias) for k,v in self.localModule.items(): if k != '*': local_modules = local_modules + '\n' + to_string(k, v) else: for mod_from in v: local_modules = local_modules + '\n' + f'from {mod_from} import {k}' if std_lib_modules: modules = modules + "\n#Standard Library modules" + std_lib_modules if third_party_modules: modules = modules + "\n\n#Third Party modules" + third_party_modules if local_modules: modules = modules + "\n\n#local modules" + local_modules + '\n' return modules def copyCode(self, importer): self.importModule, self.stdlibModule = importer.getModules() """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class transformer(): def __init__(self, indent=0, tab_size=4): self.df_name = 'df' self.tab = ' ' * tab_size self.codeText = "" self.transformers = [] self.TxCols = [] self.imputers = {} self.input_files = {} self.output_files = {} self.function_code = '' self.addInputFiles({'inputData' : 'rawData.dat', 'metaData' : 'modelMetaData.json','log' : 'aion.log','transformedData' : 'transformedData.dat','normalization' : 'normalization.pkl'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def __addValidateConfigCode(self): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = read_json(config_file)\ \n return config" return text def getPrefixModules(self): modules = [ {'module':'Path', 'mod_from':'pathlib'} ,{'module':'pandas', 'mod_as':'pd'} ,{'module':'warnings'} ,{'module':'json'} ,{'module':'logging'} ,{'module':'joblib'} ,{'module':'MinMaxScaler', 'mod_from':'sklearn.preprocessing'} ] return modules def addPrefixCode(self, indent=1): self.codeText += """ def transformation(config, targetPath, log): dataLoc = targetPath / IOFiles['inputData'] if not dataLoc.exists(): return {'Status': 'Failure', 'Message': 'Data location does not exists.'} df = read_data(dataLoc) log.log_dataframe(df) target_feature = config['target_feature'] dateTimeFeature=config['dateTimeFeature'] df.set_index(dateTimeFeature, inplace=True) df = df.dropna() df=df.fillna(df.mean()) if len(target_feature) == 1: trainX = df[target_feature].to_numpy().reshape(-1,1) else: trainX = df[target_feature].to_numpy() scaler = MinMaxScaler(feature_range=(0, 1)) trainX = scaler.fit_transform(trainX) normalization_file_name = str(targetPath / IOFiles['normalization']) joblib.dump(scaler, normalization_file_name) df[target_feature] = trainX log.log_dataframe(df) csv_path = str(targetPath / IOFiles['transformedData']) write_data(df, csv_path, index=True) status = {'Status': 'Success', 'DataFilePath': IOFiles['transformedData'], 'target_feature': target_feature,'dateTimeFeature':dateTimeFeature, "Normalization_file":normalization_file_name } meta_data['transformation'] = {} meta_data['transformation']['Status'] = status write_json(meta_data, str(targetPath / IOFiles['metaData'])) log.info(f'Transformed data saved at {csv_path}') log.info(f'output: {status}') return json.dumps(status) """ def getMainCodeModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'sys'} ,{'module':'json'} ,{'module':'logging'} ,{'module':'argparse'} ] return modules def addMainCode(self, indent=1): self.codeText += """ if __name__ == '__main__': config = validateConfig() targetPath = Path('aion') / config['targetPath'] if not targetPath.exists(): raise ValueError(f'targetPath does not exist') meta_data_file = targetPath / IOFiles['metaData'] if meta_data_file.exists(): meta_data = read_json(meta_data_file) else: raise ValueError(f'Configuration file not found: {meta_data_file}') log_file = targetPath / IOFiles['log'] log = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem) try: print(transformation(config, targetPath, log)) except Exception as e: status = {'Status': 'Failure', 'Message': str(e)} print(json.dumps(status)) """ def addValidateConfigCode(self, indent=1): self.function_code += self.__addValidateConfigCode() def addLocalFunctionsCode(self): self.addValidateConfigCode() def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def getCode(self, indent=1): return self.function_code + '\n' + self.codeText def getDFName(self): return self.df_name """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ class global_function(): def __init__(self, tab_size=4): self.tab = ' ' * tab_size self.codeText = "" self.available_functions = { 'iqr':{'name':'iqrOutlier','code':f"\n\ndef iqrOutlier(df):\ \n{self.tab}Q1 = df.quantile(0.25)\ \n{self.tab}Q3 = df.quantile(0.75)\ \n{self.tab}IQR = Q3 - Q1\ \n{self.tab}index = ~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)\ \n{self.tab}return index"}, 'zscore':{'name':'zscoreOutlier','imports':[{'mod':'stats','mod_from':'scipy'},{'mod':'numpy'}],'code':f"\n\ndef zscoreOutlier(df):\ \n{self.tab}z = numpy.abs(stats.zscore(df))\ \n{self.tab}index = (z < 3).all(axis=1)\ \n{self.tab}return index"}, 'iforest':{'name':'iforestOutlier','imports':[{'mod':'IsolationForest','mod_from':'sklearn.ensemble'}],'code':f"\n\ndef iforestOutlier(df):\ \n{self.tab}from sklearn.ensemble import IsolationForest\ \n{self.tab}isolation_forest = IsolationForest(n_estimators=100)\ \n{self.tab}isolation_forest.fit(df)\ \n{self.tab}y_pred_train = isolation_forest.predict(df)\ \n{self.tab}return y_pred_train == 1"}, 'minMaxImputer':{'name':'minMaxImputer','code':f"\n\nclass minMaxImputer(TransformerMixin):\ \n{self.tab}def __init__(self, strategy='max'):\ \n{self.tab}{self.tab}self.strategy = strategy\ \n{self.tab}def fit(self, X, y=None):\ \n{self.tab}{self.tab}self.feature_names_in_ = X.columns\ \n{self.tab}{self.tab}if self.strategy == 'min':\ \n{self.tab}{self.tab}{self.tab}self.statistics_ = X.min()\ \n{self.tab}{self.tab}else:\ \n{self.tab}{self.tab}{self.tab}self.statistics_ = X.max()\ \n{self.tab}{self.tab}return self\ \n{self.tab}def transform(self, X):\ \n{self.tab}{self.tab}import numpy\ \n{self.tab}{self.tab}return numpy.where(X.isna(), self.statistics_, X)"}, 'DummyEstimator':{'name':'DummyEstimator','code':f"\n\nclass DummyEstimator(BaseEstimator):\ \n{self.tab}def fit(self): pass\ \n{self.tab}def score(self): pass"}, 'start_reducer':{'name':'start_reducer','code':"\n\ \ndef start_reducer(df,target_feature,corr_threshold=0.85,var_threshold=0.05):\ \n import numpy as np\ \n import pandas as pd\ \n import itertools\ \n from sklearn.feature_selection import VarianceThreshold\ \n\ \n train_features = df.columns.tolist()\ \n train_features.remove(target_feature)\ \n df = df.loc[:, (df != df.iloc[0]).any()] #remove constant feature\ \n numeric_features = df.select_dtypes(include='number').columns.tolist()\ \n non_numeric_features = df.select_dtypes(exclude='number').columns.tolist()\ \n if numeric_features and var_threshold:\ \n qconstantFilter = VarianceThreshold(threshold=var_threshold)\ \n tempDf=df[numeric_features]\ \n qconstantFilter.fit(tempDf)\ \n numeric_features = [x for x,y in zip(numeric_features,qconstantFilter.get_support()) if y]\ \n if numeric_features:\ \n numColPairs = list(itertools.product(numeric_features, numeric_features))\ \n for item in numColPairs:\ \n if(item[0] == item[1]):\ \n numColPairs.remove(item)\ \n tempArray = []\ \n for item in numColPairs:\ \n tempCorr = np.abs(df[item[0]].corr(df[item[1]]))\ \n if(tempCorr > corr_threshold):\ \n tempArray.append(item[0])\ \n tempArray = np.unique(tempArray).tolist()\ \n nonsimilarNumericalCols = list(set(numeric_features) - set(tempArray))\ \n groupedFeatures = []\ \n if tempArray:\ \n corrDic = {}\ \n for feature in tempArray:\ \n temp = []\ \n for col in tempArray:\ \n tempCorr = np.abs(df[feature].corr(df[col]))\ \n temp.append(tempCorr)\ \n corrDic[feature] = temp\ \n #Similar correlation df\ \n corrDF = pd.DataFrame(corrDic,index = tempArray)\ \n corrDF.loc[:,:] = np.tril(corrDF, k=-1)\ \n alreadyIn = set()\ \n similarFeatures = []\ \n for col in corrDF:\ \n perfectCorr = corrDF[col][corrDF[col] > corr_threshold].index.tolist()\ \n if perfectCorr and col not in alreadyIn:\ \n alreadyIn.update(set(perfectCorr))\ \n perfectCorr.append(col)\ \n similarFeatures.append(perfectCorr)\ \n updatedSimFeatures = []\ \n for items in similarFeatures:\ \n if(target_feature != '' and target_feature in items):\ \n for p in items:\ \n updatedSimFeatures.append(p)\ \n else:\ \n updatedSimFeatures.append(items[0])\ \n newTempFeatures = list(set(updatedSimFeatures + nonsimilarNumericalCols))\ \n updatedFeatures = list(set(newTempFeatures + non_numeric_features))\ \n else:\ \n updatedFeatures = list(set(columns) - set(constFeatures)-set(qconstantColumns))\ \n else:\ \n updatedFeatures = list(set(columns) - set(constFeatures)-set(qconstantColumns))\ \n return updatedFeatures"}, 'feature_importance_class':{'name':'feature_importance_class','code':"\n\ \ndef feature_importance_class(df, numeric_features, cat_features,target_feature,pValTh,corrTh):\ \n import pandas as pd\ \n from sklearn.feature_selection import chi2\ \n from sklearn.feature_selection import f_classif\ \n from sklearn.feature_selection import mutual_info_classif\ \n \ \n impFeatures = []\ \n if cat_features:\ \n categoricalData=df[cat_features]\ \n chiSqCategorical=chi2(categoricalData,df[target_feature])[1]\ \n corrSeries=pd.Series(chiSqCategorical, index=cat_features)\ \n impFeatures.append(corrSeries[corrSeries<pValTh].index.tolist())\ \n if numeric_features:\ \n quantData=df[numeric_features]\ \n fclassScore=f_classif(quantData,df[target_feature])[1]\ \n miClassScore=mutual_info_classif(quantData,df[target_feature])\ \n fClassSeries=pd.Series(fclassScore,index=numeric_features)\ \n miClassSeries=pd.Series(miClassScore,index=numeric_features)\ \n impFeatures.append(fClassSeries[fClassSeries<pValTh].index.tolist())\ \n impFeatures.append(miClassSeries[miClassSeries>corrTh].index.tolist())\ \n pearsonScore=df.corr() \ \n targetPScore=abs(pearsonScore[target_feature])\ \n impFeatures.append(targetPScore[targetPScore<pValTh].index.tolist())\ \n return list(set(sum(impFeatures, [])))"}, 'feature_importance_reg':{'name':'feature_importance_reg','code':"\n\ \ndef feature_importance_reg(df, numeric_features, target_feature,pValTh,corrTh):\ \n import pandas as pd\ \n from sklearn.feature_selection import f_regression\ \n from sklearn.feature_selection import mutual_info_regression\ \n \ \n impFeatures = []\ \n if numeric_features:\ \n quantData =df[numeric_features]\ \n fregScore=f_regression(quantData,df[target_feature])[1]\ \n miregScore=mutual_info_regression(quantData,df[target_feature])\ \n fregSeries=pd.Series(fregScore,index=numeric_features)\ \n miregSeries=pd.Series(miregScore,index=numeric_features)\ \n impFeatures.append(fregSeries[fregSeries<pValTh].index.tolist())\ \n impFeatures.append(miregSeries[miregSeries>corrTh].index.tolist())\ \n pearsonScore=df.corr()\ \n targetPScore=abs(pearsonScore[target_feature])\ \n impFeatures.append(targetPScore[targetPScore<pValTh].index.tolist())\ \n return list(set(sum(impFeatures, [])))"}, 'scoring_criteria':{'name':'scoring_criteria','imports':[{'mod':'make_scorer','mod_from':'sklearn.metrics'},{'mod':'roc_auc_score','mod_from':'sklearn.metrics'}], 'code':"\n\ \ndef scoring_criteria(score_param, problem_type, class_count):\ \n if problem_type == 'classification':\ \n scorer_mapping = {\ \n 'recall':{'binary_class': 'recall', 'multi_class': 'recall_weighted'},\ \n 'precision':{'binary_class': 'precision', 'multi_class': 'precision_weighted'},\ \n 'f1_score':{'binary_class': 'f1', 'multi_class': 'f1_weighted'},\ \n 'roc_auc':{'binary_class': 'roc_auc', 'multi_class': 'roc_auc_ovr_weighted'}\ \n }\ \n if (score_param.lower() == 'roc_auc') and (class_count > 2):\ \n score_param = make_scorer(roc_auc_score, needs_proba=True,multi_class='ovr',average='weighted')\ \n else:\ \n class_type = 'binary_class' if class_count == 2 else 'multi_class'\ \n if score_param in scorer_mapping.keys():\ \n score_param = scorer_mapping[score_param][class_type]\ \n else:\ \n score_param = 'accuracy'\ \n return score_param"}, 'log_dataframe':{'name':'log_dataframe','code':f"\n\ \ndef log_dataframe(df, msg=None):\ \n import io\ \n buffer = io.StringIO()\ \n df.info(buf=buffer)\ \n if msg:\ \n log_text = f'Data frame after {{msg}}:'\ \n else:\ \n log_text = 'Data frame:'\ \n log_text += '\\n\\t'+str(df.head(2)).replace('\\n','\\n\\t')\ \n log_text += ('\\n\\t' + buffer.getvalue().replace('\\n','\\n\\t'))\ \n get_logger().info(log_text)"}, 'BayesSearchCV':{'name':'BayesSearchCV','imports':[{'mod':'cross_val_score','mod_from':'sklearn.model_selection'},{'mod':'fmin','mod_from':'hyperopt'},{'mod':'tpe','mod_from':'hyperopt'},{'mod':'hp','mod_from':'hyperopt'},{'mod':'STATUS_OK','mod_from':'hyperopt'},{'mod':'Trials','mod_from':'hyperopt'},{'mod':'numpy','mod_as':'np'}],'code':"\n\ \nclass BayesSearchCV():\ \n\ \n def __init__(self, estimator, params, scoring, n_iter, cv):\ \n self.estimator = estimator\ \n self.params = params\ \n self.scoring = scoring\ \n self.iteration = n_iter\ \n self.cv = cv\ \n self.best_estimator_ = None\ \n self.best_score_ = None\ \n self.best_params_ = None\ \n\ \n def __min_fun(self, params):\ \n score=cross_val_score(self.estimator, self.X, self.y,scoring=self.scoring,cv=self.cv)\ \n acc = score.mean()\ \n return {'loss':-acc,'score': acc, 'status': STATUS_OK,'model' :self.estimator,'params': params}\ \n\ \n def fit(self, X, y):\ \n trials = Trials()\ \n self.X = X\ \n self.y = y\ \n best = fmin(self.__min_fun,self.params,algo=tpe.suggest, max_evals=self.iteration, trials=trials)\ \n result = sorted(trials.results, key = lambda x: x['loss'])[0]\ \n self.best_estimator_ = result['model']\ \n self.best_score_ = result['score']\ \n self.best_params_ = result['params']\ \n self.best_estimator_.fit(X, y)\ \n\ \n def hyperOptParamConversion( paramSpace):\ \n paramDict = {}\ \n for j in list(paramSpace.keys()):\ \n inp = paramSpace[j]\ \n isLog = False\ \n isLin = False\ \n isRan = False\ \n isList = False\ \n isString = False\ \n try:\ \n # check if functions are given as input and reassign paramspace\ \n v = paramSpace[j]\ \n if 'logspace' in paramSpace[j]:\ \n paramSpace[j] = v[v.find('(') + 1:v.find(')')].replace(' ', '')\ \n isLog = True\ \n elif 'linspace' in paramSpace[j]:\ \n paramSpace[j] = v[v.find('(') + 1:v.find(')')].replace(' ', '')\ \n isLin = True\ \n elif 'range' in paramSpace[j]:\ \n paramSpace[j] = v[v.find('(') + 1:v.find(')')].replace(' ', '')\ \n isRan = True\ \n elif 'list' in paramSpace[j]:\ \n paramSpace[j] = v[v.find('(') + 1:v.find(')')].replace(' ', '')\ \n isList = True\ \n elif '[' and ']' in paramSpace[j]:\ \n paramSpace[j] = v.split('[')[1].split(']')[0].replace(' ', '')\ \n isList = True\ \n x = paramSpace[j].split(',')\ \n except:\ \n x = paramSpace[j]\ \n str_arg = paramSpace[j]\ \n\ \n # check if arguments are string\ \n try:\ \n test = eval(x[0])\ \n except:\ \n isString = True\ \n\ \n if isString:\ \n paramDict.update({j: hp.choice(j, x)})\ \n else:\ \n res = eval(str_arg)\ \n if isLin:\ \n y = eval('np.linspace' + str(res))\ \n paramDict.update({j: hp.uniform(j, eval(x[0]), eval(x[1]))})\ \n elif isLog:\ \n y = eval('np.logspace' + str(res))\ \n paramDict.update(\ \n {j: hp.uniform(j, 10 ** eval(x[0]), 10 ** eval(x[1]))})\ \n elif isRan:\ \n y = eval('np.arange' + str(res))\ \n paramDict.update({j: hp.choice(j, y)})\ \n # check datatype of argument\ \n elif isinstance(eval(x[0]), bool):\ \n y = list(map(lambda i: eval(i), x))\ \n paramDict.update({j: hp.choice(j, eval(str(y)))})\ \n elif isinstance(eval(x[0]), float):\ \n res = eval(str_arg)\ \n if len(str_arg.split(',')) == 3 and not isList:\ \n y = eval('np.linspace' + str(res))\ \n #print(y)\ \n paramDict.update({j: hp.uniform(j, eval(x[0]), eval(x[1]))})\ \n else:\ \n y = list(res) if isinstance(res, tuple) else [res]\ \n paramDict.update({j: hp.choice(j, y)})\ \n else:\ \n res = eval(str_arg)\ \n if len(str_arg.split(',')) == 3 and not isList:\ \n y = eval('np.linspace' +str(res)) if eval(x[2]) >= eval(x[1]) else eval('np.arange'+str(res))\ \n else:\ \n y = list(res) if isinstance(res, tuple) else [res]\ \n paramDict.update({j: hp.choice(j, y)})\ \n return paramDict"}, 's2n':{'name':'s2n','imports':[{'mod':'word2number','mod_as':'w2n'},{'mod':'numpy','mod_as':'np'}],'code':"\n\ \ndef s2n(value):\ \n try:\ \n x=eval(value)\ \n return x\ \n except:\ \n try:\ \n return w2n.word_to_num(value)\ \n except:\ \n return np.nan"}, 'readWrite':{'name':'readWrite','imports':[{'mod':'json'},{'mod':'pandas','mod_as':'pd'}],'code':"\n\ \ndef read_json(file_path):\ \n data = None\ \n with open(file_path,'r') as f:\ \n data = json.load(f)\ \n return data\ \n\ \ndef write_json(data, file_path):\ \n with open(file_path,'w') as f:\ \n json.dump(data, f)\ \n\ \ndef read_data(file_path, encoding='utf-8', sep=','):\ \n return pd.read_csv(file_path, encoding=encoding, sep=sep)\ \n\ \ndef write_data(data, file_path, index=False):\ \n return data.to_csv(file_path, index=index)\ \n\ \n#Uncomment and change below code for google storage\ \n#def write_data(data, file_path, index=False):\ \n# file_name= file_path.name\ \n# data.to_csv('output_data.csv')\ \n# storage_client = storage.Client()\ \n# bucket = storage_client.bucket('aion_data')\ \n# bucket.blob('prediction/'+file_name).upload_from_filename('output_data.csv', content_type='text/csv')\ \n# return data\ \n\ \ndef is_file_name_url(file_name):\ \n supported_urls_starts_with = ('gs://','https://','http://')\ \n return file_name.startswith(supported_urls_starts_with)\ \n"}, 'logger':{'name':'set_logger','imports':[{'mod':'logging'}],'code':f"\n\ \nlog = None\ \ndef set_logger(log_file, mode='a'):\ \n global log\ \n logging.basicConfig(filename=log_file, filemode=mode, format='%(asctime)s %(name)s- %(message)s', level=logging.INFO, datefmt='%d-%b-%y %H:%M:%S')\ \n log = logging.getLogger(Path(__file__).parent.name)\ \n return log\ \n\ \ndef get_logger():\ \n return log\n"}, 'mlflowSetPath':{'name':'mlflowSetPath','code':f"\n\ndef mlflowSetPath(path, name):\ \n{self.tab}db_name = str(Path(path)/'mlruns')\ \n{self.tab}mlflow.set_tracking_uri('file:///' + db_name)\ \n{self.tab}mlflow.set_experiment(str(Path(path).name))\ \n"}, 'mlflow_create_experiment':{'name':'mlflow_create_experiment','code':f"\n\ndef mlflow_create_experiment(config, path, name):\ \n{self.tab}tracking_uri, artifact_uri, registry_uri = get_mlflow_uris(config, path)\ \n{self.tab}mlflow.tracking.set_tracking_uri(tracking_uri)\ \n{self.tab}mlflow.tracking.set_registry_uri(registry_uri)\ \n{self.tab}client = mlflow.tracking.MlflowClient()\ \n{self.tab}experiment = client.get_experiment_by_name(name)\ \n{self.tab}if experiment:\ \n{self.tab}{self.tab}experiment_id = experiment.experiment_id\ \n{self.tab}else:\ \n{self.tab}{self.tab}experiment_id = client.create_experiment(name, artifact_uri)\ \n{self.tab}return client, experiment_id\ \n"}, 'get_mlflow_uris':{'name':'get_mlflow_uris','code':f"\n\ndef get_mlflow_uris(config, path):\ \n artifact_uri = None\ \n tracking_uri_type = config.get('tracking_uri_type',None)\ \n if tracking_uri_type == 'localDB':\ \n tracking_uri = 'sqlite:///' + str(path.resolve()/'mlruns.db')\ \n elif tracking_uri_type == 'server' and config.get('tracking_uri', None):\ \n tracking_uri = config['tracking_uri']\ \n if config.get('artifacts_uri', None):\ \n if Path(config['artifacts_uri']).exists():\ \n artifact_uri = 'file:' + config['artifacts_uri']\ \n else:\ \n artifact_uri = config['artifacts_uri']\ \n else:\ \n artifact_uri = 'file:' + str(path.resolve()/'mlruns')\ \n else:\ \n tracking_uri = 'file:' + str(path.resolve()/'mlruns')\ \n artifact_uri = None\ \n if config.get('registry_uri', None):\ \n registry_uri = config['registry_uri']\ \n else:\ \n registry_uri = 'sqlite:///' + str(path.resolve()/'registry.db')\ \n return tracking_uri, artifact_uri, registry_uri\ \n"}, 'logMlflow':{'name':'logMlflow','code':f"\n\ndef logMlflow( params, metrices, estimator,tags={{}}, algoName=None):\ \n{self.tab}run_id = None\ \n{self.tab}for k,v in params.items():\ \n{self.tab}{self.tab}mlflow.log_param(k, v)\ \n{self.tab}for k,v in metrices.items():\ \n{self.tab}{self.tab}mlflow.log_metric(k, v)\ \n{self.tab}if 'CatBoost' in algoName:\ \n{self.tab}{self.tab}model_info = mlflow.catboost.log_model(estimator, 'model')\ \n{self.tab}else:\ \n{self.tab}{self.tab}model_info = mlflow.sklearn.log_model(sk_model=estimator, artifact_path='model')\ \n{self.tab}tags['processed'] = 'no'\ \n{self.tab}tags['registered'] = 'no'\ \n{self.tab}mlflow.set_tags(tags)\ \n{self.tab}if model_info:\ \n{self.tab}{self.tab}run_id = model_info.run_id\ \n{self.tab}return run_id\ \n"}, 'classification_metrices':{'name':'classification_metrices','imports':[{'mod':'sklearn'},{'mod':'math'}],'code':"\ndef get_classification_metrices( actual_values, predicted_values):\ \n result = {}\ \n accuracy_score = sklearn.metrics.accuracy_score(actual_values, predicted_values)\ \n avg_precision = sklearn.metrics.precision_score(actual_values, predicted_values,\ \n average='macro')\ \n avg_recall = sklearn.metrics.recall_score(actual_values, predicted_values,\ \n average='macro')\ \n avg_f1 = sklearn.metrics.f1_score(actual_values, predicted_values,\ \n average='macro')\ \n\ \n result['accuracy'] = math.floor(accuracy_score*10000)/100\ \n result['precision'] = math.floor(avg_precision*10000)/100\ \n result['recall'] = math.floor(avg_recall*10000)/100\ \n result['f1'] = math.floor(avg_f1*10000)/100\ \n return result\ \n"}, 'regression_metrices':{'name':'regression_metrices','imports':[{'mod':'numpy', 'mod_as':'np'}],'code':"\ndef get_regression_metrices( actual_values, predicted_values):\ \n result = {}\ \n\ \n me = np.mean(predicted_values - actual_values)\ \n sde = np.std(predicted_values - actual_values, ddof = 1)\ \n\ \n abs_err = np.abs(predicted_values - actual_values)\ \n mae = np.mean(abs_err)\ \n sdae = np.std(abs_err, ddof = 1)\ \n\ \n abs_perc_err = 100.*np.abs(predicted_values - actual_values) / actual_values\ \n mape = np.mean(abs_perc_err)\ \n sdape = np.std(abs_perc_err, ddof = 1)\ \n\ \n result['mean_error'] = me\ \n result['mean_abs_error'] = mae\ \n result['mean_abs_perc_error'] = mape\ \n result['error_std'] = sde\ \n result['abs_error_std'] = sdae\ \n result['abs_perc_error_std'] = sdape\ \n return result\ \n"} } def add_function(self, name, importer=None): if name in self.available_functions.keys(): self.codeText += self.available_functions[name]['code'] if importer: if 'imports' in self.available_functions[name].keys(): for module in self.available_functions[name]['imports']: mod_name = module['mod'] mod_from = module.get('mod_from', None) mod_as = module.get('mod_as', None) importer.addModule(mod_name, mod_from=mod_from, mod_as=mod_as) def get_function_name(self, name): if name in self.available_functions.keys(): return self.available_functions[name]['name'] return None def getCode(self): return self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class register(): def __init__(self, importer, indent=0, tab_size=4): self.tab = " "*tab_size self.codeText = "" self.function_code = "" self.importer = importer self.input_files = {} self.output_files = {} self.addInputFiles({'log' : 'aion.log', 'metaData' : 'modelMetaData.json','metrics': 'metrics.json','production': 'production.json'}) def addInputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def addOutputFiles(self, files): if not isinstance(files, dict): raise TypeError(f"Required dict type got {type(files)} type") for k,v in files.items(): self.input_files[k] = v def getInputFiles(self): text = 'IOFiles = ' if not self.input_files: text += '{ }' else: text += json.dumps(self.input_files, indent=4) return text def getOutputFiles(self): text = 'output_file = ' if not self.output_files: text += '{ }' else: text += json.dumps(self.output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def __addValidateConfigCode(self, models=None): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = utils.read_json(config_file)\ \n return config\ " return text def addLocalFunctionsCode(self, models): self.function_code += self.__addValidateConfigCode(models) def addPrefixCode(self, smaller_is_better=False, indent=1): compare = 'min' if smaller_is_better else 'max' self.codeText += f""" def get_best_model(run_path): models_path = [d for d in run_path.iterdir() if d.is_dir] scores = {{}} for model in models_path: metrics = utils.read_json(model/IOFiles['metrics']) if metrics.get('score', None): scores[model.stem] = metrics['score'] best_model = {compare}(scores, key=scores.get) return best_model def __merge_logs(log_file_sequence,path, files): if log_file_sequence['first'] in files: with open(path/log_file_sequence['first'], 'r') as f: main_log = f.read() files.remove(log_file_sequence['first']) for file in files: with open(path/file, 'r') as f: main_log = main_log + f.read() (path/file).unlink() with open(path/log_file_sequence['merged'], 'w') as f: f.write(main_log) def merge_log_files(folder, models): log_file_sequence = {{ 'first': 'aion.log', 'merged': 'aion.log' }} log_file_suffix = '_aion.log' log_files = [x+log_file_suffix for x in models if (folder/(x+log_file_suffix)).exists()] log_files.append(log_file_sequence['first']) __merge_logs(log_file_sequence, folder, log_files) def register(config, targetPath, log): meta_data_file = targetPath / IOFiles['metaData'] if meta_data_file.exists(): meta_data = utils.read_json(meta_data_file) else: raise ValueError(f'Configuration file not found: {{meta_data_file}}') run_id = meta_data['monitoring']['runId'] usecase = config['targetPath'] current_run_path = targetPath/'runs'/str(run_id) register_model_name = get_best_model(current_run_path) models = config['models'] merge_log_files(targetPath, models) meta_data['register'] = {{'runId':run_id, 'model': register_model_name}} utils.write_json(meta_data, targetPath/IOFiles['metaData']) utils.write_json({{'Model':register_model_name,'runNo':str(run_id)}}, targetPath/IOFiles['production']) status = {{'Status':'Success','Message':f'Model Registered: {{register_model_name}}'}} log.info(f'output: {{status}}') return json.dumps(status) """ def getMainCodeModules(self): modules = [{'module':'Path', 'mod_from':'pathlib'} ,{'module':'json'} ] return modules def addMainCode(self, models, indent=1): self.codeText += """ if __name__ == '__main__': config = validateConfig() targetPath = Path('aion') / config['targetPath'] if not targetPath.exists(): raise ValueError(f'targetPath does not exist') log_file = targetPath / IOFiles['log'] log = utils.logger(log_file, mode='a', logger_name=Path(__file__).parent.stem) try: print(register(config, targetPath, log)) except Exception as e: status = {'Status': 'Failure', 'Message': str(e)} print(json.dumps(status)) """ def addStatement(self, statement, indent=1): self.codeText += f"\n{self.tab * indent}{statement}" def getCode(self, indent=1): return self.function_code + '\n' + self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from mlac.timeseries.core.imports import importModule from mlac.timeseries.core.load_data import tabularDataReader from mlac.timeseries.core.transformer import transformer as profiler from mlac.timeseries.core.selector import selector from mlac.timeseries.core.trainer import learner from mlac.timeseries.core.register import register from mlac.timeseries.core.deploy import deploy from mlac.timeseries.core.drift_analysis import drift from mlac.timeseries.core.functions import global_function from mlac.timeseries.core.data_reader import data_reader from mlac.timeseries.core.utility import utility_function """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import json class tabularDataReader(): def __init__(self, tab_size=4): self.tab = ' ' * tab_size self.function_code = '' self.codeText = '' self.code_generated = False def getInputFiles(self): IOFiles = { "rawData": "rawData.dat", "metaData" : "modelMetaData.json", "log" : "aion.log", "outputData" : "rawData.dat", "monitoring":"monitoring.json", "prodData": "prodData", "prodDataGT":"prodDataGT" } text = 'IOFiles = ' if not IOFiles: text += '{ }' else: text += json.dumps(IOFiles, indent=4) return text def getOutputFiles(self): output_files = { 'metaData' : 'modelMetaData.json', 'log' : 'aion.log', 'outputData' : 'rawData.dat' } text = 'output_file = ' if not output_files: text += '{ }' else: text += json.dumps(output_files, indent=4) return text def getInputOutputFiles(self, indent=0): text = '\n' text += self.getInputFiles() if indent: text = text.replace('\n', self.tab * indent + '\n') return text def __addValidateConfigCode(self): text = "\n\ \ndef validateConfig():\ \n config_file = Path(__file__).parent/'config.json'\ \n if not Path(config_file).exists():\ \n raise ValueError(f'Config file is missing: {config_file}')\ \n config = read_json(config_file)\ \n if not config['targetPath']:\ \n raise ValueError(f'Target Path is not configured')\ \n return config" return text def addMainCode(self, indent=1): self.codeText += """ if __name__ == '__main__': config = validateConfig() targetPath = Path('aion') / config['targetPath'] targetPath.mkdir(parents=True, exist_ok=True) if not targetPath.exists(): raise ValueError(f'targetPath does not exist') meta_data_file = targetPath / IOFiles['metaData'] if not meta_data_file.exists(): raise ValueError(f'Configuration file not found: {meta_data_file}') log_file = targetPath / IOFiles['log'] log = logger(log_file, mode='a', logger_name=Path(__file__).parent.stem) try: print(load_data(config, targetPath, log)) except Exception as e: status = {'Status': 'Failure', 'Message': str(e)} print(json.dumps(status)) """ def addLoadDataCode(self): self.codeText += """ #This function will read the data and save the data on persistent storage def load_data(config, targetPath, log): meta_data_file = targetPath / IOFiles['metaData'] meta_data = read_json(meta_data_file) if meta_data.get('monitoring', False) and not meta_data['monitoring'].get('retrain', False): raise ValueError('New data is not enougth to retrain model') df = read_data(config['dataLocation']) status = {} output_data_path = targetPath / IOFiles['outputData'] log.log_dataframe(df) required_features = list(set(config['selected_features'] + config['dateTimeFeature'] + config['target_feature'])) log.info('Dataset features required: ' + ','.join(required_features)) missing_features = [x for x in required_features if x not in df.columns.tolist()] if missing_features: raise ValueError(f'Some feature/s is/are missing: {missing_features}') log.info('Removing unused features: ' + ','.join(list(set(df.columns) - set(required_features)))) df = df[required_features] log.info(f'Required features: {required_features}') try: log.info(f'Saving Dataset: {str(output_data_path)}') write_data(df, output_data_path, index=False) status = {'Status': 'Success', 'DataFilePath': IOFiles['outputData'], 'Records': len(df)} except: raise ValueError('Unable to create data file') meta_data['load_data'] = {} meta_data['load_data']['selected_features'] = [x for x in config['selected_features'] if x != config['target_feature']] meta_data['load_data']['Status'] = status write_json(meta_data, meta_data_file) output = json.dumps(status) log.info(output) return output """ def addValidateConfigCode(self, indent=1): self.function_code += self.__addValidateConfigCode() def addLocalFunctionsCode(self): self.addValidateConfigCode() def addStatement(self, statement, indent=1): self.codeText += '\n' + self.tab * indent + statement def getCode(self): return self.function_code + '\n' + self.codeText """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json from mlac.timeseries.core import * from .utility import * def get_deploy_params(config): param_keys = ["modelVersion","problem_type","target_feature","lag_order","noofforecasts"] data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] data['ipAddress'] = '127.0.0.1' data['portNo'] = '8094' return data def import_trainer_module(importer): non_sklearn_modules = get_variable('non_sklearn_modules') if non_sklearn_modules: for mod in non_sklearn_modules: module = get_module_mapping(mod) mod_from = module.get('mod_from',None) mod_as = module.get('mod_as',None) importer.addModule(module['module'], mod_from=mod_from, mod_as=mod_as) imported_modules = [ ] def run_deploy(config): generated_files = [] importer = importModule() deployer = deploy() importModules(importer, imported_modules) usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'ModelServing' deploy_path.mkdir(parents=True, exist_ok=True) # create the utility file importer.addLocalModule('utility', mod_as='utils') utility_obj = utility_function('Prediction') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create empty init file required for creating a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") importModules(importer,deployer.getPredictionCodeModules()) code = file_header(usecase) code += importer.getCode() code += deployer.getInputOutputFiles() deployer.addPredictionCode() code += deployer.getCode() # create prediction file with open(deploy_path/"predict.py", 'w') as f: f.write(code) generated_files.append("predict.py") # create create service file with open(deploy_path/"aionCode.py", 'w') as f: f.write(file_header(usecase) + deployer.getServiceCode()) generated_files.append("aionCode.py") importer.addModule('seaborn') importer.addModule('sklearn') # create requirements file req_file = deploy_path/"requirements.txt" with open(req_file, "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") # create config file config_file = deploy_path/"config.json" config_data = get_deploy_params(config) with open (config_file, "w") as f: json.dump(config_data, f, indent=4) generated_files.append("config.json") # create docker file create_docker_file('Prediction', deploy_path,config['modelName'], generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json from mlac.timeseries.core import * from mlac.timeseries.app import utility as utils def get_model_name(algo, method): if method == 'modelBased': return algo + '_' + 'MLBased' if method == 'statisticalBased': return algo + '_' + 'StatisticsBased' else: return algo def get_training_params(config, algo): param_keys = ["modelVersion","problem_type","target_feature","train_features","scoring_criteria","test_ratio","optimization_param","dateTimeFeature"]#BugID:13217 data = {key:value for (key,value) in config.items() if key in param_keys} data['algorithms'] = {algo: config['algorithms'][algo]} data['targetPath'] = config['modelName'] return data def update_score_comparer(scorer): smaller_is_better_scorer = ['neg_mean_squared_error','mse','neg_root_mean_squared_error','rmse','neg_mean_absolute_error','mae'] if scorer.lower() in smaller_is_better_scorer: utils.update_variable('smaller_is_better', True) else: utils.update_variable('smaller_is_better', False) def run_trainer(config): trainer = learner() importer = importModule() function = global_function() utils.importModules(importer,trainer.getPrefixModules()) update_score_comparer(config['scoring_criteria']) model_name = list(config['algorithms'].keys())[0] if model_name == 'MLP': utils.importModules(importer,trainer.getMlpCodeModules()) trainer.addMlpCode() elif model_name == 'LSTM': utils.importModules(importer,trainer.getLstmCodeModules()) trainer.addLstmCode() trainer.addMainCode() usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/('ModelTraining'+'_' + model_name) deploy_path.mkdir(parents=True, exist_ok=True) generated_files = [] # create the utility file importer.addLocalModule('utility', mod_as='utils') utility_obj = utility_function('train') with open(deploy_path/"utility.py", 'w') as f: f.write(utils.file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create empty init file to make a package with open(deploy_path/"__init__.py", 'w') as f: f.write(utils.file_header(usecase)) generated_files.append("__init__.py") importer.addModule("warnings") code = importer.getCode() code += 'warnings.filterwarnings("ignore")\n' code += f"\nmodel_name = '{model_name}'\n" utils.append_variable('models_name',model_name) out_files = {'log':f'{model_name}_aion.log','model':f'{model_name}_model.pkl','metrics':'metrics.json','metaDataOutput':f'{model_name}_modelMetaData.json','production':'production.json'} trainer.addOutputFiles(out_files) code += trainer.getInputOutputFiles() code += function.getCode() trainer.addLocalFunctionsCode() code += trainer.getCode() with open(deploy_path/"aionCode.py", "w") as f: f.write(code) generated_files.append("aionCode.py") with open(deploy_path/"requirements.txt", "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") with open (deploy_path/"config.json", "w") as f: json.dump(get_training_params(config, model_name), f, indent=4) generated_files.append("config.json") utils.create_docker_file('train', deploy_path,config['modelName'], generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json import platform from mlac.timeseries.core import * from .utility import * output_file_map = { 'feature_reducer' : {'feature_reducer' : 'feature_reducer.pkl'} } def get_selector_params(config): param_keys = ["modelVersion","problem_type","target_feature","train_features","cat_features","n_components"] data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] return data def run_selector(config): select = selector() importer = importModule() function = global_function() importModules(importer,select.getPrefixModules()) importModules(importer, select.getSuffixModules()) importModules(importer, select.getMainCodeModules()) select.addPrefixCode() select.addSuffixCode() select.addMainCode() generated_files = [] usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'FeatureEngineering' deploy_path.mkdir(parents=True, exist_ok=True) # create the utility file importer.addLocalModule('*', mod_from='utility') utility_obj = utility_function('selector') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create empty init file to make a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") code = file_header(usecase) code += importer.getCode() code += select.getInputOutputFiles() code += function.getCode() select.addLocalFunctionsCode() code += select.getCode() with open(deploy_path/"aionCode.py", "w") as f: f.write(code) generated_files.append("aionCode.py") with open(deploy_path/"requirements.txt", "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") config_file = deploy_path/"config.json" config_data = get_selector_params(config) with open (config_file, "w") as f: json.dump(config_data, f, indent=4) generated_files.append("config.json") create_docker_file('selector', deploy_path,config['modelName'], generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ import datetime from pathlib import Path variables = {} def update_variable(name, value): variables[name] = value def get_variable(name, default=None): return variables.get(name, default) def append_variable(name, value): data = get_variable(name) if not data: update_variable(name, [value]) elif not isinstance(data, list): update_variable(name, [data, value]) else: data.append(value) update_variable(name, data) def addDropFeature(feature, features_list, coder, indent=1): coder.addStatement(f'if {feature} in {features_list}:', indent=indent) coder.addStatement(f'{features_list}.remove({feature})', indent=indent+1) def importModules(importer, modules_list): for module in modules_list: mod_from = module.get('mod_from',None) mod_as = module.get('mod_as',None) importer.addModule(module['module'], mod_from=mod_from, mod_as=mod_as) def file_header(use_case, module_name=None): time_str = datetime.datetime.now().isoformat(timespec='seconds', sep=' ') text = "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n" return text + f"'''\nThis file is automatically generated by AION for {use_case} usecase.\nFile generation time: {time_str}\n'''" def get_module_mapping(module): mapping = { "LogisticRegression": {'module':'LogisticRegression', 'mod_from':'sklearn.linear_model'} ,"GaussianNB": {'module':'GaussianNB', 'mod_from':'sklearn.naive_bayes'} ,"DecisionTreeClassifier": {'module':'DecisionTreeClassifier', 'mod_from':'sklearn.tree'} ,"SVC": {'module':'SVC', 'mod_from':'sklearn.svm'} ,"KNeighborsClassifier": {'module':'KNeighborsClassifier', 'mod_from':'sklearn.neighbors'} ,"GradientBoostingClassifier": {'module':'GradientBoostingClassifier', 'mod_from':'sklearn.ensemble'} ,'RandomForestClassifier':{'module':'RandomForestClassifier','mod_from':'sklearn.ensemble'} ,'XGBClassifier':{'module':'XGBClassifier','mod_from':'xgboost'} ,'LGBMClassifier':{'module':'LGBMClassifier','mod_from':'lightgbm'} ,'CatBoostClassifier':{'module':'CatBoostClassifier','mod_from':'catboost'} ,"LinearRegression": {'module':'LinearRegression', 'mod_from':'sklearn.linear_model'} ,"Lasso": {'module':'Lasso', 'mod_from':'sklearn.linear_model'} ,"Ridge": {'module':'Ridge', 'mod_from':'sklearn.linear_model'} ,"DecisionTreeRegressor": {'module':'DecisionTreeRegressor', 'mod_from':'sklearn.tree'} ,'RandomForestRegressor':{'module':'RandomForestRegressor','mod_from':'sklearn.ensemble'} ,'XGBRegressor':{'module':'XGBRegressor','mod_from':'xgboost'} ,'LGBMRegressor':{'module':'LGBMRegressor','mod_from':'lightgbm'} ,'CatBoostRegressor':{'module':'CatBoostRegressor','mod_from':'catboost'} } return mapping.get(module, None) def create_docker_file(name, path,usecasename,files=[],text_feature=False): text = "" if name == 'load_data': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='RUN pip install --no-cache-dir -r requirements.txt' elif name == 'transformer': text='FROM python:3.8-slim-buster\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' if text_feature: text+='COPY AIX-0.1-py3-none-any.whl AIX-0.1-py3-none-any.whl\n' text+='\n' text+='''RUN \ ''' if text_feature: text += ''' git && pip install requests && pip install git+https://github.com/MCFreddie777/language-check.git\ && ''' text+=''' pip install --no-cache-dir -r requirements.txt\ ''' if text_feature: text += ''' && python -m nltk.downloader stopwords && python -m nltk.downloader punkt && python -m nltk.downloader wordnet && python -m nltk.downloader averaged_perceptron_tagger\ ''' text+='\n' elif name == 'selector': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='RUN pip install --no-cache-dir -r requirements.txt' elif name == 'train': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='RUN pip install --no-cache-dir -r requirements.txt' elif name == 'register': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='RUN pip install --no-cache-dir -r requirements.txt' elif name == 'Prediction': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' if text_feature: text+='COPY AIX-0.1-py3-none-any.whl AIX-0.1-py3-none-any.whl\n' text+='''RUN \ ''' if text_feature: text += ''' git && pip install requests && pip install git+https://github.com/MCFreddie777/language-check.git\ && ''' text+='''pip install --no-cache-dir -r requirements.txt\ ''' if text_feature: text += ''' && python -m nltk.downloader stopwords && python -m nltk.downloader punkt && python -m nltk.downloader wordnet && python -m nltk.downloader averaged_perceptron_tagger\ ''' text+='\n' text+='ENTRYPOINT ["python", "aionCode.py","-ip","0.0.0.0","-pn","8094"]\n' elif name == 'input_drift': text='FROM python:3.8-slim-buster' text+='\n' text+='LABEL "usecase"="'+str(usecasename)+'"' text+='\n' text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"' text+='\n' for file in files: text+=f'\nCOPY {file} {file}' text+='\n' text+='RUN pip install --no-cache-dir -r requirements.txt' file_name = Path(path)/'Dockerfile' with open(file_name, 'w') as f: f.write(text) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json from mlac.timeseries.core import * from .utility import * def get_drift_params(config): param_keys = ["modelVersion","problem_type","retrainThreshold","dataLocation"] data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] return data def run_drift_analysis(config): importer = importModule() monitor = drift() monitor.addLocalFunctionsCode() monitor.addPrefixCode() monitor.addMainCode() importModules(importer, monitor.getMainCodeModules()) importer.addModule('warnings') generated_files = [] usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'ModelMonitoring' deploy_path.mkdir(parents=True, exist_ok=True) # create the utility file importer.addLocalModule('utility', mod_as='utils') utility_obj = utility_function('load_data') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create empty init file required for creating a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") code = importer.getCode() code += '\nwarnings.filterwarnings("ignore")\n' code += monitor.getInputOutputFiles() code += monitor.getCode() # create serving file with open(deploy_path/"aionCode.py", 'w') as f: f.write(file_header(usecase) + code) generated_files.append("aionCode.py") # create requirements file req_file = deploy_path/"requirements.txt" with open(req_file, "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") # create config file with open (deploy_path/"config.json", "w") as f: json.dump(get_drift_params(config), f, indent=4) generated_files.append("config.json") # create docker file create_docker_file('input_drift', deploy_path,config['modelName'], generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json import platform from mlac.timeseries.core import * from .utility import * output_file_map = { 'text' : {'text' : 'text_profiler.pkl'}, 'targetEncoder' : {'targetEncoder' : 'targetEncoder.pkl'}, 'featureEncoder' : {'featureEncoder' : 'inputEncoder.pkl'}, 'normalizer' : {'normalizer' : 'normalizer.pkl'} } def add_common_imports(importer): common_importes = [ {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'argparse', 'mod_from': None, 'mod_as': None}, {'module': 'platform', 'mod_from': None, 'mod_as': None } ] for mod in common_importes: importer.addModule(mod['module'], mod_from=mod['mod_from'], mod_as=mod['mod_as']) def get_transformer_params(config): param_keys = ["modelVersion","problem_type","target_feature","train_features","text_features","profiler","test_ratio","dateTimeFeature"] #BugID:13217 data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] return data def run_transformer(config): transformer = profiler() importer = importModule() function = global_function() importModules(importer, transformer.getPrefixModules()) importer.addModule('warnings') transformer.addPrefixCode() importModules(importer, transformer.getMainCodeModules()) transformer.addMainCode() usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'DataTransformation' deploy_path.mkdir(parents=True, exist_ok=True) generated_files = [] # create the utility file importer.addLocalModule('*', mod_from='utility') utility_obj = utility_function('transformer') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create empty init file to make a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") code = file_header(usecase) code += "\nimport os\nos.path.abspath(os.path.join(__file__, os.pardir))\n" #chdir to import from current dir code += importer.getCode() code += '\nwarnings.filterwarnings("ignore")\n' code += transformer.getInputOutputFiles() code += function.getCode() transformer.addLocalFunctionsCode() code += transformer.getCode() with open(deploy_path/"aionCode.py", "w") as f: f.write(code) generated_files.append("aionCode.py") with open(deploy_path/"requirements.txt", "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") config_file = deploy_path/"config.json" config_data = get_transformer_params(config) with open (config_file, "w") as f: json.dump(config_data, f, indent=4) generated_files.append("config.json") create_docker_file('transformer', deploy_path,config['modelName'], generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json from mlac.timeseries.core import * from .utility import * def get_register_params(config, models): param_keys = ["modelVersion","problem_type"] data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] data['models'] = models return data def run_register(config): importer = importModule() registration = register(importer) models = get_variable('models_name') smaller_is_better = get_variable('smaller_is_better', False) registration.addLocalFunctionsCode(models) registration.addPrefixCode(smaller_is_better) registration.addMainCode(models) importModules(importer, registration.getMainCodeModules()) importer.addModule('warnings') generated_files = [] usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'ModelRegistry' deploy_path.mkdir(parents=True, exist_ok=True) # create the utility file importer.addLocalModule('utility', mod_as='utils') utility_obj = utility_function('register') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create empty init file required for creating a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") code = importer.getCode() code += '\nwarnings.filterwarnings("ignore")\n' code += registration.getInputOutputFiles() code += registration.getCode() # create serving file with open(deploy_path/"aionCode.py", 'w') as f: f.write(file_header(usecase) + code) generated_files.append("aionCode.py") # create requirements file req_file = deploy_path/"requirements.txt" with open(req_file, "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") # create config file with open (deploy_path/"config.json", "w") as f: json.dump(get_register_params(config, models), f, indent=4) generated_files.append("config.json") # create docker file create_docker_file('register', deploy_path,config['modelName'], generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from .load_data import run_loader from .transformer import run_transformer from .selector import run_selector from .trainer import run_trainer from .register import run_register from .deploy import run_deploy from .drift_analysis import run_drift_analysis """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from pathlib import Path import json import platform from mlac.timeseries.core import * from .utility import * imported_modules = [ {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'argparse', 'mod_from': None, 'mod_as': None}, {'module': 'platform', 'mod_from': None, 'mod_as': None } ] def get_load_data_params(config): param_keys = ["modelVersion","problem_type","target_feature","selected_features","dateTimeFeature","dataLocation"] data = {key:value for (key,value) in config.items() if key in param_keys} data['targetPath'] = config['modelName'] return data def run_loader(config): generated_files = [] importer = importModule() loader = tabularDataReader() importModules(importer, imported_modules) usecase = config['modelName']+'_'+config['modelVersion'] deploy_path = Path(config["deploy_path"])/'MLaC'/'DataIngestion' deploy_path.mkdir(parents=True, exist_ok=True) # create the utility file importer.addLocalModule('*', mod_from='utility') utility_obj = utility_function('load_data') with open(deploy_path/"utility.py", 'w') as f: f.write(file_header(usecase) + utility_obj.get_code()) generated_files.append("utility.py") # create the production data reader file importer.addLocalModule('dataReader', mod_from='data_reader') readers = ['sqlite','influx'] if 's3' in config.keys(): readers.append('s3') reader_obj = data_reader(readers) with open(deploy_path/"data_reader.py", 'w') as f: f.write(file_header(usecase) + reader_obj.get_code()) generated_files.append("data_reader.py") # create empty init file to make a package with open(deploy_path/"__init__.py", 'w') as f: f.write(file_header(usecase)) generated_files.append("__init__.py") code = file_header(usecase) code += importer.getCode() code += loader.getInputOutputFiles() loader.addLocalFunctionsCode() loader.addLoadDataCode() loader.addMainCode() code += loader.getCode() with open(deploy_path/"aionCode.py", "w") as f: f.write(code) generated_files.append("aionCode.py") with open(deploy_path/"requirements.txt", "w") as f: req=importer.getBaseModule(extra_importers=[utility_obj.get_importer(), reader_obj.get_importer()]) f.write(req) generated_files.append("requirements.txt") config_file = deploy_path/"config.json" config_data = get_load_data_params(config) with open (config_file, "w") as f: json.dump(config_data, f, indent=4) generated_files.append("config.json") create_docker_file('load_data', deploy_path,config['modelName'],generated_files) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from .imports import importModule from .load_data import tabularDataReader from .transformer import transformer as profiler from .selector import selector from .trainer import learner from .deploy import deploy from .functions import global_function ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import joblib import pandas as pd import sys import math import time import pandas as pd import numpy as np from sklearn.metrics import confusion_matrix from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, confusion_matrix from sklearn.svm import SVC from sklearn.linear_model import LinearRegression import argparse import json def mltesting(modelfile,datafile,features,target): model = joblib.load(modelfile) ProblemName = model.__class__.__name__ if ProblemName in ['LogisticRegression','SGDClassifier','SVC','DecissionTreeClassifier','RandomForestClassifier','GaussianNB','KNeighborsClassifier','DecisionTreeClassifier','GradientBoostingClassifier','XGBClassifier','LGBMClassifier','CatBoostClassifier']: Problemtype = 'Classification' elif ProblemName in ['LinearRegression','Lasso','Ridge','DecisionTreeRegressor','RandomForestRegressor','GradientBoostingRegressor','XGBRegressor','LGBMRegressor','CatBoostRegressor']: Problemtype = 'Regression' else: Problemtype = 'Unknown' if Problemtype == 'Classification': Params = model.get_params() try: df = pd.read_csv(datafile,encoding='utf-8',skipinitialspace = True) if ProblemName == 'LogisticRegression' or ProblemName == 'DecisionTreeClassifier' or ProblemName == 'RandomForestClassifier' or ProblemName == 'GaussianNB' or ProblemName == 'KNeighborsClassifier' or ProblemName == 'GradientBoostingClassifier' or ProblemName == 'SVC': features = model.feature_names_in_ elif ProblemName == 'XGBClassifier': features = model.get_booster().feature_names elif ProblemName == 'LGBMClassifier': features = model.feature_name_ elif ProblemName == 'CatBoostClassifier': features = model.feature_names_ modelfeatures = features dfp = df[modelfeatures] tar = target target = df[tar] predic = model.predict(dfp) output = {} matrixconfusion = pd.DataFrame(confusion_matrix(predic,target)) matrixconfusion = matrixconfusion.to_json(orient='index') classificationreport = pd.DataFrame(classification_report(target,predic,output_dict=True)).transpose() classificationreport = round(classificationreport,2) classificationreport = classificationreport.to_json(orient='index') output["Precision"] = "%.2f" % precision_score(target, predic,average='weighted') output["Recall"] = "%.2f" % recall_score(target, predic,average='weighted') output["Accuracy"] = "%.2f" % accuracy_score(target, predic) output["ProblemName"] = ProblemName output["Status"] = "Success" output["Params"] = Params output["Problemtype"] = Problemtype output["Confusionmatrix"] = matrixconfusion output["classificationreport"] = classificationreport # import statistics # timearray = [] # for i in range(0,5): # start = time.time() # predic1 = model.predict(dfp.head(1)) # end = time.time() # timetaken = (round((end - start) * 1000,2),'Seconds') # timearray.append(timetaken) # print(timearray) start = time.time() for i in range(0,5): predic1 = model.predict(dfp.head(1)) end = time.time() timetaken = (round((end - start) * 1000,2),'Seconds') # print(timetaken) start1 = time.time() for i in range(0,5): predic2 = model.predict(dfp.head(10)) end1 = time.time() timetaken1 = (round((end1 - start1) * 1000,2) ,'Seconds') # print(timetaken1) start2 = time.time() for i in range(0,5): predic3 = model.predict(dfp.head(100)) end2 = time.time() timetaken2 = (round((end2 - start2) * 1000,2) ,'Seconds') # print(timetaken2) output["onerecord"] = timetaken output["tenrecords"] = timetaken1 output["hundrecords"] = timetaken2 print(json.dumps(output)) except Exception as e: output = {} output['Problemtype']='Classification' output['Status']= "Fail" output["ProblemName"] = ProblemName output["Msg"] = 'Detected Model : {} \\n Problem Type : Classification \\n Error : {}'.format(ProblemName, str(e).replace('"','//"').replace('\n', '\\n')) print(output["Msg"]) print(json.dumps(output)) elif Problemtype == 'Regression': Params = model.get_params() try: df = pd.read_csv(datafile,encoding='utf-8',skipinitialspace = True) if ProblemName == 'LinearRegression' or ProblemName == 'Lasso' or ProblemName == 'Ridge' or ProblemName == 'DecisionTreeRegressor' or ProblemName == 'RandomForestRegressor' or ProblemName == 'GaussianNB' or ProblemName == 'KNeighborsRegressor' or ProblemName == 'GradientBoostingRegressor': features = model.feature_names_in_ elif ProblemName == 'XGBRegressor': features = model.get_booster().feature_names elif ProblemName == 'LGBMRegressor': features = model.feature_name_ elif ProblemName == 'CatBoostRegressor': features = model.feature_names_ modelfeatures = features dfp = df[modelfeatures] tar = target target = df[tar] predict = model.predict(dfp) mse = mean_squared_error(target, predict) mae = mean_absolute_error(target, predict) rmse = math.sqrt(mse) r2 = r2_score(target,predict,multioutput='variance_weighted') output = {} output["MSE"] = "%.2f" % mean_squared_error(target, predict) output["MAE"] = "%.2f" % mean_absolute_error(target, predict) output["RMSE"] = "%.2f" % math.sqrt(mse) output["R2"] = "%.2f" %r2_score(target,predict,multioutput='variance_weighted') output["ProblemName"] = ProblemName output["Problemtype"] = Problemtype output["Params"] = Params output['Status']='Success' start = time.time() predic1 = model.predict(dfp.head(1)) end = time.time() timetaken = (round((end - start) * 1000,2) ,'Seconds') # print(timetaken) start1 = time.time() predic2 = model.predict(dfp.head(10)) end1 = time.time() timetaken1 = (round((end1 - start1) * 1000,2),'Seconds') # print(timetaken1) start2 = time.time() predic3 = model.predict(dfp.head(100)) end2 = time.time() timetaken2 = (round((end2 - start2) * 1000,2) ,'Seconds') # print(timetaken2) output["onerecord"] = timetaken output["tenrecords"] = timetaken1 output["hundrecords"] = timetaken2 print(json.dumps(output)) except Exception as e: output = {} output['Problemtype']='Regression' output['Status']='Fail' output["ProblemName"] = ProblemName output["Msg"] = 'Detected Model : {} \\n Problem Type : Regression \\n Error : {}'.format(ProblemName, str(e).replace('"','//"').replace('\n', '\\n')) print(json.dumps(output)) else: output = {} output['Problemtype']='Unknown' output['Status']='Fail' output['Params'] = '' output["ProblemName"] = ProblemName output["Msg"] = 'Detected Model : {} \\n Error : {}'.format(ProblemName, 'Model not supported') print(json.dumps(output)) return(json.dumps(output)) def baseline_testing(modelFile,csvFile,features,target): features = [x.strip() for x in features.split(',')] return mltesting(modelFile,csvFile,features,target) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' #from sklearn.externals import joblib import joblib # import pyreadstat # import sys # import math import time import pandas as pd import numpy as np from sklearn.metrics import confusion_matrix from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, confusion_matrix from sklearn.svm import SVC from sklearn.linear_model import LinearRegression import argparse import json import os import pathlib from tensorflow.keras.models import load_model # from tensorflow.keras import backend as K import tensorflow as tf # from sklearn.decomposition import LatentDirichletAllocation from pathlib import Path #from aionUQ import aionUQ from uq_main import aionUQ import os from datetime import datetime from sklearn.model_selection import train_test_split parser = argparse.ArgumentParser() parser.add_argument('savFile') parser.add_argument('csvFile') parser.add_argument('features') parser.add_argument('target') args = parser.parse_args() from appbe.dataPath import DEPLOY_LOCATION if ',' in args.features: args.features = [x.strip() for x in args.features.split(',')] else: args.features = args.features.split(",") models = args.savFile if Path(models).is_file(): # if Path(args.savFile.is_file()): model = joblib.load(args.savFile) # print(model.__class__.__name__) # print('class:',model.__class__) # print(type(model).__name__) # try: # print('Classess=',model.classes_) # except: # print("Classess=N/A") # print('params:',model.get_params()) # try: # print('fea_imp =',model.feature_importances_) # except: # print("fea_imp =N/A") ProblemName = model.__class__.__name__ Params = model.get_params() # print("ProblemName: \n",ProblemName) # print("Params: \n",Params) # print('ProblemName:',model.__doc__) # print(type(ProblemName)) if ProblemName in ['LogisticRegression','SGDClassifier','SVC','DecissionTreeClassifier','RandomForestClassifier','GaussianNB','KNeighboursClassifier','DecisionTreeClassifier','GradientBoostingClassifier']: Problemtype = 'Classification' else : Problemtype = 'Regression' if Problemtype == 'Classification': df = pd.read_csv(args.csvFile) object_cols = [col for col, col_type in df.dtypes.items() if col_type == 'object'] df = df.drop(object_cols, axis=1) df = df.dropna(axis=1) df = df.reset_index(drop=True) modelfeatures = args.features # dfp = df[modelfeatures] tar = args.target # target = df[tar] y=df[tar] X = df.drop(tar, axis=1) #for dummy test,train values pass X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) uqObj=aionUQ(df,X,y,ProblemName,Params,model,modelfeatures,tar) #accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertaitny_per=uqObj.uqMain_BBMClassification(X_train, X_test, y_train, y_test,"uqtest") accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertaitny_per=uqObj.uqMain_BBMClassification() # print("UQ Classification: \n",output_jsonobject) print(accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertaitny_per) print("End of UQ Classification.\n") else: df = pd.read_csv(args.csvFile) modelfeatures = args.features # print("modelfeatures: \n",modelfeatures) # print("type modelfeatures: \n",type(modelfeatures)) dfp = df[modelfeatures] tar = args.target target = df[tar] #Not used, just dummy X,y split y=df[tar] X = df.drop(tar, axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) uqObj=aionUQ(df,dfp,target,ProblemName,Params,model,modelfeatures,tar) total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject=uqObj.uqMain_BBMRegression() print(total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject) print("End of UQ reg\n") elif Path(models).is_dir(): os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices' os.environ['TF_CPP_MIN_LOG_LEVEL']='2' model = load_model(models) ProblemName = model.__class__.__name__ Problemtype = 'Classification' # print('class:',model.__class__) # print('class1',model.__class__.__name__) # print(model.summary()) # print('ProblemName1:',model.get_config()) def Params(model: tf.keras.Model): Params = [] model.Params(print_fn=lambda x: Params.append(x)) return '\n'.join(Params) df = pd.read_csv(args.csvFile) modelfeatures = args.features dfp = df[modelfeatures] tar = args.target target = df[tar] df3 = dfp.astype(np.float32) predic = model.predict(df3) if predic.shape[-1] > 1: predic = np.argmax(predic, axis=-1) else: predic = (predic > 0.5).astype("int32") matrixconfusion = pd.DataFrame(confusion_matrix(predic,target)) matrixconfusion = matrixconfusion.to_json(orient='index') classificationreport = pd.DataFrame(classification_report(target,predic,output_dict=True)).transpose() classificationreport = round(classificationreport,2) classificationreport = classificationreport.to_json(orient='index') output = {} output["Precision"] = "%.3f" % precision_score(target, predic,average='weighted') output["Recall"] = "%.3f" % recall_score(target, predic,average='weighted') output["Accuracy"] = "%.3f" % accuracy_score(target, predic) output["ProblemName"] = ProblemName output["Params"] = Params output["Problemtype"] = Problemtype output["Confusionmatrix"] = matrixconfusion output["classificationreport"] = classificationreport print(json.dumps(output)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import logging logging.getLogger('tensorflow').disabled = True import json #from nltk.corpus import stopwords from collections import Counter from matplotlib import pyplot import sys import os import json import matplotlib.pyplot as plt from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression from uq360.algorithms.ucc_recalibration import UCCRecalibration from sklearn import datasets from sklearn.model_selection import train_test_split import pandas as pd from uq360.metrics.regression_metrics import compute_regression_metrics import numpy as np from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import roc_curve # from math import sqrt from sklearn.metrics import r2_score,mean_squared_error, explained_variance_score,mean_absolute_error # from uq360.metrics import picp, mpiw, compute_regression_metrics, plot_uncertainty_distribution, plot_uncertainty_by_feature, plot_picp_by_feature from uq360.metrics import plot_uncertainty_by_feature, plot_picp_by_feature #Added libs from MLTest import sys import time from sklearn.metrics import confusion_matrix from pathlib import Path import logging # import json class aionUQ: # def __init__(self,uqdf,targetFeature,xtrain,ytrain,xtest,ytest,uqconfig_base,uqconfig_meta,deployLocation,saved_model): def __init__(self,df,dfp,target,ProblemName,Params,model,modelfeatures,targetfeature,deployLocation): # #printprint("Inside aionUQ \n") try: #print("Inside aionUQ init\n ") self.data=df self.dfFeatures=dfp self.uqconfig_base=Params self.uqconfig_meta=Params self.targetFeature=targetfeature self.target=target self.selectedfeature=modelfeatures self.y=self.target self.X=self.dfFeatures self.log = logging.getLogger('eion') self.basemodel=model self.model_name=ProblemName self.Deployment = os.path.join(deployLocation,'log','UQ') os.makedirs(self.Deployment,exist_ok=True) self.uqgraphlocation = os.path.join(self.Deployment,'UQgraph') os.makedirs(self.uqgraphlocation,exist_ok=True) except Exception as e: self.log.info('<!------------- UQ model INIT Error ---------------> '+str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def totalUncertainty(self,df,basemodel,model_params,xtrain, xtest, ytrain, ytest,aionstatus): from sklearn.model_selection import train_test_split # To get each class values and uncertainty if (aionstatus.lower() == 'aionuq'): X_train, X_test, y_train, y_test = xtrain, xtest, ytrain, ytest # y_val = y_train.append(y_test) else: # y_val = self.y df=self.data y=df[self.targetFeature] X = df.drop(self.targetFeature, axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) key = 'criterion' #if key in model_params: try: #if model_params.has_key(key): if key in model_params: if (model_params['criterion']): uq_scoring_param=model_params.get('criterion') elif(model_params['criterion'] == None): uq_scoring_param='picp' else: uq_scoring_param='picp' else: uq_scoring_param='picp' pass except Exception as inst: uq_scoring_param='picp' # from sklearn.tree import DecisionTreeRegressor # from sklearn.linear_model import LinearRegression,Lasso,Ridge # from sklearn import linear_model # from sklearn.ensemble import RandomForestRegressor if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']: uq_scoring_param=uq_scoring_param else: uq_scoring_param='picp' uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params) # this will fit both the base and the meta model uqmodel_fit = uq_model.fit(X_train, y_train) y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test) y_hat_total_mean=np.mean(y_hat) y_hat_lb_total_mean=np.mean(y_hat_lb) y_hat_ub_total_mean=np.mean(y_hat_ub) mpiw_20_per=(y_hat_total_mean*20/100) mpiw_lower_range = y_hat_total_mean - mpiw_20_per mpiw_upper_range = y_hat_total_mean + mpiw_20_per from uq360.metrics import picp, mpiw observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub) observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub) observed_alphas_picp=round(observed_alphas_picp,2) observed_widths_mpiw=round(observed_widths_mpiw,2) picp_percentage= round(observed_alphas_picp*100) Uncertainty_percentage=round(100-picp_percentage) self.log.info('Model total observed_widths_mpiw : '+str(observed_widths_mpiw)) self.log.info('Model mpiw_lower_range : '+str(mpiw_lower_range)) self.log.info('Model mpiw_upper_range : '+str(mpiw_upper_range)) self.log.info('Model total picp_percentage : '+str(picp_percentage)) return observed_alphas_picp,observed_widths_mpiw,picp_percentage,Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range def display_results(self,X_test, y_test, y_mean, y_lower, y_upper): try: global x_feature,y_feature if (isinstance(self.selectedfeature, list) or isinstance(self.selectedfeature, tuple)): x_feature=''.join(map(str, self.selectedfeature)) else: x_feature= str(self.selectedfeature) # self.selectedfeature=str(self.selectedfeature) X_test=np.squeeze(X_test) y_feature=str(self.targetFeature) pred_dict = {x_feature: X_test, 'y': y_test, 'y_mean': y_mean, 'y_upper': y_upper, 'y_lower': y_lower } pred_df = pd.DataFrame(data=pred_dict) pred_df_sorted = pred_df.sort_values(by=x_feature) plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y'], 'o', label='Observed') plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y_mean'], '-', lw=2, label='Predicted') plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y_upper'], 'r--', lw=2, label='Upper Bound') plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y_lower'], 'r--', lw=2, label='Lower Bound') plt.legend() plt.xlabel(x_feature) plt.ylabel(y_feature) plt.title('UQ Confidence Interval Plot.') # plt.savefig('uq_test_plt.png') if os.path.exists(str(self.uqgraphlocation)+'/uq_test_plt.png'): os.remove(str(self.uqgraphlocation)+'/uq_test_plt.png') plt.savefig(str(self.Deployment)+'/uq_test_plt.png') plt.savefig(str(self.uqgraphlocation)+'/uq_test_plt.png') plt.clf() plt.cla() plt.close() pltreg=plot_picp_by_feature(X_test, y_test, y_lower, y_upper, xlabel=x_feature) #pltreg.savefig('x.png') pltr=pltreg.figure if os.path.exists(str(self.uqgraphlocation)+'/picp_per_feature.png'): os.remove(str(self.uqgraphlocation)+'/picp_per_feature.png') pltr.savefig(str(self.Deployment)+'/picp_per_feature.png') pltr.savefig(str(self.uqgraphlocation)+'/picp_per_feature.png') plt.clf() plt.cla() plt.close() except Exception as e: # #print("display exception: \n",e) self.log.info('<!------------- UQ model Display Error ---------------> '+str(e)) def classUncertainty(self,pred,score): try: outuq = {} classes = np.unique(pred) for c in classes: ids = pred == c class_score = score[ids] predc = 'Class_'+str(c) outuq[predc]=np.mean(class_score) x = np.mean(class_score) #Uncertaininty in percentage x=x*100 self.log.info('----------------> Class '+str(c)+' Confidence Score '+str(round(x))) return outuq except Exception as e: # #print("display exception: \n",e) self.log.info('<!------------- UQ classUncertainty Error ---------------> '+str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def uqMain_BBMClassification(self,x_train, x_test, y_train, y_test,aionstatus): try: # print("Inside uqMain_BBMClassification\n") # print("lenth of x_train {}, x_test {}, y_train {}, y_test {}".format(x_train, x_test, y_train, y_test)) aionstatus = str(aionstatus) if (aionstatus.lower() == 'aionuq'): X_train, X_test, y_train, y_test = x_train, x_test, y_train, y_test else: X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, random_state=0) from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelClassification from uq360.metrics.classification_metrics import plot_reliability_diagram,area_under_risk_rejection_rate_curve,plot_risk_vs_rejection_rate,expected_calibration_error,compute_classification_metrics from sklearn.ensemble import GradientBoostingClassifier from sklearn.linear_model import LogisticRegression from sklearn.linear_model import SGDClassifier from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.svm import SVC from xgboost import XGBClassifier from lightgbm import LGBMClassifier from sklearn.neighbors import KNeighborsClassifier base_modelname=__class__.__name__ base_config = self.uqconfig_base meta_config = self.uqconfig_base model_name=self.basemodel.__class__.__name__ #print(model_name) try: #geting used features model_used_features=self.basemodel.feature_names_in_ self.log.info("Base model used training features are (UQ Testing): \n"+str(model_used_features)) except: pass model_params=self.basemodel.get_params() uq_scoring_param='accuracy' basemodel=None if (model_name == "GradientBoostingClassifier"): basemodel=GradientBoostingClassifier elif (model_name == "SGDClassifier"): basemodel=SGDClassifier elif (model_name == "GaussianNB"): basemodel=GaussianNB elif (model_name == "DecisionTreeClassifier"): basemodel=DecisionTreeClassifier elif(model_name == "RandomForestClassifier"): basemodel=RandomForestClassifier elif (model_name == "SVC"): basemodel=SVC elif(model_name == "KNeighborsClassifier"): basemodel=KNeighborsClassifier elif(model_name.lower() == "logisticregression"): basemodel=LogisticRegression elif(model_name == "XGBClassifier"): basemodel=XGBClassifier elif(model_name == "LGBMClassifier"): basemodel=LGBMClassifier else: basemodel=LogisticRegression calibrated_mdl=None if (model_name == "SVC"): from sklearn.calibration import CalibratedClassifierCV basemodel=SVC(**model_params) calibrated_mdl = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3) calibrated_mdl.fit(X_train, y_train) basepredict = calibrated_mdl.predict(X_test) predprob_base = calibrated_mdl.predict_proba(X_test)[:, :] elif (model_name == "SGDClassifier"): from sklearn.calibration import CalibratedClassifierCV basemodel=SGDClassifier(**model_params) calibrated_mdl = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3) calibrated_mdl.fit(X_train, y_train) basepredict = calibrated_mdl.predict(X_test) predprob_base = calibrated_mdl.predict_proba(X_test)[:, :] else: from sklearn.calibration import CalibratedClassifierCV base_mdl = basemodel(**model_params) calibrated_mdl = CalibratedClassifierCV(base_mdl,method='sigmoid',cv=3) basemodelfit = calibrated_mdl.fit(X_train, y_train) basepredict = calibrated_mdl.predict(X_test) predprob_base=calibrated_mdl.predict_proba(X_test)[:, :] cal_model_params=calibrated_mdl.get_params() acc_score_base=accuracy_score(y_test, basepredict) base_estimator_calibrate = cal_model_params['base_estimator'] uq_model = BlackboxMetamodelClassification(base_model=self.basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params) try: X_train=X_train[model_used_features] X_test=X_test[model_used_features] except: pass uqmodel_fit = uq_model.fit(X_train, y_train,base_is_prefitted=True,meta_train_data=(X_train, y_train)) # uqmodel_fit = uq_model.fit(X_train, y_train) y_t_pred, y_t_score = uq_model.predict(X_test) acc_score=accuracy_score(y_test, y_t_pred) test_accuracy_perc=round(100*acc_score) if(aionstatus == "aionuq"): test_accuracy_perc=round(test_accuracy_perc,2) #uq_aurrrc not used for any aion gui configuration, so it initialized as 0. if we use area_under_risk_rejection_rate_curve(), it shows plot in cmd prompt,so code execution interuupted.so we make it 0. uq_aurrrc=0 pass else: bbm_c_plot = plot_risk_vs_rejection_rate( y_true=y_test, y_prob=predprob_base, selection_scores=y_t_score, y_pred=y_t_pred, plot_label=['UQ_risk_vs_rejection'], risk_func=accuracy_score, num_bins = 10 ) # This done by kiran, need to uncomment for GUI integration. # bbm_c_plot_sub = bbm_c_plot[4] bbm_c_plot_sub = bbm_c_plot if os.path.exists(str(self.uqgraphlocation)+'/plot_risk_vs_rejection_rate.png'): os.remove(str(self.uqgraphlocation)+'/plot_risk_vs_rejection_rate.png') # bbm_c_plot_sub.savefig(str(self.uqgraphlocation)+'/plot_risk_vs_rejection_rate.png') re_plot=plot_reliability_diagram(y_true=y_test, y_prob=predprob_base, y_pred=y_t_pred, plot_label=['UQModel reliability_diagram'], num_bins=10 ) # This done by kiran, need to uncomment for GUI integration. # re_plot_sub = re_plot[4] re_plot_sub = re_plot if os.path.exists(str(self.uqgraphlocation)+'/plot_reliability_diagram.png'): os.remove(str(self.uqgraphlocation)+'/plot_reliability_diagram.png') # re_plot_sub.savefig(str(DEFAULT_FILE_PATH)+'/plot_reliability_diagram.png') uq_aurrrc=area_under_risk_rejection_rate_curve( y_true=y_test, y_prob=predprob_base, y_pred=y_t_pred, selection_scores=y_t_score, attributes=None, risk_func=accuracy_score,subgroup_ids=None, return_counts=False, num_bins=10) uq_aurrrc=uq_aurrrc test_accuracy_perc=round(test_accuracy_perc) #metric_all=compute_classification_metrics(y_test, y_prob, option='all') metric_all=compute_classification_metrics(y_test, predprob_base, option='accuracy') #expected_calibration_error uq_ece=expected_calibration_error(y_test, y_prob=predprob_base,y_pred=basepredict, num_bins=10, return_counts=False) # uq_aurrrc=uq_aurrrc confidence_score=acc_score_base-uq_ece ece_confidence_score=round(confidence_score,2) # Model uncertainty using ECE score # model_uncertainty_ece = 1-ece_confidence_score #Uncertainty Using model inherent predict probability mean_predprob_total=np.mean(y_t_score) model_confidence=mean_predprob_total model_uncertainty = 1-mean_predprob_total model_confidence = round(model_confidence,2) # To get each class values and uncertainty if (aionstatus.lower() == 'aionuq'): y_val = np.append(y_train,y_test) else: y_val = self.y self.log.info('------------------> Model Confidence Score '+str(model_confidence)) outuq = self.classUncertainty(y_t_pred,y_t_score) # Another way to get conf score model_uncertainty_per=round((model_uncertainty*100),2) model_confidence_per=round((model_confidence*100),2) acc_score_per = round((acc_score*100),2) uq_ece_per=round((uq_ece*100),2) output={} recommendation = "" if (uq_ece > 0.5): # RED text recommendation = 'Model has high ece (expected calibration error) score compare to threshold (0.5),not good to be deploy. need to be add more input data across all feature ranges to train base model, also try with different classification algorithms/ensembling to reduce ECE (ECE~0).' else: # self.log.info('Model has good ECE score and accuracy, ready to deploy.\n.') if (uq_ece <= 0.1 and model_confidence >= 0.9): # Green Text recommendation = 'Model has best calibration score (near to 0) and good confidence score , ready to deploy. ' else: # Orange recommendation = 'Model has good ECE score (between 0.1-0.5), but less confidence score compare to threshold (90%). If user wants,model can be improve by adding more input data across all feature ranges and could be evaluate with different algorithms/ensembling. ' #Adding each class uncertainty value classoutput = {} for k,v in outuq.items(): classoutput[k]=(str(round((v*100),2))) output['classes'] = classoutput output['ModelConfidenceScore']=(str(model_confidence_per)) output['ExpectedCalibrationError']=str(uq_ece_per) output['ModelUncertainty']=str(model_uncertainty_per) output['Recommendation']=recommendation # output['user_msg']='Please check the plot for more understanding of model uncertainty' #output['UQ_area_under_risk_rejection_rate_curve']=round(uq_aurrrc,4) output['Accuracy']=str(acc_score_per) output['Problem']= 'Classification' #self.log.info('Model Accuracy score in percentage : '+str(test_accuracy_perc)+str(' %')) # #print("Prediction mean for the given model:",np.mean(y_hat),"\n") #self.log.info(recommendation) #self.log.info("Model_confidence_score: " +str(confidence_score)) #self.log.info("Model_uncertainty: " +str(round(model_uncertainty,2))) #self.log.info('Please check the plot for more understanding of model uncertainty.\n.') uq_jsonobject = json.dumps(output) with open(str(self.Deployment)+"/uq_classification_log.json", "w") as f: json.dump(output, f) return test_accuracy_perc,uq_ece,output,model_confidence_per,model_uncertainty_per except Exception as inst: self.log.info('\n < ---------- UQ Model Execution Failed Start--------->') self.log.info('\n<------Model Execution failed!!!.' + str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) self.log.info('\n < ---------- Model Execution Failed End --------->') def aion_confidence_plot(self,df): df=df df = df.sort_values(by=self.selectedfeature) best_values=df.Best_values.to_list() best_upper=df.Best__upper.to_list() best_lower=df.Best__lower.to_list() Total_Upper_PI=df.Total_Upper_PI.to_list() Total_Low_PI=df.Total_Low_PI.to_list() Obseved = df.Observed.to_list() plt.plot(df[x_feature], df['Observed'], 'o', label='Observed') plt.plot(df[x_feature], df['Best__upper'],'r--', lw=2, color='grey') plt.plot(df[x_feature], df['Best__lower'],'r--', lw=2, color='grey') plt.plot(df[x_feature], df['Best_values'], 'r--', lw=2, label='MeanPrediction',color='red') plt.fill_between(df[x_feature], Total_Upper_PI, Total_Low_PI, label='Good Confidence', color='lightblue', alpha=.5) plt.fill_between(df[x_feature],best_lower, best_upper,label='Best Confidence', color='orange', alpha=.5) plt.legend() plt.xlabel(self.selectedfeature) plt.ylabel(self.targetFeature) plt.title('UQ Best & Good Area Plot') if os.path.exists(str(self.uqgraphlocation)+'/uq_confidence_plt.png'): os.remove(str(self.uqgraphlocation)+'/uq_confidence_plt.png') plt.savefig(str(self.uqgraphlocation)+'/uq_confidence_plt.png') plt.savefig(str(self.Deployment)+'/uq_confidence_plt.png') def uqMain_BBMRegression(self,x_train, x_test, y_train, y_test,aionstatus): aionstatus = str(aionstatus) # if (aionstatus.lower() == 'aionuq'): # X_train, X_test, y_train, y_test = x_train, x_test, y_train, y_test # total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,x_train, x_test, y_train, y_test,aionstatus) # else: # X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, random_state=0) # modelName = "" self.log.info('<!------------- Inside BlackBox MetaModel Regression process. ---------------> ') try: from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression import pandas as pd base_modelname=__class__.__name__ base_config = self.uqconfig_base meta_config = self.uqconfig_base model_name=self.basemodel.__class__.__name__ model_params=self.basemodel.get_params() # #print("model_params['criterion']: \n",model_params['criterion']) key = 'criterion' #if key in model_params: try: #if model_params.has_key(key): if key in model_params: if (model_params['criterion']): uq_scoring_param=model_params.get('criterion') elif(model_params['criterion'] == None): uq_scoring_param='picp' else: uq_scoring_param='picp' else: uq_scoring_param='picp' pass except Exception as inst: uq_scoring_param='picp' # modelname='sklearn.linear_model'+'.'+model_name # X_train, X_test, y_train, y_test = self.xtrain,self.xtest,self.ytrain,self.ytest #Geeting trained model name and to use the model in BlackboxMetamodelRegression from sklearn.tree import DecisionTreeRegressor from sklearn.linear_model import LinearRegression,Lasso,Ridge from sklearn.ensemble import RandomForestRegressor if (model_name == "DecisionTreeRegressor"): basemodel=DecisionTreeRegressor elif (model_name == "LinearRegression"): basemodel=LinearRegression elif (model_name == "Lasso"): basemodel=Lasso elif (model_name == "Ridge"): basemodel=Ridge elif(model_name == "RandomForestRegressor"): basemodel=RandomForestRegressor else: basemodel=LinearRegression if (aionstatus.lower() == 'aionuq'): X_train, X_test, y_train, y_test = x_train, x_test, y_train, y_test total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,x_train, x_test, y_train, y_test,aionstatus) else: X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, random_state=0) total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,None, None, None, None,aionstatus) if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']: uq_scoring_param=uq_scoring_param else: uq_scoring_param='picp' uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params) # this will fit both the base and the meta model uqmodel_fit = uq_model.fit(X_train, y_train) # #print("X_train.shape: \n",X_train.shape) y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test) from uq360.metrics import picp, mpiw observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub) observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub) picp_percentage= round(observed_alphas_picp*100) Uncertainty_percentage=round(100-picp_percentage) self.log.info('<!------------- observed_picp: ---------------> '+str(observed_alphas_picp)) self.log.info('<!------------- observed_widths_mpiw: ---------------> '+str(observed_widths_mpiw)) # UQ metamodel regression have metrics as follows, “rmse”, “nll”, “auucc_gain”, “picp”, “mpiw”, “r2” #metric_all=compute_regression_metrics(y_test, y_hat,y_hat_lb, y_hat_ub,option='all',nll_fn=None) #nll - Gaussian negative log likelihood loss. metric_all=compute_regression_metrics(y_test, y_hat,y_hat_lb, y_hat_ub,option=uq_scoring_param,nll_fn=None) metric_used='' for k,v in metric_all.items(): metric_used=str(round(v,2)) self.log.info('<!------------- Metric used for regression UQ: ---------------> '+str(metric_all)) # Determine the confidence level and recommentation to the tester # test_data=y_test observed_alphas_picp=round(observed_alphas_picp,2) observed_widths_mpiw=round(observed_widths_mpiw,2) #Calculate total uncertainty for all features # total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage = self.totalUncertainty(self.data) # df1=self.data total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,x_train, x_test, y_train, y_test,aionstatus) recommendation="" output={} if (observed_alphas_picp >= 0.95 and total_picp >= 0.75): # Add GREEN text self.log.info('Model has good confidence for the selected feature, ready to deploy.\n.') recommendation = "Model has good confidence score, ready to deploy." elif ((observed_alphas_picp >= 0.50 and observed_alphas_picp <= 0.95) and (total_picp >= 0.50)): # Orange recommendation = "Model has average confidence compare to threshold (95%), need to be add more input data across all feature ranges to train base model, also try with different regression algorithms/ensembling." self.log.info('Model has average confidence score compare to threshold, need to be add more input data for training base model and again try with UQ .') else: # RED text recommendation = "Model has less confidence compare to threshold (95%), need to be add more input data across all feature ranges to train base model, also try with different regression algorithms/ensembling." self.log.info('Model has less confidence score compare to threshold, need to be add more input data for training base model and again try with UQ .') #Build uq json info dict output['ModelConfidenceScore']=(str(total_picp_percentage)+'%') output['ModelUncertainty']=(str(total_Uncertainty_percentage)+'%') output['SelectedFeatureConfidence']=(str(picp_percentage)+'%') output['SelectedFeatureUncertainty']=(str(Uncertainty_percentage)+'%') output['PredictionIntervalCoverageProbability']=observed_alphas_picp output['MeanPredictionIntervalWidth']=round(observed_widths_mpiw) output['DesirableMPIWRange: ']=(str(round(mpiw_lower_range))+str(' - ')+str(round(mpiw_upper_range))) output['Recommendation']=str(recommendation) output['Metric']=uq_scoring_param output['Score']=metric_used output['Problemtype']= 'Regression' self.log.info('Model confidence in percentage is: '+str(picp_percentage)+str(' %')) self.log.info('Model Uncertainty is:: '+str(Uncertainty_percentage)+str(' %')) #self.log.info('Please check the plot for more understanding of model uncertainty.\n.') #self.display_results(X_test, y_test, y_mean=y_hat, y_lower=y_hat_lb, y_upper=y_hat_ub) uq_jsonobject = json.dumps(output) with open(str(self.Deployment)+"/uq_reg_log.json", "w") as f: json.dump(output, f) #To get best and medium UQ range of values from total predict interval y_hat_m=y_hat.tolist() y_hat_lb=y_hat_lb.tolist() upper_bound=y_hat_ub.tolist() y_hat_ub=y_hat_ub.tolist() for x in y_hat_lb: y_hat_ub.append(x) total_pi=y_hat_ub medium_UQ_range = y_hat_ub best_UQ_range= y_hat.tolist() ymean_upper=[] ymean_lower=[] y_hat_m=y_hat.tolist() for i in y_hat_m: y_hat_m_range= (i*20/100) x=i+y_hat_m_range y=i-y_hat_m_range ymean_upper.append(x) ymean_lower.append(y) min_best_uq_dist=round(min(best_UQ_range)) max_best_uq_dist=round(max(best_UQ_range)) # initializing ranges list_medium=list(filter(lambda x:not(min_best_uq_dist<=x<=max_best_uq_dist), total_pi)) list_best = y_hat_m X_test = np.squeeze(X_test) ''' uq_dict = {x_feature:X_test,'Observed':y_test,'Best_values': y_hat_m, 'Best__upper':ymean_upper, 'Best__lower':ymean_lower, 'Total_Low_PI': y_hat_lb, 'Total_Upper_PI': upper_bound, } print(uq_dict) uq_pred_df = pd.DataFrame(data=uq_dict) uq_pred_df_sorted = uq_pred_df.sort_values(by='Best_values') uq_pred_df_sorted.to_csv(str(self.Deployment)+"/uq_pred_df.csv",index = False) csv_path=str(self.Deployment)+"/uq_pred_df.csv" df=pd.read_csv(csv_path) self.log.info('uqMain() returns: observed_alphas_picp,observed_widths_mpiw,list_medium,list_best,metric_all.\n.') #Callconfidence olot fn only for UQTest interface if (aionstatus.lower() == 'aionuq'): #No need to showcase confidence plot for aion main pass else: self.aion_confidence_plot(df) ''' return total_picp_percentage,total_Uncertainty_percentage,list_medium,list_best,metric_all,json.loads(uq_jsonobject) except Exception as inst: exc = {"status":"FAIL","message":str(inst).strip('"')} out_exc = json.dumps(exc) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import logging logging.getLogger('tensorflow').disabled = True import json #from nltk.corpus import stopwords from collections import Counter from matplotlib import pyplot import sys import os import matplotlib.pyplot as plt from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression from sklearn import datasets from sklearn.model_selection import train_test_split import pandas as pd from uq360.metrics.regression_metrics import compute_regression_metrics import numpy as np from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import roc_curve from sklearn.metrics import r2_score,mean_squared_error, explained_variance_score,mean_absolute_error from uq360.metrics import plot_uncertainty_by_feature, plot_picp_by_feature import sys import time from sklearn.metrics import confusion_matrix from pathlib import Path import logging import logging.config from os.path import expanduser import platform from sklearn.utils import shuffle class aionUQ: # def __init__(self,uqdf,targetFeature,xtrain,ytrain,xtest,ytest,uqconfig_base,uqconfig_meta,deployLocation,saved_model): def __init__(self,df,dfp,target,ProblemName,Params,model,modelfeatures,targetfeature): try: self.data=df self.dfFeatures=dfp self.uqconfig_base=Params self.uqconfig_meta=Params self.targetFeature=targetfeature self.log = logging.getLogger('aionUQ') self.target=target self.selectedfeature=modelfeatures self.y=self.target self.X=self.dfFeatures from appbe.dataPath import DEPLOY_LOCATION self.Deployment = os.path.join(DEPLOY_LOCATION,('UQTEST_'+str(int(time.time())))) os.makedirs(self.Deployment,exist_ok=True) self.basemodel=model self.model_name=ProblemName # self.X, self.y = shuffle(self.X, self.y) X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.2, random_state=0) self.xtrain = X_train self.xtest = X_test self.ytrain = y_train self.ytest = y_test # self.deployLocation=deployLocation except Exception as e: # self.log.info('<!------------- UQ model INIT Error ---------------> '+str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) # self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def totalUncertainty(self,df,basemodel,model_params): try: # from sklearn.model_selection import train_test_split # df=self.data # y=df[self.targetFeature] # X = df.drop(self.targetFeature, axis=1) if (isinstance(self.selectedfeature,list)): selectedfeature=[self.selectedfeature[0]] selectedfeature=' '.join(map(str,selectedfeature)) if (isinstance(self.targetFeature,list)): targetFeature=[self.targetFeature[0]] targetFeature=' '.join(map(str,targetFeature)) X = self.data[selectedfeature] y = self.data[targetFeature] X = X.values.reshape((-1,1)) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) key = 'criterion' #if key in model_params: try: #if model_params.has_key(key): if key in model_params: if (model_params['criterion']): uq_scoring_param=model_params.get('criterion') elif(model_params['criterion'] == None): uq_scoring_param='picp' else: uq_scoring_param='picp' else: uq_scoring_param='picp' pass except Exception as inst: uq_scoring_param='picp' # from sklearn.tree import DecisionTreeRegressor # from sklearn.linear_model import LinearRegression,Lasso,Ridge # from sklearn import linear_model # from sklearn.ensemble import RandomForestRegressor if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']: uq_scoring_param=uq_scoring_param else: uq_scoring_param='picp' uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params) # this will fit both the base and the meta model uqmodel_fit = uq_model.fit(X_train, y_train) y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test) y_hat_total_mean=np.mean(y_hat) y_hat_lb_total_mean=np.mean(y_hat_lb) y_hat_ub_total_mean=np.mean(y_hat_ub) mpiw_20_per=(y_hat_total_mean*20/100) mpiw_lower_range = y_hat_total_mean - mpiw_20_per mpiw_upper_range = y_hat_total_mean + mpiw_20_per from uq360.metrics import picp, mpiw observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub) observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub) observed_alphas_picp=round(observed_alphas_picp,2) observed_widths_mpiw=round(observed_widths_mpiw,2) picp_percentage= round(observed_alphas_picp*100) Uncertainty_percentage=round(100-picp_percentage) # self.log.info('Model total observed_widths_mpiw : '+str(observed_widths_mpiw)) # self.log.info('Model mpiw_lower_range : '+str(mpiw_lower_range)) # self.log.info('Model mpiw_upper_range : '+str(mpiw_upper_range)) # self.log.info('Model total picp_percentage : '+str(picp_percentage)) except Exception as e: print("totalUncertainty fn error: \n",e) return observed_alphas_picp,observed_widths_mpiw,picp_percentage,Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range def display_results(self,X_test, y_test, y_mean, y_lower, y_upper): try: global x_feature,y_feature if (isinstance(self.selectedfeature, list) or isinstance(self.selectedfeature, tuple)): x_feature=','.join(map(str, self.selectedfeature)) else: x_feature= str(self.selectedfeature) # self.selectedfeature=str(self.selectedfeature) X_test=np.squeeze(X_test) y_feature=str(self.targetFeature) pred_dict = {x_feature: X_test, 'y': y_test, 'y_mean': y_mean, 'y_upper': y_upper, 'y_lower': y_lower } pred_df = pd.DataFrame(data=pred_dict) x_feature1 = x_feature.split(',') pred_df_sorted = pred_df.sort_values(by=x_feature1) plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y'], 'o', label='Observed') plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y_mean'], '-', lw=2, label='Predicted') plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y_upper'], 'r--', lw=2, label='Upper Bound') plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y_lower'], 'r--', lw=2, label='Lower Bound') plt.legend() plt.xlabel(x_feature1[0]) plt.ylabel(y_feature) plt.title('UQ Confidence Interval Plot.') # plt.savefig('uq_test_plt.png') ''' if os.path.exists(str(DEFAULT_FILE_PATH)+'/uq_test_plt.png'): os.remove(str(DEFAULT_FILE_PATH)+'/uq_test_plt.png') ''' plt.savefig(str(self.Deployment)+'/uq_test_plt.png') #plt.savefig(str(DEFAULT_FILE_PATH)+'/uq_test_plt.png') confidencePlot = os.path.join(self.Deployment,'picp_per_feature.png') plt.clf() plt.cla() plt.close() pltreg=plot_picp_by_feature(X_test, y_test, y_lower, y_upper, xlabel=x_feature) #pltreg.savefig('x.png') pltr=pltreg.figure ''' if os.path.exists(str(DEFAULT_FILE_PATH)+'/picp_per_feature.png'): os.remove(str(DEFAULT_FILE_PATH)+'/picp_per_feature.png') ''' pltr.savefig(str(self.Deployment)+'/picp_per_feature.png') picpPlot = os.path.join(self.Deployment,'picp_per_feature.png') #pltr.savefig(str(DEFAULT_FILE_PATH)+'/picp_per_feature.png') plt.clf() plt.cla() plt.close() except Exception as e: print("display exception: \n",e) # self.log.info('<!------------- UQ model Display Error ---------------> '+str(e)) return confidencePlot,picpPlot def classUncertainty(self,predprob_base): # from collections import Counter predc="Class_" classes = np.unique(self.y) total = len(self.y) list_predprob=[] counter = Counter(self.y) #for loop for test class purpose for k,v in counter.items(): n_samples = len(self.y[self.y==k]) per = ((v/total) * 100) prob_c=predprob_base[:,int(k)] list_predprob.append(prob_c) # #print("Class_{} : {}/{} percentage={}% \n".format(k,n_samples,total,per )) outuq={} for k in classes: predc += str(k) mean_predprob_class=np.mean(list_predprob[int(k)]) uncertainty=1-mean_predprob_class predc+='_Uncertainty' outuq[predc]=uncertainty predc="Class_" return outuq def uqMain_BBMClassification(self): # self.log.info('<!------------- Inside BlackBox MetaModel Classification process. ---------------> ') # import matplotlib.pyplot as plt try: from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelClassification except: ##In latest UQ360, library changed from BlackboxMetamodelClassification to MetamodelClassification. from uq360.algorithms.blackbox_metamodel import MetamodelClassification # from uq360.metrics.classification_metrics import area_under_risk_rejection_rate_curve,plot_risk_vs_rejection_rate,expected_calibration_error,compute_classification_metrics from uq360.metrics.classification_metrics import plot_reliability_diagram,area_under_risk_rejection_rate_curve,plot_risk_vs_rejection_rate,expected_calibration_error,compute_classification_metrics # from sklearn import datasets # from sklearn.model_selection import train_test_split # from sklearn.metrics import accuracy_score from sklearn.ensemble import GradientBoostingClassifier from sklearn.linear_model import LogisticRegression from sklearn.linear_model import SGDClassifier from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier # from sklearn.linear_model import LogisticRegression # import pandas as pd base_modelname=__class__.__name__ base_config = self.uqconfig_base meta_config = self.uqconfig_base model_name=self.basemodel.__class__.__name__ model_params=self.basemodel.get_params() try: #geting used features model_used_features=self.basemodel.feature_names_in_ except: pass X_train, X_test, y_train, y_test = self.xtrain,self.xtest,self.ytrain,self.ytest uq_scoring_param='accuracy' basemodel=None if (model_name == "GradientBoostingClassifier"): basemodel=GradientBoostingClassifier elif (model_name == "SGDClassifier"): basemodel=SGDClassifier elif (model_name == "GaussianNB"): basemodel=GaussianNB elif (model_name == "DecisionTreeClassifier"): basemodel=DecisionTreeClassifier elif(model_name == "RandomForestClassifier"): basemodel=RandomForestClassifier elif (model_name == "SVC"): basemodel=SVC elif(model_name == "KNeighborsClassifier"): basemodel=KNeighborsClassifier elif(model_name == "LogisticRegression"): basemodel=LogisticRegression else: basemodel=LogisticRegression try: try: ##Removed meta_config because leave meta model config as default ml model params uq_model = BlackboxMetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params) except: uq_model = BlackboxMetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params, meta_config=model_params) except: ##In latest version BlackboxMetamodelClassification name modified as MetamodelClassification try: ##Removed meta_config because leave meta model config as default ml model params uq_model = MetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params) except: uq_model = MetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params, meta_config=model_params) # this will fit both the base and the meta model try: X_train=X_train[model_used_features] X_test=X_test[model_used_features] except: pass uqmodel_fit = uq_model.fit(X_train, y_train,base_is_prefitted=True,meta_train_data=(X_train, y_train)) # uqmodel_fit = uq_model.fit(X_train, y_train) #Test data pred, score y_t_pred, y_t_score = uq_model.predict(X_test) #predict probability # uq_pred_prob=uq_model.predict_proba(X_test) # predprob_base=basemodel.predict_proba(X_test)[:, :] #if (model_name == "SVC" or model_name == "SGDClassifier"): # if model_name in ['SVC','SGDClassifier']: if (model_name == "SVC"): from sklearn.calibration import CalibratedClassifierCV basemodel=SVC(**model_params) calibrated_svc = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3) calibrated_svc.fit(X_train, y_train) basepredict = basemodel.predict(X_test) predprob_base = calibrated_svc.predict_proba(X_test)[:, :] elif (model_name == "SGDClassifier"): from sklearn.calibration import CalibratedClassifierCV basemodel=SGDClassifier(**model_params) calibrated_svc = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3) calibrated_svc.fit(X_train, y_train) basepredict = basemodel.predict(X_test) predprob_base = calibrated_svc.predict_proba(X_test)[:, :] else: base_mdl = basemodel(**model_params) basemodelfit = base_mdl.fit(X_train, y_train) basepredict = base_mdl.predict(X_test) predprob_base=base_mdl.predict_proba(X_test)[:, :] acc_score=accuracy_score(y_test, y_t_pred) test_accuracy_perc=round(100*acc_score) ''' bbm_c_plot = plot_risk_vs_rejection_rate( y_true=y_test, y_prob=predprob_base, selection_scores=y_t_score, y_pred=y_t_pred, plot_label=['UQ_risk_vs_rejection'], risk_func=accuracy_score, num_bins = 10 ) # This done by kiran, need to uncomment for GUI integration. try: bbm_c_plot_sub = bbm_c_plot[4] bbm_c_plot.savefig(str(self.Deployment)+'/plot_risk_vs_rejection_rate.png') riskPlot = os.path.join(self.Deployment,'plot_risk_vs_rejection_rate.png') except Exception as e: print(e) pass riskPlot = '' ''' riskPlot = '' ''' try: re_plot=plot_reliability_diagram(y_true=y_test, y_prob=predprob_base, y_pred=y_t_pred, plot_label=['UQModel reliability_diagram'], num_bins=10) # This done by kiran, need to uncomment for GUI integration. re_plot_sub = re_plot[4] # re_plot_sub = re_plot re_plot_sub.savefig(str(self.Deployment)+'/plot_reliability_diagram.png') reliability_plot = os.path.join(self.Deployment,'plot_reliability_diagram.png') except Exception as e: print(e) pass reliability_plot = '' ''' reliability_plot = '' uq_aurrrc=area_under_risk_rejection_rate_curve( y_true=y_test, y_prob=predprob_base, y_pred=y_t_pred, selection_scores=y_t_score, attributes=None, risk_func=accuracy_score,subgroup_ids=None, return_counts=False, num_bins=10) uq_aurrrc=uq_aurrrc test_accuracy_perc=round(test_accuracy_perc) #metric_all=compute_classification_metrics(y_test, y_prob, option='all') metric_all=compute_classification_metrics(y_test, predprob_base, option='accuracy') #expected_calibration_error uq_ece=expected_calibration_error(y_test, y_prob=predprob_base,y_pred=y_t_pred, num_bins=10, return_counts=False) uq_aurrrc=uq_aurrrc confidence_score=acc_score-uq_ece ece_confidence_score=round(confidence_score,2) # Model uncertainty using ECE score # model_uncertainty_ece = 1-ece_confidence_score # #print("model_uncertainty1: \n",model_uncertainty_ece) #Uncertainty Using model inherent predict probability mean_predprob_total=np.mean(predprob_base) model_uncertainty = 1-mean_predprob_total model_confidence=mean_predprob_total model_confidence = round(model_confidence,2) # To get each class values and uncertainty outuq = self.classUncertainty(predprob_base) # Another way to get conf score model_uncertainty_per=round((model_uncertainty*100),2) # model_confidence_per=round((model_confidence*100),2) model_confidence_per=round((ece_confidence_score*100),2) acc_score_per = round((acc_score*100),2) uq_ece_per=round((uq_ece*100),2) output={} recommendation = "" if (uq_ece > 0.5): # RED text recommendation = 'Model has high ece (expected calibration error) score compare to threshold (50%),not good to deploy. Add more input data across all feature ranges to train base model, also try with different classification algorithms/ensembling to reduce ECE (ECE~0).' msg = 'Bad' else: # self.log.info('Model has good ECE score and accuracy, ready to deploy.\n.') if (uq_ece <= 0.1 and model_confidence >= 0.9): # Green Text recommendation = 'Model has best calibration score (near to 0) and good confidence score , ready to deploy. ' msg = 'Best' else: # Orange recommendation = 'Model has average confidence score (ideal is >90% confidence) and good ECE score (ideal is <10% error).Model can be improved by adding more training data across all feature ranges and re-training the model.' msg = 'Good' #Adding each class uncertainty value output['Problem']= 'Classification' output['recommend']= 'recommend' output['msg']= msg output['UQ_Area_Under_Risk_Rejection_Rate_Curve']=round(uq_aurrrc,4) output['Model_Total_Confidence']=(str(model_confidence_per)+str('%')) output['Expected_Calibration_Error']=(str(uq_ece_per)+str('%')) output['Model_Total_Uncertainty']=(str(model_uncertainty_per)+str('%')) # output['Risk Plot'] = str(riskPlot) # output['Reliability Plot'] = str(reliability_plot) for k,v in outuq.items(): output[k]=(str(round((v*100),2))+str(' %')) output['Recommendation']=recommendation # output['user_msg']='Please check the plot for more understanding of model uncertainty' output['Metric_Accuracy_Score']=(str(acc_score_per)+str(' %')) outputs = json.dumps(output) with open(str(self.Deployment)+"/uq_classification_log.json", "w") as f: json.dump(output, f) return test_accuracy_perc,uq_ece,outputs def aion_confidence_plot(self,df): try: global x_feature df=df df = df.sort_values(by=self.selectedfeature) best_values=df.Best_values.to_list() best_upper=df.Best__upper.to_list() best_lower=df.Best__lower.to_list() Total_Upper_PI=df.Total_Upper_PI.to_list() Total_Low_PI=df.Total_Low_PI.to_list() Obseved = df.Observed.to_list() x_feature1 = x_feature.split(',') plt.plot(df[x_feature1[0]], df['Observed'], 'o', label='Observed') plt.plot(df[x_feature1[0]], df['Best__upper'],'r--', lw=2, color='grey') plt.plot(df[x_feature1[0]], df['Best__lower'],'r--', lw=2, color='grey') plt.plot(df[x_feature1[0]], df['Best_values'], 'r--', lw=2, label='MeanPrediction',color='red') plt.fill_between(df[x_feature1[0]], Total_Upper_PI, Total_Low_PI, label='Good Confidence', color='lightblue', alpha=.5) plt.fill_between(df[x_feature1[0]],best_lower, best_upper,label='Best Confidence', color='orange', alpha=.5) plt.legend() plt.xlabel(x_feature1[0]) plt.ylabel(self.targetFeature) plt.title('UQ Best & Good Area Plot') ''' if os.path.exists(str(DEFAULT_FILE_PATH)+'/uq_confidence_plt.png'): os.remove(str(DEFAULT_FILE_PATH)+'/uq_confidence_plt.png') plt.savefig(str(DEFAULT_FILE_PATH)+'/uq_confidence_plt.png') ''' plt.savefig(str(self.Deployment)+'/uq_confidence_plt.png') uq_confidence_plt = os.path.join(str(self.Deployment),'uq_confidence_plt.png') except Exception as inst: print('-----------dsdas->',inst) uq_confidence_plt = '' return uq_confidence_plt def uqMain_BBMRegression(self): # modelName = "" # self.log.info('<!------------- Inside BlockBox MetaModel Regression process. ---------------> ') try: from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression import pandas as pd base_modelname=__class__.__name__ base_config = self.uqconfig_base meta_config = self.uqconfig_base model_name=self.basemodel.__class__.__name__ model_params=self.basemodel.get_params() # #print("model_params['criterion']: \n",model_params['criterion']) key = 'criterion' #if key in model_params: try: #if model_params.has_key(key): if key in model_params: if (model_params['criterion']): uq_scoring_param=model_params.get('criterion') elif(model_params['criterion'] == None): uq_scoring_param='picp' else: uq_scoring_param='picp' else: uq_scoring_param='picp' pass except Exception as inst: uq_scoring_param='picp' # modelname='sklearn.linear_model'+'.'+model_name # self.xtrain = self.xtrain.values.reshape((-1,1)) # self.xtest = self.xtest.values.reshape((-1,1)) if (isinstance(self.selectedfeature,list)): selectedfeature=[self.selectedfeature[0]] selectedfeature=' '.join(map(str,selectedfeature)) if (isinstance(self.targetFeature,list)): targetFeature=[self.targetFeature[0]] targetFeature=' '.join(map(str,targetFeature)) X = self.data[selectedfeature] y = self.data[targetFeature] X = X.values.reshape((-1,1)) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) #Geeting trained model name and to use the model in BlackboxMetamodelRegression from sklearn.tree import DecisionTreeRegressor from sklearn.linear_model import LinearRegression,Lasso,Ridge from sklearn.ensemble import RandomForestRegressor if (model_name == "DecisionTreeRegressor"): basemodel=DecisionTreeRegressor elif (model_name == "LinearRegression"): basemodel=LinearRegression elif (model_name == "Lasso"): basemodel=Lasso elif (model_name == "Ridge"): basemodel=Ridge elif(model_name == "RandomForestRegressor"): basemodel=RandomForestRegressor else: basemodel=LinearRegression if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']: if (uq_scoring_param.lower() == 'picp'): uq_scoring_param='prediction interval coverage probability score (picp)' else: uq_scoring_param=uq_scoring_param else: uq_scoring_param='prediction interval coverage probability score (picp)' uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params) # this will fit both the base and the meta model uqmodel_fit = uq_model.fit(X_train, y_train) y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test) from uq360.metrics import picp, mpiw observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub) observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub) picp_percentage= round(observed_alphas_picp*100) Uncertainty_percentage=round(100-picp_percentage) # UQ metamodel regression have metrics as follows, “rmse”, “nll”, “auucc_gain”, “picp”, “mpiw”, “r2” metric_all=compute_regression_metrics(y_test, y_hat,y_hat_lb, y_hat_ub,option=uq_scoring_param,nll_fn=None) metric_used='' for k,v in metric_all.items(): metric_used=str(round(v,2)) # Determine the confidence level and recommentation to the tester # test_data=y_test observed_alphas_picp=round(observed_alphas_picp,2) observed_widths_mpiw=round(observed_widths_mpiw,2) #Calculate total uncertainty for all features # total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage = self.totalUncertainty(self.data) # df1=self.data total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params) recommendation="" observed_widths_mpiw = round((observed_widths_mpiw/1000000)*100) if observed_widths_mpiw > 100: observed_widths_mpiw = 100 output={} if (observed_alphas_picp >= 0.90 and total_picp >= 0.75): # GREEN text recommendation = "Model has good confidence and MPIW score, ready to deploy." msg='Good' elif ((observed_alphas_picp >= 0.50 and observed_alphas_picp <= 0.90) and (total_picp >= 0.50)): # Orange recommendation = " Model has average confidence compare to threshold (ideal is both model confidence and MPIW should be >90%) .Model can be improved by adding more training data across all feature ranges and re-training the model." msg = 'Average' else: # RED text recommendation = "Model has less confidence compare to threshold (ideal is both model confidence and MPIW should be >90%), need to be add more input data across all feature ranges and retrain base model, also try with different regression algorithms/ensembling." msg = 'Bad' #Build uq json info dict output['Model_total_confidence']=(str(total_picp_percentage)+'%') output['Model_total_Uncertainty']=(str(total_Uncertainty_percentage)+'%') output['Selected_feature_confidence']=(str(picp_percentage)+'%') output['Selected_feature_Uncertainty']=(str(Uncertainty_percentage)+'%') output['Prediction_Interval_Coverage_Probability']=observed_alphas_picp output['Mean_Prediction_Interval_Width']=str(observed_widths_mpiw)+'%' output['Desirable_MPIW_range']=(str(round(mpiw_lower_range))+str(' - ')+str(round(mpiw_upper_range))) output['Recommendation']=str(recommendation) output['Metric_used']=uq_scoring_param output['Metric_value']=metric_used output['Problem']= 'Regression' output['recommend']= 'recommend' output['msg'] = msg with open(str(self.Deployment)+"/uq_reg_log.json", "w") as f: json.dump(output, f) #To get best and medium UQ range of values from total predict interval y_hat_m=y_hat.tolist() y_hat_lb=y_hat_lb.tolist() upper_bound=y_hat_ub.tolist() y_hat_ub=y_hat_ub.tolist() for x in y_hat_lb: y_hat_ub.append(x) total_pi=y_hat_ub medium_UQ_range = y_hat_ub best_UQ_range= y_hat.tolist() ymean_upper=[] ymean_lower=[] y_hat_m=y_hat.tolist() for i in y_hat_m: y_hat_m_range= (i*20/100) x=i+y_hat_m_range y=i-y_hat_m_range ymean_upper.append(x) ymean_lower.append(y) min_best_uq_dist=round(min(best_UQ_range)) max_best_uq_dist=round(max(best_UQ_range)) # initializing ranges list_medium=list(filter(lambda x:not(min_best_uq_dist<=x<=max_best_uq_dist), total_pi)) list_best = y_hat_m ''' print(X_test) print(X_test) X_test = np.squeeze(X_test) print(x_feature) ''' uq_dict = pd.DataFrame(X_test) #print(uq_dict) uq_dict['Observed'] = y_test uq_dict['Best_values'] = y_hat_m uq_dict['Best__upper'] = ymean_upper uq_dict['Best__lower'] = ymean_lower uq_dict['Total_Low_PI'] = y_hat_lb uq_dict['Total_Upper_PI'] = upper_bound ''' uq_dict = {x_feature:X_test,'Observed':y_test,'Best_values': y_hat_m, 'Best__upper':ymean_upper, 'Best__lower':ymean_lower, 'Total_Low_PI': y_hat_lb, 'Total_Upper_PI': upper_bound, }''' uq_pred_df = pd.DataFrame(data=uq_dict) uq_pred_df_sorted = uq_pred_df.sort_values(by='Best_values') uq_pred_df_sorted.to_csv(str(self.Deployment)+"/uq_pred_df.csv",index = False) csv_path=str(self.Deployment)+"/uq_pred_df.csv" df=pd.read_csv(csv_path) # self.log.info('uqMain() returns: observed_alphas_picp,observed_widths_mpiw,list_medium,list_best,metric_all.\n.') # confidenceplot = self.aion_confidence_plot(df) # output['Confidence Plot']= confidenceplot uq_jsonobject = json.dumps(output) print("UQ regression problem training completed...\n") return observed_alphas_picp,observed_widths_mpiw,list_medium,list_best,metric_all,uq_jsonobject except Exception as inst: print('-------',inst) exc = {"status":"FAIL","message":str(inst).strip('"')} out_exc = json.dumps(exc) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import numpy as np from mlxtend.frequent_patterns import apriori, association_rules from mlxtend.preprocessing import TransactionEncoder import matplotlib.pyplot as plt import json import logging import os,sys def hot_encode(x): if(int(x)<= 0): return 0 if(int(x)>= 1): return 1 class associationrules: def __init__(self,dataframe,association_rule_conf,modelparam,invoiceNoFeature,itemFeature): self.minSupport = modelparam['minSupport'] self.metric = modelparam['metric'] self.minThreshold = modelparam['minThreshold'] self.data = dataframe self.invoiceNoFeature = invoiceNoFeature self.itemFeature = itemFeature self.log = logging.getLogger('eion') def apply_associationRules(self,outputLocation): self.data= self.data[[self.itemFeature,self.invoiceNoFeature]] self.data[self.itemFeature] = self.data[self.itemFeature].str.strip() self.data.dropna(axis = 0, subset =[self.invoiceNoFeature], inplace = True) self.data[self.invoiceNoFeature] = self.data[self.invoiceNoFeature].astype('str') self.data = self.data.groupby([self.invoiceNoFeature,self.itemFeature]).size() self.data=self.data.unstack().reset_index().fillna('0').set_index(self.invoiceNoFeature) self.data = self.data.applymap(hot_encode) ohe_df = self.data ''' print(self.data) sys.exit() items = [] for col in list(self.data): ucols = self.data[col].dropna().unique() #print('ucols :',ucols) if len(ucols) > 0: items = items + list(set(ucols) - set(items)) #items = self.data.apply(lambda col: col.unique()) #print(items) #items = (self.data[self.masterColumn].unique()) #print(items) self.log.info("-------> Total Unique Items: "+str(len(items))) encoded_vals = [] for index, row in self.data.iterrows(): labels = {} uncommons = list(set(items) - set(row)) commons = list(set(items).intersection(row)) for uc in uncommons: labels[uc] = 0 for com in commons: labels[com] = 1 encoded_vals.append(labels) ohe_df = pd.DataFrame(encoded_vals) #print(ohe_df) ''' freq_items = apriori(ohe_df, min_support=self.minSupport, use_colnames=True) self.log.info('Status:- |... AssociationRule Algorithm applied: Apriori') if not freq_items.empty: self.log.info("\n------------ Frequent Item Set --------------- ") self.log.info(freq_items) save_freq_items = pd.DataFrame() save_freq_items["itemsets"] = freq_items["itemsets"].apply(lambda x: ', '.join(list(x))).astype("unicode") outputfile = os.path.join(outputLocation,'frequentItems.csv') save_freq_items.to_csv(outputfile) self.log.info('-------> FreqentItems File Name:'+outputfile) rules = association_rules(freq_items, metric=self.metric, min_threshold=self.minThreshold) if not rules.empty: #rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) self.log.info("\n------------ Rules --------------- ") for index, row in rules.iterrows(): self.log.info("------->Rule: "+ str(row['antecedents']) + " -> " + str(row['consequents'])) self.log.info("---------->Support: "+ str(row['support'])) self.log.info("---------->Confidence: "+ str(row['confidence'])) self.log.info("---------->Lift: "+ str(row['lift'])) #rules['antecedents'] = list(rules['antecedents']) #rules['consequents'] = list(rules['consequents']) rules["antecedents"] = rules["antecedents"].apply(lambda x: ', '.join(list(x))).astype("unicode") rules["consequents"] = rules["consequents"].apply(lambda x: ', '.join(list(x))).astype("unicode") self.log.info("\n------------ Rules End --------------- ") outputfile = os.path.join(outputLocation,'associationRules.csv') self.log.info('-------> AssciationRule File Name:'+outputfile) rules.to_csv(outputfile) else: self.log.info("\n------------ Frequent Item Set --------------- ") self.log.info("Status:- |... There are no association found in frequent items above that threshold (minThreshold)") else: self.log.info("\n------------ Frequent Item Set --------------- ") self.log.info("Status:- |... There are no frequent items above that threshold (minSupport)") evaulatemodel = '{"Model":"Apriori","Score":"NA"}' return(evaulatemodel) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import sys import json import datetime,time,timeit import itertools #Sci-Tools imports import numpy as np import pandas as pd import math from statsmodels.tsa.stattools import adfuller from scipy.stats.stats import pearsonr from numpy import cumsum, log, polyfit, sqrt, std, subtract from numpy.random import randn #SDP1 class import from feature_engineering.featureImportance import featureImp from sklearn.feature_selection import VarianceThreshold import logging class featureReducer(): def __init__(self): self.pandasNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] self.log = logging.getLogger('eion') def startReducer(self,df,data_columns,target,var_threshold): self.log.info('\n---------- Feature Reducer Start ----------') dataframe = df columns=data_columns target = target corrThreshold=1.0 categoricalFeatures=[] nonNumericFeatures=[] constFeatures=[] qconstantColumns=[] DtypesDic={} numericFeatures=[] nonNumericalFeatures=[] similarFeatureGroups=[] try: dataFDtypes=self.dataFramecolType(dataframe) for item in dataFDtypes: DtypesDic[item[0]] = item[1] if item[1] in self.pandasNumericDtypes: numericFeatures.append(item[0]) else: nonNumericFeatures.append(item[0]) #Checking for constant data features for col in columns: try: distCount = len(dataframe[col].unique()) if(distCount == 1): constFeatures.append(col) except Exception as inst: self.log.info('Unique Testing Fail for Col '+str(col)) numericalDataCols,nonNumericalDataCols = [],[] #Removing constant data features if(len(constFeatures) != 0): self.log.info( '-------> Constant Features: '+str(constFeatures)) numericalDataCols = list(set(numericFeatures) - set(constFeatures)) nonNumericalDataCols = list(set(nonNumericFeatures) - set(constFeatures)) else: numericalDataCols = list(set(numericFeatures)) nonNumericalDataCols = list(set(nonNumericFeatures)) if(len(numericalDataCols) > 1): if var_threshold !=0: qconstantFilter = VarianceThreshold(threshold=var_threshold) tempDf=df[numericalDataCols] qconstantFilter.fit(tempDf) qconstantColumns = [column for column in numericalDataCols if column not in tempDf.columns[qconstantFilter.get_support()]] if(len(qconstantColumns) != 0): if target != '' and target in qconstantColumns: qconstantColumns.remove(target) self.log.info( '-------> Low Variant Features: '+str(qconstantColumns)) self.log.info('Status:- |... Low variance feature treatment done: '+str(len(qconstantColumns))+' low variance features found') numericalDataCols = list(set(numericalDataCols) - set(qconstantColumns)) else: self.log.info('Status:- |... Low variance feature treatment done: Found zero or 1 numeric feature') #Minimum of two columns required for data integration if(len(numericalDataCols) > 1): numColPairs = list(itertools.product(numericalDataCols, numericalDataCols)) noDupList = [] for item in numColPairs: if(item[0] != item[1]): noDupList.append(item) numColPairs = noDupList tempArray = [] for item in numColPairs: tempCorr = np.abs(dataframe[item[0]].corr(dataframe[item[1]])) if(tempCorr > corrThreshold): tempArray.append(item[0]) tempArray = np.unique(tempArray) nonsimilarNumericalCols = list(set(numericalDataCols) - set(tempArray)) ''' Notes: tempArray: List of all similar/equal data features nonsimilarNumericalCols: List of all non-correlatable data features ''' #Grouping similar/equal features groupedFeatures = [] if(len(numericalDataCols) != len(nonsimilarNumericalCols)): #self.log.info( '-------> Similar/Equal Features: Not Any') #Correlation dictionary corrDic = {} for feature in tempArray: temp = [] for col in tempArray: tempCorr = np.abs(dataframe[feature].corr(dataframe[col])) temp.append(tempCorr) corrDic[feature] = temp #Similar correlation dataframe corrDF = pd.DataFrame(corrDic,index = tempArray) corrDF.loc[:,:] = np.tril(corrDF, k=-1) alreadyIn = set() similarFeatures = [] for col in corrDF: perfectCorr = corrDF[col][corrDF[col] > corrThreshold].index.tolist() if perfectCorr and col not in alreadyIn: alreadyIn.update(set(perfectCorr)) perfectCorr.append(col) similarFeatures.append(perfectCorr) self.log.info( '-------> No Similar/Equal Features: '+str(len(similarFeatures))) for i in range(0,len(similarFeatures)): similarFeatureGroups.append(similarFeatures[i]) #self.log.info((str(i+1)+' '+str(similarFeatures[i]))) self.log.info('-------> Similar/Equal Features: '+str(similarFeatureGroups)) self.log.info('-------> Non Similar Features :'+str(nonsimilarNumericalCols)) updatedSimFeatures = [] for items in similarFeatures: if(target != '' and target in items): for p in items: updatedSimFeatures.append(p) else: updatedSimFeatures.append(items[0]) newTempFeatures = list(set(updatedSimFeatures + nonsimilarNumericalCols)) updatedNumFeatures = newTempFeatures #self.log.info( '\n <--- Merged similar/equal features into one ---> ') updatedFeatures = list(set(newTempFeatures + nonNumericalDataCols)) self.log.info('Status:- |... Similar feature treatment done: '+str(len(similarFeatures))+' similar features found') else: updatedNumFeatures = numericalDataCols updatedFeatures = list(set(columns) - set(constFeatures)-set(qconstantColumns)) self.log.info( '-------> Similar/Equal Features: Not Any') self.log.info('Status:- |... Similar feature treatment done: No similar features found') else: updatedNumFeatures = numericalDataCols updatedFeatures = list(set(columns) - set(constFeatures)-set(qconstantColumns)) self.log.info( '\n-----> Need minimum of two numerical features for data integration.') self.log.info('Status:- |... Similar feature treatment done: Found zero or 1 numeric feature') self.log.info('---------- Feature Reducer End ----------\n') return updatedNumFeatures,updatedFeatures,similarFeatureGroups except Exception as inst: self.log.info("feature Reducer failed "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return [],[] def dataFramecolType(self,dataFrame): dataFDtypes=[] try: dataColumns=list(dataFrame.columns) for i in dataColumns: dataType=dataFrame[i].dtypes dataFDtypes.append(tuple([i,str(dataType)])) return dataFDtypes except: self.log.info("error in dataFramecolyType") return dataFDtypes ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import sys import json import datetime,time,timeit import itertools #Sci-Tools imports import numpy as np import pandas as pd import math from statsmodels.tsa.stattools import adfuller from scipy.stats.stats import pearsonr from numpy import cumsum, log, polyfit, sqrt, std, subtract from numpy.random import randn from sklearn.metrics import normalized_mutual_info_score from sklearn.feature_selection import mutual_info_regression import logging #SDP1 class import from feature_engineering.featureImportance import featureImp from feature_engineering.featureReducer import featureReducer from sklearn.linear_model import Lasso, LogisticRegression from sklearn.feature_selection import SelectFromModel from sklearn.ensemble import ExtraTreesClassifier from sklearn.decomposition import PCA from sklearn.decomposition import TruncatedSVD from sklearn.decomposition import FactorAnalysis from sklearn.decomposition import FastICA from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.preprocessing import MinMaxScaler from sklearn.feature_selection import RFE def ranking(ranks, names, order=1): minmax = MinMaxScaler() ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0] ranks = map(lambda x: round(x,2), ranks) return dict(zip(names, ranks)) # noinspection PyPep8Naming class featureSelector(): def __init__(self): self.pandasNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] self.log = logging.getLogger('eion') def startSelector(self,df,conf_json,textFeatures,targetFeature,problem_type): try: categoricalMaxLabel = int(conf_json['categoryMaxLabel']) pca='None' pcaReducerStatus = conf_json['featureEngineering']['PCA'] svdReducerStatus = conf_json['featureEngineering']['SVD'] factorReducerStatus = conf_json['featureEngineering']['FactorAnalysis'] icaReducerStatus = conf_json['featureEngineering']['ICA'] nfeatures=float(conf_json['featureEngineering']['numberofComponents']) statisticalConfig = conf_json['statisticalConfig'] corrThresholdInput = float(statisticalConfig.get('correlationThresholdFeatures',0.50)) corrThresholdTarget = float(statisticalConfig.get('correlationThresholdTarget',0.85)) pValThresholdInput = float(statisticalConfig.get('pValueThresholdFeatures',0.05)) pValThresholdTarget = float(statisticalConfig.get('pValueThresholdTarget',0.04)) varThreshold = float(statisticalConfig.get('varianceThreshold',0.01)) allFeaturesSelector = conf_json['featureSelection']['allFeatures'] correlationSelector = conf_json['featureSelection']['statisticalBased'] modelSelector = conf_json['featureSelection']['modelBased'] featureSelectionMethod = conf_json['selectionMethod']['featureSelection'] featureEngineeringSelector = conf_json['selectionMethod']['featureEngineering'] if featureSelectionMethod == 'True': featureEngineeringSelector = 'False' # if feature engineering is true then we check weather PCA is true or svd is true. By default we will run PCA if featureEngineeringSelector == 'True': if pcaReducerStatus == 'True': svdReducerStatus = 'False' factorReducerStatus=='False' icaReducerStatus == 'False' elif svdReducerStatus == 'True': pcaReducerStatus = 'False' factorReducerStatus=='False' icaReducerStatus == 'False' elif factorReducerStatus=='True': pcaReducerStatus=='False' svdReducerStatus=='False' icaReducerStatus=='False' elif icaReducerStatus=='True': pcaReducerStatus=="False" svdReducerStatus=="False" factorReducerStatus=="False" else: pcaReducerStatus = 'True' if featureSelectionMethod == 'False' and featureEngineeringSelector == 'False': featureSelectionMethod = 'True' if featureSelectionMethod == 'True': if modelSelector == 'False' and correlationSelector == 'False' and allFeaturesSelector == 'False': modelSelector = 'True' reductionMethod = 'na' bpca_features = [] #nfeatures = 0 if 'maxClasses' in conf_json: maxclasses = int(conf_json['maxClasses']) else: maxClasses = 20 target = targetFeature self.log.info('-------> Feature: '+str(target)) dataFrame = df pThresholdInput=pValThresholdInput pThresholdTarget=pValThresholdTarget cThresholdInput=corrThresholdInput cThresholdTarget=corrThresholdTarget numericDiscreteFeatures=[] similarGruops=[] numericContinuousFeatures=[] categoricalFeatures=[] nonNumericFeatures=[] apca_features = [] dTypesDic={} dataColumns = list(dataFrame.columns) features_list = list(dataFrame.columns) modelselectedFeatures=[] topFeatures=[] allFeatures=[] targetType="" # just to make sure feature engineering is false #print(svdReducerStatus) if featureEngineeringSelector.lower() == 'false' and correlationSelector.lower() == "true" and len(textFeatures) <= 0: reducerObj=featureReducer() self.log.info(featureReducer.__doc__) self.log.info('Status:- |... Feature reduction started') updatedNumericFeatures,updatedFeatures,similarGruops=reducerObj.startReducer(dataFrame,dataColumns,target,varThreshold) if len(updatedFeatures) <= 1: self.log.info('=======================================================') self.log.info('Most of the features are of low variance. Use Model based feature engineering for better result') self.log.info('=======================================================') raise Exception('Most of the features are of low variance. Use Model based feature engineering for better result') dataFrame=dataFrame[updatedFeatures] dataColumns=list(dataFrame.columns) self.log.info('Status:- |... Feature reduction completed') elif (pcaReducerStatus.lower() == "true" or svdReducerStatus.lower() == 'true' or factorReducerStatus.lower() == 'true' or icaReducerStatus.lower()=='true') and featureEngineeringSelector.lower() == 'true': # check is PCA or SVD is true pcaColumns=[] #print(svdReducerStatus.lower()) if target != "": dataColumns.remove(target) targetArray=df[target].values targetArray.shape = (len(targetArray), 1) if pcaReducerStatus.lower() == "true": if nfeatures == 0: pca = PCA(n_components='mle',svd_solver = 'full') elif nfeatures < 1: pca = PCA(n_components=nfeatures,svd_solver = 'full') else: pca = PCA(n_components=int(nfeatures)) pca.fit(df[dataColumns]) bpca_features = dataColumns.copy() pcaArray=pca.transform(df[dataColumns]) method = 'PCA' elif svdReducerStatus.lower() == 'true': if nfeatures < 2: nfeatures = 2 pca = TruncatedSVD(n_components=int(nfeatures), n_iter=7, random_state=42) pca.fit(df[dataColumns]) bpca_features = dataColumns.copy() pcaArray=pca.transform(df[dataColumns]) method = 'SVD' elif factorReducerStatus.lower()=='true': if int(nfeatures) == 0: pca=FactorAnalysis() else: pca=FactorAnalysis(n_components=int(nfeatures)) pca.fit(df[dataColumns]) bpca_features = dataColumns.copy() pcaArray=pca.transform(df[dataColumns]) method = 'FactorAnalysis' elif icaReducerStatus.lower()=='true': if int(nfeatures) == 0: pca=FastICA() else: pca=FastICA(n_components=int(nfeatures)) pca.fit(df[dataColumns]) bpca_features = dataColumns.copy() pcaArray=pca.transform(df[dataColumns]) method = 'IndependentComponentAnalysis' pcaDF=pd.DataFrame(pcaArray) #print(pcaDF) for i in range(len(pcaDF.columns)): pcaColumns.append(method+str(i)) topFeatures=pcaColumns apca_features= pcaColumns.copy() if target != '': pcaColumns.append(target) scaledDf = pd.DataFrame(np.hstack((pcaArray, targetArray)),columns=pcaColumns) else: scaledDf = pd.DataFrame(pcaArray,columns=pcaColumns) self.log.info("<--- dataframe after dimensionality reduction using "+method) self.log.info(scaledDf.head()) dataFrame=scaledDf dataColumns=list(dataFrame.columns) self.log.info('Status:- |... Feature reduction started') self.log.info('Status:- |... '+method+' done') self.log.info('Status:- |... Feature reduction completed') self.numofCols = dataFrame.shape[1] self.numOfRows = dataFrame.shape[0] dataFDtypes=[] for i in dataColumns: dataType=dataFrame[i].dtypes dataFDtypes.append(tuple([i,str(dataType)])) #Categoring datatypes for item in dataFDtypes: dTypesDic[item[0]] = item[1] if item[0] != target: if item[1] in ['int16', 'int32', 'int64'] : numericDiscreteFeatures.append(item[0]) elif item[1] in ['float16', 'float32', 'float64']: numericContinuousFeatures.append(item[0]) else: nonNumericFeatures.append(item[0]) self.numOfRows = dataFrame.shape[0] ''' cFRatio = 0.01 if(self.numOfRows < 1000): cFRatio = 0.2 elif(self.numOfRows < 10000): cFRatio = 0.1 elif(self.numOfRows < 100000): cFRatio = 0.01 ''' for i in numericDiscreteFeatures: nUnique=len(dataFrame[i].unique().tolist()) nRows=self.numOfRows if nUnique <= categoricalMaxLabel: categoricalFeatures.append(i) for i in numericContinuousFeatures: nUnique=len(dataFrame[i].unique().tolist()) nRows=self.numOfRows if nUnique <= categoricalMaxLabel: categoricalFeatures.append(i) discreteFeatures=list(set(numericDiscreteFeatures)-set(categoricalFeatures)) numericContinuousFeatures=list(set(numericContinuousFeatures)-set(categoricalFeatures)) self.log.info('-------> Numerical continuous features :'+(str(numericContinuousFeatures))[:500]) self.log.info('-------> Numerical discrete features :'+(str(discreteFeatures))[:500]) self.log.info('-------> Non numerical features :'+(str(nonNumericFeatures))[:500]) self.log.info('-------> Categorical Features :'+(str(categoricalFeatures))[:500]) if target !="" and featureEngineeringSelector.lower() == "false" and correlationSelector.lower() == "true": self.log.info('\n------- Feature Based Correlation Analysis Start ------') start = time.time() featureImpObj = featureImp() topFeatures,targetType= featureImpObj.FFImpNew(dataFrame,numericContinuousFeatures,discreteFeatures,nonNumericFeatures,categoricalFeatures,dTypesDic,target,pThresholdInput,pThresholdTarget,cThresholdInput,cThresholdTarget,categoricalMaxLabel,problem_type,maxClasses) #topFeatures,targetType= featureImpObj.FFImp(dataFrame,numericContinuousFeatures,discreteFeatures,nonNumericFeatures,categoricalFeatures,dTypesDic,target,pThreshold,cThreshold,categoricalMaxLabel,problem_type,maxClasses) self.log.info('-------> Highly Correlated Features Using Correlation Techniques'+(str(topFeatures))[:500]) executionTime=time.time() - start self.log.info('-------> Time Taken: '+str(executionTime)) self.log.info('Status:- |... Correlation based feature selection done: '+str(len(topFeatures))+' out of '+str(len(dataColumns))+' selected') self.log.info('------- Feature Based Correlation Analysis End ------>\n') if targetType == '': if problem_type.lower() == 'classification': targetType = 'categorical' if problem_type.lower() == 'regression': targetType = 'continuous' if target !="" and featureEngineeringSelector.lower() == "false" and modelSelector.lower() == "true": self.log.info('\n------- Model Based Correlation Analysis Start -------') start = time.time() updatedFeatures = dataColumns updatedFeatures.remove(target) #targetType = problem_type.lower() modelselectedFeatures=[] if targetType == 'categorical': try: xtrain=dataFrame[updatedFeatures] ytrain=dataFrame[target] etc = ExtraTreesClassifier(n_estimators=100) etc.fit(xtrain, ytrain) rfe = RFE(etc, n_features_to_select=1, verbose =0 ) rfe.fit(xtrain, ytrain) # total list of features ranks = {} ranks["RFE_LR"] = ranking(list(map(float, rfe.ranking_)), dataColumns, order=-1) for item in ranks["RFE_LR"]: if ranks["RFE_LR"][item]>0.30: #threshold as 30% modelselectedFeatures.append(item) modelselectedFeatures = list(modelselectedFeatures) self.log.info('-------> Highly Correlated Features Using Treeclassifier + RFE: '+(str(modelselectedFeatures))[:500]) except Exception as e: self.log.info('---------------->'+str(e)) selector = SelectFromModel(ExtraTreesClassifier()) xtrain=dataFrame[updatedFeatures] ytrain=dataFrame[target] selector.fit(xtrain,ytrain) modelselectedFeatures = xtrain.columns[(selector.get_support())].tolist() self.log.info('-------> Highly Correlated Features Using Treeclassifier: '+(str(modelselectedFeatures))[:500]) else: try: xtrain=dataFrame[updatedFeatures] ytrain=dataFrame[target] ls = Lasso() ls.fit(xtrain, ytrain) rfe = RFE(ls, n_features_to_select=1, verbose = 0 ) rfe.fit(xtrain, ytrain) # total list of features ranks = {} ranks["RFE_LR"] = ranking(list(map(float, rfe.ranking_)), dataColumns, order=-1) for item in ranks["RFE_LR"]: if ranks["RFE_LR"][item]>0.30: #threshold as 30% modelselectedFeatures.append(item) modelselectedFeatures = list(modelselectedFeatures) self.log.info('-------> Highly Correlated Features Using LASSO + RFE: '+(str(modelselectedFeatures))[:500]) except Exception as e: self.log.info('---------------->'+str(e)) selector = SelectFromModel(Lasso()) xtrain=dataFrame[updatedFeatures] ytrain=dataFrame[target] selector.fit(xtrain,ytrain) modelselectedFeatures = xtrain.columns[(selector.get_support())].tolist() self.log.info('-------> Highly Correlated Features Using LASSO: '+(str(modelselectedFeatures))[:500]) executionTime=time.time() - start self.log.info('-------> Time Taken: '+str(executionTime)) self.log.info('Status:- |... Model based feature selection done: '+str(len(modelselectedFeatures))+' out of '+str(len(dataColumns))+' selected') self.log.info('--------- Model Based Correlation Analysis End -----\n') if target !="" and featureEngineeringSelector.lower() == "false" and allFeaturesSelector.lower() == "true": allFeatures = features_list if target != '': allFeatures.remove(target) #print(allFeatures) if len(topFeatures) == 0 and len(modelselectedFeatures) == 0 and len(allFeatures) == 0: allFeatures = features_list return dataFrame,target,topFeatures,modelselectedFeatures,allFeatures,targetType,similarGruops,numericContinuousFeatures,discreteFeatures,nonNumericFeatures,categoricalFeatures,pca,bpca_features,apca_features,featureEngineeringSelector except Exception as inst: self.log.info('Feature selector failed: '+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' #System imports import os import sys import json import datetime,time,timeit import itertools #Sci-Tools imports import numpy as np import pandas as pd import math from sklearn.metrics import normalized_mutual_info_score from sklearn.feature_selection import f_regression,mutual_info_regression from sklearn.feature_selection import chi2,f_classif,mutual_info_classif import scipy.stats from scipy.stats import pearsonr, spearmanr, pointbiserialr, f_oneway, kendalltau, chi2_contingency import statsmodels.api as sm import statsmodels.formula.api as smf import logging def getHigherSignificanceColName(featureDict, colname1, colname2): if featureDict[colname1]<featureDict[colname2]: return colname2 else: return colname1 class featureImp(): def __init__(self): self.dTypesDic = {} self.featureImpDic={} self.indexedDic = {} self.log = logging.getLogger('eion') def FFImpNew(self,df,contFeatures,discreteFeatures,nonNumericFeatures,categoricalFeatures,dTypesDic,target,pValThInput,pValThTarget,corrThInput,corrThTarget,categoricalMaxLabel,problem_type,maxClasses): try: dataframe = df contiFeatures= contFeatures quantFeatures=discreteFeatures+contiFeatures categoricalFeatures=categoricalFeatures targetData=dataframe[target] nUnique=len(targetData.unique().tolist()) if nUnique <= categoricalMaxLabel: targetType="categorical" else: targetType="continuous" if problem_type.lower() == 'classification' and targetType == 'continuous': targetType = 'categorical' self.log.info( '-------> Change Target Type to Categorial as user defined') if problem_type.lower() == 'regression' and targetType == 'categorical': targetType = 'continuous' self.log.info( '-------> Change Target Type to Continuous as user defined') self.log.info( '-------> Target Type: '+str(targetType)) impFeatures=[] catFeature = [] numFeature = [] catFeatureXYcat = [] numFeatureXYcat = [] catFeatureXYnum= [] numFeatureXYnum = [] dropFeatureCat= [] dropFeatureNum = [] featureDict = {} if targetType =="categorical": if len(categoricalFeatures) !=0: # input vs target # chi-square for col in categoricalFeatures: contingency = pd.crosstab(dataframe[col], targetData) stat, p, dof, expected = chi2_contingency(contingency) if p <= pValThTarget: catFeatureXYcat.append(col) # categorical feature xy when target is cat featureDict[col] = p #input vs input # chi_square if len(catFeatureXYcat) != 0: length = len(catFeatureXYcat) for i in range(length): for j in range(i+1, length): contingency = pd.crosstab(dataframe[catFeatureXYcat[i]], dataframe[catFeatureXYcat[j]]) stat, p, dof, expected = chi2_contingency(contingency) if p > pValThInput: highSignificanceColName = getHigherSignificanceColName(featureDict, catFeatureXYcat[i], catFeatureXYcat[j]) dropFeatureCat.append(highSignificanceColName) break catFeature = list(set(catFeatureXYcat) - set(dropFeatureCat)) featureDict.clear() dropFeatureCat.clear() if len(quantFeatures) !=0: # input vs target # one way anova for col in quantFeatures: CategoryGroupLists = dataframe.groupby(target)[col].apply(list) AnovaResults = f_oneway(*CategoryGroupLists) if AnovaResults[1] <= pValThTarget: numFeatureXYcat.append(col) #numeric feature xy when target is cat featureDict[col] = AnovaResults[1] #input vs input # preason/spearman/ols # numeric feature xx when target is cat if len(numFeatureXYcat) != 0: df_xx = dataframe[numFeatureXYcat] rows, cols = df_xx.shape flds = list(df_xx.columns) corr_pearson = df_xx.corr(method='pearson').values corr_spearman = df_xx.corr(method='spearman').values for i in range(cols): for j in range(i+1, cols): if corr_pearson[i,j] > -corrThInput and corr_pearson[i,j] < corrThInput: if corr_spearman[i,j] > -corrThInput and corr_spearman[i,j] < corrThInput: #f = "'"+flds[i]+"'"+' ~ '+"'"+flds[j]+"'" #reg = smf.ols(formula=f, data=dataframe).fit() tmpdf = pd.DataFrame({'x':dataframe[flds[j]], 'y':dataframe[flds[i]]}) reg = smf.ols('y~x', data=tmpdf).fit() if len(reg.pvalues) > 1 and reg.pvalues[1] > pValThInput: highSignificanceColName = getHigherSignificanceColName(featureDict, flds[i], flds[j]) dropFeatureNum.append(highSignificanceColName) break else: highSignificanceColName = getHigherSignificanceColName(featureDict, flds[i], flds[j]) dropFeatureNum.append(highSignificanceColName) break else: highSignificanceColName = getHigherSignificanceColName(featureDict, flds[i], flds[j]) dropFeatureNum.append(highSignificanceColName) break numFeature = list(set(numFeatureXYcat) - set(dropFeatureNum)) dropFeatureNum.clear() featureDict.clear() impFeatures = numFeature+catFeature hCorrFeatures=list(set((impFeatures))) else: # targetType =="continuous": if len(categoricalFeatures) !=0: # input vs target # Anova for col in categoricalFeatures: #f = target+' ~ C('+col+')' #model = smf.ols(f, data=dataframe).fit() #table = sm.stats.anova_lm(model, typ=2) tmpdf = pd.DataFrame({'x':dataframe[col], 'y':dataframe[target]}) model = smf.ols('y~x', data=tmpdf).fit() table = sm.stats.anova_lm(model, typ=2) if table['PR(>F)'][0] <= pValThTarget: catFeatureXYnum.append(col) #categorical feature xy when target is numeric featureDict[col]=table['PR(>F)'][0] #input vs input # chi_square if len(catFeatureXYnum) != 0: length = len(catFeatureXYnum) for i in range(length): for j in range(i+1, length): contingency = pd.crosstab(dataframe[catFeatureXYnum[i]], dataframe[catFeatureXYnum[j]]) stat, p, dof, expected = chi2_contingency(contingency) if p > pValThInput: highSignificanceColName = getHigherSignificanceColName(featureDict, catFeatureXYnum[i], catFeatureXYnum[j]) dropFeatureCat.append(highSignificanceColName) break catFeature = list(set(catFeatureXYnum) - set(dropFeatureCat)) dropFeatureCat.clear() featureDict.clear() if len(quantFeatures) !=0: # input vs target # preason/spearman/ols for col in quantFeatures: pearson_corr = pearsonr(dataframe[col], targetData) coef = round(pearson_corr[0],5) p_value = round(pearson_corr[1],5) if coef > -corrThTarget and coef < corrThTarget: spearman_corr = spearmanr(dataframe[col], targetData) coef = round(spearman_corr[0],5) p_value = round(spearman_corr[1],5) if coef > -corrThTarget and coef < corrThTarget: #f = target+' ~ '+col #reg = smf.ols(formula=f, data=dataframe).fit() tmpdf = pd.DataFrame({'x':dataframe[col], 'y':dataframe[target]}) reg = smf.ols('y~x', data=tmpdf).fit() if len(reg.pvalues) > 1 and reg.pvalues[1] <= pValThTarget: numFeatureXYnum.append(col) # numeric feature xx when target is numeric featureDict[col]=reg.pvalues[1] else: numFeatureXYnum.append(col) featureDict[col]=p_value else: numFeatureXYnum.append(col) featureDict[col]=p_value #input vs input # preason/spearman/ols if len(numFeatureXYnum) != 0: df_xx = dataframe[numFeatureXYnum] rows, cols = df_xx.shape flds = list(df_xx.columns) corr_pearson = df_xx.corr(method='pearson').values corr_spearman = df_xx.corr(method='spearman').values for i in range(cols): for j in range(i+1, cols): if corr_pearson[i,j] > -corrThInput and corr_pearson[i,j] < corrThInput: if corr_spearman[i,j] > -corrThInput and corr_spearman[i,j] < corrThInput: #f = flds[i]+' ~ '+flds[j] #reg = smf.ols(formula=f, data=dataframe).fit() tmpdf = pd.DataFrame({'x':dataframe[flds[j]], 'y':dataframe[flds[i]]}) reg = smf.ols('y~x', data=tmpdf).fit() if len(reg.pvalues) > 1 and reg.pvalues[1] > pValThInput: highSignificanceColName = getHigherSignificanceColName(featureDict, flds[i], flds[j]) dropFeatureNum.append(highSignificanceColName) break else: highSignificanceColName = getHigherSignificanceColName(featureDict, flds[i], flds[j]) dropFeatureNum.append(highSignificanceColName) break else: highSignificanceColName = getHigherSignificanceColName(featureDict, flds[i], flds[j]) dropFeatureNum.append(highSignificanceColName) break numFeature = list(set(numFeatureXYnum) - set(dropFeatureNum)) featureDict.clear() dropFeatureNum.clear() impFeatures = numFeature+catFeature hCorrFeatures=list(set(impFeatures)) return hCorrFeatures,targetType except Exception as inst: self.log.info( '\n--> Failed calculating feature importance '+str(inst)) hCorrFeatures=[] targetType='' exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) self.log.info('\n--> Taking all the features as highest correlation features') hCorrFeatures = list(dataframe.columns) return hCorrFeatures,targetType def FFImp(self,df,contFeatures,discreteFeatures,nonNumericFeatures,categoricalFeatures,dTypesDic,target,pValTh,corrTh,categoricalMaxLabel,problem_type,maxClasses): ''' Input: dataframe, numeric continuous features, numeric discrete features Output: feature importance dictionary ''' try: dataframe =df contiFeatures= contFeatures discreteFeatures = discreteFeatures nonNumeric = nonNumericFeatures categoricalFeatures=categoricalFeatures self.dTypesDic = dTypesDic numericFeatures = contiFeatures + discreteFeatures+categoricalFeatures quantFeatures=discreteFeatures+contiFeatures scorrDict={} fScoreDict={} pcorrDict={} miDict={} targetData=dataframe[target] data=dataframe[numericFeatures] nUnique=len(targetData.unique().tolist()) nRows=targetData.shape[0] ''' print("\n ===> nUnique :") print(nUnique) print("\n ===> nRows :") print(nRows) print("\n ===> cFRatio :") print(cFRatio) print("\n ===> nUnique/nRows :") ''' #calratio = nUnique self.log.info( '-------> Target Column Unique Stats: '+str(nUnique)+' nRows: '+str(nRows)+' Unique:'+str(nUnique)) #sys.exit() if nUnique <= categoricalMaxLabel: targetType="categorical" else: targetType="continuous" if problem_type.lower() == 'classification' and targetType == 'continuous': targetType = 'categorical' self.log.info( '-------> Change Target Type to Categorial as user defined') if problem_type.lower() == 'regression' and targetType == 'categorical': targetType = 'continuous' self.log.info( '-------> Change Target Type to Continuous as user defined') self.log.info( '-------> Target Type: '+str(targetType)) impFeatures=[] featureImpDict={} if targetType =="categorical": try: if len(categoricalFeatures) !=0: categoricalData=dataframe[categoricalFeatures] chiSqCategorical=chi2(categoricalData,targetData)[1] corrSeries=pd.Series(chiSqCategorical, index=categoricalFeatures) impFeatures.append(corrSeries[corrSeries<pValTh].index.tolist()) corrDict=corrSeries.to_dict() featureImpDict['chiSquaretestPValue']=corrDict except Exception as inst: self.log.info("Found negative values in categorical variables "+str(inst)) if len(quantFeatures) !=0: try: quantData=dataframe[quantFeatures] fclassScore=f_classif(quantData,targetData)[1] miClassScore=mutual_info_classif(quantData,targetData) fClassSeries=pd.Series(fclassScore,index=quantFeatures) miClassSeries=pd.Series(miClassScore,index=quantFeatures) impFeatures.append(fClassSeries[fClassSeries<pValTh].index.tolist()) impFeatures.append(miClassSeries[miClassSeries>corrTh].index.tolist()) featureImpDict['anovaPValue']=fClassSeries.to_dict() featureImpDict['MIScore']=miClassSeries.to_dict() except MemoryError as inst: self.log.info( '-------> MemoryError in feature selection. '+str(inst)) pearsonScore=dataframe.corr() targetPScore=abs(pearsonScore[target]) impFeatures.append(targetPScore[targetPScore<pValTh].index.tolist()) featureImpDict['pearsonCoff']=targetPScore.to_dict() hCorrFeatures=list(set(sum(impFeatures, []))) else: if len(quantFeatures) !=0: try: quantData =dataframe[quantFeatures] fregScore=f_regression(quantData,targetData)[1] miregScore=mutual_info_regression(quantData,targetData) fregSeries=pd.Series(fregScore,index=quantFeatures) miregSeries=pd.Series(miregScore,index=quantFeatures) impFeatures.append(fregSeries[fregSeries<pValTh].index.tolist()) impFeatures.append(miregSeries[miregSeries>corrTh].index.tolist()) featureImpDict['anovaPValue']=fregSeries.to_dict() featureImpDict['MIScore']=miregSeries.to_dict() except MemoryError as inst: self.log.info( '-------> MemoryError in feature selection. '+str(inst)) pearsonScore=dataframe.corr() targetPScore=abs(pearsonScore[target]) impFeatures.append(targetPScore[targetPScore<pValTh].index.tolist()) featureImpDict['pearsonCoff']=targetPScore.to_dict() hCorrFeatures=list(set(sum(impFeatures, []))) return hCorrFeatures,targetType except Exception as inst: self.log.info( '\n--> Failed calculating feature importance '+str(inst)) hCorrFeatures=[] targetType='' exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return hCorrFeatures,targetType ''' Importance degree Computes set of relational parameters pearson correlation, mutual information ''' def importanceDegree(self,dataframe,feature1,feature2): try: tempList = [] #Parameter 1: pearson correlation pcorr = self.pearsonCoff(dataframe,feature1,feature2) tempList.append(pcorr) #Parameter 2: mutual information #Testing mi = self.mutualInfo(dataframe,feature1,feature2,self.dTypesDic) tempList.append(mi) #return the highest parameter return np.max(tempList) except: return 0.0 ''' Compute pearson correlation ''' def pearsonCoff(self,dataframe,feature1,feature2): try: value=dataframe[feature1].corr(dataframe[feature2]) return np.abs(value) except: return 0.0 ''' Compute mutual information ''' def mutualInfo(self,dataframe,feature1,feature2,typeDic): try: numType = {'int64': 'discrete','int32' : 'discrete','int16' : 'discrete','float16' : 'continuous','float32' : 'continuous','float64' : 'continuous'} featureType1 = numType[typeDic[feature1]] featureType2 = numType[typeDic[feature2]] bufferList1=dataframe[feature1].values.tolist() bufferList2=dataframe[feature2].values.tolist() #Case 1: Only if both are discrete if(featureType1 == 'discrete' and featureType2 == 'discrete'): tempResult = discreteMI(bufferList1,bufferList2) return np.mean(tempResult) #Case 2: If one of the features is continuous elif(featureType1 == 'continuous' and featureType2 == 'discrete'): tempResult = self.categoricalMI(bufferList1,bufferList2) return np.mean(tempResult) else: tempResult = self.continuousMI(bufferList1,bufferList2) return np.mean(tempResult) except: return 0.0 def continuousMI(self,bufferList1,bufferList2): mi = 0.0 #Using mutual info regression from feature selection mi = mutual_info_regression(self.vec(bufferList1),bufferList2) return mi def categoricalMI(self,bufferList1,bufferList2): mi = 0.0 #Using mutual info classification from feature selection mi = mutual_info_classif(self.vec(bufferList1),bufferList2) return mi def discreteMI(self,bufferList1,bufferList2): mi = 0.0 #Using scikit normalized mutual information function mi = normalized_mutual_info_score(bufferList1,bufferList2) return mi def vec(self,x): return [[i] for i in x] ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import warnings warnings.filterwarnings("ignore") import json import os import sys import pandas as pd import numpy as np from pandas import json_normalize from autogluon.text import TextPredictor import os.path def predict(data): try: if os.path.splitext(data)[1] == ".tsv": df=pd.read_csv(data,encoding='utf-8',sep='\t') elif os.path.splitext(data)[1] == ".csv": df=pd.read_csv(data,encoding='utf-8') else: if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) df = json_normalize(jsonData) model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),'text_prediction') predictor = TextPredictor.load(model_path) predictions = predictor.predict(df) df['predict'] = predictions outputjson = df.to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} output = json.dumps(outputjson) print("predictions:",output) return(output) except KeyError as e: output = {"status":"FAIL","message":str(e).strip('"')} print("predictions:",json.dumps(output)) return (json.dumps(output)) except Exception as e: output = {"status":"FAIL","message":str(e).strip('"')} print("predictions:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = predict(sys.argv[1]) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import warnings warnings.filterwarnings("ignore") import json import os import sys import pandas as pd from pandas import json_normalize #from selector import selector #from inputprofiler import inputprofiler #from trained_model import trained_model #from output_format import output_format from autogluon.tabular import TabularDataset, TabularPredictor from autogluon.core.utils.utils import setup_outputdir from autogluon.core.utils.loaders import load_pkl from autogluon.core.utils.savers import save_pkl import os.path class MultilabelPredictor(): """ Tabular Predictor for predicting multiple columns in table. Creates multiple TabularPredictor objects which you can also use individually. You can access the TabularPredictor for a particular label via: `multilabel_predictor.get_predictor(label_i)` Parameters ---------- labels : List[str] The ith element of this list is the column (i.e. `label`) predicted by the ith TabularPredictor stored in this object. path : str Path to directory where models and intermediate outputs should be saved. If unspecified, a time-stamped folder called "AutogluonModels/ag-[TIMESTAMP]" will be created in the working directory to store all models. Note: To call `fit()` twice and save all results of each fit, you must specify different `path` locations or don't specify `path` at all. Otherwise files from first `fit()` will be overwritten by second `fit()`. Caution: when predicting many labels, this directory may grow large as it needs to store many TabularPredictors. problem_types : List[str] The ith element is the `problem_type` for the ith TabularPredictor stored in this object. eval_metrics : List[str] The ith element is the `eval_metric` for the ith TabularPredictor stored in this object. consider_labels_correlation : bool Whether the predictions of multiple labels should account for label correlations or predict each label independently of the others. If True, the ordering of `labels` may affect resulting accuracy as each label is predicted conditional on the previous labels appearing earlier in this list (i.e. in an auto-regressive fashion). Set to False if during inference you may want to individually use just the ith TabularPredictor without predicting all the other labels. kwargs : Arguments passed into the initialization of each TabularPredictor. """ multi_predictor_file = 'multilabel_predictor.pkl' def __init__(self, labels, path, problem_types=None, eval_metrics=None, consider_labels_correlation=True, **kwargs): if len(labels) < 2: raise ValueError("MultilabelPredictor is only intended for predicting MULTIPLE labels (columns), use TabularPredictor for predicting one label (column).") self.path = setup_outputdir(path, warn_if_exist=False) self.labels = labels self.consider_labels_correlation = consider_labels_correlation self.predictors = {} # key = label, value = TabularPredictor or str path to the TabularPredictor for this label if eval_metrics is None: self.eval_metrics = {} else: self.eval_metrics = {labels[i] : eval_metrics[i] for i in range(len(labels))} problem_type = None eval_metric = None for i in range(len(labels)): label = labels[i] path_i = self.path + "Predictor_" + label if problem_types is not None: problem_type = problem_types[i] if eval_metrics is not None: eval_metric = self.eval_metrics[i] self.predictors[label] = TabularPredictor(label=label, problem_type=problem_type, eval_metric=eval_metric, path=path_i, **kwargs) def fit(self, train_data, tuning_data=None, **kwargs): """ Fits a separate TabularPredictor to predict each of the labels. Parameters ---------- train_data, tuning_data : str or autogluon.tabular.TabularDataset or pd.DataFrame See documentation for `TabularPredictor.fit()`. kwargs : Arguments passed into the `fit()` call for each TabularPredictor. """ if isinstance(train_data, str): train_data = TabularDataset(train_data) if tuning_data is not None and isinstance(tuning_data, str): tuning_data = TabularDataset(tuning_data) train_data_og = train_data.copy() if tuning_data is not None: tuning_data_og = tuning_data.copy() save_metrics = len(self.eval_metrics) == 0 for i in range(len(self.labels)): label = self.labels[i] predictor = self.get_predictor(label) if not self.consider_labels_correlation: labels_to_drop = [l for l in self.labels if l!=label] else: labels_to_drop = [labels[j] for j in range(i+1,len(self.labels))] train_data = train_data_og.drop(labels_to_drop, axis=1) if tuning_data is not None: tuning_data = tuning_data_og.drop(labels_to_drop, axis=1) print(f"Fitting TabularPredictor for label: {label} ...") predictor.fit(train_data=train_data, tuning_data=tuning_data, **kwargs) self.predictors[label] = predictor.path if save_metrics: self.eval_metrics[label] = predictor.eval_metric self.save() def predict(self, data, **kwargs): """ Returns DataFrame with label columns containing predictions for each label. Parameters ---------- data : str or autogluon.tabular.TabularDataset or pd.DataFrame Data to make predictions for. If label columns are present in this data, they will be ignored. See documentation for `TabularPredictor.predict()`. kwargs : Arguments passed into the predict() call for each TabularPredictor. """ return self._predict(data, as_proba=False, **kwargs) def predict_proba(self, data, **kwargs): """ Returns dict where each key is a label and the corresponding value is the `predict_proba()` output for just that label. Parameters ---------- data : str or autogluon.tabular.TabularDataset or pd.DataFrame Data to make predictions for. See documentation for `TabularPredictor.predict()` and `TabularPredictor.predict_proba()`. kwargs : Arguments passed into the `predict_proba()` call for each TabularPredictor (also passed into a `predict()` call). """ return self._predict(data, as_proba=True, **kwargs) def evaluate(self, data, **kwargs): """ Returns dict where each key is a label and the corresponding value is the `evaluate()` output for just that label. Parameters ---------- data : str or autogluon.tabular.TabularDataset or pd.DataFrame Data to evalate predictions of all labels for, must contain all labels as columns. See documentation for `TabularPredictor.evaluate()`. kwargs : Arguments passed into the `evaluate()` call for each TabularPredictor (also passed into the `predict()` call). """ data = self._get_data(data) eval_dict = {} for label in self.labels: print(f"Evaluating TabularPredictor for label: {label} ...") predictor = self.get_predictor(label) eval_dict[label] = predictor.evaluate(data, **kwargs) if self.consider_labels_correlation: data[label] = predictor.predict(data, **kwargs) return eval_dict def save(self): """ Save MultilabelPredictor to disk. """ for label in self.labels: if not isinstance(self.predictors[label], str): self.predictors[label] = self.predictors[label].path save_pkl.save(path=self.path+self.multi_predictor_file, object=self) print(f"MultilabelPredictor saved to disk. Load with: MultilabelPredictor.load('{self.path}')") @classmethod def load(cls, path): """ Load MultilabelPredictor from disk `path` previously specified when creating this MultilabelPredictor. """ path = os.path.expanduser(path) if path[-1] != os.path.sep: path = path + os.path.sep return load_pkl.load(path=path+cls.multi_predictor_file) def get_predictor(self, label): """ Returns TabularPredictor which is used to predict this label. """ predictor = self.predictors[label] if isinstance(predictor, str): return TabularPredictor.load(path=predictor) return predictor def _get_data(self, data): if isinstance(data, str): return TabularDataset(data) return data.copy() def _predict(self, data, as_proba=False, **kwargs): data = self._get_data(data) if as_proba: predproba_dict = {} for label in self.labels: print(f"Predicting with TabularPredictor for label: {label} ...") predictor = self.get_predictor(label) if as_proba: predproba_dict[label] = predictor.predict_proba(data, as_multiclass=True, **kwargs) data[label] = predictor.predict(data, **kwargs) if not as_proba: return data[self.labels] else: return predproba_dict def predict(data): try: if os.path.splitext(data)[1] == ".tsv": df=pd.read_csv(data,encoding='utf-8',sep='\t') elif os.path.splitext(data)[1] == ".csv": df=pd.read_csv(data,encoding='utf-8') else: if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) df = json_normalize(jsonData) #df0 = df.copy() #profilerobj = inputprofiler() #df = profilerobj.apply_profiler(df) #selectobj = selector() #df = selectobj.apply_selector(df) #modelobj = trained_model() #output = modelobj.predict(df,"") # Load the Test data for Prediction # ----------------------------------------------------------------------------# test_data = df#TabularDataset(data) #'testingDataset.csv' #subsample_size = 2 # ----------------------------------------------------------------------------# # Specify the corresponding target features to be used # ----------------------------------------------------------------------------# #labels = ['education-num','education','class'] configFile = os.path.join(os.path.dirname(os.path.abspath(__file__)),'etc','predictionConfig.json') with open(configFile, 'rb') as cfile: data = json.load(cfile) labels = data['targetFeature'] # ----------------------------------------------------------------------------# for x in labels: if x in list(test_data.columns): test_data.drop(x,axis='columns', inplace=True) # ----------------------------------------------------------------------------# #test_data = test_data.sample(n=subsample_size, random_state=0) #print(test_data) #test_data_nolab = test_data.drop(columns=labels) #test_data_nolab.head() test_data_nolab = test_data # ----------------------------------------------------------------------------# # Load the trained model from where it's stored # ----------------------------------------------------------------------------# model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),'ModelPath') multi_predictor = MultilabelPredictor.load(model_path) # ----------------------------------------------------------------------------# # Start the prediction and perform the evaluation # ----------------------------------------------------------------------------# predictions = multi_predictor.predict(test_data_nolab) for label in labels: df[label+'_predict'] = predictions[label] #evaluations = multi_predictor.evaluate(test_data) #print(evaluations) #print("Evaluated using metrics:", multi_predictor.eval_metrics) # ----------------------------------------------------------------------------# # ----------------------------------------------------------------------------# #outputobj = output_format() #output = outputobj.apply_output_format(df0,output) outputjson = df.to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} output = json.dumps(outputjson) print("predictions:",output) return(output) # ----------------------------------------------------------------------------# except KeyError as e: output = {"status":"FAIL","message":str(e).strip('"')} print("predictions:",json.dumps(output)) return (json.dumps(output)) except Exception as e: output = {"status":"FAIL","message":str(e).strip('"')} print("predictions:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = predict(sys.argv[1]) import numpy as np from scipy.stats import norm from sklearn.metrics import mean_squared_error, r2_score from ..utils.misc import fitted_ucc_w_nullref def picp(y_true, y_lower, y_upper): """ Prediction Interval Coverage Probability (PICP). Computes the fraction of samples for which the grounds truth lies within predicted interval. Measures the prediction interval calibration for regression. Args: y_true: Ground truth y_lower: predicted lower bound y_upper: predicted upper bound Returns: float: the fraction of samples for which the grounds truth lies within predicted interval. """ satisfies_upper_bound = y_true <= y_upper satisfies_lower_bound = y_true >= y_lower return np.mean(satisfies_upper_bound * satisfies_lower_bound) def mpiw(y_lower, y_upper): """ Mean Prediction Interval Width (MPIW). Computes the average width of the the prediction intervals. Measures the sharpness of intervals. Args: y_lower: predicted lower bound y_upper: predicted upper bound Returns: float: the average width the prediction interval across samples. """ return np.mean(np.abs(y_lower - y_upper)) def auucc_gain(y_true, y_mean, y_lower, y_upper): """ Computes the Area Under the Uncertainty Characteristics Curve (AUUCC) gain wrt to a null reference with constant band. Args: y_true: Ground truth y_mean: predicted mean y_lower: predicted lower bound y_upper: predicted upper bound Returns: float: AUUCC gain """ u = fitted_ucc_w_nullref(y_true, y_mean, y_lower, y_upper) auucc = u.get_AUUCC() assert(isinstance(auucc, list) and len(auucc) == 2), "Failed to calculate auucc gain" assert (not np.isclose(auucc[1], 0.)), "Failed to calculate auucc gain" auucc_gain = (auucc[1]-auucc[0])/auucc[0] return auucc_gain def negative_log_likelihood_Gaussian(y_true, y_mean, y_lower, y_upper): """ Computes Gaussian negative_log_likelihood assuming symmetric band around the mean. Args: y_true: Ground truth y_mean: predicted mean y_lower: predicted lower bound y_upper: predicted upper bound Returns: float: nll """ y_std = (y_upper - y_lower) / 4.0 nll = np.mean(-norm.logpdf(y_true.squeeze(), loc=y_mean.squeeze(), scale=y_std.squeeze())) return nll def compute_regression_metrics(y_true, y_mean, y_lower, y_upper, option="all", nll_fn=None): """ Computes the metrics specified in the option which can be string or a list of strings. Default option `all` computes the ["rmse", "nll", "auucc_gain", "picp", "mpiw", "r2"] metrics. Args: y_true: Ground truth y_mean: predicted mean y_lower: predicted lower bound y_upper: predicted upper bound option: string or list of string contained the name of the metrics to be computed. nll_fn: function that evaluates NLL, if None, then computes Gaussian NLL using y_mean and y_lower. Returns: dict: dictionary containing the computed metrics. """ assert y_true.shape == y_mean.shape, "y_true shape: {}, y_mean shape: {}".format(y_true.shape, y_mean.shape) assert y_true.shape == y_lower.shape, "y_true shape: {}, y_mean shape: {}".format(y_true.shape, y_lower.shape) assert y_true.shape == y_upper.shape, "y_true shape: {}, y_mean shape: {}".format(y_true.shape, y_upper.shape) results = {} if not isinstance(option, list): if option == "all": option_list = ["rmse", "nll", "auucc_gain", "picp", "mpiw", "r2"] else: option_list = [option] if "rmse" in option_list: results["rmse"] = mean_squared_error(y_true, y_mean, squared=False) if "nll" in option_list: if nll_fn is None: nll = negative_log_likelihood_Gaussian(y_true, y_mean, y_lower, y_upper) results["nll"] = nll else: results["nll"] = np.mean(nll_fn(y_true)) if "auucc_gain" in option_list: gain = auucc_gain(y_true, y_mean, y_lower, y_upper) results["auucc_gain"] = gain if "picp" in option_list: results["picp"] = picp(y_true, y_lower, y_upper) if "mpiw" in option_list: results["mpiw"] = mpiw(y_lower, y_upper) if "r2" in option_list: results["r2"] = r2_score(y_true, y_mean) return results def _check_not_tuple_of_2_elements(obj, obj_name='obj'): """Check object is not tuple or does not have 2 elements.""" if not isinstance(obj, tuple) or len(obj) != 2: raise TypeError('%s must be a tuple of 2 elements.' % obj_name) def plot_uncertainty_distribution(dist, show_quantile_dots=False, qd_sample=20, qd_bins=7, ax=None, figsize=None, dpi=None, title='Predicted Distribution', xlims=None, xlabel='Prediction', ylabel='Density', **kwargs): """ Plot the uncertainty distribution for a single distribution. Args: dist: scipy.stats._continuous_distns. A scipy distribution object. show_quantile_dots: boolean. Whether to show quantil dots on top of the density plot. qd_sample: int. Number of dots for the quantile dot plot. qd_bins: int. Number of bins for the quantile dot plot. ax: matplotlib.axes.Axes or None, optional (default=None). Target axes instance. If None, new figure and axes will be created. figsize: tuple of 2 elements or None, optional (default=None). Figure size. dpi : int or None, optional (default=None). Resolution of the figure. title : string or None, optional (default=Prediction Distribution) Axes title. If None, title is disabled. xlims : tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.xlim()``. xlabel : string or None, optional (default=Prediction) X-axis title label. If None, title is disabled. ylabel : string or None, optional (default=Density) Y-axis title label. If None, title is disabled. Returns: matplotlib.axes.Axes: ax : The plot with prediction distribution. """ import matplotlib.pyplot as plt if ax is None: if figsize is not None: _check_not_tuple_of_2_elements(figsize, 'figsize') _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) x = np.linspace(dist.ppf(0.01), dist.ppf(0.99), 100) ax.plot(x, dist.pdf(x), **kwargs) if show_quantile_dots: from matplotlib.patches import Circle from matplotlib.collections import PatchCollection import matplotlib.ticker as ticker data = dist.rvs(size=10000) p_less_than_x = np.linspace(1 / qd_sample / 2, 1 - (1 / qd_sample / 2), qd_sample) x_ = np.percentile(data, p_less_than_x * 100) # Inverce CDF (ppf) # Create bins hist = np.histogram(x_, bins=qd_bins) bins, edges = hist radius = (edges[1] - edges[0]) / 2 ax2 = ax.twinx() patches = [] max_y = 0 for i in range(qd_bins): x_bin = (edges[i + 1] + edges[i]) / 2 y_bins = [(i + 1) * (radius * 2) for i in range(bins[i])] max_y = max(y_bins) if max(y_bins) > max_y else max_y for _, y_bin in enumerate(y_bins): circle = Circle((x_bin, y_bin), radius) patches.append(circle) p = PatchCollection(patches, alpha=0.4) ax2.add_collection(p) # Axis tweek y_scale = (max_y + radius) / max(dist.pdf(x)) ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x_ / y_scale)) ax2.yaxis.set_major_formatter(ticks_y) ax2.set_yticklabels([]) if xlims is not None: ax2.set_xlim(left=xlims[0], right=xlims[1]) else: ax2.set_xlim([min(x_) - radius, max(x) + radius]) ax2.set_ylim([0, max_y + radius]) ax2.set_aspect(1) if title is not None: ax.set_title(title) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: ax.set_ylabel(ylabel) return ax def plot_picp_by_feature(x_test, y_test, y_test_pred_lower_total, y_test_pred_upper_total, num_bins=10, ax=None, figsize=None, dpi=None, xlims=None, ylims=None, xscale="linear", title=None, xlabel=None, ylabel=None): """ Plot how prediction uncertainty varies across the entire range of a feature. Args: x_test: One dimensional ndarray. Feature column of the test dataset. y_test: One dimensional ndarray. Ground truth label of the test dataset. y_test_pred_lower_total: One dimensional ndarray. Lower bound of the total uncertainty range. y_test_pred_upper_total: One dimensional ndarray. Upper bound of the total uncertainty range. num_bins: int. Number of bins used to discritize x_test into equal-sample-sized bins. ax: matplotlib.axes.Axes or None, optional (default=None). Target axes instance. If None, new figure and axes will be created. figsize: tuple of 2 elements or None, optional (default=None). Figure size. dpi : int or None, optional (default=None). Resolution of the figure. xlims : tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.xlim()``. ylims: tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.ylim()``. xscale: Passed to ``ax.set_xscale()``. title : string or None, optional Axes title. If None, title is disabled. xlabel : string or None, optional X-axis title label. If None, title is disabled. ylabel : string or None, optional Y-axis title label. If None, title is disabled. Returns: matplotlib.axes.Axes: ax : The plot with PICP scores binned by a feature. """ from scipy.stats.mstats import mquantiles import matplotlib.pyplot as plt if ax is None: if figsize is not None: _check_not_tuple_of_2_elements(figsize, 'figsize') _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) x_uniques_sorted = np.sort(np.unique(x_test)) num_unique = len(x_uniques_sorted) sample_bin_ids = np.searchsorted(x_uniques_sorted, x_test) if len(x_uniques_sorted) > 10: # bin the values q_bins = mquantiles(x_test, np.histogram_bin_edges([], bins=num_bins-1, range=(0.0, 1.0))[1:]) q_sample_bin_ids = np.digitize(x_test, q_bins) picps = np.array([picp(y_test[q_sample_bin_ids==bin], y_test_pred_lower_total[q_sample_bin_ids==bin], y_test_pred_upper_total[q_sample_bin_ids==bin]) for bin in range(num_bins)]) unique_sample_bin_ids = np.digitize(x_uniques_sorted, q_bins) picp_replicated = [len(x_uniques_sorted[unique_sample_bin_ids == bin]) * [picps[bin]] for bin in range(num_bins)] picp_replicated = np.array([item for sublist in picp_replicated for item in sublist]) else: picps = np.array([picp(y_test[sample_bin_ids == bin], y_test_pred_lower_total[sample_bin_ids == bin], y_test_pred_upper_total[sample_bin_ids == bin]) for bin in range(num_unique)]) picp_replicated = picps ax.plot(x_uniques_sorted, picp_replicated, label='PICP') ax.axhline(0.95, linestyle='--', label='95%') ax.set_ylabel('PICP') ax.legend(loc='best') if title is None: title = 'Test data overall PICP: {:.2f} MPIW: {:.2f}'.format( picp(y_test, y_test_pred_lower_total, y_test_pred_upper_total), mpiw(y_test_pred_lower_total, y_test_pred_upper_total)) if xlims is not None: ax.set_xlim(left=xlims[0], right=xlims[1]) if ylims is not None: ax.set_ylim(bottom=ylims[0], top=ylims[1]) ax.set_title(title) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: ax.set_ylabel(ylabel) if xscale is not None: ax.set_xscale(xscale) return ax def plot_uncertainty_by_feature(x_test, y_test_pred_mean, y_test_pred_lower_total, y_test_pred_upper_total, y_test_pred_lower_epistemic=None, y_test_pred_upper_epistemic=None, ax=None, figsize=None, dpi=None, xlims=None, xscale="linear", title=None, xlabel=None, ylabel=None): """ Plot how prediction uncertainty varies across the entire range of a feature. Args: x_test: one dimensional ndarray. Feature column of the test dataset. y_test_pred_mean: One dimensional ndarray. Model prediction for the test dataset. y_test_pred_lower_total: One dimensional ndarray. Lower bound of the total uncertainty range. y_test_pred_upper_total: One dimensional ndarray. Upper bound of the total uncertainty range. y_test_pred_lower_epistemic: One dimensional ndarray. Lower bound of the epistemic uncertainty range. y_test_pred_upper_epistemic: One dimensional ndarray. Upper bound of the epistemic uncertainty range. ax: matplotlib.axes.Axes or None, optional (default=None). Target axes instance. If None, new figure and axes will be created. figsize: tuple of 2 elements or None, optional (default=None). Figure size. dpi : int or None, optional (default=None). Resolution of the figure. xlims : tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.xlim()``. xscale: Passed to ``ax.set_xscale()``. title : string or None, optional Axes title. If None, title is disabled. xlabel : string or None, optional X-axis title label. If None, title is disabled. ylabel : string or None, optional Y-axis title label. If None, title is disabled. Returns: matplotlib.axes.Axes: ax : The plot with model's uncertainty binned by a feature. """ import matplotlib.pyplot as plt if ax is None: if figsize is not None: _check_not_tuple_of_2_elements(figsize, 'figsize') _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) x_uniques_sorted = np.sort(np.unique(x_test)) y_pred_var = ((y_test_pred_upper_total - y_test_pred_lower_total) / 4.0)**2 agg_y_std = np.array([np.sqrt(np.mean(y_pred_var[x_test==x])) for x in x_uniques_sorted]) agg_y_mean = np.array([np.mean(y_test_pred_mean[x_test==x]) for x in x_uniques_sorted]) ax.plot(x_uniques_sorted, agg_y_mean, '-b', lw=2, label='mean prediction') ax.fill_between(x_uniques_sorted, agg_y_mean - 2.0 * agg_y_std, agg_y_mean + 2.0 * agg_y_std, alpha=0.3, label='total uncertainty') if y_test_pred_lower_epistemic is not None: y_pred_var_epistemic = ((y_test_pred_upper_epistemic - y_test_pred_lower_epistemic) / 4.0)**2 agg_y_std_epistemic = np.array([np.sqrt(np.mean(y_pred_var_epistemic[x_test==x])) for x in x_uniques_sorted]) ax.fill_between(x_uniques_sorted, agg_y_mean - 2.0 * agg_y_std_epistemic, agg_y_mean + 2.0 * agg_y_std_epistemic, alpha=0.3, label='model uncertainty') ax.legend(loc='best') if xlims is not None: ax.set_xlim(left=xlims[0], right=xlims[1]) if title is not None: ax.set_title(title) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: ax.set_ylabel(ylabel) if xscale is not None: ax.set_xscale(xscale) return ax import numpy as np import pandas as pd from scipy.stats import entropy from sklearn.metrics import roc_auc_score, log_loss, accuracy_score def entropy_based_uncertainty_decomposition(y_prob_samples): """ Entropy based decomposition [2]_ of predictive uncertainty into aleatoric and epistemic components. References: .. [2] Depeweg, S., Hernandez-Lobato, J. M., Doshi-Velez, F., & Udluft, S. (2018, July). Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning. In International Conference on Machine Learning (pp. 1184-1193). PMLR. Args: y_prob_samples: list of array-like of shape (n_samples, n_classes) containing class prediction probabilities corresponding to samples from the model posterior. Returns: tuple: - total_uncertainty: entropy of the predictive distribution. - aleatoric_uncertainty: aleatoric component of the total_uncertainty. - epistemic_uncertainty: epistemic component of the total_uncertainty. """ y_preds_samples_stacked = np.stack(y_prob_samples) preds_mean = np.mean(y_preds_samples_stacked, 0) total_uncertainty = entropy(preds_mean, axis=1) aleatoric_uncertainty = np.mean( np.concatenate([entropy(y_pred, axis=1).reshape(-1, 1) for y_pred in y_prob_samples], axis=1), axis=1) epistemic_uncertainty = total_uncertainty - aleatoric_uncertainty return total_uncertainty, aleatoric_uncertainty, epistemic_uncertainty def multiclass_brier_score(y_true, y_prob): """Brier score for multi-class. Args: y_true: array-like of shape (n_samples,) ground truth labels. y_prob: array-like of shape (n_samples, n_classes). Probability scores from the base model. Returns: float: Brier score. """ assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)" y_target = np.zeros_like(y_prob) y_target[:, y_true] = 1.0 return np.mean(np.sum((y_target - y_prob) ** 2, axis=1)) def area_under_risk_rejection_rate_curve(y_true, y_prob, y_pred=None, selection_scores=None, risk_func=accuracy_score, attributes=None, num_bins=10, subgroup_ids=None, return_counts=False): """ Computes risk vs rejection rate curve and the area under this curve. Similar to risk-coverage curves [3]_ where coverage instead of rejection rate is used. References: .. [3] Franc, Vojtech, and Daniel Prusa. "On discriminative learning of prediction uncertainty." In International Conference on Machine Learning, pp. 1963-1971. 2019. Args: y_true: array-like of shape (n_samples,) ground truth labels. y_prob: array-like of shape (n_samples, n_classes). Probability scores from the base model. y_pred: array-like of shape (n_samples,) predicted labels. selection_scores: scores corresponding to certainty in the predicted labels. risk_func: risk function under consideration. attributes: (optional) if risk function is a fairness metric also pass the protected attribute name. num_bins: number of bins. subgroup_ids: (optional) selectively compute risk on a subgroup of the samples specified by subgroup_ids. return_counts: set to True to return counts also. Returns: float or tuple: - aurrrc (float): area under risk rejection rate curve. - rejection_rates (list): rejection rates for each bin (returned only if return_counts is True). - selection_thresholds (list): selection threshold for each bin (returned only if return_counts is True). - risks (list): risk in each bin (returned only if return_counts is True). """ if selection_scores is None: assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)" selection_scores = y_prob[np.arange(y_prob.shape[0]), np.argmax(y_prob, axis=1)] if y_pred is None: assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)" y_pred = np.argmax(y_prob, axis=1) order = np.argsort(selection_scores)[::-1] rejection_rates = [] selection_thresholds = [] risks = [] for bin_id in range(num_bins): samples_in_bin = len(y_true) // num_bins selection_threshold = selection_scores[order[samples_in_bin * (bin_id+1)-1]] selection_thresholds.append(selection_threshold) ids = selection_scores >= selection_threshold if sum(ids) > 0: if attributes is None: if isinstance(y_true, pd.Series): y_true_numpy = y_true.values else: y_true_numpy = y_true if subgroup_ids is None: risk_value = 1.0 - risk_func(y_true_numpy[ids], y_pred[ids]) else: if sum(subgroup_ids & ids) > 0: risk_value = 1.0 - risk_func(y_true_numpy[subgroup_ids & ids], y_pred[subgroup_ids & ids]) else: risk_value = 0.0 else: risk_value = risk_func(y_true.iloc[ids], y_pred[ids], prot_attr=attributes) else: risk_value = 0.0 risks.append(risk_value) rejection_rates.append(1.0 - 1.0 * sum(ids) / len(y_true)) aurrrc = np.nanmean(risks) if not return_counts: return aurrrc else: return aurrrc, rejection_rates, selection_thresholds, risks def expected_calibration_error(y_true, y_prob, y_pred=None, num_bins=10, return_counts=False): """ Computes the reliability curve and the expected calibration error [1]_ . References: .. [1] Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger; Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1321-1330, 2017. Args: y_true: array-like of shape (n_samples,) ground truth labels. y_prob: array-like of shape (n_samples, n_classes). Probability scores from the base model. y_pred: array-like of shape (n_samples,) predicted labels. num_bins: number of bins. return_counts: set to True to return counts also. Returns: float or tuple: - ece (float): expected calibration error. - confidences_in_bins: average confidence in each bin (returned only if return_counts is True). - accuracies_in_bins: accuracy in each bin (returned only if return_counts is True). - frac_samples_in_bins: fraction of samples in each bin (returned only if return_counts is True). """ assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)" num_samples, num_classes = y_prob.shape top_scores = np.max(y_prob, axis=1) if y_pred is None: y_pred = np.argmax(y_prob, axis=1) if num_classes == 2: bins_edges = np.histogram_bin_edges([], bins=num_bins, range=(0.5, 1.0)) else: bins_edges = np.histogram_bin_edges([], bins=num_bins, range=(0.0, 1.0)) non_boundary_bin_edges = bins_edges[1:-1] bin_centers = (bins_edges[1:] + bins_edges[:-1])/2 sample_bin_ids = np.digitize(top_scores, non_boundary_bin_edges) num_samples_in_bins = np.zeros(num_bins) accuracies_in_bins = np.zeros(num_bins) confidences_in_bins = np.zeros(num_bins) for bin in range(num_bins): num_samples_in_bins[bin] = len(y_pred[sample_bin_ids == bin]) if num_samples_in_bins[bin] > 0: accuracies_in_bins[bin] = np.sum(y_true[sample_bin_ids == bin] == y_pred[sample_bin_ids == bin]) / num_samples_in_bins[bin] confidences_in_bins[bin] = np.sum(top_scores[sample_bin_ids == bin]) / num_samples_in_bins[bin] ece = np.sum( num_samples_in_bins * np.abs(accuracies_in_bins - confidences_in_bins) / num_samples ) frac_samples_in_bins = num_samples_in_bins / num_samples if not return_counts: return ece else: return ece, confidences_in_bins, accuracies_in_bins, frac_samples_in_bins, bin_centers def compute_classification_metrics(y_true, y_prob, option='all'): """ Computes the metrics specified in the option which can be string or a list of strings. Default option `all` computes the [aurrrc, ece, auroc, nll, brier, accuracy] metrics. Args: y_true: array-like of shape (n_samples,) ground truth labels. y_prob: array-like of shape (n_samples, n_classes). Probability scores from the base model. option: string or list of string contained the name of the metrics to be computed. Returns: dict: a dictionary containing the computed metrics. """ results = {} if not isinstance(option, list): if option == "all": option_list = ["aurrrc", "ece", "auroc", "nll", "brier", "accuracy"] else: option_list = [option] if "aurrrc" in option_list: results["aurrrc"] = area_under_risk_rejection_rate_curve(y_true=y_true, y_prob=y_prob) if "ece" in option_list: results["ece"] = expected_calibration_error(y_true=y_true, y_prob=y_prob) if "auroc" in option_list: results["auroc"], _ = roc_auc_score(y_true=y_true, y_score=y_prob) if "nll" in option_list: results["nll"] = log_loss(y_true=y_true, y_pred=np.argmax(y_prob, axis=1)) if "brier" in option_list: results["brier"] = multiclass_brier_score(y_true=y_true, y_prob=y_prob) if "accuracy" in option_list: results["accuracy"] = accuracy_score(y_true=y_true, y_pred=np.argmax(y_prob, axis=1)) return results def plot_reliability_diagram(y_true, y_prob, y_pred, plot_label=[""], num_bins=10): """ Plots the reliability diagram showing the calibration error for different confidence scores. Multiple curves can be plot by passing data as lists. Args: y_true: array-like or or a list of array-like of shape (n_samples,) ground truth labels. y_prob: array-like or or a list of array-like of shape (n_samples, n_classes). Probability scores from the base model. y_pred: array-like or or a list of array-like of shape (n_samples,) predicted labels. plot_label: (optional) list of names identifying each curve. num_bins: number of bins. Returns: tuple: - ece_list: ece: list containing expected calibration error for each curve. - accuracies_in_bins_list: list containing binned average accuracies for each curve. - frac_samples_in_bins_list: list containing binned sample frequencies for each curve. - confidences_in_bins_list: list containing binned average confidence for each curve. """ import matplotlib.pyplot as plt if not isinstance(y_true, list): y_true, y_prob, y_pred = [y_true], [y_prob], [y_pred] if len(plot_label) != len(y_true): raise ValueError('y_true and plot_label should be of same length.') ece_list = [] accuracies_in_bins_list = [] frac_samples_in_bins_list = [] confidences_in_bins_list = [] for idx in range(len(plot_label)): ece, confidences_in_bins, accuracies_in_bins, frac_samples_in_bins, bins = expected_calibration_error(y_true[idx], y_prob[idx], y_pred[idx], num_bins=num_bins, return_counts=True) ece_list.append(ece) accuracies_in_bins_list.append(accuracies_in_bins) frac_samples_in_bins_list.append(frac_samples_in_bins) confidences_in_bins_list.append(confidences_in_bins) fig = plt.figure(figsize=(12, 5)) plt.subplot(1, 2, 1) for idx in range(len(plot_label)): plt.plot(bins, frac_samples_in_bins_list[idx], 'o-', label=plot_label[idx]) plt.title("Confidence Histogram") plt.xlabel("Confidence") plt.ylabel("Fraction of Samples") plt.grid() plt.ylim([0.0, 1.0]) plt.legend() plt.subplot(1, 2, 2) for idx in range(len(plot_label)): plt.plot(bins, accuracies_in_bins_list[idx], 'o-', label="{} ECE = {:.2f}".format(plot_label[idx], ece_list[idx])) plt.plot(np.linspace(0, 1, 50), np.linspace(0, 1, 50), 'b.', label="Perfect Calibration") plt.title("Reliability Plot") plt.xlabel("Confidence") plt.ylabel("Accuracy") plt.grid() plt.legend() plt.show() return ece_list, accuracies_in_bins_list, frac_samples_in_bins_list, confidences_in_bins_list def plot_risk_vs_rejection_rate(y_true, y_prob, y_pred, selection_scores=None, plot_label=[""], risk_func=None, attributes=None, num_bins=10, subgroup_ids=None): """ Plots the risk vs rejection rate curve showing the risk for different rejection rates. Multiple curves can be plot by passing data as lists. Args: y_true: array-like or or a list of array-like of shape (n_samples,) ground truth labels. y_prob: array-like or or a list of array-like of shape (n_samples, n_classes). Probability scores from the base model. y_pred: array-like or or a list of array-like of shape (n_samples,) predicted labels. selection_scores: ndarray or a list of ndarray containing scores corresponding to certainty in the predicted labels. risk_func: risk function under consideration. attributes: (optional) if risk function is a fairness metric also pass the protected attribute name. num_bins: number of bins. subgroup_ids: (optional) ndarray or a list of ndarray containing subgroup_ids to selectively compute risk on a subgroup of the samples specified by subgroup_ids. Returns: tuple: - aurrrc_list: list containing the area under risk rejection rate curves. - rejection_rate_list: list containing the binned rejection rates. - selection_thresholds_list: list containing the binned selection thresholds. - risk_list: list containing the binned risks. """ import matplotlib.pyplot as plt if not isinstance(y_true, list): y_true, y_prob, y_pred, selection_scores, subgroup_ids = [y_true], [y_prob], [y_pred], [selection_scores], [subgroup_ids] if len(plot_label) != len(y_true): raise ValueError('y_true and plot_label should be of same length.') aurrrc_list = [] rejection_rate_list = [] risk_list = [] selection_thresholds_list = [] for idx in range(len(plot_label)): aursrc, rejection_rates, selection_thresholds, risks = area_under_risk_rejection_rate_curve( y_true[idx], y_prob[idx], y_pred[idx], selection_scores=selection_scores[idx], risk_func=risk_func, attributes=attributes, num_bins=num_bins, subgroup_ids=subgroup_ids[idx], return_counts=True ) aurrrc_list.append(aursrc) rejection_rate_list.append(rejection_rates) risk_list.append(risks) selection_thresholds_list.append(selection_thresholds) plt.figure(figsize=(12, 5)) plt.subplot(1, 2, 1) for idx in range(len(plot_label)): plt.plot(rejection_rate_list[idx], risk_list[idx], label="{} AURRRC={:.5f}".format(plot_label[idx], aurrrc_list[idx])) plt.legend(loc="best") plt.xlabel("Rejection Rate") if risk_func is None: ylabel = "Prediction Error Rate" else: if 'accuracy' in risk_func.__name__: ylabel = "1.0 - " + risk_func.__name__ else: ylabel = risk_func.__name__ plt.ylabel(ylabel) plt.title("Risk vs Rejection Rate Plot") plt.grid() plt.subplot(1, 2, 2) for idx in range(len(plot_label)): plt.plot(selection_thresholds_list[idx], risk_list[idx], label="{}".format(plot_label[idx])) plt.legend(loc="best") plt.xlabel("Selection Threshold") if risk_func is None: ylabel = "Prediction Error Rate" else: if 'accuracy' in risk_func.__name__: ylabel = "1.0 - " + risk_func.__name__ else: ylabel = risk_func.__name__ plt.ylabel(ylabel) plt.title("Risk vs Selection Threshold Plot") plt.grid() plt.show() return aurrrc_list, rejection_rate_list, selection_thresholds_list, risk_list from .classification_metrics import expected_calibration_error, area_under_risk_rejection_rate_curve, \ compute_classification_metrics, entropy_based_uncertainty_decomposition from .regression_metrics import picp, mpiw, compute_regression_metrics, plot_uncertainty_distribution, \ plot_uncertainty_by_feature, plot_picp_by_feature from .uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve from .uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve from copy import deepcopy import matplotlib.pyplot as plt import numpy as np from scipy.integrate import simps, trapz from sklearn.isotonic import IsotonicRegression DEFAULT_X_AXIS_NAME = 'excess' DEFAULT_Y_AXIS_NAME = 'missrate' class UncertaintyCharacteristicsCurve: """ Class with main functions of the Uncertainty Characteristics Curve (UCC). """ def __init__(self, normalize=True, precompute_bias_data=True): """ :param normalize: set initial axes normalization flag (can be changed via set_coordinates()) :param precompute_bias_data: if True, fit() will compute statistics necessary to generate bias-based UCCs (in addition to the scale-based ones). Skipping this precomputation may speed up the fit() call if bias-based UCC is not needed. """ self.axes_name2idx = {"missrate": 1, "bandwidth": 2, "excess": 3, "deficit": 4} self.axes_idx2descr = {1: "Missrate", 2: "Bandwidth", 3: "Excess", 4: "Deficit"} self.x_axis_idx = None self.y_axis_idx = None self.norm_x_axis = False self.norm_y_axis = False self.std_unit = None self.normalize = normalize self.d = None self.gt = None self.lb = None self.ub = None self.precompute_bias_data = precompute_bias_data self.set_coordinates(x_axis_name=DEFAULT_X_AXIS_NAME, y_axis_name=DEFAULT_Y_AXIS_NAME, normalize=normalize) def set_coordinates(self, x_axis_name=None, y_axis_name=None, normalize=None): """ Assigns user-specified type to the axes and normalization behavior (sticky). :param x_axis_name: None-> unchanged, or name from self.axes_name2idx :param y_axis_name: ditto :param normalize: True/False will activate/deactivate norming for specified axes. Behavior for Axes_name that are None will not be changed. Value None will leave norm status unchanged. Note, axis=='missrate' will never get normalized, even with normalize == True :return: none """ normalize = self.normalize if normalize is None else normalize if x_axis_name is None and self.x_axis_idx is None: raise ValueError("ERROR(UCC): x-axis has not been defined.") if y_axis_name is None and self.y_axis_idx is None: raise ValueError("ERROR(UCC): y-axis has not been defined.") if x_axis_name is None and y_axis_name is None and normalize is not None: # just set normalization on/off for both axes and return self.norm_x_axis = False if x_axis_name == 'missrate' else normalize self.norm_y_axis = False if y_axis_name == 'missrate' else normalize return if x_axis_name is not None: self.x_axis_idx = self.axes_name2idx[x_axis_name] self.norm_x_axis = False if x_axis_name == 'missrate' else normalize if y_axis_name is not None: self.y_axis_idx = self.axes_name2idx[y_axis_name] self.norm_y_axis = False if y_axis_name == 'missrate' else normalize def set_std_unit(self, std_unit=None): """ Sets the UCC's unit to be used when displaying normalized axes. :param std_unit: if None, the unit will be calculated as stddev of the ground truth data (ValueError raised if data has not been set at this point) or set to the user-specified value. :return: """ if std_unit is None: # set it to stddev of data if self.gt is None: raise ValueError("ERROR(UCC): No data specified - cannot set stddev unit.") self.std_unit = np.std(self.gt) if np.isclose(self.std_unit, 0.): print("WARN(UCC): data-based stddev is zero - resetting axes unit to 1.") self.std_unit = 1. else: self.std_unit = float(std_unit) def fit(self, X, gt): """ Calculates internal arrays necessary for other methods (plotting, auc, cost minimization). Re-entrant. :param X: [numsamples, 3] numpy matrix, or list of numpy matrices. Col 1: predicted values Col 2: lower band (deviate) wrt predicted value (always positive) Col 3: upper band wrt predicted value (always positive) If list is provided, all methods will output corresponding metrics as lists as well! :param gt: Ground truth array (i.e.,the 'actual' values corresponding to predictions in X :return: self """ if not isinstance(X, list): X = [X] newX = [] for x in X: assert (isinstance(x, np.ndarray) and len(x.shape) == 2 and x.shape[1] == 3 and x.shape[0] == len(gt)) newX.append(self._sanitize_input(x)) self.d = [gt - x[:, 0] for x in newX] self.lb = [x[:, 1] for x in newX] self.ub = [x[:, 2] for x in newX] self.gt = gt self.set_std_unit() self.plotdata_for_scale = [] self.plotdata_for_bias = [] # precompute plotdata: for i in range(len(self.d)): self.plotdata_for_scale.append(self._calc_plotdata(self.d[i], self.lb[i], self.ub[i], vary_bias=False)) if self.precompute_bias_data: self.plotdata_for_bias.append(self._calc_plotdata(self.d[i], self.lb[i], self.ub[i], vary_bias=True)) return self def minimize_cost(self, x_axis_cost=.5, y_axis_cost=.5, augment_cost_by_normfactor=True, search=('scale', 'bias')): """ Find minima of a linear cost function for each component. Cost function C = x_axis_cost * x_axis_value + y_axis_cost * y_axis_value. A minimum can occur in the scale-based or bias-based UCC (this can be constrained by the 'search' arg). The function returns a 'recipe' how to achieve the corresponding minimum, for each component. :param x_axis_cost: weight of one unit on x_axis :param y_axis_cost: weight of one unit on y_axis :param augment_cost_by_normfactor: when False, the cost multipliers will apply as is. If True, they will be pre-normed by the corresponding axis norm (where applicable), to account for range differences between axes. :param search: list of types over which minimization is to be performed, valid elements are 'scale' and 'bias'. :return: list of dicts - one per component, or a single dict, if there is only one component. Dict keys are - 'operation': can be 'bias' (additive) or 'scale' (multiplicative), 'modvalue': value to multiply by or to add to error bars to achieve the minimum, 'new_x'/'new_y': new coordinates (operating point) with that minimum, 'cost': new cost at minimum point, 'original_cost': original cost (original operating point). """ if self.d is None: raise ValueError("ERROR(UCC): call fit() prior to using this method.") if augment_cost_by_normfactor: if self.norm_x_axis: x_axis_cost /= self.std_unit if self.norm_y_axis: y_axis_cost /= self.std_unit print("INFO(UCC): Pre-norming costs by corresp. std deviation: new x_axis_cost = %.4f, y_axis_cost = %.4f" % (x_axis_cost, y_axis_cost)) if isinstance(search, tuple): search = list(search) if not isinstance(search, list): search = [search] min_costs = [] for d in range(len(self.d)): # original OP cost m, b, e, df = self._calc_missrate_bandwidth_excess_deficit(self.d[d], self.lb[d], self.ub[d]) original_cost = x_axis_cost * [0., m, b, e, df][self.x_axis_idx] + y_axis_cost * [0., m, b, e, df][ self.y_axis_idx] plotdata = self.plotdata_for_scale[d] cost_scale, minidx_scale = self._find_min_cost_in_component(plotdata, self.x_axis_idx, self.y_axis_idx, x_axis_cost, y_axis_cost) mcf_scale_multiplier = plotdata[minidx_scale][0] mcf_scale_x = plotdata[minidx_scale][self.x_axis_idx] mcf_scale_y = plotdata[minidx_scale][self.y_axis_idx] if 'bias' in search: if not self.precompute_bias_data: raise ValueError( "ERROR(UCC): Cannot perform minimization - instantiated without bias data computation") plotdata = self.plotdata_for_bias[d] cost_bias, minidx_bias = self._find_min_cost_in_component(plotdata, self.x_axis_idx, self.y_axis_idx, x_axis_cost, y_axis_cost) mcf_bias_add = plotdata[minidx_bias][0] mcf_bias_x = plotdata[minidx_bias][self.x_axis_idx] mcf_bias_y = plotdata[minidx_bias][self.y_axis_idx] if 'bias' in search and 'scale' in search: if cost_bias < cost_scale: min_costs.append({'operation': 'bias', 'cost': cost_bias, 'modvalue': mcf_bias_add, 'new_x': mcf_bias_x, 'new_y': mcf_bias_y, 'original_cost': original_cost}) else: min_costs.append({'operation': 'scale', 'cost': cost_scale, 'modvalue': mcf_scale_multiplier, 'new_x': mcf_scale_x, 'new_y': mcf_scale_y, 'original_cost': original_cost}) elif 'scale' in search: min_costs.append({'operation': 'scale', 'cost': cost_scale, 'modvalue': mcf_scale_multiplier, 'new_x': mcf_scale_x, 'new_y': mcf_scale_y, 'original_cost': original_cost}) elif 'bias' in search: min_costs.append({'operation': 'bias', 'cost': cost_bias, 'modvalue': mcf_bias_add, 'new_x': mcf_bias_x, 'new_y': mcf_bias_y, 'original_cost': original_cost}) else: raise ValueError("(ERROR): Unknown search element (%s) requested." % ",".join(search)) if len(min_costs) < 2: return min_costs[0] else: return min_costs def get_specific_operating_point(self, req_x_axis_value=None, req_y_axis_value=None, req_critical_value=None, vary_bias=False): """ Finds corresponding operating point on the current UCC, given a point on either x or y axis. Returns a list of recipes how to achieve the point (x,y), for each component. If there is only one component, returns a single recipe dict. :param req_x_axis_value: requested x value on UCC (normalization status is taken from current display) :param req_y_axis_value: requested y value on UCC (normalization status is taken from current display) :param vary_bias: set to True when referring to bias-induced UCC (scale UCC default) :return: list of dicts (recipes), or a single dict """ if self.d is None: raise ValueError("ERROR(UCC): call fit() prior to using this method.") if np.sum([req_x_axis_value is not None, req_y_axis_value is not None, req_critical_value is not None]) != 1: raise ValueError("ERROR(UCC): exactly one axis value must be requested at a time.") if vary_bias and not self.precompute_bias_data: raise ValueError("ERROR(UCC): Cannot vary bias - instantiated without bias data computation") xnorm = self.std_unit if self.norm_x_axis else 1. ynorm = self.std_unit if self.norm_y_axis else 1. recipe = [] for dc in range(len(self.d)): plotdata = self.plotdata_for_bias[dc] if vary_bias else self.plotdata_for_scale[dc] if req_x_axis_value is not None: tgtidx = self.x_axis_idx req_value = req_x_axis_value * xnorm elif req_y_axis_value is not None: tgtidx = self.y_axis_idx req_value = req_y_axis_value * ynorm elif req_critical_value is not None: req_value = req_critical_value tgtidx = 0 # first element in plotdata is always the critical value (scale of bias) else: raise RuntimeError("Unhandled case") closestidx = np.argmin(np.asarray([np.abs(p[tgtidx] - req_value) for p in plotdata])) recipe.append({'operation': ('bias' if vary_bias else 'scale'), 'modvalue': plotdata[closestidx][0], 'new_x': plotdata[closestidx][self.x_axis_idx] / xnorm, 'new_y': plotdata[closestidx][self.y_axis_idx] / ynorm}) if len(recipe) < 2: return recipe[0] else: return recipe def _find_min_cost_in_component(self, plotdata, idx1, idx2, cost1, cost2): """ Find s minimum cost function value and corresp. position index in plotdata :param plotdata: liste of tuples :param idx1: idx of x-axis item within the tuple :param idx2: idx of y-axis item within the tuple :param cost1: cost factor for x-axis unit :param cost2: cost factor for y-axis unit :return: min cost value, index within plotdata where minimum occurs """ raw = [cost1 * i[idx1] + cost2 * i[idx2] for i in plotdata] minidx = np.argmin(raw) return raw[minidx], minidx def _sanitize_input(self, x): """ Replaces problematic values in input data (e.g, zero error bars) :param x: single matrix of input data [n, 3] :return: sanitized version of x """ if np.isclose(np.sum(x[:, 1]), 0.): raise ValueError("ERROR(UCC): Provided lower bands are all zero.") if np.isclose(np.sum(x[:, 2]), 0.): raise ValueError("ERROR(UCC): Provided upper bands are all zero.") for i in [1, 2]: if any(np.isclose(x[:, i], 0.)): print("WARN(UCC): some band values are 0. - REPLACING with positive minimum") m = np.min(x[x[:, i] > 0, i]) x = np.where(np.isclose(x, 0.), m, x) return x def _calc_avg_excess(self, d, lb, ub): """ Excess is amount an error bar overshoots actual :param d: pred-actual array :param lb: lower band :param ub: upper band :return: average excess over array """ excess = np.zeros(d.shape) posidx = np.where(d >= 0)[0] excess[posidx] = np.where(ub[posidx] - d[posidx] < 0., 0., ub[posidx] - d[posidx]) negidx = np.where(d < 0)[0] excess[negidx] = np.where(lb[negidx] + d[negidx] < 0., 0., lb[negidx] + d[negidx]) return np.mean(excess) def _calc_avg_deficit(self, d, lb, ub): """ Deficit is error bar insufficiency: bar falls short of actual :param d: pred-actual array :param lb: lower band :param ub: upper band :return: average deficit over array """ deficit = np.zeros(d.shape) posidx = np.where(d >= 0)[0] deficit[posidx] = np.where(- ub[posidx] + d[posidx] < 0., 0., - ub[posidx] + d[posidx]) negidx = np.where(d < 0)[0] deficit[negidx] = np.where(- lb[negidx] - d[negidx] < 0., 0., - lb[negidx] - d[negidx]) return np.mean(deficit) def _calc_missrate_bandwidth_excess_deficit(self, d, lb, ub, scale=1.0, bias=0.0): """ Calculates recall at a given scale/bias, average bandwidth and average excess :param d: delta :param lb: lower band :param ub: upper band :param scale: scale * (x + bias) :param bias: :return: miss rate, average bandwidth, avg excess, avg deficit """ abslband = scale * np.where((lb + bias) < 0., 0., lb + bias) absuband = scale * np.where((ub + bias) < 0., 0., ub + bias) recall = np.sum((d >= - abslband) & (d <= absuband)) / len(d) avgbandwidth = np.mean([absuband, abslband]) avgexcess = self._calc_avg_excess(d, abslband, absuband) avgdeficit = self._calc_avg_deficit(d, abslband, absuband) return 1 - recall, avgbandwidth, avgexcess, avgdeficit def _calc_plotdata(self, d, lb, ub, vary_bias=False): """ Generates data necessary for various UCC metrics. :param d: delta (predicted - actual) vector :param ub: upper uncertainty bandwidth (above predicted) :param lb: lower uncertainty bandwidth (below predicted) - all positive (bandwidth) :param vary_bias: True will switch to additive bias instead of scale :return: list. Elements are tuples (varyvalue, missrate, bandwidth, excess, deficit) """ # step 1: collect critical scale or bias values critval = [] for i in range(len(d)): if not vary_bias: if d[i] >= 0: critval.append(d[i] / ub[i]) else: critval.append(-d[i] / lb[i]) else: if d[i] >= 0: critval.append(d[i] - ub[i]) else: critval.append(-lb[i] - d[i]) critval = sorted(critval) plotdata = [] for i in range(len(critval)): if not vary_bias: missrate, bandwidth, excess, deficit = self._calc_missrate_bandwidth_excess_deficit(d, lb, ub, scale=critval[i]) else: missrate, bandwidth, excess, deficit = self._calc_missrate_bandwidth_excess_deficit(d, lb, ub, bias=critval[i]) plotdata.append((critval[i], missrate, bandwidth, excess, deficit)) return plotdata def get_AUUCC(self, vary_bias=False, aucfct="trapz", partial_x=None, partial_y=None): """ returns approximate area under the curve on current coordinates, for each component. :param vary_bias: False == varies scale, True == varies bias :param aucfct: specifies AUC integrator (can be "trapz", "simps") :param partial_x: tuple (x_min, x_max) defining interval on x to calc a a partial AUC. The interval bounds refer to axes as visualized (ie. potentially normed) :param partial_y: tuple (y_min, y_max) defining interval on y to calc a a partial AUC. partial_x must be None. :return: list of floats with AUUCCs for each input component, or a single float, if there is only 1 component. """ if self.d is None: raise ValueError("ERROR(UCC): call fit() prior to using this method.") if vary_bias and not self.precompute_bias_data: raise ValueError("ERROR(UCC): Cannot vary bias - instantiated without bias data computation") if partial_x is not None and partial_y is not None: raise ValueError("ERROR(UCC): partial_x and partial_y can not be specified at the same time.") assert(partial_x is None or (isinstance(partial_x, tuple) and len(partial_x)==2)) assert(partial_y is None or (isinstance(partial_y, tuple) and len(partial_y)==2)) # find starting point (where the x axis value starts to actually change) rv = [] # do this for individual streams xind = self.x_axis_idx aucfct = simps if aucfct == "simps" else trapz for s in range(len(self.d)): plotdata = self.plotdata_for_bias[s] if vary_bias else self.plotdata_for_scale[s] prev = plotdata[0][xind] t = 1 cval = plotdata[t][xind] while cval == prev and t < len(plotdata) - 1: t += 1 prev = cval cval = plotdata[t][xind] startt = t - 1 # from here, it's a valid function endtt = len(plotdata) if startt >= endtt - 2: rvs = 0. # no area else: xnorm = self.std_unit if self.norm_x_axis else 1. ynorm = self.std_unit if self.norm_y_axis else 1. y=[(plotdata[i][self.y_axis_idx]) / ynorm for i in range(startt, endtt)] x=[(plotdata[i][self.x_axis_idx]) / xnorm for i in range(startt, endtt)] if partial_x is not None: from_i = self._find_closest_index(partial_x[0], x) to_i = self._find_closest_index(partial_x[1], x) + 1 elif partial_y is not None: from_i = self._find_closest_index(partial_y[0], y) to_i = self._find_closest_index(partial_y[1], y) if from_i > to_i: # y is in reverse order from_i, to_i = to_i, from_i to_i += 1 # as upper bound in array indexing else: from_i = 0 to_i = len(x) to_i = min(to_i, len(x)) if to_i < from_i: raise ValueError("ERROR(UCC): Failed to find an appropriate partial-AUC interval in the data.") if to_i - from_i < 2: raise RuntimeError("ERROR(UCC): There are too few samples (1) in the partial-AUC interval specified") rvs = aucfct(x=x[from_i:to_i], y=y[from_i:to_i]) rv.append(rvs) if len(rv) < 2: return rv[0] else: return rv @ staticmethod def _find_closest_index(value, array): """ Returns an index of the 'array' element closest in value to 'value' :param value: :param array: :return: """ return np.argmin(np.abs(np.asarray(array)-value)) def _get_single_OP(self, d, lb, ub, scale=1., bias=0.): """ Returns Operating Point for original input data, on coordinates currently set up, given a scale/bias. :param scale: :param bias: :return: single tuple (x point, y point, unit of x, unit of y) """ xnorm = self.std_unit if self.norm_x_axis else 1. ynorm = self.std_unit if self.norm_y_axis else 1. auxop = self._calc_missrate_bandwidth_excess_deficit(d, lb, ub, scale=scale, bias=bias) op = [0.] + [i for i in auxop] # mimic plotdata (first element ignored here) return (op[self.x_axis_idx] / xnorm, op[self.y_axis_idx] / ynorm, xnorm, ynorm) def get_OP(self, scale=1., bias=0.): """ Returns all Operating Points for original input data, on coordinates currently set up, given a scale/bias. :param scale: :param bias: :return: list of tuples (x point, y point, unit of x, unit of y) or a single tuple if there is only 1 component. """ if self.d is None: raise ValueError("ERROR(UCC): call fit() prior to using this method.") op = [] for dc in range(len(self.d)): op.append(self._get_single_OP(self.d[dc], self.lb[dc], self.ub[dc], scale=scale, bias=bias)) if len(op) < 2: return op[0] else: return op def plot_UCC(self, titlestr='', syslabel='model', outfn=None, vary_bias=False, markers=None, xlim=None, ylim=None, **kwargs): """ Will plot/display the UCC based on current data and coordinates. Multiple curves will be shown if there are multiple data components (via fit()) :param titlestr: Plot title string :param syslabel: list is label strings to appear in the plot legend. Can be single, if one component. :param outfn: base name of an image file to be created (will append .png before creating) :param vary_bias: True will switch to varying additive bias (default is multiplicative scale) :param markers: None or a list of marker styles to be used for each curve. List must be same or longer than number of components. Markers can be one among these ['o', 's', 'v', 'D', '+']. :param xlim: tuples or lists of specifying the range for the x axis, or None (auto) :param ylim: tuples or lists of specifying the range for the y axis, or None (auto) :param `**kwargs`: Additional arguments passed to the main plot call. :return: list of areas under the curve (or single area, if one data component) list of operating points (or single op): format of an op is tuple (xaxis value, yaxis value, xunit, yunit) """ if self.d is None: raise ValueError("ERROR(UCC): call fit() prior to using this method.") if vary_bias and not self.precompute_bias_data: raise ValueError("ERROR(UCC): Cannot vary bias - instantiated without bias data computation") if not isinstance(syslabel, list): syslabel = [syslabel] assert (len(syslabel) == len(self.d)) assert (markers is None or (isinstance(markers, list) and len(markers) >= len(self.d))) # main plot of (possibly multiple) datasets plt.figure() xnorm = self.std_unit if self.norm_x_axis else 1. ynorm = self.std_unit if self.norm_y_axis else 1. op_info = [] auucc = self.get_AUUCC(vary_bias=vary_bias) auucc = [auucc] if not isinstance(auucc, list) else auucc for s in range(len(self.d)): # original operating point x_op, y_op, x_unit, y_unit = self._get_single_OP(self.d[s], self.lb[s], self.ub[s]) op_info.append((x_op, y_op, x_unit, y_unit)) # display chart plotdata = self.plotdata_for_scale[s] if not vary_bias else self.plotdata_for_bias[s] axisX_data = [i[self.x_axis_idx] / xnorm for i in plotdata] axisY_data = [i[self.y_axis_idx] / ynorm for i in plotdata] marker = None if markers is not None: marker = markers[s] p = plt.plot(axisX_data, axisY_data, label=syslabel[s] + (" (AUC=%.3f)" % auucc[s]), marker=marker, **kwargs) if s + 1 == len(self.d): oplab = 'OP' else: oplab = None plt.plot(x_op, y_op, marker='o', color=p[0].get_color(), label=oplab, markerfacecolor='w', markeredgewidth=1.5, markeredgecolor=p[0].get_color()) axisX_label = self.axes_idx2descr[self.x_axis_idx] axisY_label = self.axes_idx2descr[self.y_axis_idx] axisX_units = "(raw)" if np.isclose(xnorm, 1.0) else "[in std deviations]" axisY_units = "(raw)" if np.isclose(ynorm, 1.0) else "[in std deviations]" axisX_label += ' ' + axisX_units axisY_label += ' ' + axisY_units if ylim is not None: plt.ylim(ylim) if xlim is not None: plt.xlim(xlim) plt.xlabel(axisX_label) plt.ylabel(axisY_label) plt.legend() plt.title(titlestr) plt.grid() if outfn is None: plt.show() else: plt.savefig(outfn) if len(auucc) < 2: auucc = auucc[0] op_info = op_info[0] return auucc, op_info import torch import torch.nn.functional as F from uq360.models.noise_models.heteroscedastic_noise_models import GaussianNoise class GaussianNoiseMLPNet(torch.nn.Module): def __init__(self, num_features, num_outputs, num_hidden): super(GaussianNoiseMLPNet, self).__init__() self.fc = torch.nn.Linear(num_features, num_hidden) self.fc_mu = torch.nn.Linear(num_hidden, num_outputs) self.fc_log_var = torch.nn.Linear(num_hidden, num_outputs) self.noise_layer = GaussianNoise() def forward(self, x): x = F.relu(self.fc(x)) mu = self.fc_mu(x) log_var = self.fc_log_var(x) return mu, log_var def loss(self, y_true=None, mu_pred=None, log_var_pred=None): return self.noise_layer.loss(y_true, mu_pred, log_var_pred, reduce_mean=True) """ Contains implementations of various utilities used by Horseshoe Bayesian layers """ import numpy as np import torch from torch.nn import Parameter td = torch.distributions gammaln = torch.lgamma def diag_gaussian_entropy(log_std, D): return 0.5 * D * (1.0 + torch.log(2 * np.pi)) + torch.sum(log_std) def inv_gamma_entropy(a, b): return torch.sum(a + torch.log(b) + torch.lgamma(a) - (1 + a) * torch.digamma(a)) def log_normal_entropy(log_std, mu, D): return torch.sum(log_std + mu + 0.5) + (D / 2) * np.log(2 * np.pi) class InvGammaHalfCauchyLayer(torch.nn.Module): """ Uses the inverse Gamma parameterization of the half-Cauchy distribution. a ~ C^+(0, b) <==> a^2 ~ IGamma(0.5, 1/lambda), lambda ~ IGamma(0.5, 1/b^2), where lambda is an auxiliary latent variable. Uses a factorized variational approximation q(ln a^2)q(lambda) = N(mu, sigma^2) IGamma(ahat, bhat). This layer places a half Cauchy prior on the scales of each output node of the layer. """ def __init__(self, out_features, b): """ :param out_fatures: number of output nodes in the layer. :param b: scale of the half Cauchy """ super(InvGammaHalfCauchyLayer, self).__init__() self.b = b self.out_features = out_features # variational parameters for q(ln a^2) self.mu = Parameter(torch.FloatTensor(out_features)) self.log_sigma = Parameter(torch.FloatTensor(out_features)) # self.log_sigma = torch.FloatTensor(out_features) # variational parameters for q(lambda). These will be updated via fixed point updates, hence not parameters. self.ahat = torch.FloatTensor([1.]) # The posterior parameter is always 1. self.bhat = torch.ones(out_features) * (1.0 / self.b ** 2) self.const = torch.FloatTensor([0.5]) self.initialize_from_prior() def initialize_from_prior(self): """ Initializes variational parameters by sampling from the prior. """ # sample from half cauchy and log to initialize the mean of the log normal sample = np.abs(self.b * (np.random.randn(self.out_features) / np.random.randn(self.out_features))) self.mu.data = torch.FloatTensor(np.log(sample)) self.log_sigma.data = torch.FloatTensor(np.random.randn(self.out_features) - 10.) def expectation_wrt_prior(self): """ Computes E[ln p(a^2 | lambda)] + E[ln p(lambda)] """ expected_a_given_lambda = -gammaln(self.const) - 0.5 * (torch.log(self.bhat) - torch.digamma(self.ahat)) + ( -0.5 - 1.) * self.mu - torch.exp(-self.mu + 0.5 * self.log_sigma.exp() ** 2) * (self.ahat / self.bhat) expected_lambda = -gammaln(self.const) - 2 * 0.5 * np.log(self.b) + (-self.const - 1.) * ( torch.log(self.bhat) - torch.digamma(self.ahat)) - (1. / self.b ** 2) * (self.ahat / self.bhat) return torch.sum(expected_a_given_lambda) + torch.sum(expected_lambda) def entropy(self): """ Computes entropy of q(ln a^2) and q(lambda) """ return self.entropy_lambda() + self.entropy_a2() def entropy_lambda(self): return inv_gamma_entropy(self.ahat, self.bhat) def entropy_a2(self): return log_normal_entropy(self.log_sigma, self.mu, self.out_features) def kl(self): """ Computes KL(q(ln(a^2)q(lambda) || IG(a^2 | 0.5, 1/lambda) IG(lambda | 0.5, 1/b^2)) """ return -self.expectation_wrt_prior() - self.entropy() def fixed_point_updates(self): # update lambda moments self.bhat = torch.exp(-self.mu + 0.5 * self.log_sigma.exp() ** 2) + (1. / self.b ** 2) class InvGammaLayer(torch.nn.Module): """ Approximates the posterior of c^2 with prior IGamma(c^2 | a , b) using a log Normal approximation q(ln c^2) = N(mu, sigma^2) """ def __init__(self, a, b, out_features=1): super(InvGammaLayer, self).__init__() self.a = torch.FloatTensor([a]) self.b = torch.FloatTensor([b]) # variational parameters for q(ln c^2) self.mu = Parameter(torch.FloatTensor(out_features)) self.log_sigma = Parameter(torch.FloatTensor(out_features)) self.out_features = out_features self.initialize_from_prior() def initialize_from_prior(self): """ Initializes variational parameters by sampling from the prior. """ self.mu.data = torch.log(self.b / (self.a + 1) * torch.ones(self.out_features)) # initialize at the mode self.log_sigma.data = torch.FloatTensor(np.random.randn(self.out_features) - 10.) def expectation_wrt_prior(self): """ Computes E[ln p(c^2 | a, b)] """ # return self.c_a * np.log(self.c_b) - gammaln(self.c_a) + ( # - self.c_a - 1) * c_mu - self.c_b * Ecinv return self.a * torch.log(self.b) - gammaln(self.a) + (- self.a - 1) \ * self.mu - self.b * torch.exp(-self.mu + 0.5 * self.log_sigma.exp() ** 2) def entropy(self): return log_normal_entropy(self.log_sigma, self.mu, 1) def kl(self): """ Computes KL(q(ln(c^2) || IG(c^2 | a, b)) """ return -self.expectation_wrt_prior().sum() - self.entropy() """ Contains implementations of various Bayesian layers """ import numpy as np import torch import torch.nn.functional as F from torch.nn import Parameter from uq360.models.bayesian_neural_networks.layer_utils import InvGammaHalfCauchyLayer, InvGammaLayer td = torch.distributions def reparam(mu, logvar, do_sample=True, mc_samples=1): if do_sample: std = torch.exp(0.5 * logvar) eps = torch.FloatTensor(std.size()).normal_() sample = mu + eps * std for _ in np.arange(1, mc_samples): sample += mu + eps * std return sample / mc_samples else: return mu class BayesianLinearLayer(torch.nn.Module): """ Affine layer with N(0, v/H) or N(0, user specified v) priors on weights and fully factorized variational Gaussian approximation """ def __init__(self, in_features, out_features, cuda=False, init_weight=None, init_bias=None, prior_stdv=None): super(BayesianLinearLayer, self).__init__() self.cuda = cuda self.in_features = in_features self.out_features = out_features # weight mean params self.weights = Parameter(torch.Tensor(out_features, in_features)) self.bias = Parameter(torch.Tensor(out_features)) # weight variance params self.weights_logvar = Parameter(torch.Tensor(out_features, in_features)) self.bias_logvar = Parameter(torch.Tensor(out_features)) # numerical stability self.fudge_factor = 1e-8 if not prior_stdv: # We will use a N(0, 1/num_inputs) prior over weights self.prior_stdv = torch.FloatTensor([1. / np.sqrt(self.weights.size(1))]) else: self.prior_stdv = torch.FloatTensor([prior_stdv]) # self.prior_stdv = torch.Tensor([1. / np.sqrt(1e+3)]) self.prior_mean = torch.FloatTensor([0.]) # for Bias use a prior of N(0, 1) self.prior_bias_stdv = torch.FloatTensor([1.]) self.prior_bias_mean = torch.FloatTensor([0.]) # init params either random or with pretrained net self.init_parameters(init_weight, init_bias) def init_parameters(self, init_weight, init_bias): # init means if init_weight is not None: self.weights.data = torch.Tensor(init_weight) else: self.weights.data.normal_(0, np.float(self.prior_stdv.numpy()[0])) if init_bias is not None: self.bias.data = torch.Tensor(init_bias) else: self.bias.data.normal_(0, 1) # init variances self.weights_logvar.data.normal_(-9, 1e-2) self.bias_logvar.data.normal_(-9, 1e-2) def forward(self, x, do_sample=True, scale_variances=False): # local reparameterization trick mu_activations = F.linear(x, self.weights, self.bias) var_activations = F.linear(x.pow(2), self.weights_logvar.exp(), self.bias_logvar.exp()) if scale_variances: activ = reparam(mu_activations, var_activations.log() - np.log(self.in_features), do_sample=do_sample) else: activ = reparam(mu_activations, var_activations.log(), do_sample=do_sample) return activ def kl(self): """ KL divergence (q(W) || p(W)) :return: """ weights_logvar = self.weights_logvar kld_weights = self.prior_stdv.log() - weights_logvar.mul(0.5) + \ (weights_logvar.exp() + (self.weights.pow(2) - self.prior_mean)) / ( 2 * self.prior_stdv.pow(2)) - 0.5 kld_bias = self.prior_bias_stdv.log() - self.bias_logvar.mul(0.5) + \ (self.bias_logvar.exp() + (self.bias.pow(2) - self.prior_bias_mean)) / ( 2 * self.prior_bias_stdv.pow(2)) \ - 0.5 return kld_weights.sum() + kld_bias.sum() class HorseshoeLayer(BayesianLinearLayer): """ Uses non-centered parametrization. w_k = v*tau_k*beta_k where k indexes an output unit and w_k and beta_k are vectors of all weights incident into the unit """ def __init__(self, in_features, out_features, cuda=False, scale=1.): super(HorseshoeLayer, self).__init__(in_features, out_features) self.cuda = cuda self.in_features = in_features self.out_features = out_features self.nodescales = InvGammaHalfCauchyLayer(out_features=out_features, b=1.) self.layerscale = InvGammaHalfCauchyLayer(out_features=1, b=scale) # prior on beta is N(0, I) when employing non centered parameterization self.prior_stdv = torch.Tensor([1]) self.prior_mean = torch.Tensor([0.]) def forward(self, x, do_sample=True, debug=False, eps_scale=None, eps_w=None): # At a particular unit k, preactivation_sample = scale_sample * pre_activation_sample # sample scales scale_mean = 0.5 * (self.nodescales.mu + self.layerscale.mu) scale_var = 0.25 * (self.nodescales.log_sigma.exp() ** 2 + self.layerscale.log_sigma.exp() ** 2) scale_sample = reparam(scale_mean, scale_var.log(), do_sample=do_sample).exp() # sample preactivations mu_activations = F.linear(x, self.weights, self.bias) var_activations = F.linear(x.pow(2), self.weights_logvar.exp(), self.bias_logvar.exp()) activ_sample = reparam(mu_activations, var_activations.log(), do_sample=do_sample) return scale_sample * activ_sample def kl(self): return super(HorseshoeLayer, self).kl() + self.nodescales.kl() + self.layerscale.kl() def fixed_point_updates(self): self.nodescales.fixed_point_updates() self.layerscale.fixed_point_updates() class RegularizedHorseshoeLayer(HorseshoeLayer): """ Uses the regularized Horseshoe distribution. The regularized Horseshoe soft thresholds the tails of the Horseshoe. For all weights w_k incident upon node k in the layer we have: w_k ~ N(0, (tau_k * v)^2 I) N(0, c^2 I), c^2 ~ InverseGamma(c_a, b). c^2 controls the scale of the thresholding. As c^2 -> infinity, the regularized Horseshoe -> Horseshoe. """ def __init__(self, in_features, out_features, cuda=False, scale=1., c_a=2., c_b=6.): super(RegularizedHorseshoeLayer, self).__init__(in_features, out_features, cuda=cuda, scale=scale) self.c = InvGammaLayer(a=c_a, b=c_b) def forward(self, x, do_sample=True, **kwargs): # At a particular unit k, preactivation_sample = scale_sample * pre_activation_sample # sample regularized scales scale_mean = self.nodescales.mu + self.layerscale.mu scale_var = self.nodescales.log_sigma.exp() ** 2 + self.layerscale.log_sigma.exp() ** 2 scale_sample = reparam(scale_mean, scale_var.log(), do_sample=do_sample).exp() c_sample = reparam(self.c.mu, 2 * self.c.log_sigma, do_sample=do_sample).exp() regularized_scale_sample = (c_sample * scale_sample) / (c_sample + scale_sample) # sample preactivations mu_activations = F.linear(x, self.weights, self.bias) var_activations = F.linear(x.pow(2), self.weights_logvar.exp(), self.bias_logvar.exp()) activ_sample = reparam(mu_activations, var_activations.log(), do_sample=do_sample) return torch.sqrt(regularized_scale_sample) * activ_sample def kl(self): return super(RegularizedHorseshoeLayer, self).kl() + self.c.kl() class NodeSpecificRegularizedHorseshoeLayer(RegularizedHorseshoeLayer): """ Uses the regularized Horseshoe distribution. The regularized Horseshoe soft thresholds the tails of the Horseshoe. For all weights w_k incident upon node k in the layer we have: w_k ~ N(0, (tau_k * v)^2 I) N(0, c_k^2 I), c_k^2 ~ InverseGamma(a, b). c_k^2 controls the scale of the thresholding. As c_k^2 -> infinity, the regularized Horseshoe -> Horseshoe Note that we now have a per-node c_k. """ def __init__(self, in_features, out_features, cuda=False, scale=1., c_a=2., c_b=6.): super(NodeSpecificRegularizedHorseshoeLayer, self).__init__(in_features, out_features, cuda=cuda, scale=scale) self.c = InvGammaLayer(a=c_a, b=c_b, out_features=out_features) import numpy as np import torch from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseFixedPrecision def compute_test_ll(y_test, y_pred_samples, std_y=1.): """ Computes test log likelihoods = (1 / Ntest) * \sum_n p(y_n | x_n, D_train) :param y_test: True y :param y_pred_samples: y^s = f(x_test, w^s); w^s ~ q(w). S x Ntest, where S is the number of samples q(w) is either a trained variational posterior or an MCMC approximation to p(w | D_train) :param std_y: True std of y (assumed known) """ S, _ = y_pred_samples.shape noise = GaussianNoiseFixedPrecision(std_y=std_y) ll = noise.loss(y_pred=y_pred_samples, y_true=y_test.unsqueeze(dim=0), reduce_sum=False) ll = torch.logsumexp(ll, dim=0) - np.log(S) # mean over num samples return torch.mean(ll) # mean over test points from abc import ABC import numpy as np import torch from torch import nn from uq360.models.bayesian_neural_networks.layers import HorseshoeLayer, BayesianLinearLayer, RegularizedHorseshoeLayer from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseGammaPrecision import numpy as np td = torch.distributions class HshoeBNN(nn.Module, ABC): """ Bayesian neural network with Horseshoe layers. """ def __init__(self, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1, hshoe_scale=1e-1, use_reg_hshoe=False): if use_reg_hshoe: layer = RegularizedHorseshoeLayer else: layer = HorseshoeLayer super(HshoeBNN, self).__init__() self.num_layers = num_layers if activation_type == 'relu': # activation self.activation = nn.ReLU() elif activation_type == 'tanh': self.activation = nn.Tanh() else: print("Activation Type not supported") self.fc_hidden = [] self.fc1 = layer(ip_dim, num_nodes, scale=hshoe_scale) for _ in np.arange(self.num_layers - 1): self.fc_hidden.append(layer(num_nodes, num_nodes)) self.fc_out = BayesianLinearLayer(num_nodes, op_dim) self.noise_layer = None def forward(self, x, do_sample=True): x = self.fc1(x, do_sample=do_sample) x = self.activation(x) for layer in self.fc_hidden: x = layer(x, do_sample=do_sample) x = self.activation(x) return self.fc_out(x, do_sample=do_sample, scale_variances=True) def kl_divergence_w(self): kld = self.fc1.kl() + self.fc_out.kl() for layer in self.fc_hidden: kld += layer.kl() return kld def fixed_point_updates(self): if hasattr(self.fc1, 'fixed_point_updates'): self.fc1.fixed_point_updates() if hasattr(self.fc_out, 'fixed_point_updates'): self.fc_out.fixed_point_updates() for layer in self.fc_hidden: if hasattr(layer, 'fixed_point_updates'): layer.fixed_point_updates() def prior_predictive_samples(self, n_sample=100): n_eval = 1000 x = torch.linspace(-2, 2, n_eval)[:, np.newaxis] y = np.zeros([n_sample, n_eval]) for i in np.arange(n_sample): y[i] = self.forward(x).data.numpy().ravel() return x.data.numpy(), y ### get and set weights ### def get_weights(self): assert len(self.fc_hidden) == 0 # only works for one layer networks. weight_dict = {} weight_dict['layerip_means'] = torch.cat([self.fc1.weights, self.fc1.bias.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerip_logvar'] = torch.cat([self.fc1.weights_logvar, self.fc1.bias_logvar.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerop_means'] = torch.cat([self.fc_out.weights, self.fc_out.bias.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerop_logvar'] = torch.cat([self.fc_out.weights_logvar, self.fc_out.bias_logvar.unsqueeze(1)], dim=1).data.numpy() return weight_dict def set_weights(self, weight_dict): assert len(self.fc_hidden) == 0 # only works for one layer networks. to_param = lambda x: nn.Parameter(torch.Tensor(x)) self.fc1.weights = to_param(weight_dict['layerip_means'][:, :-1]) self.fc1.weights = to_param(weight_dict['layerip_logvar'][:, :-1]) self.fc1.bias = to_param(weight_dict['layerip_means'][:, -1]) self.fc1.bias_logvar = to_param(weight_dict['layerip_logvar'][:, -1]) self.fc_out.weights = to_param(weight_dict['layerop_means'][:, :-1]) self.fc_out.weights = to_param(weight_dict['layerop_logvar'][:, :-1]) self.fc_out.bias = to_param(weight_dict['layerop_means'][:, -1]) self.fc_out.bias_logvar = to_param(weight_dict['layerop_logvar'][:, -1]) class HshoeRegressionNet(HshoeBNN, ABC): """ Horseshoe net with N(y_true | f(x, w), \lambda^-1); \lambda ~ Gamma(a, b) likelihoods. """ def __init__(self, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1, hshoe_scale=1e-5, use_reg_hshoe=False): super(HshoeRegressionNet, self).__init__(ip_dim=ip_dim, op_dim=op_dim, num_nodes=num_nodes, activation_type=activation_type, num_layers=num_layers, hshoe_scale=hshoe_scale, use_reg_hshoe=use_reg_hshoe) self.noise_layer = GaussianNoiseGammaPrecision(a0=6., b0=6.) def likelihood(self, x=None, y=None): out = self.forward(x) return -self.noise_layer.loss(y_pred=out, y_true=y) def neg_elbo(self, num_batches, x=None, y=None): # scale the KL terms by number of batches so that the minibatch elbo is an unbiased estiamte of the true elbo. Elik = self.likelihood(x, y) neg_elbo = (self.kl_divergence_w() + self.noise_layer.kl()) / num_batches - Elik return neg_elbo def mse(self, x, y): """ scaled rmse (scaled by 1 / std_y**2) """ E_noise_precision = 1. / self.noise_layer.get_noise_var() return (0.5 * E_noise_precision * (self.forward(x, do_sample=False) - y)**2).sum() def get_noise_var(self): return self.noise_layer.get_noise_var() class HshoeClassificationNet(HshoeBNN, ABC): """ Horseshoe net with Categorical(y_true | f(x, w)) likelihoods. Use for classification. """ def __init__(self, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1, hshoe_scale=1e-5, use_reg_hshoe=False): super(HshoeClassificationNet, self).__init__(ip_dim=ip_dim, op_dim=op_dim, num_nodes=num_nodes, activation_type=activation_type, num_layers=num_layers, hshoe_scale=hshoe_scale, use_reg_hshoe=use_reg_hshoe) self.noise_layer = torch.nn.CrossEntropyLoss(reduction='sum') def likelihood(self, x=None, y=None): out = self.forward(x) return -self.noise_layer(out, y) def neg_elbo(self, num_batches, x=None, y=None): # scale the KL terms by number of batches so that the minibatch elbo is an unbiased estiamte of the true elbo. Elik = self.likelihood(x, y) neg_elbo = (self.kl_divergence_w()) / num_batches - Elik return neg_elbo from abc import ABC import torch from torch import nn from uq360.models.bayesian_neural_networks.layers import BayesianLinearLayer from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseGammaPrecision import numpy as np td = torch.distributions class BayesianNN(nn.Module, ABC): """ Bayesian neural network with zero mean Gaussian priors over weights. """ def __init__(self, layer=BayesianLinearLayer, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1): super(BayesianNN, self).__init__() self.num_layers = num_layers if activation_type == 'relu': # activation self.activation = nn.ReLU() elif activation_type == 'tanh': self.activation = nn.Tanh() else: print("Activation Type not supported") self.fc_hidden = [] self.fc1 = layer(ip_dim, num_nodes,) for _ in np.arange(self.num_layers - 1): self.fc_hidden.append(layer(num_nodes, num_nodes, )) self.fc_out = layer(num_nodes, op_dim, ) self.noise_layer = None def forward(self, x, do_sample=True): x = self.fc1(x, do_sample=do_sample) x = self.activation(x) for layer in self.fc_hidden: x = layer(x, do_sample=do_sample) x = self.activation(x) return self.fc_out(x, do_sample=do_sample, scale_variances=True) def kl_divergence_w(self): kld = self.fc1.kl() + self.fc_out.kl() for layer in self.fc_hidden: kld += layer.kl() return kld def prior_predictive_samples(self, n_sample=100): n_eval = 1000 x = torch.linspace(-2, 2, n_eval)[:, np.newaxis] y = np.zeros([n_sample, n_eval]) for i in np.arange(n_sample): y[i] = self.forward(x).data.numpy().ravel() return x.data.numpy(), y ### get and set weights ### def get_weights(self): assert len(self.fc_hidden) == 0 # only works for one layer networks. weight_dict = {} weight_dict['layerip_means'] = torch.cat([self.fc1.weights, self.fc1.bias.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerip_logvar'] = torch.cat([self.fc1.weights_logvar, self.fc1.bias_logvar.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerop_means'] = torch.cat([self.fc_out.weights, self.fc_out.bias.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerop_logvar'] = torch.cat([self.fc_out.weights_logvar, self.fc_out.bias_logvar.unsqueeze(1)], dim=1).data.numpy() return weight_dict def set_weights(self, weight_dict): assert len(self.fc_hidden) == 0 # only works for one layer networks. to_param = lambda x: nn.Parameter(torch.Tensor(x)) self.fc1.weights = to_param(weight_dict['layerip_means'][:, :-1]) self.fc1.weights = to_param(weight_dict['layerip_logvar'][:, :-1]) self.fc1.bias = to_param(weight_dict['layerip_means'][:, -1]) self.fc1.bias_logvar = to_param(weight_dict['layerip_logvar'][:, -1]) self.fc_out.weights = to_param(weight_dict['layerop_means'][:, :-1]) self.fc_out.weights = to_param(weight_dict['layerop_logvar'][:, :-1]) self.fc_out.bias = to_param(weight_dict['layerop_means'][:, -1]) self.fc_out.bias_logvar = to_param(weight_dict['layerop_logvar'][:, -1]) class BayesianRegressionNet(BayesianNN, ABC): """ Bayesian neural net with N(y_true | f(x, w), \lambda^-1); \lambda ~ Gamma(a, b) likelihoods. """ def __init__(self, layer=BayesianLinearLayer, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1): super(BayesianRegressionNet, self).__init__(layer=layer, ip_dim=ip_dim, op_dim=op_dim, num_nodes=num_nodes, activation_type=activation_type, num_layers=num_layers, ) self.noise_layer = GaussianNoiseGammaPrecision(a0=6., b0=6.) def likelihood(self, x=None, y=None): out = self.forward(x) return -self.noise_layer.loss(y_pred=out, y_true=y) def neg_elbo(self, num_batches, x=None, y=None): # scale the KL terms by number of batches so that the minibatch elbo is an unbiased estiamte of the true elbo. Elik = self.likelihood(x, y) neg_elbo = (self.kl_divergence_w() + self.noise_layer.kl()) / num_batches - Elik return neg_elbo def mse(self, x, y): """ scaled rmse (scaled by 1 / std_y**2) """ E_noise_precision = 1. / self.noise_layer.get_noise_var() return (0.5 * E_noise_precision * (self.forward(x, do_sample=False) - y)**2).sum() def get_noise_var(self): return self.noise_layer.get_noise_var() class BayesianClassificationNet(BayesianNN, ABC): """ Bayesian neural net with Categorical(y_true | f(x, w)) likelihoods. Use for classification. """ def __init__(self, layer=BayesianLinearLayer, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1): super(BayesianClassificationNet, self).__init__(layer=layer, ip_dim=ip_dim, op_dim=op_dim, num_nodes=num_nodes, activation_type=activation_type, num_layers=num_layers) self.noise_layer = torch.nn.CrossEntropyLoss(reduction='sum') def likelihood(self, x=None, y=None): out = self.forward(x) return -self.noise_layer(out, y) def neg_elbo(self, num_batches, x=None, y=None): # scale the KL terms by number of batches so that the minibatch elbo is an unbiased estiamte of the true elbo. Elik = self.likelihood(x, y) neg_elbo = self.kl_divergence_w() / num_batches - Elik return neg_elbo import math import numpy as np import torch from scipy.special import gammaln from uq360.models.noise_models.noisemodel import AbstractNoiseModel from torch.nn import Parameter td = torch.distributions def transform(a): return torch.log(1 + torch.exp(a)) class GaussianNoiseGammaPrecision(torch.nn.Module, AbstractNoiseModel): """ N(y_true | f(x, w), \lambda^-1); \lambda ~ Gamma(a, b). Uses a variational approximation; q(lambda) = Gamma(ahat, bhat) """ def __init__(self, a0=6, b0=6, cuda=False): super(GaussianNoiseGammaPrecision, self).__init__() self.cuda = cuda self.a0 = a0 self.b0 = b0 self.const = torch.log(torch.FloatTensor([2 * math.pi])) # variational parameters self.ahat = Parameter(torch.FloatTensor([10.])) self.bhat = Parameter(torch.FloatTensor([3.])) def loss(self, y_pred=None, y_true=None): """ computes -1 * E_q(\lambda)[ln N (y_pred | y_true, \lambda^-1)], where q(lambda) = Gamma(ahat, bhat) :param y_pred: :param y_true: :return: """ n = y_pred.shape[0] ahat = transform(self.ahat) bhat = transform(self.bhat) return -1 * (-0.5 * n * self.const + 0.5 * n * (torch.digamma(ahat) - torch.log(bhat)) \ - 0.5 * (ahat/bhat) * ((y_pred - y_true) ** 2).sum()) def kl(self): ahat = transform(self.ahat) bhat = transform(self.bhat) return (ahat - self.a0) * torch.digamma(ahat) - torch.lgamma(ahat) + gammaln(self.a0) + \ self.a0 * (torch.log(bhat) - np.log(self.b0)) + ahat * (self.b0 - bhat) / bhat def get_noise_var(self): ahat = transform(self.ahat) bhat = transform(self.bhat) return (bhat / ahat).data.numpy()[0] class GaussianNoiseFixedPrecision(torch.nn.Module, AbstractNoiseModel): """ N(y_true | f(x, w), sigma_y**2); known sigma_y """ def __init__(self, std_y=1., cuda=False): super(GaussianNoiseFixedPrecision, self).__init__() self.cuda = cuda self.const = torch.log(torch.FloatTensor([2 * math.pi])) self.sigma_y = std_y def loss(self, y_pred=None, y_true=None): """ computes -1 * ln N (y_pred | y_true, sigma_y**2) :param y_pred: :param y_true: :return: """ ll = -0.5 * self.const - np.log(self.sigma_y) - 0.5 * (1. / self.sigma_y ** 2) * ((y_pred - y_true) ** 2) return -ll.sum(dim=0) def get_noise_var(self): return self.sigma_y ** 2 import math import numpy as np import torch from scipy.special import gammaln from uq360.models.noise_models.noisemodel import AbstractNoiseModel from torch.nn import Parameter td = torch.distributions def transform(a): return torch.log(1 + torch.exp(a)) class GaussianNoise(torch.nn.Module, AbstractNoiseModel): """ N(y_true | f_\mu(x, w), f_\sigma^2(x, w)) """ def __init__(self, cuda=False): super(GaussianNoise, self).__init__() self.cuda = cuda self.const = torch.log(torch.FloatTensor([2 * math.pi])) def loss(self, y_true=None, mu_pred=None, log_var_pred=None, reduce_mean=True): """ computes -1 * ln N (y_true | mu_pred, softplus(log_var_pred)) :param y_true: :param mu_pred: :param log_var_pred: :return: """ var_pred = transform(log_var_pred) ll = -0.5 * self.const - 0.5 * torch.log(var_pred) - 0.5 * (1. / var_pred) * ((mu_pred - y_true) ** 2) if reduce_mean: return -ll.mean(dim=0) else: return -ll.sum(dim=0) def get_noise_var(self, log_var_pred): return transform(log_var_pred) import abc import sys # Ensure compatibility with Python 2/3 if sys.version_info >= (3, 4): ABC = abc.ABC else: ABC = abc.ABCMeta(str('ABC'), (), {}) class AbstractNoiseModel(ABC): """ Abstract class. All noise models inherit from here. """ def __init__(self, *argv, **kwargs): """ Initialize an AbstractNoiseModel object. """ @abc.abstractmethod def loss(self, *argv, **kwargs): """ Compute loss given predictions and groundtruth labels """ raise NotImplementedError @abc.abstractmethod def get_noise_var(self, *argv, **kwargs): """ Return the current estimate of noise variance """ raise NotImplementedError import abc import sys # Ensure compatibility with Python 2/3 if sys.version_info >= (3, 4): ABC = abc.ABC else: ABC = abc.ABCMeta(str('ABC'), (), {}) class BuiltinUQ(ABC): """ BuiltinUQ is the base class for any algorithm that has UQ built into it. """ def __init__(self, *argv, **kwargs): """ Initialize a BuiltinUQ object. """ @abc.abstractmethod def fit(self, *argv, **kwargs): """ Learn the UQ related parameters.. """ raise NotImplementedError @abc.abstractmethod def predict(self, *argv, **kwargs): """ Method to obtain the predicitve uncertainty, this can return the total, epistemic and/or aleatoric uncertainty in the predictions. """ raise NotImplementedError def set_params(self, **parameters): for parameter, value in parameters.items(): setattr(self, parameter, value) return self import abc import sys # Ensure compatibility with Python 2/3 if sys.version_info >= (3, 4): ABC = abc.ABC else: ABC = abc.ABCMeta(str('ABC'), (), {}) class PostHocUQ(ABC): """ PostHocUQ is the base class for any algorithm that quantifies uncertainty of a pre-trained model. """ def __init__(self, *argv, **kwargs): """ Initialize a BuiltinUQ object. """ @abc.abstractmethod def _process_pretrained_model(self, *argv, **kwargs): """ Method to process the pretrained model that requires UQ. """ raise NotImplementedError @abc.abstractmethod def predict(self, *argv, **kwargs): """ Method to obtain the predicitve uncertainty, this can return the total, epistemic and/or aleatoric uncertainty in the predictions. """ raise NotImplementedError def set_params(self, **parameters): for parameter, value in parameters.items(): setattr(self, parameter, value) return self def get_params(self): """ This method should not take any arguments and returns a dict of the __init__ parameters. """ raise NotImplementedError from .ucc_recalibration import UCCRecalibration from collections import namedtuple from uq360.algorithms.posthocuq import PostHocUQ from uq360.utils.misc import form_D_for_auucc from uq360.metrics.uncertainty_characteristics_curve.uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve class UCCRecalibration(PostHocUQ): """ Recalibration a regression model to specified operating point using Uncertainty Characteristics Curve. """ def __init__(self, base_model): """ Args: base_model: pretrained model to be recalibrated. """ super(UCCRecalibration).__init__() self.base_model = self._process_pretrained_model(base_model) self.ucc = None def get_params(self, deep=True): return {"base_model": self.base_model} def _process_pretrained_model(self, base_model): return base_model def fit(self, X, y): """ Fit the Uncertainty Characteristics Curve. Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ y_pred_mean, y_pred_lower, y_pred_upper = self.base_model.predict(X)[:3] bwu = y_pred_upper - y_pred_mean bwl = y_pred_mean - y_pred_lower self.ucc = UncertaintyCharacteristicsCurve() self.ucc.fit(form_D_for_auucc(y_pred_mean, bwl, bwu), y.squeeze()) return self def predict(self, X, missrate=0.05): """ Generate prediction and uncertainty bounds for data X. Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. missrate: desired missrate of the new operating point, set to 0.05 by default. Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. """ C = self.ucc.get_specific_operating_point(req_y_axis_value=missrate, vary_bias=False) new_scale = C['modvalue'] y_pred_mean, y_pred_lower, y_pred_upper = self.base_model.predict(X)[:3] bwu = y_pred_upper - y_pred_mean bwl = y_pred_mean - y_pred_lower if C['operation'] == 'bias': calib_y_pred_upper = y_pred_mean + (new_scale + bwu) # lower bound width calib_y_pred_lower = y_pred_mean - (new_scale + bwl) # Upper bound width else: calib_y_pred_upper = y_pred_mean + (new_scale * bwu) # lower bound width calib_y_pred_lower = y_pred_mean - (new_scale * bwl) # Upper bound width Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_pred_mean, calib_y_pred_lower, calib_y_pred_upper) return res from .classification_calibration import ClassificationCalibration from collections import namedtuple import numpy as np from sklearn.calibration import CalibratedClassifierCV from sklearn.preprocessing import LabelEncoder from uq360.utils.misc import DummySklearnEstimator from uq360.algorithms.posthocuq import PostHocUQ class ClassificationCalibration(PostHocUQ): """Post hoc calibration of classification models. Currently wraps `CalibratedClassifierCV` from sklearn and allows non-sklearn models to be calibrated. """ def __init__(self, num_classes, fit_mode="features", method='isotonic', base_model_prediction_func=None): """ Args: num_classes: number of classes. fit_mode: features or probs. If probs the `fit` and `predict` operate on the base models probability scores, useful when these are precomputed. method: isotonic or sigmoid. base_model_prediction_func: the function that takes in the input features and produces base model's probability scores. This is ignored when operating in `probs` mode. """ super(ClassificationCalibration).__init__() if fit_mode == "probs": # In this case, the fit assumes that it receives the probability scores of the base model. # create a dummy estimator self.base_model = DummySklearnEstimator(num_classes, lambda x: x) else: self.base_model = DummySklearnEstimator(num_classes, base_model_prediction_func) self.method = method def get_params(self, deep=True): return {"num_classes": self.num_classes, "fit_mode": self.fit_mode, "method": self.method, "base_model_prediction_func": self.base_model_prediction_func} def _process_pretrained_model(self, base_model): return base_model def fit(self, X, y): """ Fits calibration model using the provided calibration set. Args: X: array-like of shape (n_samples, n_features) or (n_samples, n_classes). Features vectors of the training data or the probability scores from the base model. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ self.base_model.label_encoder_ = LabelEncoder().fit(y) self.calib_model = CalibratedClassifierCV(base_estimator=self.base_model, cv="prefit", method=self.method) self.calib_model.fit(X, y) return self def predict(self, X): """ Obtain calibrated predictions for the test points. Args: X: array-like of shape (n_samples, n_features) or (n_samples, n_classes). Features vectors of the training data or the probability scores from the base model. Returns: namedtuple: A namedtupe that holds y_pred: ndarray of shape (n_samples,) Predicted labels of the test points. y_prob: ndarray of shape (n_samples, n_classes) Predicted probability scores of the classes. """ y_prob = self.calib_model.predict_proba(X) if len(np.shape(y_prob)) == 1: y_pred_labels = y_prob > 0.5 else: y_pred_labels = np.argmax(y_prob, axis=1) Result = namedtuple('res', ['y_pred', 'y_prob']) res = Result(y_pred_labels, y_prob) return res from collections import namedtuple import numpy as np import torch import torch.nn.functional as F from scipy.stats import norm from torch.utils.data import DataLoader from torch.utils.data import TensorDataset from uq360.algorithms.builtinuq import BuiltinUQ np.random.seed(42) torch.manual_seed(42) class _MLPNet_Main(torch.nn.Module): def __init__(self, num_features, num_outputs, num_hidden): super(_MLPNet_Main, self).__init__() self.fc = torch.nn.Linear(num_features, num_hidden) self.fc_mu = torch.nn.Linear(num_hidden, num_outputs) self.fc_log_var = torch.nn.Linear(num_hidden, num_outputs) def forward(self, x): x = F.relu(self.fc(x)) mu = self.fc_mu(x) log_var = self.fc_log_var(x) return mu, log_var class _MLPNet_Aux(torch.nn.Module): def __init__(self, num_features, num_outputs, num_hidden): super(_MLPNet_Aux, self).__init__() self.fc = torch.nn.Linear(num_features, num_hidden) self.fc_log_var = torch.nn.Linear(num_hidden, num_outputs) def forward(self, x): x = F.relu(self.fc(x)) log_var = self.fc_log_var(x) return log_var class AuxiliaryIntervalPredictor(BuiltinUQ): """ Auxiliary Interval Predictor [1]_ uses an auxiliary model to encourage calibration of the main model. References: .. [1] Thiagarajan, J. J., Venkatesh, B., Sattigeri, P., & Bremer, P. T. (2020, April). Building calibrated deep models via uncertainty matching with auxiliary interval predictors. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 04, pp. 6005-6012). https://arxiv.org/abs/1909.04079 """ def __init__(self, model_type=None, main_model=None, aux_model=None, config=None, device=None, verbose=True): """ Args: model_type: The model type used to build the main model and the auxiliary model. Currently supported values are [mlp, custom]. `mlp` modeltype learns a mlp neural network using pytorch framework. For `custom` the user provide `main_model` and `aux_model`. main_model: (optional) The main prediction model. Currently support pytorch models that return mean and log variance. aux_model: (optional) The auxiliary prediction model. Currently support pytorch models that return calibrated log variance. config: dictionary containing the config parameters for the model. device: device used for pytorch models ignored otherwise. verbose: if True, print statements with the progress are enabled. """ super(AuxiliaryIntervalPredictor).__init__() self.config = config self.device = device self.verbose = verbose if model_type == "mlp": self.model_type = model_type self.main_model = _MLPNet_Main( num_features=self.config["num_features"], num_outputs=self.config["num_outputs"], num_hidden=self.config["num_hidden"], ) self.aux_model = _MLPNet_Aux( num_features=self.config["num_features"], num_outputs=self.config["num_outputs"], num_hidden=self.config["num_hidden"], ) elif model_type == "custom": self.model_type = model_type self.main_model = main_model self.aux_model = aux_model else: raise NotImplementedError def get_params(self, deep=True): return {"model_type": self.model_type, "config": self.config, "main_model": self.main_model, "aux_model": self.aux_model, "device": self.device, "verbose": self.verbose} def _main_model_loss(self, y_true, y_pred_mu, y_pred_log_var, y_pred_log_var_aux): r = torch.abs(y_true - y_pred_mu) # + 0.5 * y_pred_log_var + loss = torch.mean(0.5 * torch.exp(-y_pred_log_var) * r ** 2) + \ self.config["lambda_match"] * torch.mean(torch.abs(torch.exp(0.5 * y_pred_log_var) - torch.exp(0.5 * y_pred_log_var_aux))) return loss def _aux_model_loss(self, y_true, y_pred_mu, y_pred_log_var_aux): deltal = deltau = 2.0 * torch.exp(0.5 * y_pred_log_var_aux) upper = y_pred_mu + deltau lower = y_pred_mu - deltal width = upper - lower r = torch.abs(y_true - y_pred_mu) emce = torch.mean(torch.sigmoid((y_true - lower) * (upper - y_true) * 100000)) loss_emce = torch.abs(self.config["calibration_alpha"]-emce) loss_noise = torch.mean(torch.abs(0.5 * width - r)) loss_sharpness = torch.mean(torch.abs(upper - y_true)) + torch.mean(torch.abs(lower - y_true)) #print(emce) return loss_emce + self.config["lambda_noise"] * loss_noise + self.config["lambda_sharpness"] * loss_sharpness def fit(self, X, y): """ Fit the Auxiliary Interval Predictor model. Args: X: array-like of shape (n_samples, n_features). Features vectors of the training data. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ X = torch.from_numpy(X).float().to(self.device) y = torch.from_numpy(y).float().to(self.device) dataset_loader = DataLoader( TensorDataset(X,y), batch_size=self.config["batch_size"] ) optimizer_main_model = torch.optim.Adam(self.main_model.parameters(), lr=self.config["lr"]) optimizer_aux_model = torch.optim.Adam(self.aux_model.parameters(), lr=self.config["lr"]) for it in range(self.config["num_outer_iters"]): # Train the main model for epoch in range(self.config["num_main_iters"]): avg_mean_model_loss = 0.0 for batch_x, batch_y in dataset_loader: self.main_model.train() self.aux_model.eval() batch_y_pred_log_var_aux = self.aux_model(batch_x) batch_y_pred_mu, batch_y_pred_log_var = self.main_model(batch_x) main_loss = self._main_model_loss(batch_y, batch_y_pred_mu, batch_y_pred_log_var, batch_y_pred_log_var_aux) optimizer_main_model.zero_grad() main_loss.backward() optimizer_main_model.step() avg_mean_model_loss += main_loss.item()/len(dataset_loader) if self.verbose: print("Iter: {}, Epoch: {}, main_model_loss = {}".format(it, epoch, avg_mean_model_loss)) # Train the auxiliary model for epoch in range(self.config["num_aux_iters"]): avg_aux_model_loss = 0.0 for batch_x, batch_y in dataset_loader: self.aux_model.train() self.main_model.eval() batch_y_pred_log_var_aux = self.aux_model(batch_x) batch_y_pred_mu, batch_y_pred_log_var = self.main_model(batch_x) aux_loss = self._aux_model_loss(batch_y, batch_y_pred_mu, batch_y_pred_log_var_aux) optimizer_aux_model.zero_grad() aux_loss.backward() optimizer_aux_model.step() avg_aux_model_loss += aux_loss.item() / len(dataset_loader) if self.verbose: print("Iter: {}, Epoch: {}, aux_model_loss = {}".format(it, epoch, avg_aux_model_loss)) return self def predict(self, X, return_dists=False): """ Obtain predictions for the test points. In addition to the mean and lower/upper bounds, also returns full predictive distribution (return_dists=True). Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. return_dists: If True, the predictive distribution for each instance using scipy distributions is returned. Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. dists: list of predictive distribution as `scipy.stats` objects with length n_samples. Only returned when `return_dists` is True. """ self.main_model.eval() X = torch.from_numpy(X).float().to(self.device) dataset_loader = DataLoader( X, batch_size=self.config["batch_size"] ) y_mean_list = [] y_log_var_list = [] for batch_x in dataset_loader: batch_y_pred_mu, batch_y_pred_log_var = self.main_model(batch_x) y_mean_list.append(batch_y_pred_mu.data.cpu().numpy()) y_log_var_list.append(batch_y_pred_log_var.data.cpu().numpy()) y_mean = np.concatenate(y_mean_list) y_log_var = np.concatenate(y_log_var_list) y_std = np.sqrt(np.exp(y_log_var)) y_lower = y_mean - 2.0*y_std y_upper = y_mean + 2.0*y_std Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) if return_dists: dists = [norm(loc=y_mean[i], scale=y_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_dists',)) res = Result(*res, y_dists=dists) return res from .auxiliary_interval_predictor import AuxiliaryIntervalPredictor import copy from collections import namedtuple import numpy as np import torch import torch.nn.functional as F from torch.utils.data import DataLoader import torch.utils.data as data_utils from scipy.stats import norm from sklearn.preprocessing import StandardScaler from uq360.algorithms.builtinuq import BuiltinUQ from uq360.models.bayesian_neural_networks.bnn_models import horseshoe_mlp, bayesian_mlp class BnnRegression(BuiltinUQ): """ Variationally trained BNNs with Gaussian and Horseshoe [6]_ priors for regression. References: .. [6] Ghosh, Soumya, Jiayu Yao, and Finale Doshi-Velez. "Structured variational learning of Bayesian neural networks with horseshoe priors." International Conference on Machine Learning. PMLR, 2018. """ def __init__(self, config, prior="Gaussian"): """ Args: config: a dictionary specifying network and learning hyperparameters. prior: BNN priors specified as a string. Supported priors are Gaussian, Hshoe, RegHshoe """ super(BnnRegression, self).__init__() self.config = config if prior == "Gaussian": self.net = bayesian_mlp.BayesianRegressionNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers']) self.config['use_reg_hshoe'] = None elif prior == "Hshoe": self.net = horseshoe_mlp.HshoeRegressionNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers'], hshoe_scale=config['hshoe_scale']) self.config['use_reg_hshoe'] = False elif prior == "RegHshoe": self.net = horseshoe_mlp.HshoeRegressionNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers'], hshoe_scale=config['hshoe_scale'], use_reg_hshoe=config['use_reg_hshoe']) self.config['use_reg_hshoe'] = True else: raise NotImplementedError("'prior' must be a string. It can be one of Gaussian, Hshoe, RegHshoe") def get_params(self, deep=True): return {"prior": self.prior, "config": self.config} def fit(self, X, y): """ Fit the BNN regression model. Args: X: array-like of shape (n_samples, n_features). Features vectors of the training data. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ torch.manual_seed(1234) optimizer = torch.optim.Adam(self.net.parameters(), lr=self.config['step_size']) neg_elbo = torch.zeros([self.config['num_epochs'], 1]) params_store = {} for epoch in range(self.config['num_epochs']): loss = self.net.neg_elbo(num_batches=1, x=X, y=y.float().unsqueeze(dim=1)) / X.shape[0] optimizer.zero_grad() loss.backward() optimizer.step() if hasattr(self.net, 'fixed_point_updates'): # for hshoe or regularized hshoe nets self.net.fixed_point_updates() neg_elbo[epoch] = loss.item() if (epoch + 1) % 10 == 0: # print ((net.noise_layer.bhat/net.noise_layer.ahat).data.numpy()[0]) print('Epoch[{}/{}], neg elbo: {:.6f}, noise var: {:.6f}' .format(epoch + 1, self.config['num_epochs'], neg_elbo[epoch].item() / X.shape[0], self.net.get_noise_var())) params_store[epoch] = copy.deepcopy(self.net.state_dict()) # for small nets we can just store all. best_model_id = neg_elbo.argmin() # loss_val_store.argmin() # self.net.load_state_dict(params_store[best_model_id.item()]) return self def predict(self, X, mc_samples=100, return_dists=False, return_epistemic=True, return_epistemic_dists=False): """ Obtain predictions for the test points. In addition to the mean and lower/upper bounds, also returns epistemic uncertainty (return_epistemic=True) and full predictive distribution (return_dists=True). Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. mc_samples: Number of Monte-Carlo samples. return_dists: If True, the predictive distribution for each instance using scipy distributions is returned. return_epistemic: if True, the epistemic upper and lower bounds are returned. return_epistemic_dists: If True, the epistemic distribution for each instance using scipy distributions is returned. Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. y_lower_epistemic: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of epistemic component of the predictive distribution of the test points. Only returned when `return_epistemic` is True. y_upper_epistemic: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of epistemic component of the predictive distribution of the test points. Only returned when `return_epistemic` is True. dists: list of predictive distribution as `scipy.stats` objects with length n_samples. Only returned when `return_dists` is True. """ epistemic_out = np.zeros([mc_samples, X.shape[0]]) total_out = np.zeros([mc_samples, X.shape[0]]) for s in np.arange(mc_samples): pred = self.net(X).data.numpy().ravel() epistemic_out[s] = pred total_out[s] = pred + np.sqrt(self.net.get_noise_var()) * np.random.randn(pred.shape[0]) y_total_std = np.std(total_out, axis=0) y_epi_std = np.std(epistemic_out, axis=0) y_mean = np.mean(total_out, axis=0) y_lower = y_mean - 2 * y_total_std y_upper = y_mean + 2 * y_total_std y_epi_lower = y_mean - 2 * y_epi_std y_epi_upper = y_mean + 2 * y_epi_std Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) if return_epistemic: Result = namedtuple('res', Result._fields + ('lower_epistemic', 'upper_epistemic',)) res = Result(*res, lower_epistemic=y_epi_lower, upper_epistemic=y_epi_upper) if return_dists: dists = [norm(loc=y_mean[i], scale=y_total_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_dists',)) res = Result(*res, y_dists=dists) if return_epistemic_dists: epi_dists = [norm(loc=y_mean[i], scale=y_epi_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_epistemic_dists',)) res = Result(*res, y_epistemic_dists=epi_dists) return res class BnnClassification(BuiltinUQ): """ Variationally trained BNNs with Gaussian and Horseshoe [6]_ priors for classification. """ def __init__(self, config, prior="Gaussian", device=None): """ Args: config: a dictionary specifying network and learning hyperparameters. prior: BNN priors specified as a string. Supported priors are Gaussian, Hshoe, RegHshoe """ super(BnnClassification, self).__init__() self.config = config self.device = device if prior == "Gaussian": self.net = bayesian_mlp.BayesianClassificationNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers']) self.config['use_reg_hshoe'] = None elif prior == "Hshoe": self.net = horseshoe_mlp.HshoeClassificationNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers'], hshoe_scale=config['hshoe_scale']) self.config['use_reg_hshoe'] = False elif prior == "RegHshoe": self.net = horseshoe_mlp.HshoeClassificationNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers'], hshoe_scale=config['hshoe_scale'], use_reg_hshoe=config['use_reg_hshoe']) self.config['use_reg_hshoe'] = True else: raise NotImplementedError("'prior' must be a string. It can be one of Gaussian, Hshoe, RegHshoe") if "batch_size" not in self.config: self.config["batch_size"] = 50 self.net = self.net.to(device) def get_params(self, deep=True): return {"prior": self.prior, "config": self.config, "device": self.device} def fit(self, X=None, y=None, train_loader=None): """ Fits BNN regression model. Args: X: array-like of shape (n_samples, n_features) or (n_samples, n_classes). Features vectors of the training data or the probability scores from the base model. Ignored if train_loader is not None. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Ignored if train_loader is not None. train_loader: pytorch train_loader object. Returns: self """ if train_loader is None: train = data_utils.TensorDataset(torch.Tensor(X), torch.Tensor(y.values).long()) train_loader = data_utils.DataLoader(train, batch_size=self.config['batch_size'], shuffle=True) torch.manual_seed(1234) optimizer = torch.optim.Adam(self.net.parameters(), lr=self.config['step_size']) neg_elbo = torch.zeros([self.config['num_epochs'], 1]) params_store = {} for epoch in range(self.config['num_epochs']): avg_loss = 0.0 for batch_x, batch_y in train_loader: loss = self.net.neg_elbo(num_batches=len(train_loader), x=batch_x, y=batch_y) / batch_x.size(0) optimizer.zero_grad() loss.backward() optimizer.step() if hasattr(self.net, 'fixed_point_updates'): # for hshoe or regularized hshoe nets self.net.fixed_point_updates() avg_loss += loss.item() neg_elbo[epoch] = avg_loss / len(train_loader) if (epoch + 1) % 10 == 0: # print ((net.noise_layer.bhat/net.noise_layer.ahat).data.numpy()[0]) print('Epoch[{}/{}], neg elbo: {:.6f}' .format(epoch + 1, self.config['num_epochs'], neg_elbo[epoch].item())) params_store[epoch] = copy.deepcopy(self.net.state_dict()) # for small nets we can just store all. best_model_id = neg_elbo.argmin() # loss_val_store.argmin() # self.net.load_state_dict(params_store[best_model_id.item()]) return self def predict(self, X, mc_samples=100): """ Obtain calibrated predictions for the test points. Args: X: array-like of shape (n_samples, n_features) or (n_samples, n_classes). Features vectors of the training data or the probability scores from the base model. mc_samples: Number of Monte-Carlo samples. Returns: namedtuple: A namedtupe that holds y_pred: ndarray of shape (n_samples,) Predicted labels of the test points. y_prob: ndarray of shape (n_samples, n_classes) Predicted probability scores of the classes. y_prob_var: ndarray of shape (n_samples,) Variance of the prediction on the test points. y_prob_samples: ndarray of shape (mc_samples, n_samples, n_classes) Samples from the predictive distribution. """ X = torch.Tensor(X) y_prob_samples = [F.softmax(self.net(X), dim=1).detach().numpy() for _ in np.arange(mc_samples)] y_prob_samples_stacked = np.stack(y_prob_samples) prob_mean = np.mean(y_prob_samples_stacked, 0) prob_var = np.std(y_prob_samples_stacked, 0) ** 2 if len(np.shape(prob_mean)) == 1: y_pred_labels = prob_mean > 0.5 else: y_pred_labels = np.argmax(prob_mean, axis=1) Result = namedtuple('res', ['y_pred', 'y_prob', 'y_prob_var', 'y_prob_samples']) res = Result(y_pred_labels, prob_mean, prob_var, y_prob_samples) return res from collections import namedtuple import botorch import gpytorch import numpy as np import torch from botorch.models import SingleTaskGP from botorch.utils.transforms import normalize from gpytorch.constraints import GreaterThan from scipy.stats import norm from sklearn.preprocessing import StandardScaler from uq360.algorithms.builtinuq import BuiltinUQ np.random.seed(42) torch.manual_seed(42) class HomoscedasticGPRegression(BuiltinUQ): """ A wrapper around Botorch SingleTask Gaussian Process Regression [1]_ with homoscedastic noise. References: .. [1] https://botorch.org/api/models.html#singletaskgp """ def __init__(self, kernel=gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel()), likelihood=None, config=None): """ Args: kernel: gpytorch kernel function with default set to `RBFKernel` with output scale. likelihood: gpytorch likelihood function with default set to `GaussianLikelihood`. config: dictionary containing the config parameters for the model. """ super(HomoscedasticGPRegression).__init__() self.config = config self.kernel = kernel self.likelihood = likelihood self.model = None self.scaler = StandardScaler() self.X_bounds = None def get_params(self, deep=True): return {"kernel": self.kernel, "likelihood": self.likelihood, "config": self.config} def fit(self, X, y, **kwargs): """ Fit the GP Regression model. Additional arguments relevant for SingleTaskGP fitting can be passed to this function. Args: X: array-like of shape (n_samples, n_features). Features vectors of the training data. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values **kwargs: Additional arguments relevant for SingleTaskGP fitting. Returns: self """ y = self.scaler.fit_transform(y) X, y = torch.tensor(X), torch.tensor(y) self.X_bounds = X_bounds = torch.stack([X.min() * torch.ones(X.shape[1]), X.max() * torch.ones(X.shape[1])]) X = normalize(X, X_bounds) model_homo = SingleTaskGP(train_X=X, train_Y=y, covar_module=self.kernel, likelihood=self.likelihood, **kwargs) model_homo.likelihood.noise_covar.register_constraint("raw_noise", GreaterThan(1e-5)) model_homo_marginal_log_lik = gpytorch.mlls.ExactMarginalLogLikelihood(model_homo.likelihood, model_homo) botorch.fit.fit_gpytorch_model(model_homo_marginal_log_lik) model_homo_marginal_log_lik.eval() self.model = model_homo_marginal_log_lik self.inferred_observation_noise = self.scaler.inverse_transform(self.model.likelihood.noise.detach().numpy()[0].reshape(1,1)).squeeze() return self def predict(self, X, return_dists=False, return_epistemic=False, return_epistemic_dists=False): """ Obtain predictions for the test points. In addition to the mean and lower/upper bounds, also returns epistemic uncertainty (return_epistemic=True) and full predictive distribution (return_dists=True). Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. return_dists: If True, the predictive distribution for each instance using scipy distributions is returned. return_epistemic: if True, the epistemic upper and lower bounds are returned. return_epistemic_dists: If True, the epistemic distribution for each instance using scipy distributions is returned. Returns: namedtuple: A namedtuple that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. y_lower_epistemic: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of epistemic component of the predictive distribution of the test points. Only returned when `return_epistemic` is True. y_upper_epistemic: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of epistemic component of the predictive distribution of the test points. Only returned when `return_epistemic` is True. dists: list of predictive distribution as `scipy.stats` objects with length n_samples. Only returned when `return_dists` is True. """ X = torch.tensor(X) X_test_norm = normalize(X, self.X_bounds) self.model.eval() with torch.no_grad(): posterior = self.model.model.posterior(X_test_norm) y_mean = posterior.mean #y_epi_std = torch.sqrt(posterior.variance) y_lower_epistemic, y_upper_epistemic = posterior.mvn.confidence_region() predictive_posterior = self.model.model.posterior(X_test_norm, observation_noise=True) #y_std = torch.sqrt(predictive_posterior.variance) y_lower_total, y_upper_total = predictive_posterior.mvn.confidence_region() y_mean, y_lower, y_upper, y_lower_epistemic, y_upper_epistemic = self.scaler.inverse_transform(y_mean.numpy()).squeeze(), \ self.scaler.inverse_transform(y_lower_total.numpy()).squeeze(),\ self.scaler.inverse_transform(y_upper_total.numpy()).squeeze(),\ self.scaler.inverse_transform(y_lower_epistemic.numpy()).squeeze(),\ self.scaler.inverse_transform(y_upper_epistemic.numpy()).squeeze() y_epi_std = (y_upper_epistemic - y_lower_epistemic) / 4.0 y_std = (y_upper_total - y_lower_total) / 4.0 Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) if return_epistemic: Result = namedtuple('res', Result._fields + ('y_lower_epistemic', 'y_upper_epistemic',)) res = Result(*res, y_lower_epistemic=y_lower_epistemic, y_upper_epistemic=y_upper_epistemic) if return_dists: dists = [norm(loc=y_mean[i], scale=y_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_dists',)) res = Result(*res, y_dists=dists) if return_epistemic_dists: epi_dists = [norm(loc=y_mean[i], scale=y_epi_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_epistemic_dists',)) res = Result(*res, y_epistemic_dists=epi_dists) return res from .homoscedastic_gaussian_process_regression import HomoscedasticGPRegression from collections import namedtuple from sklearn.ensemble import GradientBoostingRegressor from uq360.algorithms.builtinuq import BuiltinUQ class QuantileRegression(BuiltinUQ): """Quantile Regression uses quantile loss and learns two separate models for the upper and lower quantile to obtain the prediction intervals. """ def __init__(self, model_type="gbr", config=None): """ Args: model_type: The base model used for predicting a quantile. Currently supported values are [gbr]. gbr is sklearn GradientBoostingRegressor. config: dictionary containing the config parameters for the model. """ super(QuantileRegression).__init__() if config is not None: self.config = config else: self.config = {} if "alpha" not in self.config: self.config["alpha"] = 0.95 if model_type == "gbr": self.model_type = model_type self.model_mean = GradientBoostingRegressor( loss='ls', n_estimators=self.config["n_estimators"], max_depth=self.config["max_depth"], learning_rate=self.config["learning_rate"], min_samples_leaf=self.config["min_samples_leaf"], min_samples_split=self.config["min_samples_split"] ) self.model_upper = GradientBoostingRegressor( loss='quantile', alpha=self.config["alpha"], n_estimators=self.config["n_estimators"], max_depth=self.config["max_depth"], learning_rate=self.config["learning_rate"], min_samples_leaf=self.config["min_samples_leaf"], min_samples_split=self.config["min_samples_split"] ) self.model_lower = GradientBoostingRegressor( loss='quantile', alpha=1.0 - self.config["alpha"], n_estimators=self.config["n_estimators"], max_depth=self.config["max_depth"], learning_rate=self.config["learning_rate"], min_samples_leaf=self.config["min_samples_leaf"], min_samples_split=self.config["min_samples_split"]) else: raise NotImplementedError def get_params(self, deep=True): return {"model_type": self.model_type, "config": self.config} def fit(self, X, y): """ Fit the Quantile Regression model. Args: X: array-like of shape (n_samples, n_features). Features vectors of the training data. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ self.model_mean.fit(X, y) self.model_lower.fit(X, y) self.model_upper.fit(X, y) return self def predict(self, X): """ Obtain predictions for the test points. In addition to the mean and lower/upper bounds, also returns epistemic uncertainty (return_epistemic=True) and full predictive distribution (return_dists=True). Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. """ y_mean = self.model_mean.predict(X) y_lower = self.model_lower.predict(X) y_upper = self.model_upper.predict(X) Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) return res from .quantile_regression import QuantileRegression from .infinitesimal_jackknife import InfinitesimalJackknife from collections import namedtuple import numpy as np from uq360.algorithms.posthocuq import PostHocUQ class InfinitesimalJackknife(PostHocUQ): """ Performs a first order Taylor series expansion around MLE / MAP fit. Requires the model being probed to be twice differentiable. """ def __init__(self, params, gradients, hessian, config): """ Initialize IJ. Args: params: MLE / MAP fit around which uncertainty is sought. d*1 gradients: Per data point gradients, estimated at the MLE / MAP fit. d*n hessian: Hessian evaluated at the MLE / MAP fit. d*d """ super(InfinitesimalJackknife).__init__() self.params_one = params self.gradients = gradients self.hessian = hessian self.d, self.n = gradients.shape self.dParams_dWeights = -np.linalg.solve(self.hessian, self.gradients) self.approx_dParams_dWeights = -np.linalg.solve(np.diag(np.diag(self.hessian)), self.gradients) self.w_one = np.ones([self.n]) self.config = config def get_params(self, deep=True): return {"params": self.params, "config": self.config, "gradients": self.gradients, "hessian": self.hessian} def _process_pretrained_model(self, *argv, **kwargs): pass def get_parameter_uncertainty(self): if (self.config['resampling_strategy'] == "jackknife") or (self.config['resampling_strategy'] == "jackknife+"): w_query = np.ones_like(self.w_one) resampled_params = np.zeros([self.n, self.d]) for i in np.arange(self.n): w_query[i] = 0 resampled_params[i] = self.ij(w_query) w_query[i] = 1 return np.cov(resampled_params), resampled_params elif self.config['resampling_strategy'] == "bootstrap": pass else: raise NotImplementedError("Only jackknife, jackknife+, and bootstrap resampling strategies are supported") def predict(self, X, model): """ Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. model: model object, must implement a set_parameters function Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. """ n, _ = X.shape y_all = model.predict(X) _, d_out = y_all.shape params_cov, params = self.get_parameter_uncertainty() if d_out > 1: print("Quantiles are computed independently for each dimension. May not be accurate.") y = np.zeros([params.shape[0], n, d_out]) for i in np.arange(params.shape[0]): model.set_parameters(params[i]) y[i] = model.predict(X) y_lower = np.quantile(y, q=0.5 * self.config['alpha'], axis=0) y_upper = np.quantile(y, q=(1. - 0.5 * self.config['alpha']), axis=0) y_mean = y.mean(axis=0) Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) return res def ij(self, w_query): """ Args: w_query: A n*1 vector to query parameters at. Return: new parameters at w_query """ assert w_query.shape[0] == self.n return self.params_one + self.dParams_dWeights @ (w_query-self.w_one).T def approx_ij(self, w_query): """ Args: w_query: A n*1 vector to query parameters at. Return: new parameters at w_query """ assert w_query.shape[0] == self.n return self.params_one + self.approx_dParams_dWeights @ (w_query-self.w_one).T import inspect from collections import namedtuple import numpy as np from sklearn.ensemble import GradientBoostingClassifier from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.exceptions import NotFittedError from uq360.algorithms.posthocuq import PostHocUQ class BlackboxMetamodelClassification(PostHocUQ): """ Extracts confidence scores from black-box classification models using a meta-model [4]_ . References: .. [4] Chen, Tongfei, et al. "Confidence scoring using whitebox meta-models with linear classifier probes." The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 2019. """ def _create_named_model(self, mdltype, config): """ Instantiates a model by name passed in 'mdltype'. Args: mdltype: string with name (must be supported) config: dict with args passed in the instantiation call Returns: mdl instance """ assert (isinstance(mdltype, str)) if mdltype == 'lr': mdl = LogisticRegression(**config) elif mdltype == 'gbm': mdl = GradientBoostingClassifier(**config) else: raise NotImplementedError("ERROR: Requested model type unknown: \"%s\"" % mdltype) return mdl def _get_model_instance(self, model, config): """ Returns an instance of a model based on (a) a desired name or (b) passed in class, or (c) passed in instance. :param model: string, class, or instance. Class and instance must have certain methods callable. :param config: dict with args passed in during the instantiation :return: model instance """ assert (model is not None and config is not None) if isinstance(model, str): # 'model' is a name, create it mdl = self._create_named_model(model, config) elif inspect.isclass(model): # 'model' is a class, instantiate it mdl = model(**config) else: # 'model' is an instance, register it mdl = model if not all([hasattr(mdl, key) and callable(getattr(mdl, key)) for key in self.callable_keys]): raise ValueError("ERROR: Passed model/method failed the interface test. Methods required: %s" % ','.join(self.callable_keys)) return mdl def __init__(self, base_model=None, meta_model=None, base_config=None, meta_config=None, random_seed=42): """ :param base_model: Base model. Can be: (1) None (default mdl will be set up), (2) Named model (e.g., logistic regression 'lr' or gradient boosting machine 'gbm'), (3) Base model class declaration (e.g., sklearn.linear_model.LogisticRegression). Will instantiate. (4) Model instance (instantiated outside). Will be re-used. Must have certain callable methods. Note: user-supplied classes and models must have certain callable methods ('predict', 'fit') and be capable of raising NotFittedError. :param meta_model: Meta model. Same values possible as with 'base_model' :param base_config: None or a params dict to be passed to 'base_model' at instantiation :param meta_config: None or a params dict to be passed to 'meta_model' at instantiation :param random_seed: seed used in the various pipeline steps """ super(BlackboxMetamodelClassification).__init__() self.random_seed = random_seed self.callable_keys = ['predict', 'fit'] # required methods - must be present in models passed in self.base_model_default = 'gbm' self.meta_model_default = 'lr' self.base_config_default = {'n_estimators': 300, 'max_depth': 10, 'learning_rate': 0.001, 'min_samples_leaf': 10, 'min_samples_split': 10, 'random_state': self.random_seed} self.meta_config_default = {'penalty': 'l1', 'C': 1, 'solver': 'liblinear', 'random_state': self.random_seed} self.base_config = base_config if base_config is not None else self.base_config_default self.meta_config = meta_config if meta_config is not None else self.meta_config_default self.base_model = None self.meta_model = None self.base_model = self._get_model_instance(base_model if base_model is not None else self.base_model_default, self.base_config) self.meta_model = self._get_model_instance(meta_model if meta_model is not None else self.meta_model_default, self.meta_config) def get_params(self, deep=True): return {"base_model": self.base_model, "meta_model": self.meta_model, "base_config": self.base_config, "meta_config": self.meta_config, "random_seed": self.random_seed} def _process_pretrained_model(self, X, y_hat_proba): """ Given the original input features and the base output probabilities, generate input features to train a meta model. Current implementation copies all input features and appends. :param X: numpy [nsamples, dim] :param y_hat_proba: [nsamples, nclasses] :return: array with new features [nsamples, newdim] """ assert (len(y_hat_proba.shape) == 2) assert (X.shape[0] == y_hat_proba.shape[0]) # sort the probs sample by sample faux1 = np.sort(y_hat_proba, axis=-1) # add delta between top and second candidate faux2 = np.expand_dims(faux1[:, -1] - faux1[:, -2], axis=-1) return np.hstack([X, faux1, faux2]) def fit(self, X, y, meta_fraction=0.2, randomize_samples=True, base_is_prefitted=False, meta_train_data=(None, None)): """ Fit base and meta models. :param X: input to the base model, array-like of shape (n_samples, n_features). Features vectors of the training data. :param y: ground truth for the base model, array-like of shape (n_samples,) :param meta_fraction: float in [0,1] - a fractional size of the partition carved out to train the meta model (complement will be used to train the base model) :param randomize_samples: use shuffling when creating partitions :param base_is_prefitted: Setting True will skip fitting the base model (useful for base models that have been instantiated outside/by the user and are already fitted. :param meta_train_data: User supplied data to train the meta model. Note that this option should only be used with 'base_is_prefitted'==True. Pass a tuple meta_train_data=(X_meta, y_meta) to activate. Note that (X,y,meta_fraction, randomize_samples) will be ignored in this mode. :return: self """ X = np.asarray(X) y = np.asarray(y) assert (len(meta_train_data) == 2) if meta_train_data[0] is None: X_base, X_meta, y_base, y_meta = train_test_split(X, y, shuffle=randomize_samples, test_size=meta_fraction, random_state=self.random_seed) else: if not base_is_prefitted: raise ValueError("ERROR: fit(): base model must be pre-fitted to use the 'meta_train_data' option") X_base = y_base = None X_meta = meta_train_data[0] y_meta = meta_train_data[1] # fit the base model if not base_is_prefitted: self.base_model.fit(X_base, y_base) # get input for the meta model from the base try: y_hat_meta_proba = self.base_model.predict_proba(X_meta) # determine correct-incorrect outcome - these are targets for the meta model trainer # y_hat_meta_targets = np.asarray((y_meta == np.argmax(y_hat_meta_proba, axis=-1)), dtype=np.int) -- Fix for python 3.8.11 update (in 2.9.0.8) y_hat_meta_targets = np.asarray((y_meta == np.argmax(y_hat_meta_proba, axis=-1)), dtype=int) except NotFittedError as e: raise RuntimeError("ERROR: fit(): The base model appears not pre-fitted (%s)" % repr(e)) # get input features for meta training X_meta_in = self._process_pretrained_model(X_meta, y_hat_meta_proba) # train meta model to predict 'correct' vs. 'incorrect' of the base self.meta_model.fit(X_meta_in, y_hat_meta_targets) return self def predict(self, X): """ Generate a base prediction along with uncertainty/confidence for data X. :param X: array-like of shape (n_samples, n_features). Features vectors of the test points. :return: namedtuple: A namedtuple that holds y_pred: ndarray of shape (n_samples,) Predicted labels of the test points. y_score: ndarray of shape (n_samples,) Confidence score the test points. """ y_hat_proba = self.base_model.predict_proba(X) y_hat = np.argmax(y_hat_proba, axis=-1) X_meta_in = self._process_pretrained_model(X, y_hat_proba) z_hat = self.meta_model.predict_proba(X_meta_in) index_of_class_1 = np.where(self.meta_model.classes_ == 1)[0][0] # class 1 corresponds to probab of positive/correct outcome Result = namedtuple('res', ['y_pred', 'y_score']) res = Result(y_hat, z_hat[:, index_of_class_1]) return res from .blackbox_metamodel_regression import BlackboxMetamodelRegression from .blackbox_metamodel_classification import BlackboxMetamodelClassification import inspect from collections import namedtuple import numpy as np from sklearn.ensemble import GradientBoostingRegressor from sklearn.model_selection import train_test_split from sklearn.exceptions import NotFittedError from uq360.algorithms.posthocuq import PostHocUQ class BlackboxMetamodelRegression(PostHocUQ): """ Extracts confidence scores from black-box regression models using a meta-model [2]_ . References: .. [2] Chen, Tongfei, et al. Confidence scoring using whitebox meta-models with linear classifier probes. The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 2019. """ def _create_named_model(self, mdltype, config): """ Instantiates a model by name passed in 'mdltype' :param mdltype: string with name (must be supprted) :param config: dict with args passed in the instantiation call :return: mdl instance """ assert (isinstance(mdltype, str)) if mdltype == 'gbr': mdl = GradientBoostingRegressor(**config) else: raise NotImplementedError("ERROR: Requested model type unknown: \"%s\"" % mdltype) return mdl def _get_model_instance(self, model, config): """ Returns an instance of a model based on (a) a desired name or (b) passed in class, or (c) passed in instance :param model: string, class, or instance. Class and instance must have certain methods callable. :param config: dict with args passed in during the instantiation :return: model instance """ assert (model is not None and config is not None) if isinstance(model, str): # 'model' is a name, create it mdl = self._create_named_model(model, config) elif inspect.isclass(model): # 'model' is a class, instantiate it mdl = model(**config) else: # 'model' is an instance, register it mdl = model if not all([hasattr(mdl, key) and callable(getattr(mdl, key)) for key in self.callable_keys]): raise ValueError("ERROR: Passed model/method failed the interface test. Methods required: %s" % ','.join(self.callable_keys)) return mdl def __init__(self, base_model=None, meta_model=None, base_config=None, meta_config=None, random_seed=42): """ :param base_model: Base model. Can be: (1) None (default mdl will be set up), (2) Named model (e.g., 'gbr'), (3) Base model class declaration (e.g., sklearn.linear_model.LinearRegressor). Will instantiate. (4) Model instance (instantiated outside). Will be re-used. Must have required callable methods. Note: user-supplied classes and models must have certain callable methods ('predict', 'fit') and be capable of raising NotFittedError. :param meta_model: Meta model. Same values possible as with 'base_model' :param base_config: None or a params dict to be passed to 'base_model' at instantiation :param meta_config: None or a params dict to be passed to 'meta_model' at instantiation :param random_seed: seed used in the various pipeline steps """ super(BlackboxMetamodelRegression).__init__() self.random_seed = random_seed self.callable_keys = ['predict', 'fit'] # required methods - must be present in models passed in self.base_model_default = 'gbr' self.meta_model_default = 'gbr' self.base_config_default = {'loss': 'ls', 'n_estimators': 300, 'max_depth': 10, 'learning_rate': 0.001, 'min_samples_leaf': 10, 'min_samples_split': 10, 'random_state': self.random_seed} self.meta_config_default = {'loss': 'quantile', 'alpha': 0.95, 'n_estimators': 300, 'max_depth': 10, 'learning_rate': 0.001, 'min_samples_leaf': 10, 'min_samples_split': 10, 'random_state': self.random_seed} self.base_config = base_config if base_config is not None else self.base_config_default self.meta_config = meta_config if meta_config is not None else self.meta_config_default self.base_model = None self.meta_model = None self.base_model = self._get_model_instance(base_model if base_model is not None else self.base_model_default, self.base_config) self.meta_model = self._get_model_instance(meta_model if meta_model is not None else self.meta_model_default, self.meta_config) def get_params(self, deep=True): return {"base_model": self.base_model, "meta_model": self.meta_model, "base_config": self.base_config, "meta_config": self.meta_config, "random_seed": self.random_seed} def fit(self, X, y, meta_fraction=0.2, randomize_samples=True, base_is_prefitted=False, meta_train_data=(None, None)): """ Fit base and meta models. :param X: input to the base model :param y: ground truth for the base model :param meta_fraction: float in [0,1] - a fractional size of the partition carved out to train the meta model (complement will be used to train the base model) :param randomize_samples: use shuffling when creating partitions :param base_is_prefitted: Setting True will skip fitting the base model (useful for base models that have been instantiated outside/by the user and are already fitted. :param meta_train_data: User supplied data to train the meta model. Note that this option should only be used with 'base_is_prefitted'==True. Pass a tuple meta_train_data=(X_meta, y_meta) to activate. Note that (X,y,meta_fraction, randomize_samples) will be ignored in this mode. :return: self """ X = np.asarray(X) y = np.asarray(y) assert(len(meta_train_data)==2) if meta_train_data[0] is None: X_base, X_meta, y_base, y_meta = train_test_split(X, y, shuffle=randomize_samples, test_size=meta_fraction, random_state=self.random_seed) else: if not base_is_prefitted: raise ValueError("ERROR: fit(): base model must be pre-fitted to use the 'meta_train_data' option") X_base = y_base = None X_meta = meta_train_data[0] y_meta = meta_train_data[1] # fit the base model if not base_is_prefitted: self.base_model.fit(X_base, y_base) # get input for the meta model from the base try: y_hat_meta = self.base_model.predict(X_meta) except NotFittedError as e: raise RuntimeError("ERROR: fit(): The base model appears not pre-fitted (%s)" % repr(e)) # used base input and output as meta input X_meta_in = self._process_pretrained_model(X_meta, y_hat_meta) # train meta model to predict abs diff self.meta_model.fit(X_meta_in, np.abs(y_hat_meta - y_meta)) return self def _process_pretrained_model(self, X, y_hat): """ Given the original input features and the base output probabilities, generate input features to train a meta model. Current implementation copies all input features and appends. :param X: numpy [nsamples, dim] :param y_hat: [nsamples,] :return: array with new features [nsamples, newdim] """ y_hat_meta_prime = np.expand_dims(y_hat, -1) if len(y_hat.shape) < 2 else y_hat X_meta_in = np.hstack([X, y_hat_meta_prime]) return X_meta_in def predict(self, X): """ Generate prediction and uncertainty bounds for data X. :param X: input features :return: namedtuple: A namedtuple that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. """ y_hat = self.base_model.predict(X) y_hat_prime = np.expand_dims(y_hat, -1) if len(y_hat.shape) < 2 else y_hat X_meta_in = np.hstack([X, y_hat_prime]) z_hat = self.meta_model.predict(X_meta_in) Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_hat, y_hat - z_hat, y_hat + z_hat) return res from .heteroscedastic_regression import HeteroscedasticRegression from collections import namedtuple import numpy as np import torch from scipy.stats import norm from torch.utils.data import DataLoader from torch.utils.data import TensorDataset from uq360.algorithms.builtinuq import BuiltinUQ from uq360.models.heteroscedastic_mlp import GaussianNoiseMLPNet as _MLPNet np.random.seed(42) torch.manual_seed(42) class HeteroscedasticRegression(BuiltinUQ): """ Wrapper for heteroscedastic regression. We learn to predict targets given features, assuming that the targets are noisy and that the amount of noise varies between data points. https://en.wikipedia.org/wiki/Heteroscedasticity """ def __init__(self, model_type=None, model=None, config=None, device=None, verbose=True): """ Args: model_type: The base model architecture. Currently supported values are [mlp]. mlp modeltype learns a multi-layer perceptron with a heteroscedastic Gaussian likelihood. Both the mean and variance of the Gaussian are functions of the data point ->git N(y_n | mlp_mu(x_n), mlp_var(x_n)) model: (optional) The prediction model. Currently support pytorch models that returns mean and log variance. config: dictionary containing the config parameters for the model. device: device used for pytorch models ignored otherwise. verbose: if True, print statements with the progress are enabled. """ super(HeteroscedasticRegression).__init__() self.config = config self.device = device self.verbose = verbose if model_type == "mlp": self.model_type = model_type self.model = _MLPNet( num_features=self.config["num_features"], num_outputs=self.config["num_outputs"], num_hidden=self.config["num_hidden"], ) elif model_type == "custom": self.model_type = model_type self.model = model else: raise NotImplementedError def get_params(self, deep=True): return {"model_type": self.model_type, "config": self.config, "model": self.model, "device": self.device, "verbose": self.verbose} def _loss(self, y_true, y_pred_mu, y_pred_log_var): return torch.mean(0.5 * torch.exp(-y_pred_log_var) * torch.abs(y_true - y_pred_mu) ** 2 + 0.5 * y_pred_log_var) def fit(self, X, y): """ Fit the Heteroscedastic Regression model. Args: X: array-like of shape (n_samples, n_features). Features vectors of the training data. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ X = torch.from_numpy(X).float().to(self.device) y = torch.from_numpy(y).float().to(self.device) dataset_loader = DataLoader( TensorDataset(X,y), batch_size=self.config["batch_size"] ) optimizer = torch.optim.Adam(self.model.parameters(), lr=self.config["lr"]) for epoch in range(self.config["num_epochs"]): avg_loss = 0.0 for batch_x, batch_y in dataset_loader: self.model.train() batch_y_pred_mu, batch_y_pred_log_var = self.model(batch_x) loss = self.model.loss(batch_y, batch_y_pred_mu, batch_y_pred_log_var) optimizer.zero_grad() loss.backward() optimizer.step() avg_loss += loss.item()/len(dataset_loader) if self.verbose: print("Epoch: {}, loss = {}".format(epoch, avg_loss)) return self def predict(self, X, return_dists=False): """ Obtain predictions for the test points. In addition to the mean and lower/upper bounds, also returns epistemic uncertainty (return_epistemic=True) and full predictive distribution (return_dists=True). Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. return_dists: If True, the predictive distribution for each instance using scipy distributions is returned. Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. dists: list of predictive distribution as `scipy.stats` objects with length n_samples. Only returned when `return_dists` is True. """ self.model.eval() X = torch.from_numpy(X).float().to(self.device) dataset_loader = DataLoader( X, batch_size=self.config["batch_size"] ) y_mean_list = [] y_log_var_list = [] for batch_x in dataset_loader: batch_y_pred_mu, batch_y_pred_log_var = self.model(batch_x) y_mean_list.append(batch_y_pred_mu.data.cpu().numpy()) y_log_var_list.append(batch_y_pred_log_var.data.cpu().numpy()) y_mean = np.concatenate(y_mean_list) y_log_var = np.concatenate(y_log_var_list) y_std = np.sqrt(np.exp(y_log_var)) y_lower = y_mean - 2.0*y_std y_upper = y_mean + 2.0*y_std Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) if return_dists: dists = [norm(loc=y_mean[i], scale=y_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_dists',)) res = Result(*res, y_dists=dists) return res from .meps_dataset import MEPSDataset # Adapted from https://github.com/Trusted-AI/AIX360/blob/master/aix360/datasets/meps_dataset.py # Utilization target is kept as a continuous target. import os import pandas as pd def default_preprocessing(df): """ 1.Create a new column, RACE that is 'White' if RACEV2X = 1 and HISPANX = 2 i.e. non Hispanic White and 'non-White' otherwise 2. Restrict to Panel 19 3. RENAME all columns that are PANEL/ROUND SPECIFIC 4. Drop rows based on certain values of individual features that correspond to missing/unknown - generally < -1 5. Compute UTILIZATION. """ def race(row): if ((row['HISPANX'] == 2) and (row['RACEV2X'] == 1)): #non-Hispanic Whites are marked as WHITE; all others as NON-WHITE return 'White' return 'Non-White' df['RACEV2X'] = df.apply(lambda row: race(row), axis=1) df = df.rename(columns = {'RACEV2X' : 'RACE'}) df = df[df['PANEL'] == 19] # RENAME COLUMNS df = df.rename(columns = {'FTSTU53X' : 'FTSTU', 'ACTDTY53' : 'ACTDTY', 'HONRDC53' : 'HONRDC', 'RTHLTH53' : 'RTHLTH', 'MNHLTH53' : 'MNHLTH', 'CHBRON53' : 'CHBRON', 'JTPAIN53' : 'JTPAIN', 'PREGNT53' : 'PREGNT', 'WLKLIM53' : 'WLKLIM', 'ACTLIM53' : 'ACTLIM', 'SOCLIM53' : 'SOCLIM', 'COGLIM53' : 'COGLIM', 'EMPST53' : 'EMPST', 'REGION53' : 'REGION', 'MARRY53X' : 'MARRY', 'AGE53X' : 'AGE', 'POVCAT15' : 'POVCAT', 'INSCOV15' : 'INSCOV'}) df = df[df['REGION'] >= 0] # remove values -1 df = df[df['AGE'] >= 0] # remove values -1 df = df[df['MARRY'] >= 0] # remove values -1, -7, -8, -9 df = df[df['ASTHDX'] >= 0] # remove values -1, -7, -8, -9 df = df[(df[['FTSTU','ACTDTY','HONRDC','RTHLTH','MNHLTH','HIBPDX','CHDDX','ANGIDX','EDUCYR','HIDEG', 'MIDX','OHRTDX','STRKDX','EMPHDX','CHBRON','CHOLDX','CANCERDX','DIABDX', 'JTPAIN','ARTHDX','ARTHTYPE','ASTHDX','ADHDADDX','PREGNT','WLKLIM', 'ACTLIM','SOCLIM','COGLIM','DFHEAR42','DFSEE42','ADSMOK42', 'PHQ242','EMPST','POVCAT','INSCOV']] >= -1).all(1)] #for all other categorical features, remove values < -1 def utilization(row): return row['OBTOTV15'] + row['OPTOTV15'] + row['ERTOT15'] + row['IPNGTD15'] + row['HHTOTD15'] df['TOTEXP15'] = df.apply(lambda row: utilization(row), axis=1) df = df.rename(columns = {'TOTEXP15' : 'UTILIZATION'}) df = df[['REGION','AGE','SEX','RACE','MARRY', 'FTSTU','ACTDTY','HONRDC','RTHLTH','MNHLTH','HIBPDX','CHDDX','ANGIDX', 'MIDX','OHRTDX','STRKDX','EMPHDX','CHBRON','CHOLDX','CANCERDX','DIABDX', 'JTPAIN','ARTHDX','ARTHTYPE','ASTHDX','ADHDADDX','PREGNT','WLKLIM', 'ACTLIM','SOCLIM','COGLIM','DFHEAR42','DFSEE42','ADSMOK42','PCS42', 'MCS42','K6SUM42','PHQ242','EMPST','POVCAT','INSCOV','UTILIZATION','PERWT15F']] return df class MEPSDataset(): """ The Medical Expenditure Panel Survey (MEPS) [#]_ data consists of large scale surveys of families and individuals, medical providers, and employers, and collects data on health services used, costs & frequency of services, demographics, health status and conditions, etc., of the respondents. This specific dataset contains MEPS survey data for calendar year 2015 obtained in rounds 3, 4, and 5 of Panel 19, and rounds 1, 2, and 3 of Panel 20. See :file:`uq360/datasets/data/meps_data/README.md` for more details on the dataset and instructions on downloading/processing the data. References: .. [#] `Medical Expenditure Panel Survey data <https://meps.ahrq.gov/mepsweb/>`_ """ def __init__(self, custom_preprocessing=default_preprocessing, dirpath=None): self._dirpath = dirpath if not self._dirpath: self._dirpath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data', 'meps_data') self._filepath = os.path.join(self._dirpath, 'h181.csv') try: df = pd.read_csv(self._filepath, sep=',', na_values=[]) except IOError as err: print("IOError: {}".format(err)) print("To use this class, please place the heloc_dataset.csv:") print("file, as-is, in the folder:") print("\n\t{}\n".format(os.path.abspath(os.path.join( os.path.abspath(__file__), 'data', 'meps_data')))) import sys sys.exit(1) if custom_preprocessing: self._data = custom_preprocessing(df) def data(self): return self._data import autograd import autograd.numpy as np import numpy.random as npr import scipy.optimize sigmoid = lambda x: 0.5 * (np.tanh(x / 2.) + 1) get_num_train = lambda inputs: inputs.shape[0] logistic_predictions = lambda params, inputs: sigmoid(np.dot(inputs, params)) class LogisticRegression: def __init__(self): self.params = None def set_parameters(self, params): self.params = params def predict(self, X): if self.params is not None: # Outputs probability of a label being true according to logistic model return np.atleast_2d(sigmoid(np.dot(X, self.params))).T else: raise RuntimeError("Params need to be fit before predictions can be made.") def loss(self, params, weights, inputs, targets): # Training loss is the negative log-likelihood of the training labels. preds = logistic_predictions(params, inputs) label_probabilities = preds * targets + (1 - preds) * (1 - targets) return -np.sum(weights * np.log(label_probabilities + 1e-16)) def fit(self, weights, init_params, inputs, targets, verbose=True): training_loss_fun = lambda params: self.loss(params, weights, inputs, targets) # Define a function that returns gradients of training loss using Autograd. training_gradient_fun = autograd.grad(training_loss_fun, 0) # optimize params if verbose: print("Initial loss:", self.loss(init_params, weights, inputs, targets)) # opt_params = sgd(training_gradient_fun, params, hyper=1, num_iters=5000, step_size=0.1) res = scipy.optimize.minimize(fun=training_loss_fun, jac=training_gradient_fun, x0=init_params, tol=1e-10, options={'disp': verbose}) opt_params = res.x if verbose: print("Trained loss:", self.loss(opt_params, weights, inputs, targets)) self.params = opt_params return opt_params def get_test_acc(self, params, test_targets, test_inputs): preds = np.round(self.predict(test_inputs).T).astype(np.int) err = np.abs(test_targets - preds).sum() return 1 - err/ test_targets.shape[1] #### Required for IJ computation ### def compute_hessian(self, params_one, weights_one, inputs, targets): return autograd.hessian(self.loss, argnum=0)(params_one, weights_one, inputs, targets) def compute_jacobian(self, params_one, weights_one, inputs, targets): return autograd.jacobian(autograd.jacobian(self.loss, argnum=0), argnum=1)\ (params_one, weights_one, inputs, targets).squeeze() ################################################### @staticmethod def synthetic_lr_data(N=10000, D=10): x = 1. * npr.randn(N, D) x_test = 1. * npr.randn(int(0.3 * N), D) w = npr.randn(D, 1) y = sigmoid((x @ w)).ravel() y = npr.binomial(n=1, p=y) # corrupt labels y_test = sigmoid(x_test @ w).ravel() # y_test = np.round(y_test) y_test = npr.binomial(n=1, p=y_test) return x, np.atleast_2d(y), x_test, np.atleast_2d(y_test) import autograd import autograd.numpy as np import scipy.optimize from autograd import grad from autograd.scipy.special import logsumexp from sklearn.cluster import KMeans class HMM: """ A Hidden Markov Model with Gaussian observations with unknown means and known precisions. """ def __init__(self, X, config_dict=None): self.N, self.T, self.D = X.shape self.K = config_dict['K'] # number of HMM states self.I = np.eye(self.K) self.Precision = np.zeros([self.D, self.D, self.K]) self.X = X if config_dict['precision'] is None: for k in np.arange(self.K): self.Precision[:, :, k] = np.eye(self.D) else: self.Precision = config_dict['precision'] self.dParams_dWeights = None self.alphaT = None # Store the final beliefs. self.beta1 = None # store the first timestep beliefs from the beta recursion. self.forward_trellis = {} # stores \alpha self.backward_trellis = {} # stores \beta def initialize_params(self, seed=1234): np.random.seed(seed) param_dict = {} A = np.random.randn(self.K, self.K) # use k-means to initialize the mean parameters X = self.X.reshape([-1, self.D]) kmeans = KMeans(n_clusters=self.K, random_state=seed, n_init=15).fit(X) labels = kmeans.labels_ _, counts = np.unique(labels, return_counts=True) pi = counts phi = kmeans.cluster_centers_ param_dict['A'] = np.exp(A) param_dict['pi0'] = pi param_dict['phi'] = phi return self.pack_params(param_dict) def unpack_params(self, params): param_dict = dict() K = self.K # For unpacking simplex parameters: have packed them as # log(pi[:-1]) - log(pi[-1]). unnorm_A = np.exp(np.append(params[:K**2-K].reshape(K, K-1), np.zeros((K, 1)), axis=1) ) Z = np.sum(unnorm_A[:, :-1], axis=1) unnorm_A /= Z[:, np.newaxis] norm_A = unnorm_A / unnorm_A.sum(axis=1, keepdims=True) param_dict['A'] = norm_A unnorm_pi = np.exp(np.append(params[K**2-K:K**2-1], 0.0)) Z = np.sum(unnorm_pi[:-1]) unnorm_pi /= Z param_dict['pi0'] = unnorm_pi / unnorm_pi.sum() param_dict['phi'] = params[K**2-K+K-1:].reshape(self.D, K) return param_dict def weighted_alpha_recursion(self, xseq, pi, phi, Sigma, A, wseq, store_belief=False): """ Computes the weighted marginal probability of the sequence xseq given parameters; weights wseq turn on or off the emissions p(x_t | z_t) (weighting scheme B) :param xseq: T * D :param pi: K * 1 :param phi: D * K :param wseq: T * 1 :param A: :return: """ ll = self.log_obs_lik(xseq[:, :, np.newaxis], phi[np.newaxis, :, :], Sigma) alpha = np.log(pi.ravel()) + wseq[0] * ll[0] if wseq[0] == 0: self.forward_trellis[0] = alpha[:, np.newaxis] for t in np.arange(1, self.T): alpha = logsumexp(alpha[:, np.newaxis] + np.log(A), axis=0) + wseq[t] * ll[t] if wseq[t] == 0: # store the trellis, would be used to compute the posterior z_t | x_1...x_t-1, x_t+1, ...x_T self.forward_trellis[t] = alpha[:, np.newaxis] if store_belief: # store the final belief self.alphaT = alpha return logsumexp(alpha) def weighted_beta_recursion(self, xseq, pi, phi, Sigma, A, wseq, store_belief=False): """ Runs beta recursion; weights wseq turn on or off the emissions p(x_t | z_t) (weighting scheme B) :param xseq: T * D :param pi: K * 1 :param phi: D * K :param wseq: T * 1 :param A: :return: """ ll = self.log_obs_lik(xseq[:, :, np.newaxis], phi[np.newaxis, :, :], Sigma) beta = np.zeros_like(pi.ravel()) # log(\beta) of all ones. max_t = ll.shape[0] if wseq[max_t - 1] == 0: # store the trellis, would be used to compute the posterior z_t | x_1...x_t-1, x_t+1, ...x_T self.backward_trellis[max_t - 1] = beta[:, np.newaxis] for i in np.arange(1, max_t): t = max_t - i - 1 beta = logsumexp((beta + wseq[t + 1] * ll[t + 1])[np.newaxis, :] + np.log(A), axis=1) if wseq[t] == 0: # store the trellis, would be used to compute the posterior z_t | x_1...x_t-1, x_t+1, ...x_T self.backward_trellis[t] = beta[:, np.newaxis] # account for the init prob beta = (beta + wseq[0] * ll[0]) + np.log(pi.ravel()) if store_belief: # store the final belief self.beta1 = beta return logsumexp(beta) def weighted_loss(self, params, weights): """ For LOOCV / IF computation within a single sequence. Uses weighted alpha recursion :param params: :param weights: :return: """ param_dict = self.unpack_params(params) logp = self.get_prior_contrib(param_dict) logp = logp + self.weighted_alpha_recursion(self.X[0], param_dict['pi0'], param_dict['phi'], self.Precision, param_dict['A'], weights) return -logp def loss_at_missing_timesteps(self, weights, params): """ :param weights: zeroed out weights indicate missing values :param params: packed parameters :return: """ # empty forward and backward trellis self.clear_trellis() param_dict = self.unpack_params(params) # populate forward and backward trellis lpx = self.weighted_alpha_recursion(self.X[0], param_dict['pi0'], param_dict['phi'], self.Precision, param_dict['A'], weights, store_belief=True ) lpx_alt = self.weighted_beta_recursion(self.X[0], param_dict['pi0'], param_dict['phi'], self.Precision, param_dict['A'], weights, store_belief=True) assert np.allclose(lpx, lpx_alt) # sanity check test_ll = [] # compute loo likelihood ll = self.log_obs_lik(self.X[0][:, :, np.newaxis], param_dict['phi'], self.Precision) # compute posterior p(z_t | x_1,...t-1, t+1,...T) \forall missing t tsteps = [] for t in self.forward_trellis.keys(): lpz_given_x = self.forward_trellis[t] + self.backward_trellis[t] - lpx test_ll.append(logsumexp(ll[t] + lpz_given_x.ravel())) tsteps.append(t) # empty forward and backward trellis self.clear_trellis() return -np.array(test_ll) def fit(self, weights, init_params=None, num_random_restarts=1, verbose=False, maxiter=None): if maxiter: options_dict = {'disp': verbose, 'gtol': 1e-10, 'maxiter': maxiter} else: options_dict = {'disp': verbose, 'gtol': 1e-10} # Define a function that returns gradients of training loss using Autograd. training_loss_fun = lambda params: self.weighted_loss(params, weights) training_gradient_fun = grad(training_loss_fun, 0) if init_params is None: init_params = self.initialize_params() if verbose: print("Initial loss: ", training_loss_fun(init_params)) res = scipy.optimize.minimize(fun=training_loss_fun, jac=training_gradient_fun, x0=init_params, tol=1e-10, options=options_dict) if verbose: print('grad norm =', np.linalg.norm(res.jac)) return res.x def clear_trellis(self): self.forward_trellis = {} self.backward_trellis = {} #### Required for IJ computation ### def compute_hessian(self, params_one, weights_one): return autograd.hessian(self.weighted_loss, argnum=0)(params_one, weights_one) def compute_jacobian(self, params_one, weights_one): return autograd.jacobian(autograd.jacobian(self.weighted_loss, argnum=0), argnum=1)\ (params_one, weights_one).squeeze() ################################################### @staticmethod def log_obs_lik(x, phi, Sigma): """ :param x: T*D*1 :param phi: 1*D*K :param Sigma: D*D*K --- precision matrices per state :return: ll """ centered_x = x - phi ll = -0.5 * np.einsum('tdk, tdk, ddk -> tk', centered_x, centered_x, Sigma ) return ll @staticmethod def pack_params(params_dict): param_list = [(np.log(params_dict['A'][:, :-1]) - np.log(params_dict['A'][:, -1])[:, np.newaxis]).ravel(), np.log(params_dict['pi0'][:-1]) - np.log(params_dict['pi0'][-1]), params_dict['phi'].ravel()] return np.concatenate(param_list) @staticmethod def get_prior_contrib(param_dict): logp = 0.0 # Prior logp += -0.5 * (np.linalg.norm(param_dict['phi'], axis=0) ** 2).sum() logp += (1.1 - 1) * np.log(param_dict['A']).sum() logp += (1.1 - 1) * np.log(param_dict['pi0']).sum() return logp @staticmethod def get_indices_in_held_out_fold(T, pct_to_drop, contiguous=False): """ :param T: length of the sequence :param pct_to_drop: % of T in the held out fold :param contiguous: if True generate a block of indices to drop else generate indices by iid sampling :return: o (the set of indices in the fold) """ if contiguous: l = np.floor(pct_to_drop / 100. * T) anchor = np.random.choice(np.arange(l + 1, T)) o = np.arange(anchor - l, anchor).astype(int) else: # i.i.d LWCV o = np.random.choice(T - 2, size=np.int(pct_to_drop / 100. * T), replace=False) + 1 return o @staticmethod def synthetic_hmm_data(K, T, D, sigma0=None, seed=1234, varainces_of_mean=1.0, diagonal_upweight=False): """ :param K: Number of HMM states :param T: length of the sequence """ N = 1 # For structured IJ we will remove data / time steps from a single sequence np.random.seed(seed) if sigma0 is None: sigma0 = np.eye(D) A = np.random.dirichlet(alpha=np.ones(K), size=K) if diagonal_upweight: A = A + 3 * np.eye(K) # add 3 to the diagonal and renormalize to encourage self transitions A = A / A.sum(axis=1) pi0 = np.random.dirichlet(alpha=np.ones(K)) mus = np.random.normal(size=(K, D), scale=np.sqrt(varainces_of_mean)) zs = np.empty((N, T), dtype=np.int) X = np.empty((N, T, D)) for n in range(N): zs[n, 0] = int(np.random.choice(np.arange(K), p=pi0)) X[n, 0] = np.random.multivariate_normal(mean=mus[zs[n, 0]], cov=sigma0) for t in range(1, T): zs[n, t] = int(np.random.choice(np.arange(K), p=A[zs[n, t - 1], :])) X[n, t] = np.random.multivariate_normal(mean=mus[zs[n, t]], cov=sigma0) return {'X': X, 'state_assignments': zs, 'A': A, 'initial_state_assignment': pi0, 'means': mus} import abc import sys # Ensure compatibility with Python 2/3 if sys.version_info >= (3, 4): ABC = abc.ABC else: ABC = abc.ABCMeta(str('ABC'), (), {}) from copy import deepcopy import numpy as np import numpy.random as npr def make_batches(n_data, batch_size): return [slice(i, min(i+batch_size, n_data)) for i in range(0, n_data, batch_size)] def generate_regression_data(seed, data_count=500): """ Generate data from a noisy sine wave. :param seed: random number seed :param data_count: number of data points. :return: """ np.random.seed(seed) noise_var = 0.1 x = np.linspace(-4, 4, data_count) y = 1*np.sin(x) + np.sqrt(noise_var)*npr.randn(data_count) train_count = int (0.2 * data_count) idx = npr.permutation(range(data_count)) x_train = x[idx[:train_count], np.newaxis ] x_test = x[ idx[train_count:], np.newaxis ] y_train = y[ idx[:train_count] ] y_test = y[ idx[train_count:] ] mu = np.mean(x_train, 0) std = np.std(x_train, 0) x_train = (x_train - mu) / std x_test = (x_test - mu) / std mu = np.mean(y_train, 0) std = np.std(y_train, 0) y_train = (y_train - mu) / std train_stats = dict() train_stats['mu'] = mu train_stats['sigma'] = std return x_train, y_train, x_test, y_test, train_stats def form_D_for_auucc(yhat, zhatl, zhatu): # a handy routine to format data as needed by the UCC fit() method D = np.zeros([yhat.shape[0], 3]) D[:, 0] = yhat.squeeze() D[:, 1] = zhatl.squeeze() D[:, 2] = zhatu.squeeze() return D def fitted_ucc_w_nullref(y_true, y_pred_mean, y_pred_lower, y_pred_upper): """ Instantiates an UCC object for the target predictor plus a 'null' (constant band) reference :param y_pred_lower: :param y_pred_mean: :param y_pred_upper: :param y_true: :return: ucc object fitted for two systems: target + null reference """ # form matrix for ucc: X_for_ucc = form_D_for_auucc(y_pred_mean.squeeze(), y_pred_mean.squeeze() - y_pred_lower.squeeze(), y_pred_upper.squeeze() - y_pred_mean.squeeze()) # form matrix for a 'null' system (constant band) X_null = deepcopy(X_for_ucc) X_null[:,1:] = np.std(y_pred_mean) # can be set to any other constant (no effect on AUUCC) # create an instance of ucc and fit data from uq360.metrics.uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve as ucc u = ucc() u.fit([X_for_ucc, X_null], y_true.squeeze()) return u def make_sklearn_compatible_scorer(task_type, metric, greater_is_better=True, **kwargs): """ Args: task_type: (str) regression or classification. metric: (str): choice of metric can be one of these - [aurrrc, ece, auroc, nll, brier, accuracy] for classification and ["rmse", "nll", "auucc_gain", "picp", "mpiw", "r2"] for regression. greater_is_better: is False the scores are negated before returning. **kwargs: additional arguments specific to some metrics. Returns: sklearn compatible scorer function. """ from uq360.metrics.classification_metrics import compute_classification_metrics from uq360.metrics.regression_metrics import compute_regression_metrics def sklearn_compatible_score(model, X, y_true): """ Args: model: The model being scored. Currently uq360 and sklearn models are supported. X: Input features. y_true: ground truth values for the target. Returns: Computed score of the model. """ from uq360.algorithms.builtinuq import BuiltinUQ from uq360.algorithms.posthocuq import PostHocUQ if isinstance(model, BuiltinUQ) or isinstance(model, PostHocUQ): # uq360 models if task_type == "classification": score = compute_classification_metrics( y_true=y_true, y_prob=model.predict(X).y_prob, option=metric, **kwargs )[metric] elif task_type == "regression": y_mean, y_lower, y_upper = model.predict(X) score = compute_regression_metrics( y_true=y_true, y_mean=y_mean, y_lower=y_lower, y_upper=y_upper, option=metric, **kwargs )[metric] else: raise NotImplementedError else: # sklearn models if task_type == "classification": score = compute_classification_metrics( y_true=y_true, y_prob=model.predict_proba(X), option=metric, **kwargs )[metric] else: if metric in ["rmse", "r2"]: score = compute_regression_metrics( y_true=y_true, y_mean=model.predict(X), y_lower=None, y_upper=None, option=metric, **kwargs )[metric] else: raise NotImplementedError("{} is not supported for sklearn regression models".format(metric)) if not greater_is_better: score = -score return score return sklearn_compatible_score class DummySklearnEstimator(ABC): def __init__(self, num_classes, base_model_prediction_fn): self.base_model_prediction_fn = base_model_prediction_fn self.classes_ = [i for i in range(num_classes)] def fit(self): pass def predict_proba(self, X): return self.base_model_prediction_fn(X) from builtins import range import autograd.numpy as np def adam(grad, x, callback=None, num_iters=100, step_size=0.001, b1=0.9, b2=0.999, eps=10**-8, polyak=False): """Adapted from autograd.misc.optimizers""" m = np.zeros(len(x)) v = np.zeros(len(x)) for i in range(num_iters): g = grad(x, i) if callback: callback(x, i, g, polyak) m = (1 - b1) * g + b1 * m # First moment estimate. v = (1 - b2) * (g**2) + b2 * v # Second moment estimate. mhat = m / (1 - b1**(i + 1)) # Bias correction. vhat = v / (1 - b2**(i + 1)) x = x - step_size*mhat/(np.sqrt(vhat) + eps) return x import matplotlib.pyplot as plt import numpy as np import numpy.random as npr import torch as torch def make_data_gap(seed, data_count=100): import GPy npr.seed(0) x = np.hstack([np.linspace(-5, -2, int(data_count/2)), np.linspace(2, 5, int(data_count/2))]) x = x[:, np.newaxis] k = GPy.kern.RBF(input_dim=1, variance=1., lengthscale=1.) K = k.K(x) L = np.linalg.cholesky(K + 1e-5 * np.eye(data_count)) # draw a noise free random function from a GP eps = np.random.randn(data_count) f = L @ eps # use a homoskedastic Gaussian noise model N(f(x)_i, \sigma^2). \sigma^2 = 0.1 eps_noise = np.sqrt(0.1) * np.random.randn(data_count) y = f + eps_noise y = y[:, np.newaxis] plt.plot(x, f, 'ko', ms=2) plt.plot(x, y, 'ro') plt.title("GP generated Data") plt.pause(1) return torch.FloatTensor(x), torch.FloatTensor(y), torch.FloatTensor(x), torch.FloatTensor(y) def make_data_sine(seed, data_count=450): # fix the random seed np.random.seed(seed) noise_var = 0.1 X = np.linspace(-4, 4, data_count) y = 1*np.sin(X) + np.sqrt(noise_var)*npr.randn(data_count) train_count = int (0.2 * data_count) idx = npr.permutation(range(data_count)) X_train = X[idx[:train_count], np.newaxis ] X_test = X[ idx[train_count:], np.newaxis ] y_train = y[ idx[:train_count] ] y_test = y[ idx[train_count:] ] mu = np.mean(X_train, 0) std = np.std(X_train, 0) X_train = (X_train - mu) / std X_test = (X_test - mu) / std mu = np.mean(y_train, 0) std = np.std(y_train, 0) # mu = 0 # std = 1 y_train = (y_train - mu) / std y_test = (y_test -mu) / std train_stats = dict() train_stats['mu'] = torch.FloatTensor([mu]) train_stats['sigma'] = torch.FloatTensor([std]) return torch.FloatTensor(X_train), torch.FloatTensor(y_train), torch.FloatTensor(X_test), torch.FloatTensor(y_test),\ train_stats ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' #System imports import os import sys import json import datetime,time,timeit import itertools import numpy as np import pandas as pd import math from sklearn.preprocessing import MinMaxScaler,StandardScaler from sklearn.preprocessing import PowerTransformer import logging class dataTransformer(): def __init__(self): self.log = logging.getLogger('eion') def startTransformer(self,df,features,target,transType): scaler ='None' if target in features: features.remove(target) transFeatures=features transDfColumns=[] dataframe=df[transFeatures] #targetArray=np.array(df[target]) #targetArray.shape = (len(targetArray), 1) self.log.info("Data Normalization has started") if transType.lower() =='standardscaler': scaler = StandardScaler().fit(dataframe) transDf = scaler.transform(dataframe) elif transType.lower() =='minmax': scaler=MinMaxScaler().fit(dataframe) transDf = scaler.transform(dataframe) elif transType.lower() =='lognormal': print(dataframe) scaler = PowerTransformer(method='yeo-johnson', standardize=False).fit(dataframe) transDf = scaler.transform(dataframe) else: self.log.info("Need to implement") #features.append(target) #scaledDf = pd.DataFrame(np.hstack((transDf, targetArray)),columns=features) return transDf,features,scaler import pandas as pd tab = ' ' VALID_AGGREGATION_METHODS = ['mean','sum'] VALID_GRANULARITY_UNITS = ['second','minute','hour','day','week','month','year'] VALID_INTERPOLATE_KWARGS = {'linear':{},'spline':{'order':5},'timebased':{}} VALID_INTERPOLATE_METHODS = list( VALID_INTERPOLATE_KWARGS.keys()) def get_one_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def get_boolean(value): if (isinstance(value, str) and value.lower() == 'true') or (isinstance(value, bool) and value == True): return True else: return False def get_source_delta( data: pd.DataFrame): MAX_SAMPLE_TRY = 20 if len( data) <= 1: return None time_delta = data.index[-1] - data.index[-2] count = {} for i in range(len(data)): if i == MAX_SAMPLE_TRY or i == data.index[-1]: break delta = data.index[i+1] - data.index[i] if delta not in count.keys(): count[delta] = 1 else: count[delta] += 1 if count: return max(count, key=count.get) else: return None class timeSeries(): def __init__( self, config, datetime, log=None): self.datetime = datetime self.validate_config(config) self.log = log def validate_config( self, config): if not self.datetime or self.datetime.lower() == 'na': raise ValueError('date time feature is not provided') self.config = {} method = get_one_true_option(config.get('interpolation',None)) self.config['interpolate'] = {} self.config['interpolate']['enabled'] = method in VALID_INTERPOLATE_METHODS self.config['interpolate']['method'] = method self.config['rolling'] = {} self.config['rolling']['enabled'] = get_boolean( config.get('rollingWindow',False)) self.config['rolling']['size'] = int( config.get('rollingWindowSize',1)) if self.config['rolling']['size'] < 1: raise ValueError('Rolling window size should be greater than 0.') self.config['aggregation'] = {} aggregation = config.get('aggregation',{}) agg_method = get_one_true_option(aggregation['type']) self.config['aggregation'] = {} self.config['aggregation']['enabled'] = agg_method in VALID_AGGREGATION_METHODS self.config['aggregation']['method'] = agg_method granularity = aggregation.get('granularity',{}) granularity_unit = get_one_true_option( granularity.get('unit',None)) if granularity_unit in VALID_GRANULARITY_UNITS: granularity_mapping = {'second':'S','minute':'Min','hour':'H','day':'D','week':'W','month':'M','year':'Y'} size = int(granularity.get('size',10)) granularity_unit = granularity_mapping.get(granularity_unit,granularity_unit) self.config['aggregation']['granularity'] = {} self.config['aggregation']['granularity']['unit'] = granularity_unit self.config['aggregation']['granularity']['size'] = size def log_info(self, msg, type='info'): if self.log: if type == 'error': self.log.error( msg) else: self.log.info( msg) else: print( msg) def is_down_sampling(self, data, size, granularity_unit): down_sampling = False if granularity_unit in ['M', 'Y']: return True else: target_delta = pd.Timedelta(size , granularity_unit) source_delta = get_source_delta(data) if not source_delta: raise ValueError('Could not find the data frame time frequency') return source_delta < target_delta def run( self, data): if self.datetime not in data.columns: raise ValueError(f"Date time feature '{self.datetime}' is not present in data") try: # data[self.datetime] = pd.to_datetime( data[self.datetime]) ##For bug 13513 - If the datetime needs UTC timestamp process, except part will handle. try: #for non utc timestamp data[self.datetime] = pd.to_datetime( data[self.datetime]) except: #for utc timestamp data[self.datetime] = pd.to_datetime( data[self.datetime],utc=True) data.set_index( self.datetime, inplace=True) except: raise ValueError(f"can not convert '{self.datetime}' to dateTime") if self.config.get('interpolate',{}).get('enabled',False): method = self.config['interpolate']['method'] self.log_info(f"Applying Interpolation using {method}") methods_mapping = {'timebased': 'time'} self.config['interpolate']['mapped_method'] = methods_mapping.get(method, method) data.interpolate(method=self.config['interpolate']['mapped_method'], inplace=True, **VALID_INTERPOLATE_KWARGS[method]) if self.config.get('rolling',{}).get('enabled',False): if self.config['rolling']['size'] > len( data): raise ValueError('Rolling window size is greater than dataset size') self.log_info(f"Applying rolling window( moving avg) with size {self.config['rolling']['size']}") data = data.rolling( self.config['rolling']['size']).mean() data = data.iloc[self.config['rolling']['size'] - 1:] aggregation = self.config.get('aggregation',{}) if aggregation.get('enabled',False): method = aggregation.get('method','mean') self.rule = str(aggregation['granularity']['size']) + aggregation['granularity']['unit'] if self.is_down_sampling(data, aggregation['granularity']['size'], aggregation['granularity']['unit']): self.log_info(f"Applying down sampling( {self.rule})") if method == 'mean': data = data.resample( self.rule).mean() elif method == 'sum': data = data.resample( self.rule).sum() else: self.log_info(f"Applying up sampling using forward fill method( {self.rule})") data = data.resample( self.rule).ffill() data.reset_index( inplace=True, names=self.datetime) return data def get_code(self, indent=0): tab = ' ' code = '' code += f""" def preprocess( data): try: #for non utc timestamp data['{self.datetime}'] = pd.to_datetime( data['{self.datetime}']) except: data['{self.datetime}'] = pd.to_datetime( data['{self.datetime}'],utc=True) data.set_index( '{self.datetime}', inplace=True) """ if self.config.get('interpolate',{}).get('enabled',False): code += tab + f"data.interpolate(method='{self.config['interpolate']['mapped_method']}', inplace=True, **{VALID_INTERPOLATE_KWARGS[self.config['interpolate']['method']]})\n" if self.config.get('rolling',{}).get('enabled',False): code += tab + f"data = data.rolling( {self.config['rolling']['size']}).mean().iloc[{self.config['rolling']['size'] - 1}:]\n" if self.config.get('aggregation',{}).get('enabled',False): code += tab + f"data = data.resample( '{self.rule}').{self.config.get('aggregation',{}).get('method','mean')}()\n" code += tab + f"data.reset_index( inplace=True, names='{self.datetime}')\n" code += tab + "return data\n" return code ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import os import sys import string import spacy #import en_core_web_sm from spacy.lang.en.stop_words import STOP_WORDS from spacy.lang.en import English try: from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS except: from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer from sklearn.base import TransformerMixin from nltk.stem import WordNetLemmatizer import re from collections import defaultdict from nltk.corpus import wordnet as wn from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import LabelBinarizer from nltk.tokenize import word_tokenize from nltk import pos_tag from nltk.corpus import stopwords class textDataProfiler(): def __init__(self): self.data=None #self.nlp=en_core_web_sm.load() self.punctuations = string.punctuation self.stopwords = list(STOP_WORDS) def startTextProfiler(self,df,target): try: dataColumns = list(df.columns) print(' \n No of rows and columns in dataFrame',df.shape) print('\n features in dataFrame',dataColumns) dataFDtypes=self.dataFramecolType(df) print('\n feature types in dataFrame',dataFDtypes) trainX=df['text'] trainY=df[target] return trainX,trainY except Exception as inst: print('startTextProfiler code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) def dataFramecolType(self,dataFrame): dataFDtypes=[] try: dataColumns=list(dataFrame.columns) for i in dataColumns: dataType=dataFrame[i].dtypes dataFDtypes.append(tuple([i,str(dataType)])) return dataFDtypes except Exception as e: print("error in dataFramecolyType",e) return dataFDtypes def textTokenizer(self,text): try: parser = English() tokens = parser(text) tokens = [ word.lemma_.lower().strip() if word.lemma_ != "-PRON-" else word.lower_ for word in tokens ] tokens = [ word for word in tokens if word not in self.stopwords and word not in self.punctuations ] return tokens except Exception as inst: print('textDataProfiler code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) return {} def cleanText(self,text): try: text=str(text).strip().lower() for punctuation in string.punctuation: text = text.replace(punctuation, '') return text except Exception as inst: print('cleanText code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) def textTokenization(self,text): try: tokenizedText=word_tokenize(text) return tokenizedText except Exception as inst: print('textDataProfiler code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) return {} def textLemmitizer(self,text): try: tag_map = defaultdict(lambda : wn.NOUN) tag_map['J'] = wn.ADJ tag_map['V'] = wn.VERB tag_map['R'] = wn.ADV Final_words = [] word_Lemmatized = WordNetLemmatizer() for word, tag in pos_tag(text): if word not in stopwords.words('english') and word.isalpha(): word_Final = word_Lemmatized.lemmatize(word,tag_map[tag[0]]) Final_words.append(word_Final) return str(Final_words) except Exception as inst: print('textLemmitizer code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) return {} class TextCleaner(TransformerMixin): def clean_text(self,text): try: text=str(text).strip().lower() text = text.replace("isn't", "is not") text = text.replace("aren't", "are not") text = text.replace("ain't", "am not") text = text.replace("won't", "will not") text = text.replace("didn't", "did not") text = text.replace("shan't", "shall not") text = text.replace("haven't", "have not") text = text.replace("hadn't", "had not") text = text.replace("hasn't", "has not") text = text.replace("don't", "do not") text = text.replace("wasn't", "was not") text = text.replace("weren't", "were not") text = text.replace("doesn't", "does not") text = text.replace("'s", " is") text = text.replace("'re", " are") text = text.replace("'m", " am") text = text.replace("'d", " would") text = text.replace("'ll", " will") text = re.sub(r'^https?:\/\/.*[\r\n]*', ' ', text, flags=re.MULTILINE) text = re.sub(r'[\w\.-]+@[\w\.-]+', ' ', text, flags=re.MULTILINE) for punctuation in string.punctuation: text = text.replace(punctuation,' ') text = re.sub(r'[^A-Za-z0-9\s]',r' ',text) text = re.sub(r'\n',r' ',text) text = re.sub(r'[0-9]',r' ',text) wordnet_lemmatizer = WordNetLemmatizer() text = " ".join([wordnet_lemmatizer.lemmatize(w, pos='v') for w in text.split()]) return text except Exception as inst: print('TextCleaner clean_text code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) def text_cleaner(self,text): text = self.clean_text(text) stop_words = set(stopwords.words('english')) text_tokens = word_tokenize(text) out=' '.join(str(j) for j in text_tokens if j not in stop_words and (len(j)!=1)) return(out) def transform(self, X, **transform_params): # Cleaning Text return [self.clean_text(text) for text in X] def fit(self, X, y=None, **fit_params): return self def get_params(self, deep=True): return {} ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import random from matplotlib import pyplot as plt import cv2 import albumentations as A import os import pandas as pd from pathlib import Path class ImageAugmentation(): def __init__(self, dataLocation, csvFile): self.AugmentationOptions = {"Flip": {"operation": A.HorizontalFlip, "suffix":"_flip"}, "Rotate": {"operation": A.Rotate, "suffix":"_rotate"}, "Shift": {"operation": A.RGBShift, "suffix":"_shift"}, "Crop": {"operation": [A.CenterCrop, A.RandomSizedBBoxSafeCrop], "suffix":"_crop"}, "Contrast": {"operation": A.RandomContrast, "suffix":"_cont"}, "Brightness": {"operation": A.RandomBrightness, "suffix":"_bright"}, "Blur": {"operation": A.GaussianBlur, "suffix":"_blur"} } self.dataLocation = dataLocation self.csvFile = csvFile def __applyAugmentationClass(self, image, augmentation,limit): if augmentation in list(self.AugmentationOptions.keys()): if augmentation == "Crop": height, width, _ = image.shape crop_percentage = random.uniform(0.6, 0.9) transform = self.AugmentationOptions[augmentation]["operation"][0](height=int(height*crop_percentage), width=int(width*crop_percentage) ) elif augmentation == "Blur": transform = self.AugmentationOptions[augmentation]["operation"](blur_limit = limit) elif augmentation in ["Contrast","Brightness"]: transform = self.AugmentationOptions[augmentation]["operation"](limit = limit) else: transform = self.AugmentationOptions[augmentation]["operation"]() return transform(image=image) def __applyAugmentation(self, image, augmentation,limit,bboxes=None, category_ids=None, seed=7): transformOptions = [] if bboxes: bbox_params = A.BboxParams(format='pascal_voc', label_fields=['category_ids']) else: bbox_params = None if augmentation in list(self.AugmentationOptions.keys()): if augmentation == "Crop": height, width, _ = image.shape crop_percentage = random.uniform(0.6, 0.9) transformOptions.append(self.AugmentationOptions[augmentation]["operation"][1](height=int(height*crop_percentage), width=int(width*crop_percentage) )) elif augmentation == "Blur": transformOptions.append(self.AugmentationOptions[augmentation]["operation"](blur_limit = limit)) elif augmentation in ["Contrast","Brightness"]: transformOptions.append(self.AugmentationOptions[augmentation]["operation"](limit = limit)) else: transformOptions.append(self.AugmentationOptions[augmentation]["operation"]()) transform = A.Compose( transformOptions, bbox_params=bbox_params, ) random.seed(seed) return transform(image=image, bboxes=bboxes, category_ids=category_ids) else: return None def getBBox(self, df, imageLoc, category_name_to_id): subDf = df[df['loc']==imageLoc] boxes = [] category = [] for index, row in subDf.iterrows(): boxes.append( [row['xmin'],row['ymin'],row['xmax'],row['ymax']]) category.append(category_name_to_id[row['Label']]) return boxes, category def __objAug(self, imageLoc, df, classes_names, category_id_to_name, category_name_to_id,limit,numberofImages,op): for x in range(numberofImages): bbox, category_ids = self.getBBox(df, imageLoc, category_name_to_id) image = cv2.imread(imageLoc) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) transformed = self.__applyAugmentation(image, op,limit,bbox, category_ids) transformed['image'] = cv2.cvtColor(transformed['image'], cv2.COLOR_RGB2BGR) count = 1 row = df[df['loc']==imageLoc].iloc[0] filename = (Path(imageLoc).stem +'_'+str(x)+ self.AugmentationOptions[op]["suffix"] + Path(imageLoc).suffix) newImage = str(Path(imageLoc).parent/filename) for index,bbox in enumerate(transformed['bboxes']): data = {'File':filename, 'xmin':bbox[0],'ymin':bbox[1],'xmax':bbox[2],'ymax':bbox[3],'Label':category_id_to_name[transformed['category_ids'][index]],'id':count,'height':row['height'],'width':row['width'], 'angle':0.0, 'loc': newImage, 'AugmentedImage': True} count += 1 df=df.append(data, ignore_index=True) cv2.imwrite(newImage, transformed['image']) return df def __objectDetection(self, images, df, optionDf, classes_names, suffix='',augConf={}): category_id_to_name = {v+1:k for v,k in enumerate(classes_names)} category_name_to_id = {k:v+1 for v,k in enumerate(classes_names)} for i, imageLoc in enumerate(images): for key in optionDf.columns: if optionDf.iloc[i][key]: if key in augConf: limit = eval(augConf[key].get('limit','0.2')) numberofImages = int(augConf[key].get('noOfImages',1)) else: limit = 0.2 numberofImages = 1 df = self.__objAug(imageLoc, df, classes_names, category_id_to_name,category_name_to_id,limit,numberofImages,op=key) return df def __augClassificationImage(self, imageLoc, df,limit,imageindex,op): data = {} image = cv2.imread(imageLoc) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) transformed = self.__applyAugmentationClass(image, op,limit) transformed['image'] = cv2.cvtColor(transformed['image'], cv2.COLOR_RGB2BGR) location = Path(imageLoc).parent filename = (Path(imageLoc).stem +'_'+'str(imageindex)'+ self.AugmentationOptions[op]["suffix"] + Path(imageLoc).suffix) cv2.imwrite(str(location/'AION'/'AugumentedImages'/filename), transformed['image']) data['File'] = filename data['Label'] = df[df['File']==Path(imageLoc).name]["Label"].iloc[0] data['AugmentedImage'] = True data['loc'] = str(location/filename) return data def __classification(self, images, df, optionDf,augConf,csv_file=None, outputDir=None): for i, imageLoc in enumerate(images): for key in optionDf.columns: if optionDf.iloc[i][key]: if key in augConf: limit = eval(augConf[key].get('limit','0.2')) numberofImages = int(augConf[key].get('noOfImages',1)) else: limit = 0.2 numberofImages = 1 for x in range(numberofImages): rows = self.__augClassificationImage(imageLoc, df,limit,x,op=key) df=df.append(rows, ignore_index=True) return df def removeAugmentedImages(self, df): removeDf = df[df['AugmentedImage'] == True]['loc'].unique().tolist() #df[df['imageAugmentationOriginalImage'] != True][loocationField].apply(lambda x: Path(x).unlink()) for file in removeDf: if file: Path(file).unlink() def augment(self, modelType="imageclassification",params=None,csvSavePath = None,augConf={}): if isinstance(params, dict) and any(params.values()): df = pd.read_csv(self.csvFile) if not self.dataLocation.endswith('/'): images = self.dataLocation+'/' else: images = self.dataLocation if modelType == "imageclassification": images = images + df['File'] else: images = images + df['File'] df['loc'] = images images = set(images.tolist()) option = {} for key in list(self.AugmentationOptions.keys()): option[key] = params.get(key, False) optionDf = pd.DataFrame(columns=list(option.keys())) for i in range(len(images)): optionDf = optionDf.append(option, ignore_index=True) if modelType == "imageclassification": df = self.__classification(images, df, optionDf,augConf) else: classes_names = sorted(df['Label'].unique().tolist()) df = self.__objectDetection(images, df, optionDf, classes_names,'',augConf) df.to_csv(self.csvFile, index=False) return self.csvFile ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' #System imports import logging from distutils.util import strtobool import pandas as pd from text import TextProcessing def get_one_true_option(d, default_value): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value class textProfiler(): def __init__(self): self.log = logging.getLogger('eion') def textCleaning(self, textCorpus): textProcessor = TextProcessing.TextProcessing() textCorpus = textProcessor.transform(textCorpus) return(textCorpus) def textProfiler(self, textCorpus, conf_json, pipeList, max_features): cleaning_kwargs = {} textCleaning = conf_json.get('textCleaning') self.log.info("Text Preprocessing config: ",textCleaning) cleaning_kwargs['fRemoveNoise'] = strtobool(textCleaning.get('removeNoise', 'True')) cleaning_kwargs['fNormalize'] = strtobool(textCleaning.get('normalize', 'True')) cleaning_kwargs['fReplaceAcronym'] = strtobool(textCleaning.get('replaceAcronym', 'False')) cleaning_kwargs['fCorrectSpelling'] = strtobool(textCleaning.get('correctSpelling', 'False')) cleaning_kwargs['fRemoveStopwords'] = strtobool(textCleaning.get('removeStopwords', 'True')) cleaning_kwargs['fRemovePunctuation'] = strtobool(textCleaning.get('removePunctuation', 'True')) cleaning_kwargs['fRemoveNumericTokens'] = strtobool(textCleaning.get('removeNumericTokens', 'True')) cleaning_kwargs['normalizationMethod'] = get_one_true_option(textCleaning.get('normalizeMethod'), 'lemmatization').capitalize() removeNoiseConfig = textCleaning.get('removeNoiseConfig') if type(removeNoiseConfig) is dict: cleaning_kwargs['removeNoise_fHtmlDecode'] = strtobool(removeNoiseConfig.get('decodeHTML', 'True')) cleaning_kwargs['removeNoise_fRemoveHyperLinks'] = strtobool(removeNoiseConfig.get('removeHyperLinks', 'True')) cleaning_kwargs['removeNoise_fRemoveMentions'] = strtobool(removeNoiseConfig.get('removeMentions', 'True')) cleaning_kwargs['removeNoise_fRemoveHashtags'] = strtobool(removeNoiseConfig.get('removeHashtags', 'True')) cleaning_kwargs['removeNoise_RemoveOrReplaceEmoji'] = 'remove' if strtobool(removeNoiseConfig.get('removeEmoji', 'True')) else 'replace' cleaning_kwargs['removeNoise_fUnicodeToAscii'] = strtobool(removeNoiseConfig.get('unicodeToAscii', 'True')) cleaning_kwargs['removeNoise_fRemoveNonAscii'] = strtobool(removeNoiseConfig.get('removeNonAscii', 'True')) acronymConfig = textCleaning.get('acronymConfig') if type(acronymConfig) is dict: cleaning_kwargs['acronymDict'] = acronymConfig.get('acronymDict', None) stopWordsConfig = textCleaning.get('stopWordsConfig') if type(stopWordsConfig) is dict: cleaning_kwargs['stopwordsList'] = stopWordsConfig.get('stopwordsList', []) cleaning_kwargs['extend_or_replace_stopwordslist'] = 'extend' if strtobool(stopWordsConfig.get('extend', 'True')) else 'replace' removeNumericConfig = textCleaning.get('removeNumericConfig') if type(removeNumericConfig) is dict: cleaning_kwargs['removeNumeric_fIncludeSpecialCharacters'] = strtobool(removeNumericConfig.get('removeNumeric_IncludeSpecialCharacters', 'True')) removePunctuationConfig = textCleaning.get('removePunctuationConfig') if type(removePunctuationConfig) is dict: cleaning_kwargs['fRemovePuncWithinTokens'] = strtobool(removePunctuationConfig.get('removePuncWithinTokens', 'False')) cleaning_kwargs['fExpandContractions'] = strtobool(textCleaning.get('expandContractions', 'False')) if cleaning_kwargs['fExpandContractions']: cleaning_kwargs['expandContractions_googleNewsWordVectorPath'] = GOOGLE_NEWS_WORD_VECTORS_PATH libConfig = textCleaning.get('libConfig') if type(libConfig) is dict: cleaning_kwargs['tokenizationLib'] = get_one_true_option(libConfig.get('tokenizationLib'), 'nltk') cleaning_kwargs['lemmatizationLib'] = get_one_true_option(libConfig.get('lemmatizationLib'), 'nltk') cleaning_kwargs['stopwordsRemovalLib'] = get_one_true_option(libConfig.get('stopwordsRemovalLib'), 'nltk') textProcessor = TextProcessing.TextProcessing(**cleaning_kwargs) textCorpus = textProcessor.transform(textCorpus) pipeList.append(("TextProcessing",textProcessor)) textFeatureExtraction = conf_json.get('textFeatureExtraction') if strtobool(textFeatureExtraction.get('pos_tags', 'False')): pos_tags_lib = get_one_true_option(textFeatureExtraction.get('pos_tags_lib'), 'nltk') posTagger = TextProcessing.PosTagging( pos_tags_lib) textCorpus = posTagger.transform(textCorpus) pipeList.append(("posTagger",posTagger)) ngram_min = 1 ngram_max = 1 if strtobool(textFeatureExtraction.get('n_grams', 'False')): n_grams_config = textFeatureExtraction.get("n_grams_config") ngram_min = int(n_grams_config.get('min_n', 1)) ngram_max = int(n_grams_config.get('max_n', 1)) if (ngram_min < 1) or ngram_min > ngram_max: ngram_min = 1 ngram_max = 1 invalidNgramWarning = 'WARNING : invalid ngram config.\nUsing the default values min_n={}, max_n={}'.format(ngram_min, ngram_max) self.log.info(invalidNgramWarning) ngram_range_tuple = (ngram_min, ngram_max) textConversionMethod = conf_json.get('textConversionMethod') conversion_method = get_one_true_option(textConversionMethod, None) if conversion_method.lower() == "countvectors": X, vectorizer = TextProcessing.ExtractFeatureCountVectors(textCorpus, ngram_range=ngram_range_tuple, max_features=max_features) pipeList.append(("vectorizer",vectorizer)) df1 = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names()) df1 = df1.add_suffix('_vect') self.log.info('----------> Conversion Method: CountVectors') elif conversion_method.lower() in ["word2vec","fasttext","glove"]: embedding_method = conversion_method wordEmbeddingVecotrizer = TextProcessing.wordEmbedding(embedding_method) wordEmbeddingVecotrizer.checkAndDownloadPretrainedModel() X = wordEmbeddingVecotrizer.transform(textCorpus) df1 = pd.DataFrame(X) df1 = df1.add_suffix('_vect') pipeList.append(("vectorizer",wordEmbeddingVecotrizer)) self.log.info('----------> Conversion Method: '+str(conversion_method)) elif conversion_method.lower() == "sentencetransformer": from sentence_transformers import SentenceTransformer model = SentenceTransformer('sentence-transformers/msmarco-distilroberta-base-v2') X = model.encode(textCorpus) df1 = pd.DataFrame(X) df1 = df1.add_suffix('_vect') pipeList.append(("vectorizer",model)) self.log.info('----------> Conversion Method: SentenceTransformer') elif conversion_method.lower() == 'tf_idf': X, vectorizer = TextProcessing.ExtractFeatureTfIdfVectors(textCorpus,ngram_range=ngram_range_tuple, max_features=max_features) pipeList.append(("vectorizer",vectorizer)) df1 = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names()) df1 = df1.add_suffix('_vect') self.log.info('----------> Conversion Method: TF_IDF') else: df1 = pd.DataFrame() df1['tokenize'] = textCorpus self.log.info('----------> Conversion Method: NA') return df1, pipeList,conversion_method ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import glob import pandas as pd import io import xml.etree.ElementTree as ET import argparse os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) import tensorflow as tf from PIL import Image from object_detection.utils import dataset_util, label_map_util from collections import namedtuple from pathlib import Path def class_text_to_int(row_label, label_map_dict): return label_map_dict[row_label] def split(df, group): data = namedtuple('data', ['File', 'object']) gb = df.groupby(group) return [data(File, gb.get_group(x)) for File, x in zip(gb.groups.keys(), gb.groups)] def create_tf_example(group, path, label_map_dict): with tf.io.gfile.GFile(os.path.join(path, '{}'.format(group.File)), 'rb') as fid: encoded_jpg = fid.read() encoded_jpg_io = io.BytesIO(encoded_jpg) image = Image.open(encoded_jpg_io) width, height = image.size File = group.File.encode('utf8') image_format = b'jpg' xmins = [] xmaxs = [] ymins = [] ymaxs = [] classes_text = [] classes = [] for index, row in group.object.iterrows(): xmin_n = min(row['xmin'], row['xmax']) xmax_n = max(row['xmin'], row['xmax']) ymin_n = min(row['ymin'], row['ymax']) ymax_n = max(row['ymin'], row['ymax']) xmin_new = min(xmin_n, width) xmax_new = min(xmax_n, width) ymin_new = min(ymin_n, height) ymax_new = min(ymax_n, height) xmn = xmin_new / width xmins.append(xmn) xmx = xmax_new / width xmaxs.append(xmx) ymn = ymin_new / height ymins.append(ymn) ymx = ymax_new / height ymaxs.append(ymx) classes_text.append(row['Label'].encode('utf8')) classes.append(class_text_to_int(row['Label'], label_map_dict)) tf_example = tf.train.Example(features=tf.train.Features(feature={ 'image/height': dataset_util.int64_feature(height), 'image/width': dataset_util.int64_feature(width), 'image/filename': dataset_util.bytes_feature(File), 'image/source_id': dataset_util.bytes_feature(File), 'image/encoded': dataset_util.bytes_feature(encoded_jpg), 'image/format': dataset_util.bytes_feature(image_format), 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins), 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs), 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins), 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs), 'image/object/class/text': dataset_util.bytes_list_feature(classes_text), 'image/object/class/label': dataset_util.int64_list_feature(classes), })) return tf_example def labelFile(classes_names, label_map_path): pbtxt_content = "" for i, class_name in enumerate(classes_names): pbtxt_content = ( pbtxt_content + "item {{\n id: {0}\n name: '{1}'\n}}\n\n".format(i + 1, class_name) ) pbtxt_content = pbtxt_content.strip() with open(label_map_path, "w") as f: f.write(pbtxt_content) def createLabelFile(train_df, save_path): labelmap_path = str(Path(save_path)/ 'label_map.pbtxt') classes_names = sorted(train_df['Label'].unique().tolist()) labelFile(classes_names, labelmap_path) return labelmap_path, len(classes_names) def generate_TF_record(image_dir, output_dir, train_df, test_df, labelmap_path): outputPath = str(Path(output_dir)/ 'train.tfrecord') writer = tf.io.TFRecordWriter( outputPath) grouped = split(train_df, 'File') label_map = label_map_util.load_labelmap(labelmap_path ) label_map_dict = label_map_util.get_label_map_dict(label_map) for group in grouped: tf_example = create_tf_example(group, image_dir, label_map_dict) writer.write(tf_example.SerializeToString()) writer.close() if len(test_df): outputPath = str(Path(output_dir)/ 'test.tfrecord') writer = tf.io.TFRecordWriter( outputPath) grouped = split(test_df, 'File') for group in grouped: tf_example = create_tf_example(group, image_dir, label_map_dict) writer.write(tf_example.SerializeToString()) writer.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import io import json import logging import pandas as pd import sys import numpy as np from pathlib import Path from word2number import w2n from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OrdinalEncoder from sklearn.preprocessing import OneHotEncoder from sklearn.impute import SimpleImputer, KNNImputer from sklearn.pipeline import Pipeline, FeatureUnion from sklearn.preprocessing import FunctionTransformer from sklearn.preprocessing import MinMaxScaler,StandardScaler from sklearn.preprocessing import PowerTransformer from sklearn.compose import ColumnTransformer from sklearn.base import TransformerMixin from sklearn.ensemble import IsolationForest from category_encoders import TargetEncoder try: import transformations.data_profiler_functions as cs except: import data_profiler_functions as cs if 'AION' in sys.modules: try: from appbe.app_config import DEBUG_ENABLED except: DEBUG_ENABLED = False else: DEBUG_ENABLED = False log_suffix = f'[{Path(__file__).stem}] ' class profiler(): def __init__(self, xtrain, ytrain=None, target=None, encode_target = False, config={}, keep_unprocessed=[],data_path=None,log=None): if not isinstance(xtrain, pd.DataFrame): raise ValueError(f'{log_suffix}supported data type is pandas.DataFrame but provide data is of {type(xtrain)} type') if xtrain.empty: raise ValueError(f'{log_suffix}Data frame is empty') if target and target in xtrain.columns: self.target = xtrain[target] xtrain.drop(target, axis=1, inplace=True) self.target_name = target elif ytrain: self.target = ytrain self.target_name = 'target' else: self.target = pd.Series() self.target_name = None self.data_path = data_path self.encode_target = encode_target self.label_encoder = None self.data = xtrain self.keep_unprocessed = keep_unprocessed self.colm_type = {} for colm, infer_type in zip(self.data.columns, self.data.dtypes): self.colm_type[colm] = infer_type self.numeric_feature = [] self.cat_feature = [] self.text_feature = [] self.wordToNumericFeatures = [] self.added_features = [] self.pipeline = [] self.dropped_features = {} self.train_features_type={} self.__update_type() self.config = config self.featureDict = config.get('featureDict', []) self.output_columns = [] self.feature_expender = [] self.text_to_num = {} self.force_numeric_conv = [] if log: self.log = log else: self.log = logging.getLogger('eion') self.type_conversion = {} self.log_input_feat_info() def log_input_feat_info(self): if self.featureDict: feature_df = pd.DataFrame(self.featureDict) log_text = '\nPreprocessing options:' log_text += '\n\t'+str(feature_df.head( len(self.featureDict))).replace('\n','\n\t') self.log.info(log_text) def log_dataframe(self, msg=None): buffer = io.StringIO() self.data.info(buf=buffer) if msg: log_text = f'Data frame after {msg}:' else: log_text = 'Data frame:' log_text += '\n\t'+str(self.data.head(2)).replace('\n','\n\t') log_text += ('\n\t' + buffer.getvalue().replace('\n','\n\t')) self.log.info(log_text) def transform(self): if self.is_target_available(): if self.target_name: self.log.info(f"Target feature name: '{self.target_name}'") self.log.info(f"Target feature size: {len(self.target)}") else: self.log.info(f"Target feature not present") self.log_dataframe() print(self.data.info()) try: self.process() except Exception as e: self.log.error(e, exc_info=True) raise pipe = FeatureUnion(self.pipeline) try: if self.text_feature: from text.textProfiler import set_pretrained_model set_pretrained_model(pipe) conversion_method = self.get_conversion_method() process_data = pipe.fit_transform(self.data, y=self.target) # save for testing if DEBUG_ENABLED: if not isinstance(process_data, np.ndarray): process_data = process_data.toarray() df = pd.DataFrame(process_data) df.to_csv('debug_preprocessed.csv', index=False) if self.text_feature and conversion_method == 'latentsemanticanalysis': n_size = self.get_tf_idf_output_size( pipe) dimensions = self.get_tf_idf_dimensions() if n_size < dimensions or n_size > dimensions: dimensions = n_size from sklearn.decomposition import TruncatedSVD reducer = TruncatedSVD( n_components = dimensions) reduced_data = reducer.fit_transform( process_data[:,-n_size:]) text_process_idx = [t[0] for t in pipe.transformer_list].index('text_process') pipe.transformer_list[text_process_idx][1].steps.append(('feature_reducer',reducer)) if not isinstance(process_data, np.ndarray): process_data = process_data.toarray() process_data = np.concatenate((process_data[:,:-n_size], reduced_data), axis=1) last_step = self.feature_expender.pop() self.feature_expender.append({'feature_reducer':list(last_step.values())[0]}) except EOFError as e: if "Compressed file ended before the end-of-stream marker was reached" in str(e): raise EOFError('Pretrained model is not downloaded properly') self.update_output_features_names(pipe) if not isinstance(process_data, np.ndarray): process_data = process_data.toarray() df = pd.DataFrame(process_data, index=self.data.index, columns=self.output_columns) if self.is_target_available() and self.target_name: df[self.target_name] = self.target if self.keep_unprocessed: df[self.keep_unprocessed] = self.data[self.keep_unprocessed] self.log_numerical_fill() self.log_categorical_fill() self.log_normalization() return df, pipe, self.label_encoder def log_type_conversion(self): if self.log: self.log.info('----------- Inspecting Features -----------') self.log.info('----------- Type Conversion -----------') count = 0 for k, v in self.type_conversion.items(): if v[0] != v[1]: self.log.info(f'-------> {k} -> from {v[0]} to {v[1]} : {v[2]}') self.log.info('Status:- |... Feature inspection done') def check_config(self): removeDuplicate = self.config.get('removeDuplicate', False) self.config['removeDuplicate'] = cs.get_boolean(removeDuplicate) self.config['misValueRatio'] = float(self.config.get('misValueRatio', cs.default_config['misValueRatio'])) self.config['numericFeatureRatio'] = float(self.config.get('numericFeatureRatio', cs.default_config['numericFeatureRatio'])) self.config['categoryMaxLabel'] = int(self.config.get('categoryMaxLabel', cs.default_config['categoryMaxLabel'])) featureDict = self.config.get('featureDict', []) if isinstance(featureDict, dict): self.config['featureDict'] = [] if isinstance(featureDict, str): self.config['featureDict'] = [] def process(self): #remove duplicate not required at the time of prediction self.check_config() self.remove_constant_feature() self.remove_empty_feature(self.config['misValueRatio']) self.remove_index_features() self.dropna() if self.config['removeDuplicate']: self.drop_duplicate() #self.check_categorical_features() #self.string_to_numeric() self.process_target() self.train_features_type = {k:v for k,v in zip(self.data.columns, self.data.dtypes)} self.parse_process_step_config() self.process_drop_fillna() self.log_type_conversion() self.update_num_fill_dict() if DEBUG_ENABLED: print(self.num_fill_method_dict) self.update_cat_fill_dict() self.create_pipeline() self.text_pipeline(self.config) self.apply_outlier() if DEBUG_ENABLED: self.log.info(self.process_method) self.log.info(self.pipeline) def is_target_available(self): return (isinstance(self.target, pd.Series) and not self.target.empty) or len(self.target) def process_target(self, operation='encode', arg=None): if self.is_target_available(): # drop null values self.__update_index( self.target.notna(), 'target') if self.encode_target: self.label_encoder = LabelEncoder() self.target = self.label_encoder.fit_transform(self.target) return self.label_encoder return None def is_target_column(self, column): return column == self.target_name def fill_default_steps(self): num_fill_method = cs.get_one_true_option(self.config.get('numericalFillMethod',{})) normalization_method = cs.get_one_true_option(self.config.get('normalization',{}),'none') for colm in self.numeric_feature: if num_fill_method: self.fill_missing_value_method(colm, num_fill_method.lower()) if normalization_method: self.fill_normalizer_method(colm, normalization_method.lower()) cat_fill_method = cs.get_one_true_option(self.config.get('categoricalFillMethod',{})) cat_encode_method = cs.get_one_true_option(self.config.get('categoryEncoding',{})) for colm in self.cat_feature: if cat_fill_method: self.fill_missing_value_method(colm, cat_fill_method.lower()) if cat_encode_method: self.fill_encoder_value_method(colm, cat_encode_method.lower(), default=True) def parse_process_step_config(self): self.process_method = {} user_provided_data_type = {} for feat_conf in self.featureDict: colm = feat_conf.get('feature', '') if not self.is_target_column(colm): if colm in self.data.columns: user_provided_data_type[colm] = feat_conf['type'] if user_provided_data_type: self.update_user_provided_type(user_provided_data_type) self.fill_default_steps() for feat_conf in self.featureDict: colm = feat_conf.get('feature', '') if not self.is_target_column(colm): if colm in self.data.columns: if feat_conf.get('fillMethod', None): self.fill_missing_value_method(colm, feat_conf['fillMethod'].lower()) if feat_conf.get('categoryEncoding', None): self.fill_encoder_value_method(colm, feat_conf['categoryEncoding'].lower()) if feat_conf.get('normalization', None): self.fill_normalizer_method(colm, feat_conf['normalization'].lower()) if feat_conf.get('outlier', None): self.fill_outlier_method(colm, feat_conf['outlier'].lower()) if feat_conf.get('outlierOperation', None): self.fill_outlier_process(colm, feat_conf['outlierOperation'].lower()) def get_tf_idf_dimensions(self): dim = cs.get_one_true_option(self.config.get('embeddingSize',{}).get('TF_IDF',{}), 'default') return {'default': 300, '50d':50, '100d':100, '200d':200, '300d':300}[dim] def get_tf_idf_output_size(self, pipe): start_index = {} for feat_expender in self.feature_expender: if feat_expender: step_name = list(feat_expender.keys())[0] index = list(feat_expender.values())[0] for transformer_step in pipe.transformer_list: if transformer_step[1].steps[-1][0] in step_name: start_index[index] = {transformer_step[1].steps[-1][0]: transformer_step[1].steps[-1][1].get_feature_names_out()} if start_index: for key,value in start_index.items(): for k,v in value.items(): if k == 'vectorizer': return len(v) return 0 def update_output_features_names(self, pipe): columns = self.output_columns start_index = {} index_shifter = 0 for feat_expender in self.feature_expender: if feat_expender: step_name = list(feat_expender.keys())[0] for key,value in start_index.items(): for k,v in value.items(): index_shifter += len(v) index = list(feat_expender.values())[0] for transformer_step in pipe.transformer_list: if transformer_step[1].steps[-1][0] in step_name: start_index[index + index_shifter] = {transformer_step[1].steps[-1][0]: transformer_step[1].steps[-1][1].get_feature_names_out()} #print(start_index) if start_index: for key,value in start_index.items(): for k,v in value.items(): if k == 'vectorizer': v = [f'{x}_vect' for x in v] self.output_columns[key:key] = v self.added_features = [*self.added_features, *v] def text_pipeline(self, conf_json): if self.text_feature: from text.textProfiler import textProfiler from text.textProfiler import textCombine pipeList = [] text_pipe = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", self.text_feature) ], remainder="drop")), ("text_fillNa",SimpleImputer(strategy='constant', fill_value='')), ("merge_text_feature", textCombine())]) obj = textProfiler() pipeList = obj.cleaner(conf_json, pipeList, self.data_path) pipeList = obj.embedding(conf_json, pipeList) last_step = "merge_text_feature" for pipe_elem in pipeList: text_pipe.steps.append((pipe_elem[0], pipe_elem[1])) last_step = pipe_elem[0] text_transformer = ('text_process', text_pipe) self.pipeline.append(text_transformer) self.feature_expender.append({last_step:len(self.output_columns)}) def create_pipeline(self): num_pipe = {} for k,v in self.num_fill_method_dict.items(): for k1,v1 in v.items(): if k1 and k1 != 'none': num_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_num_imputer(k)), (k1, self.get_num_scaler(k1)) ]) else: num_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_num_imputer(k)) ]) self.output_columns.extend(v1) cat_pipe = {} for k,v in self.cat_fill_method_dict.items(): for k1,v1 in v.items(): cat_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_cat_imputer(k)), (k1, self.get_cat_encoder(k1)) ]) if k1 not in ['onehotencoding']: self.output_columns.extend(v1) else: self.feature_expender.append({k1:len(self.output_columns)}) for key, pipe in num_pipe.items(): self.pipeline.append((key, pipe)) for key, pipe in cat_pipe.items(): self.pipeline.append((key, pipe)) "Drop: feature during training but replace with zero during prediction " def process_drop_fillna(self): drop_column = [] if 'numFill' in self.process_method.keys(): for col, method in self.process_method['numFill'].items(): if method == 'drop': self.process_method['numFill'][col] = 'zero' drop_column.append(col) if 'catFill' in self.process_method.keys(): for col, method in self.process_method['catFill'].items(): if method == 'drop': self.process_method['catFill'][col] = 'zero' drop_column.append(col) if drop_column: self.data.dropna(subset=drop_column, inplace=True) def update_num_fill_dict(self): self.num_fill_method_dict = {} if 'numFill' in self.process_method.keys(): for f in cs.supported_method['fillNa']['numeric']: self.num_fill_method_dict[f] = {} for en in cs.supported_method['normalization']: self.num_fill_method_dict[f][en] = [] for col in self.numeric_feature: numFillDict = self.process_method.get('numFill',{}) normalizationDict = self.process_method.get('normalization',{}) if f == numFillDict.get(col, '') and en == normalizationDict.get(col,''): self.num_fill_method_dict[f][en].append(col) if not self.num_fill_method_dict[f][en] : del self.num_fill_method_dict[f][en] if not self.num_fill_method_dict[f]: del self.num_fill_method_dict[f] def update_cat_fill_dict(self): self.cat_fill_method_dict = {} if 'catFill' in self.process_method.keys(): for f in cs.supported_method['fillNa']['categorical']: self.cat_fill_method_dict[f] = {} for en in cs.supported_method['categoryEncoding']: self.cat_fill_method_dict[f][en] = [] for col in self.cat_feature: catFillDict = self.process_method.get('catFill',{}) catEncoderDict = self.process_method.get('catEncoder',{}) if f == catFillDict.get(col, '') and en == catEncoderDict.get(col,''): self.cat_fill_method_dict[f][en].append(col) if not self.cat_fill_method_dict[f][en] : del self.cat_fill_method_dict[f][en] if not self.cat_fill_method_dict[f]: del self.cat_fill_method_dict[f] def __update_type(self): self.numeric_feature = list( set(self.data.select_dtypes(include='number').columns.tolist()) - set(self.keep_unprocessed)) self.cat_feature = list( set(self.data.select_dtypes(include='category').columns.tolist()) - set(self.keep_unprocessed)) self.text_feature = list( set(self.data.select_dtypes(include='object').columns.tolist()) - set(self.keep_unprocessed)) self.datetime_feature = list( set(self.data.select_dtypes(include='datetime').columns.tolist()) - set(self.keep_unprocessed)) def update_user_provided_type(self, data_types): allowed_types = ['numerical','categorical', 'text'] skipped_types = ['date','index'] type_mapping = {'numerical': np.dtype('float'), 'float': np.dtype('float'),'categorical': 'category', 'text':np.dtype('object'),'date':'datetime64[ns]','index': np.dtype('int64'),} mapped_type = {k:type_mapping[v] for k,v in data_types.items() if v in allowed_types} skipped_features = [k for k,v in data_types.items() if v in skipped_types] if skipped_features: self.keep_unprocessed.extend( skipped_features) self.keep_unprocessed = list(set(self.keep_unprocessed)) self.update_type(mapped_type, 'user provided data type') def get_type(self, as_list=False): if as_list: return [self.colm_type.values()] else: return self.colm_type def update_type(self, data_types={}, reason=''): invalid_features = [x for x in data_types.keys() if x not in self.data.columns] if invalid_features: valid_feat = list(set(data_types.keys()) - set(invalid_features)) valid_feat_type = {k:v for k,v in data_types if k in valid_feat} else: valid_feat_type = data_types for k,v in valid_feat_type.items(): if v != self.colm_type[k].name: try: self.data.astype({k:v}) self.colm_type.update({k:self.data[k].dtype}) self.type_conversion[k] = (self.colm_type[k] , v, 'Done', reason) except: self.type_conversion[k] = (self.colm_type[k] , v, 'Fail', reason) if v == np.dtype('float64') and self.colm_type[k].name == 'object': if self.check_numeric( k): self.data[ k] = pd.to_numeric(self.data[ k], errors='coerce') self.type_conversion[k] = (self.colm_type[k] , v, 'Done', reason) self.force_numeric_conv.append( k) else: raise ValueError(f"Can not convert '{k}' feature to 'numeric' as numeric values are less than {self.config['numericFeatureRatio'] * 100}%") self.data = self.data.astype(valid_feat_type) self.__update_type() def check_numeric(self, feature): col_values = self.data[feature].copy() col_values = pd.to_numeric(col_values, errors='coerce') if col_values.count() >= (self.config['numericFeatureRatio'] * len(col_values)): return True return False def string_to_numeric(self): def to_number(x): try: return w2n.word_to_num(x) except: return np.nan for col in self.text_feature: col_values = self.data[col].copy() col_values = pd.to_numeric(col_values, errors='coerce') if col_values.count() >= (self.config['numericFeatureRatio'] * len(col_values)): self.text_to_num[col] = 'float64' self.wordToNumericFeatures.append(col) if self.text_to_num: columns = list(self.text_to_num.keys()) self.data[columns] = self.data[columns].apply(lambda x: to_number(x), axis=1, result_type='broadcast') self.update_type(self.text_to_num) self.log.info('----------- Inspecting Features -----------') for col in self.text_feature: self.log.info(f'-------> Feature : {col}') if col in self.text_to_num: self.log.info('----------> Numeric Status :Yes') self.log.info('----------> Data Type Converting to numeric :Yes') else: self.log.info('----------> Numeric Status :No') self.log.info(f'\nStatus:- |... Feature inspection done for numeric data: {len(self.text_to_num)} feature(s) converted to numeric') self.log.info(f'\nStatus:- |... Feature word to numeric treatment done: {self.text_to_num}') self.log.info('----------- Inspecting Features End -----------') def check_categorical_features(self): num_data = self.data.select_dtypes(include='number') num_data_unique = num_data.nunique() num_to_cat_col = {} for i, value in enumerate(num_data_unique): if value < self.config['categoryMaxLabel']: num_to_cat_col[num_data_unique.index[i]] = 'category' if num_to_cat_col: self.update_type(num_to_cat_col, 'numerical to categorical') str_to_cat_col = {} str_data = self.data.select_dtypes(include='object') str_data_unique = str_data.nunique() for i, value in enumerate(str_data_unique): if value < self.config['categoryMaxLabel']: str_to_cat_col[str_data_unique.index[i]] = 'category' for colm in str_data.columns: if self.data[colm].str.len().max() < cs.default_config['str_to_cat_len_max']: str_to_cat_col[colm] = 'category' if str_to_cat_col: self.update_type(str_to_cat_col, 'text to categorical') def drop_features(self, features=[], reason='unspecified'): if isinstance(features, str): features = [features] feat_to_remove = [x for x in features if x in self.data.columns] if feat_to_remove: self.data.drop(feat_to_remove, axis=1, inplace=True) for feat in feat_to_remove: self.dropped_features[feat] = reason self.log_drop_feature(feat_to_remove, reason) self.__update_type() def __update_index(self, indices, reason=''): if isinstance(indices, (bool, pd.core.series.Series)) and len(indices) == len(self.data): if not indices.all(): self.data = self.data[indices] if self.is_target_available(): self.target = self.target[indices] self.log_update_index((indices == False).sum(), reason) def dropna(self): self.data.dropna(how='all',inplace=True) if self.is_target_available(): self.target = self.target[self.data.index] def drop_duplicate(self): index = self.data.duplicated(keep='first') self.__update_index( ~index, reason='duplicate') def log_drop_feature(self, columns, reason): self.log.info(f'---------- Dropping {reason} features ----------') self.log.info(f'\nStatus:- |... {reason} feature treatment done: {len(columns)} {reason} feature(s) found') self.log.info(f'-------> Drop Features: {columns}') self.log.info(f'Data Frame Shape After Dropping (Rows,Columns): {self.data.shape}') def log_update_index(self,count, reason): if count: if reason == 'target': self.log.info('-------> Null Target Rows Drop:') self.log.info(f'-------> Dropped rows count: {count}') elif reason == 'duplicate': self.log.info('-------> Duplicate Rows Drop:') self.log.info(f'-------> Dropped rows count: {count}') elif reason == 'outlier': self.log.info(f'-------> Dropped rows count: {count}') self.log.info('Status:- |... Outlier treatment done') self.log.info(f'-------> Data Frame Shape After Dropping samples(Rows,Columns): {self.data.shape}') def log_normalization(self): if self.process_method.get('normalization', None): self.log.info(f'\nStatus:- !... Normalization treatment done') for method in cs.supported_method['normalization']: cols = [] for col, m in self.process_method['normalization'].items(): if m == method: cols.append(col) if cols and method != 'none': self.log.info(f'Running {method} on features: {cols}') def log_numerical_fill(self): if self.process_method.get('numFill', None): self.log.info(f'\nStatus:- !... Fillna for numeric feature done') for method in cs.supported_method['fillNa']['numeric']: cols = [] for col, m in self.process_method['numFill'].items(): if m == method: cols.append(col) if cols: self.log.info(f'-------> Running {method} on features: {cols}') def log_categorical_fill(self): if self.process_method.get('catFill', None): self.log.info(f'\nStatus:- !... FillNa for categorical feature done') for method in cs.supported_method['fillNa']['categorical']: cols = [] for col, m in self.process_method['catFill'].items(): if m == method: cols.append(col) if cols: self.log.info(f'-------> Running {method} on features: {cols}') def remove_constant_feature(self): unique_values = self.data.nunique() constant_features = [] for i, value in enumerate(unique_values): if value == 1: constant_features.append(unique_values.index[i]) if constant_features: self.drop_features(constant_features, "constant") def remove_empty_feature(self, misval_ratio=1.0): missing_ratio = self.data.isnull().sum() / len(self.data) missing_ratio = {k:v for k,v in zip(self.data.columns, missing_ratio)} empty_features = [k for k,v in missing_ratio.items() if v > misval_ratio] if empty_features: self.drop_features(empty_features, "empty") def remove_index_features(self): index_feature = [] for feat in self.numeric_feature: if self.data[feat].nunique() == len(self.data): #if (self.data[feat].sum()- sum(self.data.index) == (self.data.iloc[0][feat]-self.data.index[0])*len(self.data)): # index feature can be time based count = (self.data[feat] - self.data[feat].shift() == 1).sum() if len(self.data) - count == 1: index_feature.append(feat) self.drop_features(index_feature, "index") def fill_missing_value_method(self, colm, method): if colm in self.numeric_feature: if method in cs.supported_method['fillNa']['numeric']: if 'numFill' not in self.process_method.keys(): self.process_method['numFill'] = {} if method == 'na' and self.process_method['numFill'].get(colm, None): pass # don't overwrite else: self.process_method['numFill'][colm] = method if colm in self.cat_feature: if method in cs.supported_method['fillNa']['categorical']: if 'catFill' not in self.process_method.keys(): self.process_method['catFill'] = {} if method == 'na' and self.process_method['catFill'].get(colm, None): pass else: self.process_method['catFill'][colm] = method def check_encoding_method(self, method, colm,default=False): if not self.is_target_available() and (method.lower() == list(cs.target_encoding_method_change.keys())[0]): method = cs.target_encoding_method_change[method.lower()] if default: self.log.info(f"Applying Label encoding instead of Target encoding on feature '{colm}' as target feature is not present") return method def fill_encoder_value_method(self,colm, method, default=False): if colm in self.cat_feature: if method.lower() in cs.supported_method['categoryEncoding']: if 'catEncoder' not in self.process_method.keys(): self.process_method['catEncoder'] = {} if method == 'na' and self.process_method['catEncoder'].get(colm, None): pass else: self.process_method['catEncoder'][colm] = self.check_encoding_method(method, colm,default) else: self.log.info(f"-------> categorical encoding method '{method}' is not supported. supported methods are {cs.supported_method['categoryEncoding']}") def fill_normalizer_method(self,colm, method): if colm in self.numeric_feature: if method in cs.supported_method['normalization']: if 'normalization' not in self.process_method.keys(): self.process_method['normalization'] = {} if (method == 'na' or method == 'none') and self.process_method['normalization'].get(colm, None): pass else: self.process_method['normalization'][colm] = method else: self.log.info(f"-------> Normalization method '{method}' is not supported. supported methods are {cs.supported_method['normalization']}") def apply_outlier(self): inlier_indice = np.array([True] * len(self.data)) if self.process_method.get('outlier', None): self.log.info('-------> Feature wise outlier detection:') for k,v in self.process_method['outlier'].items(): if k in self.numeric_feature: if v == 'iqr': index = cs.findiqrOutlier(self.data[k]) elif v == 'zscore': index = cs.findzscoreOutlier(self.data[k]) elif v == 'disable': index = None if k in self.process_method['outlierOperation'].keys(): if self.process_method['outlierOperation'][k] == 'dropdata': inlier_indice = np.logical_and(inlier_indice, index) elif self.process_method['outlierOperation'][k] == 'average': mean = self.data[k].mean() index = ~index self.data.loc[index,[k]] = mean self.log.info(f'-------> {k}: Replaced by Mean {mean}: total replacement {index.sum()}') elif self.process_method['outlierOperation'][k] == 'nochange' and v != 'disable': self.log.info(f'-------> Total outliers in "{k}": {(~index).sum()}') if self.config.get('outlierDetection',None): if self.config['outlierDetection'].get('IsolationForest','False') == 'True': if self.numeric_feature: index = cs.findiforestOutlier(self.data[self.numeric_feature]) inlier_indice = np.logical_and(inlier_indice, index) self.log.info(f'-------> Numeric feature based Outlier detection(IsolationForest):') if inlier_indice.sum() != len(self.data): self.__update_index(inlier_indice, 'outlier') def fill_outlier_method(self,colm, method): if colm in self.numeric_feature: if method in cs.supported_method['outlier_column_wise']: if 'outlier' not in self.process_method.keys(): self.process_method['outlier'] = {} if method not in ['Disable', 'na']: self.process_method['outlier'][colm] = method else: self.log.info(f"-------> outlier detection method '{method}' is not supported for column wise. supported methods are {cs.supported_method['outlier_column_wise']}") def fill_outlier_process(self,colm, method): if colm in self.numeric_feature: if method in cs.supported_method['outlierOperation']: if 'outlierOperation' not in self.process_method.keys(): self.process_method['outlierOperation'] = {} self.process_method['outlierOperation'][colm] = method else: self.log.info(f"-------> outlier process method '{method}' is not supported for column wise. supported methods are {cs.supported_method['outlierOperation']}") def get_cat_imputer(self,method): if method == 'mode': return SimpleImputer(strategy='most_frequent') elif method == 'zero': return SimpleImputer(strategy='constant', fill_value=0) def get_cat_encoder(self,method): if method == 'labelencoding': return OrdinalEncoder() elif method == 'onehotencoding': return OneHotEncoder(sparse=False,handle_unknown="ignore") elif method == 'targetencoding': if not self.is_target_available(): raise ValueError('Can not apply Target Encoding when target feature is not present') return TargetEncoder() def get_num_imputer(self,method): if method == 'mode': return SimpleImputer(strategy='most_frequent') elif method == 'mean': return SimpleImputer(strategy='mean') elif method == 'median': return SimpleImputer(strategy='median') elif method == 'knnimputer': return KNNImputer() elif method == 'zero': return SimpleImputer(strategy='constant', fill_value=0) def get_num_scaler(self,method): if method == 'minmax': return MinMaxScaler() elif method == 'standardscaler': return StandardScaler() elif method == 'lognormal': return PowerTransformer(method='yeo-johnson', standardize=False) def recommenderStartProfiler(self,modelFeatures): return cs.recommenderStartProfiler(self,modelFeatures) def folderPreprocessing(self,folderlocation,folderdetails,deployLocation): return cs.folderPreprocessing(self,folderlocation,folderdetails,deployLocation) def textSimilarityStartProfiler(self, doc_col_1, doc_col_2): return cs.textSimilarityStartProfiler(self, doc_col_1, doc_col_2) def get_conversion_method(self): return cs.get_one_true_option(self.config.get('textConversionMethod','')).lower() def set_features(features,profiler=None): return cs.set_features(features,profiler) import os import sys import numpy as np import scipy import pandas as pd from pathlib import Path default_config = { 'misValueRatio': '1.0', 'numericFeatureRatio': '1.0', 'categoryMaxLabel': '20', 'str_to_cat_len_max': 10 } target_encoding_method_change = {'targetencoding': 'labelencoding'} supported_method = { 'fillNa': { 'categorical' : ['mode','zero','na'], 'numeric' : ['median','mean','knnimputer','zero','drop','na'], }, 'categoryEncoding': ['labelencoding','targetencoding','onehotencoding','na','none'], 'normalization': ['standardscaler','minmax','lognormal', 'na','none'], 'outlier_column_wise': ['iqr','zscore', 'disable', 'na'], 'outlierOperation': ['dropdata', 'average', 'nochange'] } def findiqrOutlier(df): Q1 = df.quantile(0.25) Q3 = df.quantile(0.75) IQR = Q3 - Q1 index = ~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))) return index def findzscoreOutlier(df): z = np.abs(scipy.stats.zscore(df)) index = (z < 3) return index def findiforestOutlier(df): from sklearn.ensemble import IsolationForest isolation_forest = IsolationForest(n_estimators=100) isolation_forest.fit(df) y_pred_train = isolation_forest.predict(df) return y_pred_train == 1 def get_one_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def get_boolean(value): if (isinstance(value, str) and value.lower() == 'true') or (isinstance(value, bool) and value == True): return True else: return False def recommenderStartProfiler(self,modelFeatures): try: self.log.info('----------> FillNA:0') self.data = self.data.fillna(value=0) self.log.info('Status:- !... Missing value treatment done') self.log.info('----------> Remove Empty Row') self.data = self.data.dropna(axis=0,how='all') self.log.info('Status:- !... Empty feature treatment done') userId,itemId,rating = modelFeatures.split(',') self.data[itemId] = self.data[itemId].astype(np.int32) self.data[userId] = self.data[userId].astype(np.int32) self.data[rating] = self.data[rating].astype(np.float32) return self.data except Exception as inst: self.log.info("Error: dataProfiler failed "+str(inst)) return(self.data) def folderPreprocessing(self,folderlocation,folderdetails,deployLocation): try: dataset_directory = Path(folderlocation) dataset_csv_file = dataset_directory/folderdetails['label_csv_file_name'] tfrecord_directory = Path(deployLocation)/'Video_TFRecord' from savp import PreprocessSAVP import csv csvfile = open(dataset_csv_file, newline='') csv_reader = csv.DictReader(csvfile) PreprocessSAVP(dataset_directory,csv_reader,tfrecord_directory) dataColumns = list(self.data.columns) VideoProcessing = True return dataColumns,VideoProcessing,tfrecord_directory except Exception as inst: self.log.info("Error: dataProfiler failed "+str(inst)) def textSimilarityStartProfiler(self, doc_col_1, doc_col_2): import os try: features = [doc_col_1, doc_col_2] pipe = None dataColumns = list(self.data.columns) self.numofCols = self.data.shape[1] self.numOfRows = self.data.shape[0] from transformations.textProfiler import textProfiler self.log.info('-------> Execute Fill NA With Empty String') self.data = self.data.fillna(value=" ") self.log.info('Status:- |... Missing value treatment done') self.data[doc_col_1] = textProfiler().textCleaning(self.data[doc_col_1]) self.data[doc_col_2] = textProfiler().textCleaning(self.data[doc_col_2]) self.log.info('-------> Concatenate: ' + doc_col_1 + ' ' + doc_col_2) self.data['text'] = self.data[[doc_col_1, doc_col_2]].apply(lambda row: ' '.join(row.values.astype(str)), axis=1) from tensorflow.keras.preprocessing.text import Tokenizer pipe = Tokenizer() pipe.fit_on_texts(self.data['text'].values) self.log.info('-------> Tokenizer: Fit on Concatenate Field') self.log.info('Status:- |... Tokenizer the text') self.data[doc_col_1] = self.data[doc_col_1].astype(str) self.data[doc_col_1] = self.data[doc_col_1].astype(str) return (self.data, pipe, self.target_name, features) except Exception as inst: self.log.info("StartProfiler failed " + str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) def set_features(features,profiler=None): if profiler: features = [x for x in features if x not in profiler.added_features] return features + profiler.text_feature return features ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import pandas as pd import sys import os import warnings import logging from pathlib import Path import random from sklearn.model_selection import train_test_split import operator import re import pdfplumber class dataReader(): def __init__(self): self.dataDf =None self.log = logging.getLogger('eion') def readCsv(self,dataPath,featureList,targetColumn): data=pd.read_csv(dataPath) dataDf=data[featureList] predictDf=data[targetColumn] return dataDf,predictDf def rowsfilter(self,filters,dataframe): self.log.info('\n-------> No of rows before filtering: '+str(dataframe.shape[0])) #task-13479 filterexpression='' firstexpressiondone = False for x in filters: if firstexpressiondone: filterexpression += ' ' if x['combineOperator'].lower() == 'and': filterexpression += '&' elif x['combineOperator'].lower() == 'or': filterexpression += '|' filterexpression += ' ' firstexpressiondone = True filterexpression += x['feature'] filterexpression += ' ' if x['condition'].lower() == 'equals': filterexpression += '==' elif x['condition'].lower() == 'notequals': filterexpression += '!=' elif x['condition'].lower() == 'lessthan': filterexpression += '<' elif x['condition'].lower() == 'lessthanequalto': filterexpression += '<=' elif x['condition'].lower() == 'greaterthan': filterexpression += '>' elif x['condition'].lower() == 'greaterthanequalto': filterexpression += '>=' filterexpression += ' ' if dataframe[x['feature']].dtype in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']: filterexpression += x['value'] else: filterexpression += '\''+x['value']+'\'' dataframe = dataframe.query(filterexpression) self.log.info('-------> Row filter: '+str(filterexpression)) #task-13479 self.log.info('-------> No of rows after filtering: '+str(dataframe.shape[0])) return dataframe,filterexpression def grouping(self,grouper,dataframe): grouperbyjson= {} groupbyfeatures = grouper['groupby'] dataframe = dataframe.reset_index() features = dataframe.columns.tolist() aggjson = {} for feature, featureType in zip(features,dataframe.dtypes): if feature == groupbyfeatures or feature == 'index': continue if dataframe[feature].empty == True: continue if dataframe[feature].isnull().all() == True: continue if featureType in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']: temp = {} temp[feature+'_size'] = 'size' temp[feature+'_sum'] = 'sum' temp[feature+'_max'] = 'max' temp[feature+'_min'] = 'min' temp[feature+'_mean'] = 'mean' aggjson[feature] = temp else: temp = {} temp[feature+'_size'] = 'size' temp[feature+'_unique'] = 'nunique' aggjson[feature] = temp groupbystring = 'groupby([\''+groupbyfeatures+'\']).agg('+str(aggjson)+')' grouperbyjson['groupbystring'] = groupbystring dataframe = dataframe.groupby([groupbyfeatures]).agg(aggjson) dataframe.columns = dataframe.columns.droplevel(0) dataframe = dataframe.reset_index() ''' if operation.lower() == 'size': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).size() elif operation.lower() == 'mean': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).mean() elif operation.lower() == 'max': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).max() elif operation.lower() == 'min': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).min() dataframe = dataframe.rename("groupby_value") dataframe = dataframe.to_frame() dataframe = dataframe.reset_index() ''' return dataframe,grouperbyjson def timeGrouping(self,timegrouper,dataframe): grouperbyjson= {} dateTime = timegrouper['dateTime'] frequency = timegrouper['freq'] groupbyfeatures = timegrouper['groupby'] grouperbyjson['datetime'] = dateTime if dataframe[dateTime].dtypes in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']: dtlenth = dataframe[dateTime].iloc[0] dtlenth = np.int64(dtlenth) dtlenth = len(str(dtlenth)) if dtlenth == 13: dataframe['date'] = pd.to_datetime(dataframe[dateTime],unit='ms') grouperbyjson['unit'] = 'ms' elif dtlenth == 10: dataframe['date'] = pd.to_datetime(dataframe[dateTime],unit='s') grouperbyjson['unit'] = 's' else: dataframe['date'] = pd.to_datetime(dataframe[dateTime]) grouperbyjson['unit'] = '' else: dataframe['date'] = pd.to_datetime(dataframe[dateTime]) grouperbyjson['unit'] = '' dataframe = dataframe.reset_index() dataframe.set_index('date',inplace=True) features = dataframe.columns.tolist() aggjson = {} for feature, featureType in zip(features,dataframe.dtypes): if feature == groupbyfeatures or feature == dateTime or feature == 'index': continue if dataframe[feature].empty == True: continue if dataframe[feature].isnull().all() == True: continue if featureType in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']: temp = {'size','sum','max','min','mean'} aggjson[feature] = temp else: temp = {'size','nunique'} aggjson[feature] = temp if groupbyfeatures == '': groupbystring = 'groupby([pd.Grouper(freq=\''+frequency+'\')]).agg('+str(aggjson)+')' else: groupbystring = 'groupby([pd.Grouper(freq=\''+frequency+'\'),\''+groupbyfeatures+'\']).agg('+str(aggjson)+')' grouperbyjson['groupbystring'] = groupbystring print(grouperbyjson) if groupbyfeatures == '': dataframe = dataframe.groupby([pd.Grouper(freq=frequency)]).agg(aggjson) else: dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).agg(aggjson) dataframe.columns = ['_'.join(col) for col in dataframe.columns] dataframe = dataframe.reset_index() self.log.info(dataframe.head(10)) ''' if operation.lower() == 'size': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).size() elif operation.lower() == 'mean': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).mean() elif operation.lower() == 'max': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).max() elif operation.lower() == 'min': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).min() dataframe = dataframe.rename("groupby_value") dataframe = dataframe.to_frame() dataframe = dataframe.reset_index() ''' return dataframe,grouperbyjson def readDf(self,dataF,featureList,targetColumn): dataDf = dataF[featureList] predictDf =dataF[targetColumn] return dataDf,predictDf def csvTodf(self,dataPath,delimiter,textqualifier): ''' if os.path.splitext(dataPath)[1] == ".tsv": dataFrame=pd.read_csv(dataPath,encoding='latin1',sep='\t') else: dataFrame=pd.read_csv(dataPath,encoding='latin1') ''' if os.path.splitext(dataPath)[1] == ".py": f = open(dataPath, "r") pythoncode = f.read() f.close() ldict = {} exec(pythoncode, globals(), ldict) dataFrame = ldict['dfpy'] else: dataFrame=pd.read_csv(dataPath,encoding='utf-8',sep=delimiter,quotechar=textqualifier, skipinitialspace = True,na_values=['-','?'],encoding_errors= 'replace') dataFrame.rename(columns=lambda x: x.strip(), inplace=True) return dataFrame def read_file(self, fileName): fileName = Path(fileName) if fileName.suffix == '.pdf': pdf = pdfplumber.open(fileName) text = '' for index, page in enumerate(pdf.pages): if index: text += ' ' text += page.extract_text() else: with open(fileName, "r",encoding="utf-8") as f: text = f.read() return text def documentsTodf(self,folderlocation,labelFilePath): dataDf = pd.DataFrame() error_message = "" dataset_csv_file = os.path.join(folderlocation,labelFilePath) labels = pd.read_csv(dataset_csv_file) dataDict = {} keys = ["File","Label"] for key in keys: dataDict[key] = [] for i in range(len(labels)): filename = os.path.join(folderlocation,labels.loc[i,"File"]) dataDict["File"].append(self.read_file(filename)) dataDict["Label"].append(labels.loc[i,"Label"]) dataDf = pd.DataFrame.from_dict(dataDict) error_message = "" return dataDf, error_message def removeFeatures(self,df,datetimeFeature,indexFeature,modelFeatures,targetFeature): self.log.info("\n---------- Prepare Features ----------") if(str(datetimeFeature).lower() != 'na'): datetimeFeature = datetimeFeature.split(",") datetimeFeature = list(map(str.strip, datetimeFeature)) for dtfeature in datetimeFeature: if dtfeature in df.columns: self.log.info("-------> Remove Date Time Feature: "+dtfeature) df = df.drop(columns=dtfeature) if(str(indexFeature).lower() != 'na'): indexFeature = indexFeature.split(",") indexFeature = list(map(str.strip, indexFeature)) for ifeature in indexFeature: if ifeature in df.columns: self.log.info("-------> Remove Index Feature: "+ifeature) df = df.drop(columns=ifeature) if(str(modelFeatures).lower() != 'na'): self.log.info("-------> Model Features: "+str(modelFeatures)) modelFeatures = modelFeatures.split(",") modelFeatures = list(map(str.strip, modelFeatures)) if(targetFeature != '' and str(targetFeature).lower() != 'na'): targetFeature = targetFeature.split(",") targetFeature = list(map(str.strip, targetFeature)) for ifeature in targetFeature: if ifeature not in modelFeatures: modelFeatures.append(ifeature) if(str(indexFeature).lower() != 'na'): for ifeature in indexFeature: if ifeature in modelFeatures: modelFeatures.remove(ifeature) if(str(datetimeFeature).lower() != 'na'): for dtfeature in datetimeFeature: if dtfeature in modelFeatures: modelFeatures.remove(dtfeature) df = df[modelFeatures] self.log.info("---------- Prepare Features End ----------") return(df) def splitImageDataset(self, df, ratio, modelType): if modelType.lower() == "objectdetection": images = df['File'].unique().tolist() trainImages = random.sample(images, int(len(images) * ratio)) mask = [0] * len(df) for i in range(len(df)): mask[i] = df.iloc[i]['File'] in trainImages trainDf = df.iloc[mask] testDf = df.iloc[[not elem for elem in mask]] return trainDf, testDf else: return train_test_split(df, test_size=(1 - ratio)) def createTFRecord(self, train_image_dir, output_dir, csv_file, testPercentage, AugEnabled,keepAugImages,operations, modelType,augConf={}): from transformations import generate_tfrecord from transformations.imageAug import ImageAugmentation if isinstance(csv_file, pd.DataFrame): df = csv_file else: df = pd.read_csv(os.path.join(train_image_dir,csv_file)) labelmap_path, num_classes = generate_tfrecord.createLabelFile(df, output_dir) train_df, test_df = self.splitImageDataset(df, testPercentage/100.0, modelType) if AugEnabled: augFile = os.path.join(output_dir,"tempTrainDf.csv") train_df.to_csv(augFile) ia = ImageAugmentation(train_image_dir, augFile) augFile = ia.augment(modelType, operations,None,augConf) train_df = pd.read_csv(augFile) generate_tfrecord.generate_TF_record(train_image_dir, output_dir, train_df, test_df, labelmap_path) if AugEnabled and not keepAugImages: ia.removeAugmentedImages(train_df) return train_df, num_classes ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import sys from pathlib import Path import urllib.request import tarfile import json import subprocess import os from os.path import expanduser import platform class ODpretrainedModels(): def __init__(self, location=None): if location: if isinstance(location, Path): self.pretrained_models_location = location.as_posix() else: self.pretrained_models_location = location else: p = subprocess.run([sys.executable, "-m", "pip","show","AION"],capture_output=True, text=True) if p.returncode == 0: Output = p.stdout.split('\n') for x in Output: y = x.split(':',1) if(y[0]=='Location'): self.pretrained_models_location = y[1].strip()+"/AION/pretrained_models/object_detection" break if Path(self.pretrained_models_location).is_dir(): self.config_file_location = self.pretrained_models_location+'/supported_models.json' with open(self.config_file_location) as json_data: self.supportedModels = json.load(json_data) home = expanduser("~") if platform.system() == 'Windows': self.modelsPath = os.path.join(home,'AppData','Local','HCLT','AION','PreTrainedModels','ObjectDetection') else: self.modelsPath = os.path.join(home,'HCLT','AION','PreTrainedModels','ObjectDetection') if os.path.isdir(self.modelsPath) == False: os.makedirs(self.modelsPath) def __save_config(self): with open(self.config_file_location, 'w') as json_file: json.dump(self.supportedModels, json_file) def __download(self, modelName): try: url = self.supportedModels[modelName]["url"] file = self.supportedModels[modelName]["file"] local_file_path = Path(self.modelsPath)/(file+".tar.gz") urllib.request.urlretrieve(url, local_file_path) except: raise ValueError("{} model download error, check your internet connection".format(modelName)) return local_file_path def __extract(self, modelName, file_location, extract_dir): try: tarFile = tarfile.open(file_location) tarFile.extractall(extract_dir) tarFile.close() Path.unlink(file_location) return True except: return False def download(self, modelName): if modelName in list(self.supportedModels.keys()): p = Path(self.modelsPath).glob('**/*') modelsDownloaded = [x.name for x in p if x.is_dir()] if self.supportedModels[modelName]['file'] not in modelsDownloaded: file = self.__download(modelName) self.supportedModels[modelName]["downloaded"] = True if self.__extract(modelName, file, self.modelsPath): self.supportedModels[modelName]["extracted"] = True self.__save_config() else: self.__save_config() raise ValueError("{} model downloaded but extraction failed,please try again".format(modelName)) else: raise ValueError("{} is not supported for object detection".format(modelName)) return self.supportedModels[modelName] def get_info(self,modeltype): models_info = {} p = Path(self.pretrained_models_location) downloaded_models = [x.name for x in p.iterdir() if x.is_dir()] for model in list(self.supportedModels.keys()): if (self.supportedModels[model]['type'] == modeltype) or (modeltype == ''): models_info[model] = self.supportedModels[model]['extracted'] return models_info def is_model_exist(self, model_name): models = self.get_info('') status = "NOT_SUPPORTED" if model_name in models: if self.supportedModels[model_name]['extracted']: status = "READY" else: status = "NOT_READY" return status def clear_config(self, model_name): self.supportedModels[model_name]['extracted'] = False self.supportedModels[model_name]['downloaded'] = False self.__save_config() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import sys import logging import json import joblib from pathlib import Path import platform from datetime import datetime as dt import time from pathlib import Path import argparse from operator import itemgetter import re import fitz from io import StringIO from nltk.tokenize import sent_tokenize import pandas as pd from scipy import spatial import urllib.request import zipfile import shutil requirements = """ scipy pandas pathlib """ def pdf2txtInternal(pdffile): keyword = ['Discussion','4. Discussion','DISCUSSION','Results','RESULTS','Introduction','introduction','methods', 'method','result','results','limitation','Conclusions','conclusion','Conclusions','Acknowledgements', 'Acknowledgement','ACKNOWLEDGMENT','ACKNOWLEDGMENTS','References','REFERENCES'] print(pdffile) filename1 = Path(pdffile) csvInpClassFileName = filename1.stem csvOutpClassFileName = "ClassResult" + filename1.stem +".csv" styles = {} font_counts = {} granularity=False doc = fitz.open(pdffile) for i in range(1,len(doc)+1): page = doc[i-1] blocks = page.get_text("dict")["blocks"] for b in blocks: # iterate through the text blocks if b['type'] == 0: # block contains text for l in b["lines"]: # iterate through the text lines for s in l["spans"]: # iterate through the text spans if granularity: identifier = "{0}_{1}_{2}_{3}".format(s['size'], s['flags'], s['font'], s['color']) styles[identifier] = {'size': s['size'], 'flags': s['flags'], 'font': s['font'], 'color': s['color']} else: identifier = "{0}".format(s['size']) styles[identifier] = {'size': s['size'], 'font': s['font']} font_counts[identifier] = font_counts.get(identifier, 0) + 1 # count the fonts usage font_counts = sorted(font_counts.items(), key=itemgetter(1), reverse=True) doc.close() if len(font_counts) < 1: raise ValueError("Zero discriminating fonts found!") p_style = styles[font_counts[0][0]] # get style for most used font by count (paragraph) p_size = p_style['size'] results = [] # list of tuples that store the information as (text, font size, font name) total_data =[] para_data =[] search_data =[] only_text =[] v={} pdf = fitz.open(pdffile) # filePath is a string that contains the path to the pdf for page in pdf: dict = page.get_text("dict") blocks = dict["blocks"] for block in blocks: if "lines" in block.keys(): spans = block['lines'] for span in spans: data = span['spans'] for lines in data: if lines['size']>=p_size: total_data.append([[lines['text']], [lines['size'], lines['font']]]) search_data.append([[lines['text']], [str(int(lines['size']))]]) para_data.append([lines['text']]) #, [lines['size']]]) for keywords in keyword: if keywords == lines['text']: # only store font information of a specific keyword results.append([[lines['text']], [lines['size'], lines['font']]]) only_text.append([lines['text']]) pdf.close() headers=[''] intros =['Abstract','abstract'] header = [''] headers_info =[] for line in total_data: if results[-1][1] == line[1]: headers_info.append(line) headers.extend(line[0]) if str(results[-1][0]).isupper(): headers =([item for item in headers if re.findall(r"(?<![^\s,])[A-Z]+(?![^\s,])", item)]) headers.insert(0,'') m1 = [x for x in headers if x=='Abstract'] if len(m1)!=0: headers.pop(0) else: headers = headers elif str(results[-1][0][0][0]).isdigit(): headers = ([item for item in headers if re.findall(r"([0-9])" , item)]) headers.insert(0,'') else: m1 = [x for x in headers if x=='Abstract'] if len(m1)!=0: headers.pop(0) else: headers = headers header_size=(headers_info[0][1][0]) paragraph =[] check =[] str1 =' ' for data in (para_data): paragraph.extend(data) str2 = str1.join(paragraph) repl = [['- ', '-'], [' +', ' '], [' \.', '.']] for i in repl: str2 = re.sub(i[0], i[1], str2) for al in search_data: rec=(''.join(str(x) for x in al[1])) if float(rec) >=(p_size) or float(rec)>= header_size: check.extend(al[0]) str3 = str1.join(check) str3 = str1.join(check) repl = [['- ', '-'], [' +', ' '], [' \.', '.']] for i in repl: str3 = re.sub(i[0], i[1], str3) dataTosend=[] data = [] for cols in range(2,len(headers)+1): start = headers[cols-2] #.replace(' ','') #'SUBJECTS AND METHODS' end = headers[cols-1] if start in ['Acknowledgements', 'Acknowledgement', 'ACKNOWLEDGMENT','ACKNOWLEDGMENTS', 'References', 'REFERENCES']: break if start=='': #.replace(' ','') res=(str2[str2.find(start)+len(start):str2.rfind(end)]) data.append(['Abstract', res]) tmp='Abstract' + ':'+ ' ' + res dataTosend.append(tmp) else: res=(str2[str2.rfind(start)+len(start):str2.rfind(end)]) data.append([start, res]) tmp=start + ':'+ ' ' + res dataTosend.append(tmp) tokens = [] # sent tokenization and csv file creation updated for idx in range(len(data)): head = data[idx][0] para = data[idx][1] exp = sent_tokenize(para) for val in exp: tokens.append([head, val]) sent_data = [] for head, sent in tokens: break_sent = r'\. [A-Z]|\.[A-Z]' # break senteance if 2 or more in a same column. match = re.findall(break_sent, sent) if len(match) >= 1: for i in range (len(match)): idx, _ = re.search(break_sent, sent).span() sent_data.append( sent[:int(idx)+1].strip()) sent = sent[int(idx)+1:].strip() if (re.search('^[a-z]|^[,;]', sent)): # add incomplete sentence if sent_data != []: last_val = sent_data.pop() new_val = last_val[1] +' '+ sent sent_data.append( new_val) else: sent_data.append( sent) return sent_data def get_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def extract_data(location): files = [x for x in Path(location).iterdir() if x.suffix == '.pdf'] if not files: raise ValueError(f'no pdf file found in directory {location}') sentences = [] for file in files: data=pdf2txtInternal(file) sentences.append(data) return [item for sublist in sentences for item in sublist] def keyWordGeneration( keywords,deploy_loc, pretrained_loc): keywords = keywords.split() noOfKeyword = len(keywords) embeddings = {} word = '' print(pretrained_loc) with open(pretrained_loc, 'r', encoding="utf8") as f: header = f.readline() header = header.split(' ') vocab_size = int(header[0]) embed_size = int(header[1]) for i in range(vocab_size): data = f.readline().strip().split(' ') word = data[0] embeddings[word] = [float(x) for x in data[1:]] readData=pd.DataFrame([],columns=['Keyword']) for i in range(noOfKeyword): neighbours = (sorted(embeddings.keys(), key=lambda word: spatial.distance.euclidean(embeddings[word], embeddings[keywords[i]])) )[1:6] readData = readData.append({'Keyword': keywords[i]}, ignore_index=True) for j in range(len(neighbours)): readData = readData.append({'Keyword': neighbours[j]}, ignore_index=True) readData.to_csv( Path(deploy_loc)/"keywordDataBase.csv",encoding='utf-8',index=False) return set( readData['Keyword']) def dataClassifyWithKw(sentences, keywords): df = pd.DataFrame(sentences, columns=['File']) pattern = '|'.join(keywords) df['Label'] = df.File.str.contains(pattern) return df def to_dataframe(data_loc, keywords, pretrained_type, embedding_size=300, deploy_loc=None, train=True): pretrained_loc = checkAndDownloadPretrainedModel(pretrained_type, embedding_size) sentences = extract_data(data_loc) if train: keywords = keyWordGeneration( keywords,deploy_loc, pretrained_loc) df = dataClassifyWithKw(sentences, keywords) return df def get_pretrained_model_path(): from AION.appfe.appbe.dataPath import DATA_DIR modelsPath = Path(DATA_DIR)/'PreTrainedModels'/'TextProcessing' if not modelsPath.exists(): modelsPath.mkdir(parents=True, exist_ok=True) return modelsPath def checkAndDownloadPretrainedModel(preTrainedModel, embedding_size=300): models = {'glove':{50:'glove.6B.50d.w2vformat.txt',100:'glove.6B.100d.w2vformat.txt',200:'glove.6B.200d.w2vformat.txt',300:'glove.6B.300d.w2vformat.txt'}, 'fasttext':{300:'wiki-news-300d-1M.vec'}} supported_models = [x for y in models.values() for x in y.values()] embedding_sizes = {x:y.keys() for x,y in models.items()} if embedding_size not in embedding_sizes[preTrainedModel]: raise ValueError(f"Embedding size '{embedding_size}' not supported for {preTrainedModel}") selected_model = models[preTrainedModel.lower()][embedding_size] modelsPath = get_pretrained_model_path() p = Path(modelsPath).glob('**/*') modelsDownloaded = [x.name for x in p if x.name in supported_models] local_file_path = None if selected_model not in modelsDownloaded: if preTrainedModel.lower() == "glove": try: location = Path(modelsPath) local_file_path = location/f"glove.6B.{embedding_size}d.w2vformat.txt" file_test, header_test = urllib.request.urlretrieve(f'https://aion-pretrained-models.s3.ap-south-1.amazonaws.com/text/glove.6B.{embedding_size}d.w2vformat.txt', local_file_path) except Exception as e: raise ValueError("Error: unable to download glove pretrained model, please try again or download it manually and placed it at {}. ".format(location)+str(e)) elif preTrainedModel.lower() == "fasttext": try: location = Path(modelsPath) local_file_path = location/"wiki-news-300d-1M.vec.zip" url = 'https://aion-pretrained-models.s3.ap-south-1.amazonaws.com/text/wiki-news-300d-1M.vec.zip' file_test, header_test = urllib.request.urlretrieve(url, local_file_path) with zipfile.ZipFile(local_file_path) as zip_ref: zip_ref.extractall(location) Path(local_file_path).unlink() except Exception as e: raise ValueError("Error: unable to download fastText pretrained model, please try again or download it manually and placed it at {}. ".format(location)+str(e)) return Path(modelsPath)/selected_model def get_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def get_params(profiler): pretrained_model = get_true_option(profiler.get('textConversionMethod', {}), 'Glove') embedding_size = get_true_option(profiler['embeddingSize'][pretrained_model], 50) pretrained_model = pretrained_model.lower() if pretrained_model == 'fasttext': embedding_size = 300 elif pretrained_model == 'glove': sizes = {'default':300, '50d':50, '100d':100,'200d':200, '300d':300} embedding_size = sizes[embedding_size] keywords = profiler['KeyWords'] return "delhi dialysis", pretrained_model, embedding_size def deploy(deploy_path, pretrained_model, embedding_size, output_columns,model_file, bert_length): from AION.mlac.ml.core.imports import importModule def create_predict(pretrained_model, embedding_size): importer = importModule() common_importes = [ {'module': 'sys', 'mod_from': None, 'mod_as': None}, {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'} ] for mod in common_importes: importer.addModule(mod['module'], mod_from=mod['mod_from'], mod_as=mod['mod_as']) local_importes = [ {'module': 'selector', 'mod_from': 'script.selector', 'mod_as': None}, {'module': 'inputprofiler', 'mod_from': 'script.inputprofiler', 'mod_as': None}, {'module': 'trained_model', 'mod_from': 'script.trained_model', 'mod_as': None}, {'module': 'summarize', 'mod_from': None, 'mod_as': None} ] for mod in local_importes: importer.addLocalModule(mod['module'], mod_from=mod['mod_from'], mod_as=mod['mod_as']) text = f""" def predict(data): try: dataLocation = Path(data) if not dataLocation.is_dir(): raise ValueError('Input should be a valid directory') keywords_file = Path(__file__).parent/'keywordDataBase.csv' if not keywords_file.exists(): raise ValueError('keywordDataBase.csv is missing in trained model output') keywords_df = pd.read_csv(keywords_file) if 'Keyword' not in keywords_df.columns: raise ValueError('keywordDataBase.csv file in output folder is corrupt') pretrained_type = '{pretrained_model.lower()}' embedding_sz = {embedding_size} keywords = keywords_df['Keyword'].tolist() df = summarize.to_dataframe(dataLocation, keywords, pretrained_type, embedding_sz, train=False) df0 = df.copy() profilerobj = inputprofiler() df = profilerobj.apply_profiler(df) selectobj = selector() df = selectobj.apply_selector(df) modelobj = trained_model() output = modelobj.predict(df,df0) outputjson = {{"status":"SUCCESS","data":output}} print("predictions:",outputjson) except KeyError as e: output = {{"status":"FAIL","message":str(e).strip('"')}} print("predictions:",json.dumps(output)) return (json.dumps(output)) except Exception as e: output = {{"status":"FAIL","message":str(e).strip('"')}} print("predictions:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = predict(sys.argv[1]) """ code = importer.getCode() code += text return code def create_profiler(output_columns): importer = importModule() common_importes = [ {'module': 'scipy', 'mod_from': None, 'mod_as': None}, {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'} ] for mod in common_importes: importer.addModule(mod['module'], mod_from=mod['mod_from'], mod_as=mod['mod_as']) text = f""" class inputprofiler(object): def __init__(self): self.model = None preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if preprocess_path.exists(): self.model = joblib.load(preprocess_path) else: raise ValueError('Preprocess model not found') def apply_profiler(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) if self.model: df = self.model.transform(df) if isinstance(df, scipy.sparse.spmatrix): df = pd.DataFrame(df.toarray(), columns={output_columns}) else: df = pd.DataFrame(df, columns={output_columns}) return(df) """ code = importer.getCode() code += text return code def create_selector(output_columns): importer = importModule() common_importes = [ {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'} ] for mod in common_importes: importer.addModule(mod['module'], mod_from=mod['mod_from'], mod_as=mod['mod_as']) text = f""" class selector(object): def apply_selector(self,df): df = df[{output_columns}] return(df) """ code = importer.getCode() code += text return code def create_train(model_file, bert_length): importer = importModule() common_importes = [ {'module': 'os', 'mod_from': None, 'mod_as': None}, {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}, {'module': 'Summarizer', 'mod_from': 'summarizer', 'mod_as': None } ] for mod in common_importes: importer.addModule(mod['module'], mod_from=mod['mod_from'], mod_as=mod['mod_as']) text = f""" class trained_model(object): def __init__(self): self.model = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','{model_file}')) def predict(self, X, df_org): X = X.astype(np.float32) df_org['predicted'] = pd.DataFrame(self.model.predict(X)) textToSum="" for i in df_org.index: if (df_org['predicted'][i] or df_org['Label'][i]) : textToSum=textToSum + " " + df_org["File"][i] bert_model = Summarizer() bert_summary=bert_model(textToSum, min_length={bert_length}) return bert_summary """ code = importer.getCode() code += text return code deploy_path = Path(deploy_path) aion_prediction = deploy_path/'aion_predict.py' profiler_file = deploy_path/'script'/'inputprofiler.py' selector_file = deploy_path/'script'/'selector.py' trainer_file = deploy_path/'script'/'trained_model.py' with open(aion_prediction, 'w') as f: f.write(create_predict(pretrained_model, embedding_size)) with open(profiler_file, 'w') as f: f.write(create_profiler(output_columns)) with open(selector_file, 'w') as f: f.write(create_selector(output_columns)) with open(trainer_file, 'w') as f: f.write(create_train(model_file, bert_length)) cwf = Path(__file__) shutil.copy(cwf, deploy_path/cwf.name) # require dataLocation for reading files # require deployLocation for saving keywords # require pretrained model location # require pretrained model type # require keywwords if __name__ == '__main__': dataLocation = r'C:\Harish\aion\task\task\summarization\reference\pdfs' deployLocation = r'C:\Users\vashistah\AppData\Local\HCLT\AION\uses' pretrained_loc = r"C:\Users\vashistah\AppData\Local\HCLT\AION\PreTrainedModels\TextProcessing" pretrained_type = 'glove' keywords = 'delhi dialysis' data = to_dataframe(dataLocation, keywords, pretrained_type,300, deployLocation, train=True) print(data) data.to_csv(Path(deployLocation)/'output.csv', index=False) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import numpy as np import os import datetime, time, timeit from sklearn.model_selection import KFold from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score from sklearn.metrics import classification_report import pickle import logging class recommendersystem(): def __init__(self,features,svd_params): self.features = features self.svd_input = svd_params self.log = logging.getLogger('eion') print ("recommendersystem starts \n") #To extract dict key,values def extract_params(self,dict): self.dict=dict for k,v in self.dict.items(): return k,v def recommender_model(self,df,outputfile): from sklearn.metrics.pairwise import cosine_similarity from utils.file_ops import save_csv USER_ITEM_MATRIX = 'user_item_matrix' ITEM_SIMILARITY_MATRIX = 'item_similarity_matrix' selectedColumns = self.features.split(',') data = pd.DataFrame() for i in range(0,len(selectedColumns)): data[selectedColumns[i]] = df[selectedColumns[i]] dataset = data self.log.info('-------> Top(5) Rows') self.log.info(data.head(5)) start = time.time() self.log.info('\n----------- Recommender System Training Starts -----------') #--------------- Task 11190:recommender system changes Start ---Usnish------------------# # selectedColumns = ['userId', 'movieId', 'rating'] df_eda = df.groupby(selectedColumns[1]).agg(mean_rating=(selectedColumns[2], 'mean'),number_of_ratings=(selectedColumns[2], 'count')).reset_index() self.log.info('-------> Top 10 most rated Items:') self.log.info(df_eda.sort_values(by='number_of_ratings', ascending=False).head(10)) matrix = data.pivot_table(index=selectedColumns[1], columns=selectedColumns[0], values=selectedColumns[2]) relative_file = os.path.join(outputfile, 'data', USER_ITEM_MATRIX + '.csv') matrix.to_csv(relative_file) item_similarity_cosine = cosine_similarity(matrix.fillna(0)) item_similarity_cosine = pd.DataFrame(item_similarity_cosine,columns=pd.Series([i + 1 for i in range(item_similarity_cosine.shape[0])],name='ItemId'),index=pd.Series([i + 1 for i in range(item_similarity_cosine.shape[0])],name='ItemId')) self.log.info('---------> Item-Item Similarity matrix created:') self.log.info(item_similarity_cosine.head(5)) relative_file = os.path.join(outputfile, 'data', ITEM_SIMILARITY_MATRIX + '.csv') save_csv(item_similarity_cosine,relative_file) # --------------- recommender system changes End ---Usnish------------------# executionTime=time.time() - start self.log.info("------->Execution Time: "+str(executionTime)) self.log.info('----------- Recommender System Training End -----------\n') return "filename",matrix,"NA","","" ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import pickle import pandas as pd import sys import time import os from os.path import expanduser import platform from sklearn.preprocessing import binarize import logging import tensorflow as tf from sklearn.model_selection import train_test_split from tensorflow.keras import preprocessing from sklearn.metrics import roc_auc_score from sklearn.metrics import accuracy_score from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.layers import Input, Embedding, LSTM, Lambda import tensorflow.keras.backend as K from tensorflow.keras.models import Model from tensorflow.keras.optimizers import Adam from tensorflow.keras.layers import Concatenate from tensorflow.keras.layers import Input, Dense, Flatten, GlobalMaxPool2D, GlobalAvgPool2D, Concatenate, Multiply, Dropout, Subtract, Add, Conv2D from sklearn.metrics.pairwise import cosine_similarity, cosine_distances import tensorflow.keras.backend as K from tensorflow.keras.models import Model, Sequential from tensorflow.keras import layers, utils, callbacks, optimizers, regularizers ## Keras subclassing based siamese network class siameseNetwork(Model): def __init__(self, activation,inputShape, num_iterations): self.activation=activation self.log = logging.getLogger('eion') super(siameseNetwork, self).__init__() i1 = layers.Input(shape=inputShape) i2 = layers.Input(shape=inputShape) featureExtractor = self.build_feature_extractor(inputShape, num_iterations) f1 = featureExtractor(i1) f2 = featureExtractor(i2) #distance vect distance = layers.Concatenate()([f1, f2]) cosine_loss = tf.keras.losses.CosineSimilarity(axis=1) c_loss=cosine_loss(f1, f2) similarity = tf.keras.layers.Dot(axes=1,normalize=True)([f1,f2]) outputs = layers.Dense(1, activation="sigmoid")(distance) self.model = Model(inputs=[i1, i2], outputs=outputs) ##Build dense sequential layers def build_feature_extractor(self, inputShape, num_iterations): layers_config = [layers.Input(inputShape)] for i, n_units in enumerate(num_iterations): layers_config.append(layers.Dense(n_units)) layers_config.append(layers.Dropout(0.2)) layers_config.append(layers.BatchNormalization()) layers_config.append(layers.Activation(self.activation)) model = Sequential(layers_config, name='feature_extractor') return model def call(self, x): return self.model(x) def euclidean_distance(vectors): (f1, f2) = vectors sumSquared = K.sum(K.square(f1 - f2), axis=1, keepdims=True) return K.sqrt(K.maximum(sumSquared, K.epsilon())) def cosine_similarity(vectors): (f1, f2) = vectors f1 = K.l2_normalize(f1, axis=-1) f2 = K.l2_normalize(f2, axis=-1) return K.mean(f1 * f2, axis=-1, keepdims=True) def cos_dist_output_shape(shapes): shape1, shape2 = shapes return (shape1[0],1) class eion_similarity_siamese: def __init__(self): self.log = logging.getLogger('eion') def siamese_model(self,df,col1,col2,targetColumn,conf,pipe,deployLocation,iterName,iterVersion,testPercentage,predicted_data_file): try: self.log.info('-------> Read Embedded File') home = expanduser("~") if platform.system() == 'Windows': modelsPath = os.path.join(home,'AppData','Local','HCLT','AION','PreTrainedModels','TextSimilarity') else: modelsPath = os.path.join(home,'HCLT','AION','PreTrainedModels','TextSimilarity') if os.path.isdir(modelsPath) == False: os.makedirs(modelsPath) embedding_file_path = os.path.join(modelsPath,'glove.6B.100d.txt') if not os.path.exists(embedding_file_path): from pathlib import Path import urllib.request import zipfile location = modelsPath local_file_path = os.path.join(location,"glove.6B.zip") file_test, header_test = urllib.request.urlretrieve('http://nlp.stanford.edu/data/wordvecs/glove.6B.zip', local_file_path) with zipfile.ZipFile(local_file_path, 'r') as zip_ref: zip_ref.extractall(location) os.unlink(os.path.join(location,"glove.6B.zip")) if os.path.isfile(os.path.join(location,"glove.6B.50d.txt")): os.unlink(os.path.join(location,"glove.6B.50d.txt")) if os.path.isfile(os.path.join(location,"glove.6B.300d.txt")): os.unlink(os.path.join(location,"glove.6B.300d.txt")) if os.path.isfile(os.path.join(location,"glove.6B.200d.txt")): os.unlink(os.path.join(location,"glove.6B.200d.txt")) X = df[[col1,col2]] Y = df[targetColumn] testPercentage = testPercentage self.log.info('\n-------------- Test Train Split ----------------') if testPercentage == 0: xtrain=X ytrain=Y xtest=X ytest=Y else: testSize=testPercentage/100 self.log.info('-------> Split Type: Random Split') self.log.info('-------> Train Percentage: '+str(testSize)) X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=testSize) self.log.info('-------> Train Data Shape: '+str(X_train.shape)+' ---------->') self.log.info('-------> Test Data Shape: '+str(X_test.shape)+' ---------->') self.log.info('-------------- Test Train Split End ----------------\n') self.log.info('\n-------------- Train Validate Split ----------------') X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.20, random_state=42) self.log.info('-------> Train Data Shape: '+str(X_train.shape)+' ---------->') self.log.info('-------> Validate Data Shape: '+str(X_val.shape)+' ---------->') self.log.info('-------------- Train Validate Split End----------------\n') self.log.info('Status:- |... Train / test split done: '+str(100-testPercentage)+'% train,'+str(testPercentage)+'% test') train_sentence1 = pipe.texts_to_sequences(X_train[col1].values) train_sentence2 = pipe.texts_to_sequences(X_train[col2].values) val_sentence1 = pipe.texts_to_sequences(X_val[col1].values) val_sentence2 = pipe.texts_to_sequences(X_val[col2].values) len_vec = [len(sent_vec) for sent_vec in train_sentence1] max_len = np.max(len_vec) len_vec = [len(sent_vec) for sent_vec in train_sentence2] if (max_len < np.max(len_vec)): max_len = np.max(len_vec) train_sentence1 = pad_sequences(train_sentence1, maxlen=max_len, padding='post') train_sentence2 = pad_sequences(train_sentence2, maxlen=max_len, padding='post') val_sentence1 = pad_sequences(val_sentence1, maxlen=max_len, padding='post') val_sentence2 = pad_sequences(val_sentence2, maxlen=max_len, padding='post') y_train = y_train.values y_val = y_val.values activation = str(conf['activation']) model = siameseNetwork(activation,inputShape=train_sentence1.shape[1], num_iterations=[10]) model.compile( loss="binary_crossentropy", optimizer=optimizers.Adam(learning_rate=0.0001), metrics=["accuracy"]) es = callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=1, restore_best_weights=True) rlp = callbacks.ReduceLROnPlateau( monitor='val_loss', factor=0.1, patience=2, min_lr=1e-10, mode='min', verbose=1 ) x_valid=X_val y_valid=y_val n_epoch = int(conf['num_epochs']) batch_size = int(conf['batch_size']) similarityIndex = conf['similarityIndex'] model.fit([train_sentence1,train_sentence2],y_train.reshape(-1,1), epochs = n_epoch,batch_size=batch_size, validation_data=([val_sentence1, val_sentence2],y_val.reshape(-1,1)),callbacks=[es, rlp]) scores = model.evaluate([val_sentence1, val_sentence2], y_val.reshape(-1,1), verbose=0) self.log.info('-------> Model Score Matrix: Accuracy') self.log.info('-------> Model Score (Validate Data) : '+str(scores[1])) self.log.info('Status:- |... Algorithm applied: SIAMESE') test_sentence1 = pipe.texts_to_sequences(X_test[col1].values) test_sentence2 = pipe.texts_to_sequences(X_test[col2].values) test_sentence1 = pad_sequences(test_sentence1, maxlen=max_len, padding='post') test_sentence2 = pad_sequences(test_sentence2, maxlen=max_len, padding='post') prediction = model.predict([test_sentence1, test_sentence2 ]) n_epoch = conf['num_epochs'] batch_size = conf['batch_size'] activation = conf['activation'] similarityIndex = conf['similarityIndex'] self.log.info('-------> similarityIndex : '+str(similarityIndex)) prediction = np.where(prediction > similarityIndex,1,0) rocauc_sco = roc_auc_score(y_test,prediction) acc_sco = accuracy_score(y_test, prediction) predict_df = pd.DataFrame() predict_df['actual'] = y_test predict_df['predict'] = prediction predict_df.to_csv(predicted_data_file) self.log.info('-------> Model Score Matrix: Accuracy') self.log.info('-------> Model Score (Validate Data) : '+str(scores[1])) self.log.info('Status:- |... Algorithm applied: SIAMESE') test_sentence1 = pipe.texts_to_sequences(X_test[col1].values) test_sentence2 = pipe.texts_to_sequences(X_test[col2].values) test_sentence1 = pad_sequences(test_sentence1, maxlen=max_len, padding='post') test_sentence2 = pad_sequences(test_sentence2, maxlen=max_len, padding='post') prediction = model.predict([test_sentence1, test_sentence2 ]) prediction = np.where(prediction > similarityIndex,1,0) rocauc_sco = roc_auc_score(y_test,prediction) acc_sco = accuracy_score(y_test, prediction) predict_df = pd.DataFrame() predict_df['actual'] = y_test predict_df['predict'] = prediction predict_df.to_csv(predicted_data_file) self.log.info("predict_df: \n"+str(predict_df)) sco = acc_sco self.log.info('-------> Test Data Accuracy Score : '+str(acc_sco)) self.log.info('Status:- |... Testing Score: '+str(acc_sco)) self.log.info('-------> Test Data ROC AUC Score : '+str(rocauc_sco)) matrix = '"Accuracy":'+str(acc_sco)+',"ROC AUC":'+str(rocauc_sco) prediction = model.predict([train_sentence1, train_sentence2]) prediction = np.where(prediction > similarityIndex,1,0) train_rocauc_sco = roc_auc_score(y_train,prediction) train_acc_sco = accuracy_score(y_train, prediction) self.log.info('-------> Train Data Accuracy Score : '+str(train_acc_sco)) self.log.info('-------> Train Data ROC AUC Score : '+str(train_rocauc_sco)) trainmatrix = '"Accuracy":'+str(train_acc_sco)+',"ROC AUC":'+str(train_rocauc_sco) model_tried = '{"Model":"SIAMESE","Score":'+str(sco)+'}' saved_model = 'textsimilarity_'+iterName+'_'+iterVersion # filename = os.path.join(deployLocation,'model','textsimilarity_'+iterName+'_'+iterVersion+'.sav') # filename = os.path.join(deployLocation,'model','textsimilarity_'+iterName+'_'+iterVersion+'.h5') ## Because we are using subclassing layer api, please use dir (as below) to store deep learn model instead of .h5 model. filename = os.path.join(deployLocation,'model','textsimilarity_'+iterName+'_'+iterVersion) model.save(filename) # model.save_weights(filename) model_name = 'SIAMESE MODEL' return(model_name,scores[1],matrix,trainmatrix,model_tried,saved_model,filename,max_len,similarityIndex) except Exception as inst: self.log.info("SIAMESE failed " + str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json from pathlib import Path def get_metrics(request): output = {} output_path = Path(request.session['deploypath'])/"etc"/"output.json" if not output_path.exists(): raise ValueError('output json path does not exist, something unexpected happen') with open(output_path) as file: config = json.load(file) output['problem_type'] = config.get('data',{}).get('ModelType') output['best_model'] = config.get('data',{}).get('BestModel') output['hyper_params'] = config.get('data',{}).get('params') output['best_score'] = str(round(float(config.get('data',{}).get('BestScore')), 2)) output['scoring_method'] = config.get('data',{}).get('ScoreType') if output['problem_type'] == 'classification': output['mcc_score'] = str(round(float(config.get('data',{}).get('matrix',{}).get('MCC_SCORE', 0.0)), 2)) else: output['mcc_score'] = 'NA' return output import json import os def get_brier_score(request): try: displaypath = os.path.join(request.session['deploypath'], "etc", "output.json") with open(displaypath) as file: config = json.load(file) problem_type = config["data"]["ModelType"] brier_score = config["data"]["matrix"]["BRIER_SCORE"] print(problem_type,brier_score) except Exception as e: #print(str(e)) raise ValueError(str(e)) return problem_type, brier_score ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import numpy as np from appbe.eda import ux_eda from sklearn.preprocessing import LabelEncoder import json import matplotlib.pyplot as plt import os import mpld3 import subprocess import os import sys import re import json import pandas as pd from appbe.eda import ux_eda from aif360.datasets import StandardDataset from aif360.metrics import ClassificationMetric from aif360.datasets import BinaryLabelDataset def get_metrics(request): dataFile = os.path.join(request.session['deploypath'], "data", "preprocesseddata.csv.gz") predictionScriptPath = os.path.join(request.session['deploypath'], 'aion_predict.py') displaypath = os.path.join(request.session['deploypath'], "etc", "display.json") f = open(displaypath, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) Target_feature = configSettings['targetFeature'] outputStr = subprocess.check_output([sys.executable, predictionScriptPath, dataFile]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() predict_dict = json.loads(outputStr) df = pd.read_csv(dataFile) df_p = pd.DataFrame.from_dict(predict_dict['data']) d3_url = request.GET.get('d3_url') mpld3_url = request.GET.get('mpld3_url') df_temp = request.GET.get('feature') global metricvalue metricvalue = request.GET.get('metricvalue') Protected_feature = df_temp df_p = df_p.drop(columns=[Target_feature, 'remarks', 'probability']) df_p.rename(columns={'prediction': Target_feature}, inplace=True) eda_obj = ux_eda(dataFile, optimize=1) features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catfeatures = eda_obj.getFeatures() features_to_Encode = features categorical_names = {} encoders = {} for feature in features_to_Encode: le = LabelEncoder() le.fit(df[feature]) df[feature] = le.transform(df[feature]) le.fit(df_p[feature]) df_p[feature] = le.transform(df_p[feature]) categorical_names[feature] = le.classes_ encoders[feature] = le new_list = [item for item in categorical_names[Protected_feature] if not(pd.isnull(item)) == True] claas_size = len(new_list) if claas_size > 10: return 'HeavyFeature' metrics = fair_metrics(categorical_names, Protected_feature,Target_feature, claas_size, df, df_p) figure = plot_fair_metrics(metrics) html_graph = mpld3.fig_to_html(figure,d3_url=d3_url,mpld3_url=mpld3_url) return html_graph def fair_metrics(categorical_names, Protected_feature,Target_feature, claas_size, df, df_p): cols = [metricvalue] obj_fairness = [[0]] fair_metrics = pd.DataFrame(data=obj_fairness, index=['objective'], columns=cols) for indx in range(claas_size): priv_group = categorical_names[Protected_feature][indx] privileged_class = np.where(categorical_names[Protected_feature] == priv_group)[0] data_orig = StandardDataset(df, label_name=Target_feature, favorable_classes=[1], protected_attribute_names=[Protected_feature], privileged_classes=[privileged_class]) attr = data_orig.protected_attribute_names[0] idx = data_orig.protected_attribute_names.index(attr) privileged_groups = [{attr:data_orig.privileged_protected_attributes[idx][0]}] unprivileged_size = data_orig.unprivileged_protected_attributes[0].size unprivileged_groups = [] for idx2 in range(unprivileged_size): unprivileged_groups.extend([{attr:data_orig.unprivileged_protected_attributes[idx][idx2]}]) bld = BinaryLabelDataset(df=df, label_names=[Target_feature], protected_attribute_names=[Protected_feature]) bld_p = BinaryLabelDataset(df=df_p, label_names=[Target_feature], protected_attribute_names=[Protected_feature]) ClsMet = ClassificationMetric(bld, bld_p,unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) if metricvalue == "Theil Index": row = pd.DataFrame([[ClsMet.theil_index()]], columns = cols , index = [priv_group]) elif metricvalue == "Equal Opportunity Difference": row = pd.DataFrame([[ClsMet.equal_opportunity_difference()]], columns = cols , index = [priv_group]) elif metricvalue == "Disparate Impact": row = pd.DataFrame([[ClsMet.disparate_impact()]], columns = cols , index = [priv_group]) elif metricvalue == "Statistical Parity Difference": row = pd.DataFrame([[ClsMet.statistical_parity_difference()]], columns = cols , index = [priv_group]) #fair_metrics = fair_metrics.append(row) fair_metrics = pd.concat([fair_metrics,row]) return fair_metrics def plot_fair_metrics(fair_metrics): import matplotlib.patches as patches plt.style.use('default') import seaborn as sns fig, ax = plt.subplots(figsize=(10,4), ncols=1, nrows=1) plt.subplots_adjust( left = 0.125, bottom = 0.1, right = 0.9, top = 0.9, wspace = .5, hspace = 1.1 ) y_title_margin = 1.2 plt.suptitle("Fairness metrics", y = 1.09, fontsize=20) sns.set(style="dark") cols = fair_metrics.columns.values obj = fair_metrics.loc['objective'] if metricvalue == "Theil Index": size_rect = [0.5] rect = [-0.1] bottom = [-0.1] top = [2] bound = [[-0.1,0.1]] elif metricvalue == "Equal Opportunity Difference": size_rect = [0.2] rect = [-0.1] bottom = [-1] top = [1] bound = [[-0.1,0.1]] elif metricvalue == "Disparate Impact": size_rect = [0.4] rect = [0.8] bottom = [0] top = [2] bound = [[-0.1,0.1]] elif metricvalue == "Statistical Parity Difference": size_rect = [0.2] rect = [-0.1] bottom = [-1] top = [1] bound = [[-0.1,0.1]] for attr in fair_metrics.index[1:len(fair_metrics)].values: check = [bound[i][0] < fair_metrics.loc[attr][i] < bound[i][1] for i in range(0,1)] for i in range(0,1): plt.subplot(1, 1, i+1) xx = fair_metrics.index[1:len(fair_metrics)].values.tolist() yy = fair_metrics.iloc[1:len(fair_metrics)][cols[i]].values.tolist() palette = sns.color_palette('husl', len(xx)) ax = sns.pointplot(x=fair_metrics.index[1:len(fair_metrics)], y=yy, palette=palette, hue=xx) index = 0 for p in zip(ax.get_xticks(), yy): if (p[1] > 2.0): _color = palette.as_hex()[index] _val = 'Outlier(' + str(round(p[1],3)) + ')' ax.text(p[0]-0.5, 0.02, _val, color=_color) else: ax.text(p[0], p[1]+0.05, round(p[1],3), color='k') index = index + 1 plt.ylim(bottom[i], top[i]) plt.setp(ax.patches, linewidth=0) ax.get_xaxis().set_visible(False) ax.legend(loc='right', bbox_to_anchor=(1, 0.8), ncol=1) ax.add_patch(patches.Rectangle((-5,rect[i]), 10, size_rect[i], alpha=0.3, facecolor="green", linewidth=1, linestyle='solid')) # plt.axhline(obj[i], color='black', alpha=0.3) plt.title(cols[i], fontname="Times New Roman", size=20,fontweight="bold") ax.set_ylabel('') ax.set_xlabel('') return fig import base64 import io import json import os import urllib import joblib import numpy as np import pandas as pd from SALib.analyze import sobol class sensitivityAnalysis(): def __init__(self, model, problemType, data, target, featureName): self.model = model self.probemType = problemType self.data = data self.target = target self.featureName = featureName self.paramvales = [] self.X = [] self.Y = [] self.problem = {} def preprocess(self): self.X = self.data[self.featureName].values self.Y = self.data[self.target].values bounds = [[np.min(self.X[:, i]), np.max(self.X[:, i])] for i in range(self.X.shape[1])] self.problem = { 'num_vars': self.X.shape[1], 'names': self.featureName, 'bounds': bounds } def generate_samples(self,size): from SALib.sample import sobol self.param_values = sobol.sample(self.problem, size) def calSiClass(self, satype,isML,isDL): try: D = self.problem['num_vars'] S = np.zeros(self.X.shape[1]) for class_label in np.unique(self.Y): if isML: y_pred_poba = self.model.predict_proba(self.param_values)[:, class_label] if isDL: y_pred_poba = self.model.predict(self.param_values)[:,class_label] if not y_pred_poba.size % (2 * D + 2) == 0: lim = y_pred_poba.size - y_pred_poba.size % (2 * D + 2) y_pred_poba = y_pred_poba[:lim] Si = sobol.analyze(self.problem, y_pred_poba) if satype.lower() == 'first': S += Si['S1'] else: S += Si['ST'] S /= len(np.unique(self.Y)) return S except Exception as e: print('Error in calculating Si for Classification: ', str(e)) raise ValueError(str(e)) def calSiReg(self, satype,isML,isDL): try: D = self.problem['num_vars'] Y = np.array([self.model.predict(X_sample.reshape(1, -1)) for X_sample in self.param_values]) Y = Y.reshape(-1) if not Y.size % (2 * D + 2) == 0: lim = Y.size - Y.size % (2 * D + 2) Y = Y[:lim] Si = sobol.analyze(self.problem, Y) if satype.lower() == 'first': S = Si['S1'] else: S = Si['ST'] return S except Exception as e: print('Error in calculating Si for Regression: ', str(e)) raise ValueError(str(e)) def plotSi(self, S, saType): try: import matplotlib.pyplot as plt if saType.lower() == 'first': title, label = 'Sensitivity Analysis', 'First order' else: title, label = 'Sensitivity Analysis', 'Total order' x = np.arange(len(self.problem['names'])) width = 0.35 fig, ax = plt.subplots() ax.bar(x - width / 2, S, width, label=label) ax.set_xticks(x) ax.set_xlabel('Features') ax.set_ylabel('Sensitivity Indices') ax.set_title(title) ax.set_xticklabels(self.problem['names'], rotation=45, ha="right") ax.legend() plt.tight_layout() image = io.BytesIO() plt.savefig(image, format='png') image.seek(0) string = base64.b64encode(image.read()) SAimage = 'data:image/png;base64,' + urllib.parse.quote(string) except Exception as e: print(e) SAimage = '' return SAimage def checkModelType(modelName): isML= False isDL = False if modelName in ["Neural Network", "Convolutional Neural Network (1D)", "Recurrent Neural Network","Recurrent Neural Network (GRU)", "Recurrent Neural Network (LSTM)", "Neural Architecture Search", "Deep Q Network", "Dueling Deep Q Network"]: isDL = True elif modelName in ["Linear Regression","Lasso","Ridge","Logistic Regression", "Naive Bayes", "Decision Tree", "Random Forest", "Support Vector Machine", "K Nearest Neighbors", "Gradient Boosting", "Extreme Gradient Boosting (XGBoost)", "Light Gradient Boosting (LightGBM)", "Categorical Boosting (CatBoost)","Bagging (Ensemble)"]: isML = True return isML,isDL def startSA(request): try: displaypath = os.path.join(request.session['deploypath'], "etc", "display.json") if not os.path.exists(displaypath): raise Exception('Config file not found.') with open(displaypath) as file: config = json.load(file) probelmType = config['problemType'] if probelmType.lower() not in ['classification','regression']: raise Exception(f"Probolem Type: {probelmType} not supported") isML,isDL = checkModelType(config['modelname']) sample_size = 1024 if isML: model = joblib.load(os.path.join(request.session['deploypath'], 'model', config['saved_model'])) sample_size = 2048 if isDL: from tensorflow.keras.models import load_model model = load_model(os.path.join(request.session['deploypath'], 'model', config['saved_model'])) sample_size = 512 target = config['targetFeature'] featureName = config['modelFeatures'] dataPath = os.path.join(request.session['deploypath'], 'data', 'postprocesseddata.csv.gz') if not os.path.exists(dataPath): raise Exception('Data file not found.') from utils.file_ops import read_df_compressed read_status,dataFrame = read_df_compressed(dataPath) obj = sensitivityAnalysis(model, probelmType, dataFrame, target, featureName) obj.preprocess() obj.generate_samples(sample_size) submitType = str(request.GET.get('satype')) saType = 'first' if submitType == 'first' else 'total' if probelmType.lower() == 'classification': SA_values = obj.calSiClass(saType,isML,isDL) else: SA_values = obj.calSiReg(saType,isML,isDL) if SA_values.size and saType: graph = obj.plotSi(SA_values, saType) if graph: outputJson = {'Status': "Success", "graph": graph} else: outputJson = {'Status': "Error", "graph": '','reason':'Error in Plotting Graph'} else: outputJson = {'Status': "Error", "graph": '','reason':'Error in calculating Si values'} output_json = json.dumps(outputJson) return output_json except Exception as e: print(str(e)) raise ValueError(str(e)) import numpy as np import joblib import pandas as pd from appbe.eda import ux_eda from sklearn.preprocessing import MinMaxScaler, LabelEncoder # from pathlib import Path import configparser import json import matplotlib.pyplot as plt import numpy as np import os def trustedai_uq(request): try: displaypath = os.path.join(request.session['deploypath'], "etc", "display.json") f = open(displaypath, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) TargetFeature = configSettings['targetFeature'] problemType = configSettings['problemType'] raw_data_loc = configSettings['preprocessedData'] dataLocation = configSettings['postprocessedData'] selectedfeatures = request.GET.get('values') if problemType.lower() == "classification": model = (os.path.join(request.session['deploypath'], 'model', configSettings['saved_model'])) df = pd.read_csv(dataLocation) trainfea = df.columns.tolist() feature = json.loads(selectedfeatures) # feature = ",".join(featurs) # features = ['PetalLengthCm','PetalWidthCm'] targ = TargetFeature tar =[targ] from bin.aion_uncertainties import aion_uq outputStr = aion_uq(model,dataLocation,feature,tar) return outputStr if problemType.lower() == "regression": model = (os.path.join(request.session['deploypath'], 'model', configSettings['saved_model'])) df = pd.read_csv(dataLocation) trainfea = df.columns.tolist() feature = json.loads(selectedfeatures) # feature = ",".join(featurs) # features = ['PetalLengthCm','PetalWidthCm'] targ = TargetFeature tar =[targ] from bin.aion_uncertainties import aion_uq outputStr = aion_uq(model,dataLocation,feature,tar) print(outputStr) return outputStr except Exception as e: print('error',e) return e ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json import logging import os import shutil import time import importlib from sys import platform from pathlib import Path from distutils.util import strtobool import config_manager.pipeline_config_reader as cs # Base class for EION configuration Manager which read the needed f params from eion.json, initialize the parameterlist, read the respective params, store in variables and return back to caller function or external modules. class AionConfigManager: def getDebiasingDetail(self): return cs.getDebiasingDetail(self) # eion configuration Constractor def __init__(self): self.log = logging.getLogger('eion') self.data = '' self.problemType = '' self.basic = [] self.advance=[] self.summarize = False #To get the inliner labels for eion anomaly detection def get_text_feature(self): self.text_features = [] feat_dict = self.advance['profiler']['featureDict'] for feat in feat_dict: if feat.get('type') == 'text': if feat.get('feature'): self.text_features.append(feat['feature']) return self.text_features def validate_config(self): status = True error_id = '' msg = '' conversion_method = self.__get_true_option(self.advance.get('profiler',{}).get('textConversionMethod',{})) is_text_feature = self.get_text_feature() if is_text_feature and conversion_method.lower() == 'fasttext': status = importlib.util.find_spec('fasttext') if not status: error_id = 'fasttext' msg = 'fastText is not installed. Please install fastText' return status,error_id, msg def getTextlocation(self): text_data = self.basic["dataLocation"] return text_data def getTextSummarize(self): algo = self.basic['algorithms']['textSummarization'] for key in algo: if algo[key] == 'True': algoname = key method = self.advance['textSummarization']['summaryLength'] for key in method: if method[key] == 'True': methodname = key return algoname,methodname def getAssociationRuleFeatures(self): if 'invoiceNoFeature' in self.basic['algorithms']['recommenderSystem']['associationRulesConfig']: invoiceNoFeature = self.basic['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'] else: invoiceNoFeature ='' if 'itemFeature' in self.basic['algorithms']['recommenderSystem']['associationRulesConfig']: itemFeature = self.basic['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature'] else: itemFeature ='' return invoiceNoFeature,itemFeature def getFirstDocumentFeature(self): return cs.getFirstDocumentFeature(self) def getSecondDocumentFeature(self): return cs.getSecondDocumentFeature(self) def getEionTextSimilarityConfig(self): return self.advance['textSimilarityConfig'] def getEionTextSummarizationConfig(self): return self.basic['dataLocation'],self.basic['deployLocation'] ,self.basic['textSummarization']['KeyWords'],self.basic['textSummarization']['pathForKeywordFile'] def getEionInliers(self): return cs.getEionInliers(self) #To get the selected models for eion anomaly detection def getEionanomalyModels(self): self.anomalyModels = self.mlmodels return (self.anomalyModels) # To get parameter list of configuration module from json, this will be passed as dict{} def getEionProfilerConfigurarion(self): return cs.getEionProfilerConfigurarion(self) def getAIONTestTrainPercentage(self): return cs.getAIONTestTrainPercentage(self) def getModelEvaluationConfig(self): try: return request.POST.get('mydata',{}) except Exception as e: return({}) def getAIONDataBalancingMethod(self): return cs.getAIONDataBalancingMethod(self) def updateFeatureSelection(self, selectorConfig,codeConfigure,vectorizer=False): if vectorizer: selectorConfig['selectionMethod']['featureSelection'] = 'True' selectorConfig['featureSelection']['allFeatures'] = 'True' selectorConfig['featureSelection']['statisticalBased'] = 'False' selectorConfig['featureSelection']['modelBased'] = 'False' codeConfigure.update_config("feature_selector", ['allFeatures']) # To get parameter list of selector module params def getEionSelectorConfiguration(self): return cs.getEionSelectorConfiguration(self) def createDeploymentFolders(self,deployFolder,iterName,iterVersion): usecase = '{}{}{}'.format(iterName, '_' if iterVersion != '' else '', iterVersion) folders = ['data','log','model','script','etc'] skip_delete = ['log'] deployLocation = Path(deployFolder)/iterName/iterVersion deployLocation.mkdir(parents=True, exist_ok=True) # delete previous failed/trained use case outputs except log folder # as logging is already enabled for current usecase for x in deployLocation.iterdir(): if x.is_file(): # bug 13315 delete existing files x.unlink() elif x.is_dir(): if x.stem not in skip_delete: shutil.rmtree( x) for folder in folders: (deployLocation/folder).mkdir( parents=True, exist_ok=True) (deployLocation/'log'/'img').mkdir( parents=True, exist_ok=True) data_location = deployLocation/'data' paths = { 'usecase': str(deployLocation.parent), 'deploy': str(deployLocation), 'data': str(deployLocation/'data'), 'image': str(deployLocation/'log'/'img'), } files = { 'original': str(data_location/'preprocesseddata.csv.gz'), 'profiled': str(data_location/'postprocesseddata.csv.gz'), 'reduction': str(data_location/'reductiondata.csv'), 'trained': str(data_location/'trainingdata.csv'), 'predicted': str(data_location/'predicteddata.csv.gz'), 'logs': str(deployLocation/'log'/'model_training_logs.log'), 'output': str(deployLocation/'etc'/'output.json'), } return( paths['usecase'],paths['deploy'],paths['data'],paths['image'],files['original'],files['profiled'],files['trained'],files['predicted'],files['logs'],files['output'],files['reduction']) # To get parameter list of learner module params def getEionLearnerConfiguration(self): try: if(self.advance['mllearner_config']): mllearner_config = self.advance['mllearner_config'] if 'categoryBalancingMethod' not in mllearner_config: mllearner_config['categoryBalancingMethod'] = 'oversample' if 'testPercentage' not in mllearner_config: mllearner_config['testPercentage'] = 20 if 'missingTargetCategory' not in mllearner_config: mllearner_config['missingTargetCategory'] = '' mllearner_config['modelParams']['classifierModelParams']['Deep Q Network'] = self.advance['rllearner_config']['modelParams']['classifierModelParams']['Deep Q Network'] mllearner_config['modelParams']['classifierModelParams']['Neural Architecture Search'] = self.advance['dllearner_config']['modelParams']['classifierModelParams']['Neural Architecture Search'] mllearner_config['modelParams']['classifierModelParams']['Dueling Deep Q Network'] = self.advance['rllearner_config']['modelParams']['classifierModelParams']['Dueling Deep Q Network'] mllearner_config['modelParams']['regressorModelParams']['Deep Q Network'] = self.advance['rllearner_config']['modelParams']['regressorModelParams']['Deep Q Network'] mllearner_config['modelParams']['regressorModelParams']['Dueling Deep Q Network'] = self.advance['rllearner_config']['modelParams']['regressorModelParams']['Dueling Deep Q Network'] mllearner_config['modelParams']['regressorModelParams']['Neural Architecture Search'] = self.advance['dllearner_config']['modelParams']['regressorModelParams']['Neural Architecture Search'] return mllearner_config else: return('NA') except KeyError: return('NA') except Exception as inst: self.log.info( '\n-----> getEionLearnerConfiguration failed!!!.'+str(inst)) return('NA') def getEionDeepLearnerConfiguration(self): return cs.getEionDeepLearnerConfiguration(self) def gettimegrouper(self): return cs.gettimegrouper(self) def getgrouper(self): return cs.getgrouper(self) def getfilter(self): return cs.getfilter(self) def getNumberofForecasts(self): return cs.getNumberofForecasts(self) ##To get multivariate feature based anomaly detection status def getMVFeaturebasedAD(self): return cs.getMVFeaturebasedAD(self) def getModulesDetails(self): problem_type = self.problemType visualizationstatus = self.getEionVisualizationStatus() profiler_status = self.getEionProfilerStatus() selector_status = self.getEionSelectorStatus() learner_status = self.mllearner deeplearner_status = self.dllearner targetFeature = self.getTargetFeatures() deploy_status = self.getEionDeploymentStatus() VideoProcessing = False similarityIdentificationStatus = False contextualSearchStatus = False anomalyDetectionStatus = False if problem_type.lower() == 'survivalanalysis': survival_analysis_status = True selector_status = False associationRuleStatus = 'disable' timeseriesStatus = 'disable' learner_status = False deeplearner_status = False else: survival_analysis_status = False if problem_type.lower() == 'textsimilarity': selector_status = False learner_status = False deeplearner_status = False timeseriesStatus = 'disable' associationRuleStatus = 'disable' inputDriftStatus = 'disable' textSimilarityStatus = True else: textSimilarityStatus = False if problem_type.lower() == 'inputdrift': inputDriftStatus = True profiler_status = False selector_status = False learner_status = False deeplearner_status = False timeseriesStatus = 'disable' associationRuleStatus = 'disable' deploy_status = False visualizationstatus = False else: inputDriftStatus = False if problem_type.lower() == 'outputdrift': outputDriftStatus = True profiler_status = False selector_status = False learner_status = False deeplearner_status = False timeseriesStatus = 'disable' associationRuleStatus = 'disable' deploy_status = False visualizationstatus = False else: outputDriftStatus = False if problem_type.lower() == 'recommendersystem': recommenderStatus = True #profiler_status = 'disable' selector_status = False learner_status = False deeplearner_status = False timeseriesStatus = 'disable' associationRuleStatus = 'disable' #Task 11190 visualizationstatus = False else: recommenderStatus = False ''' if profiler_status.lower() == 'enable': profiler_status = True else: profiler_status = False if selector_status.lower() == 'enable': selector_status = True else: selector_status = False if visualizationstatus.lower() == 'enable': visualizationstatus = True else: visualizationstatus = False ''' if learner_status: if(problem_type == 'NA'): learner_status = True elif(problem_type.lower() in ['classification','regression','clustering','anomalydetection', 'topicmodelling', 'objectdetection', 'timeseriesanomalydetection']): #task 11997 learner_status = True else: learner_status = False if problem_type.lower() == 'anomalydetection' or problem_type.lower() == 'timeseriesanomalydetection': #task 11997 anomalyDetectionStatus = True if deeplearner_status: if(problem_type.lower() == 'na'): deeplearner_status = True elif(problem_type.lower() in ['classification','regression']): deeplearner_status = True else: deeplearner_status = False if(targetFeature == ''): deeplearner_status = False if problem_type.lower() == 'timeseriesforecasting': #task 11997 timeseriesStatus = True profiler_status = True #task 12627 selector_status = False learner_status = False deeplearner_status = False associationRuleStatus = 'disable' else: timeseriesStatus = False if problem_type.lower() == 'videoforecasting': forecastingStatus = True timeseriesStatus = False profiler_status = True selector_status = False learner_status = False deeplearner_status = False associationRuleStatus = 'disable' else: forecastingStatus = False if problem_type.lower() == 'imageclassification': imageClassificationStatus = True timeseriesStatus = False profiler_status = False selector_status = False learner_status = False deeplearner_status = False associationRuleStatus = 'disable' else: imageClassificationStatus = False if problem_type.lower() == 'associationrules': associationRuleStatus = True timeseriesStatus = False profiler_status = False selector_status = False learner_status = False deeplearner_status = False visualizationstatus = False else: associationRuleStatus = False if problem_type.lower() == 'statetransition': stateTransitionStatus = True objectDetectionStatus = False imageClassificationStatus = False timeseriesStatus = False profiler_status = False selector_status = False learner_status = False deeplearner_status = False associationRuleStatus = False visualizationstatus = False else: stateTransitionStatus = False if problem_type.lower() == 'objectdetection': objectDetectionStatus = True imageClassificationStatus = False timeseriesStatus = False profiler_status = False selector_status = False learner_status = False deeplearner_status = False associationRuleStatus = False visualizationstatus = False else: objectDetectionStatus = False if problem_type.lower() == 'similarityidentification': similarityIdentificationStatus = True objectDetectionStatus = False imageClassificationStatus = False timeseriesStatus = False selector_status = False learner_status = False deeplearner_status = False associationRuleStatus = False visualizationstatus = False self.updateEmbeddingForDocSimilarity() else: similarityIdentificationStatus = False if problem_type.lower() == 'contextualsearch': contextualSearchStatus = True objectDetectionStatus = False imageClassificationStatus = False timeseriesStatus = False selector_status = False learner_status = False deeplearner_status = False associationRuleStatus = False visualizationstatus = False self.updateEmbeddingForContextualsearch() else: contextualSearchStatus = False if problem_type.lower() == 'textsummarization': textSummarization = True profiler_status = False selector_status = False else: textSummarization = False ''' if deploy_status.lower() == 'enable': deploy_status = True else: deploy_status = False ''' #print(inputDriftStatus) return problem_type,targetFeature,profiler_status,selector_status,learner_status,deeplearner_status,timeseriesStatus,textSummarization,survival_analysis_status,textSimilarityStatus,inputDriftStatus,outputDriftStatus,recommenderStatus,visualizationstatus,deploy_status,associationRuleStatus,imageClassificationStatus,forecastingStatus,objectDetectionStatus,stateTransitionStatus,similarityIdentificationStatus,contextualSearchStatus,anomalyDetectionStatus def __get_true_option(self, d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def updateEmbeddingForDocSimilarity(self): method = self.__get_true_option(self.basic['algorithms']['similarityIdentification']) textConversionMethods = self.advance['profiler']['textConversionMethod'] print("------------"+method+'---------------') for key in textConversionMethods: if key == method: self.advance['profiler']['textConversionMethod'][key] = "True" else: self.advance['profiler']['textConversionMethod'][key] = "False" if method.lower() == 'bm25': self.advance['profiler']['textConversionMethod']['bm25'] = "True" def updateEmbeddingForContextualsearch(self): method = self.__get_true_option(self.basic['algorithms']['contextualSearch']) textConversionMethods = self.advance['profiler']['textConversionMethod'] print("------------"+method+'---------------') for key in textConversionMethods: if key == method: self.advance['profiler']['textConversionMethod'][key] = "True" else: self.advance['profiler']['textConversionMethod'][key] = "False" if method.lower() == 'bm25': self.advance['profiler']['textConversionMethod']['bm25'] = "True" def get_conversion_method(self): return self.__get_true_option( self.advance['profiler']['textConversionMethod']) def getAlgoName(self, problem_type=None): if problem_type == None: problem_type = self.__get_true_option(self.basic['algorithms']) return self.__get_true_option(self.basic['algorithms'][problem_type]) def getScoringCreteria(self): return self.scoringCreteria def getVectorDBCosSearchStatus(self,problemType): if self.basic['preprocessing'][problemType]['VectorDB'] == 'True': return True else: return False def getVectorDBFeatureDelimitInDoc(self): return ' ~&~ ' def getEionDeployerConfiguration(self): return cs.getEionDeployerConfiguration(self) def getEionAssociationRuleConfiguration(self): return cs.getEionAssociationRuleConfiguration(self) def getEionAssociationRuleModelParams(self): try: associationConfg = self.advance['associationrule'] if 'modelParams' in associationConfg: modelParams = associationConfg['modelParams'] if(str(type(modelParams)) != "<class 'dict'>"): modelParams = [] elif(len(modelParams) == 0): modelParams = [] if(len(modelParams) == 0): if 'modelparamsfile' in associationConfg: ml_algorithm_filename = associationConfg['modelparamsfile'] if(ml_algorithm_filename == ''): ml_algorithm_filename = os.path.dirname(os.path.abspath(__file__))+'/AssciationRules_Defaults.json' modelParams = json.loads(open(ml_algorithm_filename).read()) modelList = [] modelList = list(modelParams.keys()) return(modelParams,modelList) except KeyError: modelParams = [] modelList=[] return(modelParams,modelList) def getEionImageAugmentationConfiguration(self): try: enable = self.advance['ImageAugmentation'].get('Enable', "False") keepAugImages = self.advance['ImageAugmentation'].get('KeepAugmentedImages', "False") if enable == "True": operations = {} operations.update(self.advance['ImageAugmentation'].get('Noise', {})) operations.update(self.advance['ImageAugmentation'].get('Transformation', {})) if keepAugImages == 'True': keepAugImages = True if keepAugImages == 'False': keepAugImages = False return True,keepAugImages,{key: True if value.lower() == "true" else False for key, value in operations.items()},self.advance['ImageAugmentation'].get('configuration',{}) else: return False,False, {},{} except KeyError: return False,False, {},{} def getAIONRemoteTraining(self): try: if(self.advance['remoteTraining']): self.advance['remoteTraining']['Enable'] = strtobool(self.advance['remoteTraining'].get('Enable', 'False')) return self.advance['remoteTraining'] else: remoteTraining = {} remoteTraining['Enable'] = False remoteTraining['server'] = None remoteTraining['ssh'] = None return(remoteTraining) except KeyError: remoteTraining = {} remoteTraining['Enable'] = False remoteTraining['server'] = None remoteTraining['ssh'] = None return(remoteTraining) def getEionObjectDetectionConfiguration(self): return cs.getEionObjectDetectionConfiguration(self) def getEionTimeSeriesConfiguration(self): return cs.getEionTimeSeriesConfiguration(self) def getAIONAnomalyDetectionConfiguration(self): return cs.getAIONAnomalyDetectionConfiguration(self) def getAIONTSAnomalyDetectionConfiguration(self): return cs.getAIONTSAnomalyDetectionConfiguration(self) def getEionVisualizationStatus(self): return(True) def getEionVisualizationConfiguration(self): return cs.getEionVisualizationConfiguration(self) def getEionRecommenderConfiguration(self): return cs.getEionRecommenderConfiguration(self) def getAionNASConfiguration(self): return cs.getAionNASConfiguration(self) def getEionProblemType(self): try: analysis_type = self.basic['analysisType'] self.problemType = '' for key in analysis_type.keys(): if analysis_type[key] == 'True': self.problemType = key break if self.problemType: return self.problemType else: return('NA') except KeyError: return('NA') def getEionProfilerStatus(self): return cs.getEionProfilerStatus(self) def getEionSelectorStatus(self): return cs.getEionSelectorStatus(self) def getEionDeploymentStatus(self): return cs.getEionDeploymentStatus(self) def getEionTimeSeriesModelParams(self): try: selectedMLModel = self.mlmodels tsconfig = self.advance['timeSeriesForecasting'] #task 11997 if 'modelParams' in tsconfig: modelParams = tsconfig['modelParams'] if(str(type(modelParams)) != "<class 'dict'>"): modelParams = [] elif(len(modelParams) == 0): modelParams = [] if(len(modelParams) == 0): if 'modelparamsfile' in tsconfig: ml_algorithm_filename = tsconfig['modelparamsfile'] if(ml_algorithm_filename == ''): ml_algorithm_filename = os.path.dirname(os.path.abspath(__file__))+'/TS_Defaults.json' modelParams = json.loads(open(ml_algorithm_filename).read()) #Modified getting modelParams as small letters modelParams = {k.lower(): v for k, v in modelParams.items()} #print("\n modelParams: type \n",modelParams,type(modelParams)) if selectedMLModel != '': #if selectedMLModel.lower() != 'var': if ('var' not in selectedMLModel.lower()): modelList = selectedMLModel.split(",") modelList = list(map(str.strip, modelList)) #Modified getting modelList as small letters modelList = [strMP.lower() for strMP in modelList] for mod in modelList: if mod not in modelParams: self.log.info("'"+mod+"' Not Available for Particular Problem Type") modelList.remove(mod) else: modelList = selectedMLModel.split(",") #Modified modelList = [strMP.lower() for strMP in modelList] modelList = list(map(str.strip, modelList)) else: #Modified modelParams = [strMP.lower() for strMP in modelParams] modelList = list(modelParams.keys()) return(modelParams,modelList) except KeyError: modelParams = [] modelList=[] return(modelParams,modelList) #NAS status def getNASStatus(self): return cs.getNASStatus(self) def getEionImageLearnerModelParams(self): try: selectedDLModel = self.dlmodels learnerconfig = self.advance['image_config'] modelList = selectedDLModel.split(",") return(learnerconfig,modelList) except KeyError: learnerconfig = [] modelList=[] return(learnerconfig,modelList) def getAionObjectDetectionModelParams(self): try: selectedDLModel = self.dlmodels modelList = selectedDLModel.split(",") return(modelList) except KeyError: modelList=[] return(modelList) def getEionVideoLearnerModelParams(self): try: selectedDLModel = self.basic['selected_DL_Models'] learnerconfig = self.advance['video_config'] modelList = selectedDLModel.split(",") return(learnerconfig,modelList) except KeyError: learnerconfig = [] modelList=[] return(learnerconfig,modelList) def getEionDeepLearnerModelParams(self,modelType): try: numberofModels = 0 dl_algorithm_filename = '' if(modelType == 'classification'): requiredalgo = 'classifierModelParams' elif(modelType == 'regression'): requiredalgo = 'regressorModelParams' selectedmodels = 'regression' elif(modelType == 'TextClassification'): requiredalgo = 'classifierModelParams' elif(modelType == 'clustering'): requiredalgo = 'clusteringModelParams' learnerconfig = self.advance['dllearner_config'] selectedDLModel = self.dlmodels modelParams = [] modelList=[] if 'modelParams' in learnerconfig: modelParams = learnerconfig['modelParams'] if(str(type(modelParams)) != "<class 'dict'>"): modelParams = [] elif(len(modelParams) == 0): modelParams = [] if(len(modelParams) == 0): if 'modelparamsfile' in learnerconfig: if(learnerconfig['modelparamsfile'] != ""): dl_algorithm_filename = learnerconfig['modelparamsfile'] if(dl_algorithm_filename == ''): dl_algorithm_filename = os.path.dirname(os.path.abspath(__file__))+'/DL_Defaults.json' modelParams = json.loads(open(dl_algorithm_filename).read()) if requiredalgo in modelParams: modelParams = modelParams[requiredalgo] if selectedDLModel != '': modelList = selectedDLModel.split(",") modelList = list(map(str.strip, modelList)) for mod in modelList: if mod not in modelParams: self.log.info("'"+mod+"' Not Available for Particular Problem Type") modelList.remove(mod) else: modelList = list(modelParams.keys()) #modelParams = dict((k.lower(), v) for k, v in modelParams .items()) #modelList = selectedMLModel.split(",") if(len(modelList) == 0): modelList = list(modelParams.keys()) return(modelParams,modelList) except KeyError: modelParams = [] modelList=[] return(modelParams,modelList) def getEionLearnerModelParams(self,modelType): try: numberofModels = 0 ml_algorithm_filename = '' if(modelType == 'classification'): requiredalgo = 'classifierModelParams' elif(modelType == 'regression'): requiredalgo = 'regressorModelParams' elif(modelType == 'TextClassification'): requiredalgo = 'classifierModelParams' elif(modelType == 'clustering'): requiredalgo = 'clusteringModelParams' elif(modelType == 'topicmodelling'): requiredalgo = 'topicModellingParams' learnerconfig = self.advance['mllearner_config'] selectedMLModel = self.mlmodels modelParams = [] modelList=[] if 'modelParams' in learnerconfig: modelParams = learnerconfig['modelParams'] if(str(type(modelParams)) != "<class 'dict'>"): modelParams = [] elif(len(modelParams) == 0): modelParams = [] if(len(modelParams) == 0): if 'modelparamsfile' in learnerconfig: if(learnerconfig['modelparamsfile'] != ""): ml_algorithm_filename = learnerconfig['modelparamsfile'] if(ml_algorithm_filename == ''): ml_algorithm_filename = os.path.dirname(os.path.abspath(__file__))+'/ML_Defaults.json' modelParams = json.loads(open(ml_algorithm_filename).read()) if requiredalgo in modelParams: modelParams = modelParams[requiredalgo] #modelParams = dict((k.lower(), v) for k, v in modelParams .items()) #print(modelParams) #modelList = list(modelParams.keys()) #print("SelectedModels") #self.log.info(selectedmodels) #if selectedmodels in selectedMLModel: if selectedMLModel != '': modelList = selectedMLModel.split(",") modelList = list(map(str.strip, modelList)) for mod in modelList: if mod not in modelParams: self.log.info("'"+mod+"' Not Available for Particular Problem Type") modelList.remove(mod) else: modelList = list(modelParams.keys()) #modelList = selectedMLModel.split(",") if(len(modelList) ==0): modelList = list(modelParams.keys()) return(modelParams,modelList) except KeyError: modelParams = [] modelList=[] return(modelParams,modelList) def getTargetFeatures(self): return cs.getTargetFeatures(self) def getModelFeatures(self): try: if(self.basic['trainingFeatures']): modFeatures = self.basic['trainingFeatures'] modFeatures = modFeatures.split(",") modFeatures = list(map(str.strip, modFeatures)) modFeatures = ",".join([modf for modf in modFeatures]) return(modFeatures) else: return('NA') except KeyError: return('NA') def getFolderSettings(self): return cs.getFolderSettings(self) def getAIONLocationSettings(self): self.iter_name = self.basic['modelName'] self.iteration_version = self.basic['modelVersion'] if(self.basic['dataLocation']): dataLocation = self.basic['dataLocation'] else: dataLocation = 'NA' if(self.basic['deployLocation']): deployLocation = self.basic['deployLocation'] else: deployLocation = 'NA' try: if 'fileSettings' in self.basic: csv_setting = self.basic['fileSettings'] if 'delimiters' in csv_setting: delimiter = csv_setting['delimiters'] if delimiter.lower() == 'tab' or delimiter.lower() == '\t': delimiter = '\t' elif delimiter.lower() == 'semicolon' or delimiter.lower() == ';': delimiter = ';' elif delimiter.lower() == 'comma' or delimiter.lower() == ',': delimiter = ',' elif delimiter.lower() == 'space' or delimiter.lower() == ' ': delimiter = ' ' elif delimiter.lower() == 'other': if 'other' in csv_setting: delimiter = csv_setting['other'] else: delimiter = ',' elif delimiter == '': delimiter = ',' else: delimiter = ',' if 'textqualifier' in csv_setting: textqualifier = csv_setting['textqualifier'] else: textqualifier = '"' else: delimiter = ',' textqualifier = '"' except KeyError: delimiter = ',' textqualifier = '"' return(self.iter_name,self.iteration_version,dataLocation,deployLocation,delimiter,textqualifier) def getFeatures(self): try: if(self.basic['dateTimeFeature']): dtFeatures = self.basic['dateTimeFeature'] dtFeatures = dtFeatures.split(",") dtFeatures = list(map(str.strip, dtFeatures)) dtFeatures = ",".join([dtf for dtf in dtFeatures]) else: dtFeatures = 'NA' except KeyError: dtFeatures = 'NA' try: if(self.basic['indexFeature']): iFeatures = self.basic['indexFeature'] iFeatures = iFeatures.split(",") iFeatures = list(map(str.strip, iFeatures)) iFeatures = ",".join([dif for dif in iFeatures]) else: iFeatures = 'NA' except KeyError: iFeatures = 'NA' try: if(self.basic['trainingFeatures']): modFeatures = self.basic['trainingFeatures'] modFeatures = modFeatures.split(",") modFeatures = list(map(str.strip, modFeatures)) modFeatures = ",".join([modf for modf in modFeatures]) else: modFeatures = 'NA' except KeyError: modFeatures = 'NA' return(dtFeatures,iFeatures,modFeatures) def setModels(self): return cs.setModels(self) def readConfigurationFile(self,path): return cs.readConfigurationFile(self, path) def getFilterExpression(self): return cs.getFilterExpression(self) def getSurvivalEventColumn(self): return cs.getSurvivalEventColumn(self) def getSurvivalDurationColumn(self): return cs.getSurvivalDurationColumn(self) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json def getDebiasingDetail(self): try: if(self.advance['profiler']['deBiasing']): dlconfig = self.advance['profiler']['deBiasing'] return dlconfig else: return('NA') except KeyError: return('NA') def getFirstDocumentFeature(self): if 'firstDocFeature' in self.basic: firstDocFeature = self.basic['algorithms']['recommenderSystem']['textSimilarityConfig']['baseFeature'] else: firstDocFeature = '' return(firstDocFeature) def getSecondDocumentFeature(self): if 'secondDocFeature' in self.basic: secondDocFeature = self.basic['algorithms']['recommenderSystem']['textSimilarityConfig']['comparisonFeature'] else: secondDocFeature = '' return(secondDocFeature) def getEionInliers(self): if 'inlierLabels' in self.basic: self.inlierLabels = self.basic['inlierLabels'] else: self.inlierLabels = 'NA' return (self.inlierLabels) def getEionProfilerConfigurarion(self): try: if(self.advance['profiler']): return self.advance['profiler'] else: return('NA') except KeyError: return('NA') def getAIONTestTrainPercentage(self): try: return (int(self.advance.get('testPercentage',20))) except KeyError: return(20) def getAIONDataBalancingMethod(self): try: if(self.advance['categoryBalancingMethod']): return self.advance['categoryBalancingMethod'] else: return("oversample") except KeyError: return("oversample") def getEionSelectorConfiguration(self): try: if(self.advance['selector']): return self.advance['selector'] else: return('NA') except KeyError: return('NA') def getEionDeepLearnerConfiguration(self): try: if(self.advance['dllearner_config']): dlconfig = self.advance['dllearner_config'] if 'categoryBalancingMethod' not in dlconfig: dlconfig['categoryBalancingMethod'] = '' if 'testPercentage' not in dlconfig: #Unnati dlconfig['testPercentage'] = 20 #Unnati return dlconfig else: return('NA') except KeyError: return('NA') def gettimegrouper(self): try: if(self.basic['timegrouper']): return self.basic['timegrouper'] else: return 'NA' except: return 'NA' def getgrouper(self): try: if(self.basic['group']): return self.basic['group'] else: return 'NA' except: return 'NA' def getfilter(self): try: if(self.basic['filter']): return self.basic['filter'] else: return 'NA' except: return 'NA' def getNumberofForecasts(self): try: if(self.basic['noofforecasts']): return int(self.basic['noofforecasts']) else: return (-1) except: return (-1) ##To get multivariate feature based anomaly detection status def getMVFeaturebasedAD(self): try: dict_ae=self.basic['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder'] #task 11997 if(dict_ae): return (dict_ae) else: return (-1) except: return (-1) def getEionDeployerConfiguration(self): try: if(self.advance['deployer']): return self.advance['deployer'] else: return('NA') except KeyError: return('NA') def getEionAssociationRuleConfiguration(self): try: if(self.advance['associationrule']): return self.advance['associationrule'] else: return('NA') except KeyError: return('NA') def getEionObjectDetectionConfiguration(self): try: if(self.advance['objectDetection']): return self.advance['objectDetection'] else: return('NA') except KeyError: return('NA') def getEionTimeSeriesConfiguration(self): try: if(self.advance['timeSeriesForecasting']): #task 11997 return self.advance['timeSeriesForecasting'] else: return('NA') except KeyError: return('NA') def getAIONAnomalyDetectionConfiguration(self): try: if(self.advance['anomalyDetection']): return self.advance['anomalyDetection'] else: return('NA') except KeyError: return('NA') def getAIONTSAnomalyDetectionConfiguration(self): #task 11997 try: if(self.advance['timeSeriesAnomalyDetection']): return self.advance['timeSeriesAnomalyDetection'] else: return('NA') except KeyError: return('NA') def getEionVisualizationConfiguration(self): try: if(self.advance['visualization_settings']): return(self.advance['visualization_settings']) else: return('NA') except KeyError: return('NA') def getEionRecommenderConfiguration(self): try: if(self.advance['recommenderparam']): return self.advance['recommenderparam'] else: return('NA') except KeyError: return('NA') def getAionNASConfiguration(self): try: if(self.advance['neuralarchsearch']): return self.advance['neuralarchsearch'] else: return('NA') except KeyError: return('NA') def getEionProfilerStatus(self): try: if(self.basic['output']['profilerStage']): return(self.basic['output']['profilerStage']) else: return('false') except KeyError: return('false') def getEionSelectorStatus(self): try: if(self.basic['output']['selectorStage']): return(self.basic['output']['selectorStage']) else: return('disable') except KeyError: return('disable') def getEionDeploymentStatus(self): try: if(self.basic['output']['deploymentStage']): return(self.basic['output']['deploymentStage']) else: return(False) except KeyError: return(False) def __get_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def getNASStatus(self): try: if(self.dlmodels): return(self.dlmodels) else: return('NA') except KeyError: return('NA') def getTargetFeatures(self): try: if(self.basic['targetFeature']): return(self.basic['targetFeature']) else: return('') except KeyError: return('') def getFolderSettings(self): try: if(self.basic['folderSettings']): return(self.basic['folderSettings']) else: return('NA') except KeyError: return('NA') def getFilterExpression(self): try: if(self.basic['filterExpression']): return (self.basic['filterExpression']) else: return None except KeyError: return None def setModels(self): try: analysis_type = self.basic['analysisType'] #print(analysis_type) self.problemType = '' for key in analysis_type.keys(): if analysis_type[key] == 'True': self.problemType = key break if self.problemType == 'summarization': self.problemType = 'classification' self.summarize = True if self.problemType not in ['inputDrift','outputDrift']: conf_algorithm = self.basic['algorithms'][self.problemType] else: conf_algorithm = {} self.mlmodels='' self.dlmodels='' self.scoringCreteria = 'NA' if self.problemType in ['classification','regression','survivalAnalysis','timeSeriesForecasting']: #task 11997 scorCre = self.basic['scoringCriteria'][self.problemType] for key in scorCre.keys(): if scorCre[key] == 'True': self.scoringCreteria = key break if self.problemType.lower() == 'timeseriesforecasting': #task 11997 self.mllearner=False #task 11997 removed initialising self.ml models as timeSeriesForecasting if self.scoringCreteria == 'Mean Squared Error': self.scoringCreteria = 'MSE' if self.scoringCreteria == 'Root Mean Squared Error': self.scoringCreteria = 'RMSE' if self.scoringCreteria == 'Mean Absolute Error': self.scoringCreteria = 'MAE' if self.scoringCreteria == 'R-Squared': self.scoringCreteria = 'R2' if self.problemType in ['similarityIdentification','contextualSearch']: self.scoringCreteria = __get_true_option(self.basic['scoringCriteria'][self.problemType], "Cosine Similarity") if self.problemType in ['classification','regression']: for key in conf_algorithm.keys(): if conf_algorithm[key] == 'True': if key not in ['Recurrent Neural Network','Convolutional Neural Network (1D)','Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)','GoogleModelSearch_DNN']: if self.mlmodels != '': self.mlmodels += ',' self.mlmodels += key else: if self.dlmodels != '': self.dlmodels += ',' self.dlmodels += key elif self.problemType in ['videoForecasting','imageClassification','objectDetection']: for key in conf_algorithm.keys(): if conf_algorithm[key] == 'True': if self.dlmodels != '': self.dlmodels += ',' self.dlmodels += key elif self.problemType == 'recommenderSystem': problem_model = '' for key in conf_algorithm.keys(): if key not in ['itemRatingConfig','textSimilarityConfig']: if conf_algorithm[key] == 'True': problem_model = key break if problem_model == 'ItemRating': self.mlmodels = 'SVD' elif problem_model == 'AssociationRules-Apriori': self.mlmodels = 'Apriori' self.problemType = 'AssociationRules' elif problem_model == 'TextSimilarity-Siamese': self.mlmodels = 'Siamese' self.problemType = 'TextSimilarity' else: for key in conf_algorithm.keys(): if conf_algorithm[key] == 'True': if self.mlmodels != '': self.mlmodels += ',' self.mlmodels += key self.mllearner = False self.dllearner = False if self.mlmodels != '': self.mllearner = True if self.advance['mllearner_config']['Stacking (Ensemble)'] == 'True': self.mlmodels += ',' self.mlmodels += 'Stacking (Ensemble)' if self.advance['mllearner_config']['Voting (Ensemble)'] == 'True': self.mlmodels += ',' self.mlmodels += 'Voting (Ensemble)' if self.dlmodels != '': self.dllearner = True return('done') except KeyError: return('NA') def readConfigurationFile(self, path): if isinstance( path, dict): self.data = path else: with open(path, 'r') as data_file: self.data = json.load(data_file) #loading json object as python dictionary self.basic = self.data['basic'] self.advance = self.data['advance'] problemType = self.setModels() if 'output' in self.basic: if(self.basic['output']['profilerStage']): if(str(type(self.basic['output']['profilerStage'])) != "<class 'str'>"): msg = "JSON Validation Fail: Profiling Should be String and value should be either enable or disable" self.log.info(msg) return False,msg if((self.basic['output']['profilerStage'].lower() == 'true') & ('profiler' not in self.advance)): msg = "JSON Validation Fail: Profiler Configuration Not Found in Advance JSON" self.log.info(msg) return False,msg if(str(type(self.advance['profiler'])) != "<class 'dict'>"): msg = "JSON Validation Fail: Error: Profiler Configuration Syntax" self.log.info(msg) return False,msg if((self.basic['output']['profilerStage'].lower() != 'true') & (self.basic['output']['profilerStage'].lower() != 'false')): msg = "JSON Validation Fail: Profiling is Not defined Correctly, it should be either enable or disable" self.log.info(msg) return False,msg if(self.basic['output']['selectorStage']): if(str(type(self.basic['output']['selectorStage'])) != "<class 'str'>"): msg = "JSON Validation Fail: Selection Should be String and value should be either enable or disable" self.log.info(msg) return False,msg if((self.basic['output']['selectorStage'].lower() == 'true') & ('selector' not in self.advance)): msg = "JSON Validation Fail: Selector Configuration Not Found" self.log.info(msg) return False,msg if((self.basic['output']['selectorStage'].lower() != 'true') & (self.basic['output']['selectorStage'].lower() != 'false')): msg = "JSON Validation Fail:: Selection is Not defined Correctly, it should be either enable or disable" self.log.info(msg) return False,msg if(str(type(self.advance['selector'])) != "<class 'dict'>"): msg = "JSON Validation Fail: Error: Syntax of Selector" self.log.info(msg) return False,msg if 'dataLocation' not in self.basic: msg = "JSON Validation Fail: Data Location Not Defined" self.log.info(msg) return False,msg if 'deployLocation' not in self.basic: msg = "JSON Validation Fail: Deploy Location Not Defined" self.log.info(msg) return False,msg if 'deployment' in self.basic: if(str(type(self.basic['deployment'])) != "<class 'str'>"): msg = "JSON Validation Fail: deployment Should be String and value should be either enable or disable" self.log.info(msg) return False,msg if(self.basic['deployment'] == 'enable'): if 'deployer' in self.advance: if(str(type(self.advance['deployer'])) != "<class 'dict'>"): msg = "JSON Validation Fail: deployer configuration should be nexted json object" self.log.info(msg) return False,msg else: msg = "JSON Validation Fail: deployer configuration is missing" self.log.info(msg) return False,msg return True,'Good' def getSurvivalEventColumn(self): try: if(self.advance['survival_config']): survival_config = self.advance['survival_config'] if 'input' in survival_config: inp = survival_config['input'] if not isinstance(inp, dict): return None elif 'event_col' in inp: e = inp['event_col'] if not isinstance(e, str): return None return (e) else: return None else: return None else: return None except KeyError: return None def getSurvivalDurationColumn(self): try: if(self.advance['survival_config']): survival_config = self.advance['survival_config'] if 'input' in survival_config: inp = survival_config['input'] if not isinstance(inp, dict): return None elif 'duration_col' in inp: t = inp['duration_col'] if not isinstance(t, str): return None return (t) else: return None else: return None else: return None except KeyError: return None ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json import logging import os import shutil import time from sys import platform from distutils.util import strtobool # Base class for EION configuration Manager which read the needed f params from eion.json, initialize the parameterlist, read the respective params, store in variables and return back to caller function or external modules. class OTAionConfigManager: # eion configuration Constractor def __init__(self): self.log = logging.getLogger('eion') self.data = '' self.problemType = '' self.basic = [] self.advance=[] # To get parameter list of configuration module from json, this will be passed as dict{} def getEionProfilerConfigurarion(self): try: if(self.advance['profiler']): return self.advance['profiler'] else: return('NA') except KeyError: return('NA') def getAIONTestTrainPercentage(self): try: if(self.advance['testPercentage']): return int(self.advance['testPercentage']) else: return(80) except KeyError: return(80) def getAIONDataBalancingMethod(self): try: if(self.advance['categoryBalancingMethod']): return self.advance['categoryBalancingMethod'] else: return("oversample") except KeyError: return("oversample") # To get parameter list of selector module params def getEionSelectorConfiguration(self): try: if(self.advance['selector']): return self.advance['selector'] else: return('NA') except KeyError: return('NA') def createDeploymentFolders(self,deployFolder,iterName,iterVersion): usecaseFolderLocation = os.path.join(deployFolder,iterName) os.makedirs(usecaseFolderLocation,exist_ok = True) deployLocation = os.path.join(usecaseFolderLocation,str(iterVersion)) try: os.makedirs(deployLocation) except OSError as e: shutil.rmtree(deployLocation) time.sleep(2) os.makedirs(deployLocation) dataFolderLocation = os.path.join(deployLocation,'data') try: os.makedirs(dataFolderLocation) except OSError as e: print("\nDeployment Data Folder Already Exists") logFolderLocation = os.path.join(deployLocation,'log') try: os.makedirs(logFolderLocation) except OSError as e: print("\nLog Folder Already Exists") etcFolderLocation = os.path.join(deployLocation,'etc') try: os.makedirs(etcFolderLocation) except OSError as e: print("\ETC Folder Already Exists") prodFolderLocation = os.path.join(deployLocation,'production') os.makedirs(prodFolderLocation) profilerFolderLocation = os.path.join(prodFolderLocation, 'profiler') os.makedirs(profilerFolderLocation) modelFolderLocation = os.path.join(prodFolderLocation, 'model') os.makedirs(modelFolderLocation) original_data_file = os.path.join(dataFolderLocation,'preprocesseddata.csv') profiled_data_file = os.path.join(dataFolderLocation,'postprocesseddata.csv') trained_data_file = os.path.join(dataFolderLocation,'trainingdata.csv') predicted_data_file = os.path.join(dataFolderLocation,'predicteddata.csv') logFileName=os.path.join(logFolderLocation,'model_training_logs.log') outputjsonFile=os.path.join(deployLocation,'etc','output.json') return(deployLocation,dataFolderLocation,original_data_file,profiled_data_file,trained_data_file,predicted_data_file,logFileName,outputjsonFile) # To get parameter list of learner module params def getEionLearnerConfiguration(self): try: if(self.advance['onlinelearner_config']): mllearner_config = self.advance['mllearner_config'] if 'categoryBalancingMethod' not in mllearner_config: mllearner_config['categoryBalancingMethod'] = 'oversample' if 'testPercentage' not in mllearner_config: mllearner_config['testPercentage'] = 20 if 'missingTargetCategory' not in mllearner_config: mllearner_config['missingTargetCategory'] = '' return mllearner_config else: return('NA') except KeyError: return('NA') except Exception as inst: self.log.info( '\n-----> getEionLearnerConfiguration failed!!!.'+str(inst)) return('NA') def gettimegrouper(self): try: if(self.basic['timegrouper']): return self.basic['timegrouper'] else: return 'NA' except: return 'NA' def getgrouper(self): try: if(self.basic['group']): return self.basic['group'] else: return 'NA' except: return 'NA' def getfilter(self): try: if(self.basic['filter']): return self.basic['filter'] else: return 'NA' except: return 'NA' def getModulesDetails(self): problem_type = self.problemType visualizationstatus = self.getEionVisualizationStatus() profiler_status = self.getEionProfilerStatus() selector_status = self.getEionSelectorStatus() learner_status = self.mllearner targetFeature = self.getTargetFeatures() deploy_status = self.getEionDeploymentStatus() if learner_status: if(problem_type == 'NA'): learner_status = True elif(problem_type.lower() in ['classification','regression']): learner_status = True else: learner_status = False return problem_type,targetFeature,profiler_status,selector_status,learner_status,visualizationstatus,deploy_status def __get_true_option(self, d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def getAlgoName(self, problem_type=None): if problem_type == None: problem_type = self.__get_true_option(self.basic['algorithms']) return self.__get_true_option(self.basic['algorithms'][problem_type]) def getScoringCreteria(self): return (self.scoringCreteria) def getEionDeployerConfiguration(self): try: if(self.advance['deployer']): return self.advance['deployer'] else: return('NA') except KeyError: return('NA') def getAIONRemoteTraining(self): try: if(self.advance['remoteTraining']): self.advance['remoteTraining']['Enable'] = strtobool(self.advance['remoteTraining'].get('Enable', 'False')) return self.advance['remoteTraining'] else: remoteTraining = {} remoteTraining['Enable'] = False remoteTraining['server'] = None remoteTraining['ssh'] = None return(remoteTraining) except KeyError: remoteTraining = {} remoteTraining['Enable'] = False remoteTraining['server'] = None remoteTraining['ssh'] = None return(remoteTraining) def getEionVisualizationStatus(self): return(True) def getEionVisualizationConfiguration(self): try: if(self.advance['visualization_settings']): return(self.advance['visualization_settings']) else: return('NA') except KeyError: return('NA') def getEionBatchLearningStatus(self): try: if(self.basic['output']['batchLearning']): return(self.basic['output']['batchLearning']) else: return('disable') except KeyError: return('disable') def getEionProblemType(self): try: analysis_type = self.basic['analysisType'] self.problemType = '' for key in analysis_type.keys(): if analysis_type[key] == 'True': self.problemType = key break if self.problemType: return self.problemType else: return('NA') except KeyError: return('NA') def getEionProfilerStatus(self): try: if(self.basic['output']['profilerStage']): return(self.basic['output']['profilerStage']) else: return('false') except KeyError: return('false') #To get eion selector module status (enable/disable/none) def getEionSelectorStatus(self): try: if(self.basic['output']['selectorStage']): return(self.basic['output']['selectorStage']) else: return('disable') except KeyError: return('disable') def getEionDeploymentStatus(self): try: if(self.basic['output']['deploymentStage']): return(self.basic['output']['deploymentStage']) else: return(False) except KeyError: return(False) def getEionLearnerModelParams(self,modelType): try: numberofModels = 0 ml_algorithm_filename = '' if(modelType == 'classification'): requiredalgo = 'classifierModelParams' elif(modelType == 'regression'): requiredalgo = 'regressorModelParams' learnerconfig = self.advance['onlinelearner_config'] selectedMLModel = self.mlmodels modelParams = [] modelList=[] if 'modelParams' in learnerconfig: modelParams = learnerconfig['modelParams'] if(str(type(modelParams)) != "<class 'dict'>"): modelParams = [] elif(len(modelParams) == 0): modelParams = [] if(len(modelParams) == 0): if 'modelparamsfile' in learnerconfig: if(learnerconfig['modelparamsfile'] != ""): ml_algorithm_filename = learnerconfig['modelparamsfile'] if(ml_algorithm_filename == ''): ml_algorithm_filename = os.path.dirname(os.path.abspath(__file__))+'/ML_Defaults.json' modelParams = json.loads(open(ml_algorithm_filename).read()) if requiredalgo in modelParams: modelParams = modelParams[requiredalgo] if selectedMLModel != '': modelList = selectedMLModel.split(",") modelList = list(map(str.strip, modelList)) for mod in modelList: if mod not in modelParams: self.log.info("'"+mod+"' Not Available for Particular Problem Type") modelList.remove(mod) else: modelList = list(modelParams.keys()) #modelList = selectedMLModel.split(",") if(len(modelList) ==0): modelList = list(modelParams.keys()) return(modelParams,modelList) except KeyError: modelParams = [] modelList=[] return(modelParams,modelList) def getTargetFeatures(self): try: if(self.basic['targetFeature']): return(self.basic['targetFeature']) else: return('') except KeyError: return('') def getModelFeatures(self): try: if(self.basic['trainingFeatures']): modFeatures = self.basic['trainingFeatures'] modFeatures = modFeatures.split(",") modFeatures = list(map(str.strip, modFeatures)) modFeatures = ",".join([modf for modf in modFeatures]) return(modFeatures) else: return('NA') except KeyError: return('NA') def getFolderSettings(self): try: if(self.basic['folderSettings']): return(self.basic['folderSettings']) else: return('NA') except KeyError: return('NA') def getAIONLocationSettings(self): self.iter_name = self.basic['modelName'] self.iteration_version = self.basic['modelVersion'] if(self.basic['dataLocation']): dataLocation = self.basic['dataLocation'] else: dataLocation = 'NA' if(self.basic['deployLocation']): deployLocation = self.basic['deployLocation'] else: deployLocation = 'NA' try: if 'csv_settings' in self.basic: csv_setting = self.basic['csv_settings'] if 'delimiters' in csv_setting: delimiter = csv_setting['delimiters'] if delimiter.lower() == 'tab': delimiter = '\t' elif delimiter.lower() == 'semicolon': delimiter = ';' elif delimiter.lower() == 'comma': delimiter = ',' elif delimiter.lower() == 'space': delimiter = ' ' elif delimiter.lower() == 'other': if 'other' in csv_setting: delimiter = csv_setting['other'] else: delimiter = ',' else: delimiter = ',' else: delimiter = ',' if 'textqualifier' in csv_setting: textqualifier = csv_setting['textqualifier'] else: textqualifier = '"' else: delimiter = ',' textqualifier = '"' except KeyError: delimiter = ',' textqualifier = '"' return(self.iter_name,self.iteration_version,dataLocation,deployLocation,delimiter,textqualifier) def getFeatures(self): try: if(self.basic['dateTimeFeature']): dtFeatures = self.basic['dateTimeFeature'] dtFeatures = dtFeatures.split(",") dtFeatures = list(map(str.strip, dtFeatures)) dtFeatures = ",".join([dtf for dtf in dtFeatures]) else: dtFeatures = 'NA' except KeyError: dtFeatures = 'NA' try: if(self.basic['indexFeature']): iFeatures = self.basic['indexFeature'] iFeatures = iFeatures.split(",") iFeatures = list(map(str.strip, iFeatures)) iFeatures = ",".join([dif for dif in iFeatures]) else: iFeatures = 'NA' except KeyError: iFeatures = 'NA' try: if(self.basic['trainingFeatures']): modFeatures = self.basic['trainingFeatures'] modFeatures = modFeatures.split(",") modFeatures = list(map(str.strip, modFeatures)) modFeatures = ",".join([modf for modf in modFeatures]) else: modFeatures = 'NA' except KeyError: modFeatures = 'NA' return(dtFeatures,iFeatures,modFeatures) def setModels(self): try: analysis_type = self.basic['analysisType'] #print(analysis_type) self.problemType = '' for key in analysis_type.keys(): if analysis_type[key] == 'True': self.problemType = key break if self.problemType not in ['inputDrift','outputDrift']: conf_algorithm = self.basic['algorithms'][self.problemType] else: conf_algorithm = {} self.mlmodels='' self.dlmodels='' self.scoringCreteria = 'NA' if self.problemType in ['classification','regression']: scorCre = self.basic['scoringCriteria'][self.problemType] for key in scorCre.keys(): if scorCre[key] == 'True': self.scoringCreteria = key break #print(self.problemType) #print(self.scoringCreteria) if self.scoringCreteria == 'Mean Squared Error': self.scoringCreteria = 'MSE' if self.scoringCreteria == 'Root Mean Squared Error': self.scoringCreteria = 'RMSE' if self.scoringCreteria == 'Mean Absolute Error': self.scoringCreteria = 'MAE' if self.scoringCreteria == 'R-Squared': self.scoringCreteria = 'R2' if self.problemType in ['classification','regression']: for key in conf_algorithm.keys(): if conf_algorithm[key] == 'True': if self.mlmodels != '': self.mlmodels += ',' self.mlmodels += key else: for key in conf_algorithm.keys(): if conf_algorithm[key] == 'True': if self.mlmodels != '': self.mlmodels += ',' self.mlmodels += key self.mllearner = False if self.mlmodels != '': self.mllearner = True return('done') except KeyError: return('NA') def readConfigurationFile(self,path): with open(path, 'rb') as data_file: try: self.data = json.load(data_file) #loading json object as python dictionary #print(self.data) self.basic = self.data['basic'] self.advance = self.data['advance'] problemType = self.setModels() if(self.basic['output']['profilerStage']): if(str(type(self.basic['output']['profilerStage'])) != "<class 'str'>"): msg = "JSON Validation Fail: Profiling Should be String and value should be either enable or disable" self.log.info(msg) return False,msg if((self.basic['output']['profilerStage'].lower() == 'true') & ('profiler' not in self.advance)): msg = "JSON Validation Fail: Profiler Configuration Not Found in Advance JSON" self.log.info(msg) return False,msg if(str(type(self.advance['profiler'])) != "<class 'dict'>"): msg = "JSON Validation Fail: Error: Profiler Configuration Syntax" self.log.info(msg) return False,msg if((self.basic['output']['profilerStage'].lower() != 'true') & (self.basic['output']['profilerStage'].lower() != 'false')): msg = "JSON Validation Fail: Profiling is Not defined Correctly, it should be either enable or disable" self.log.info(msg) return False,msg if(self.basic['output']['selectorStage']): if(str(type(self.basic['output']['selectorStage'])) != "<class 'str'>"): msg = "JSON Validation Fail: Selection Should be String and value should be either enable or disable" self.log.info(msg) return False,msg if((self.basic['output']['selectorStage'].lower() == 'true') & ('selector' not in self.advance)): msg = "JSON Validation Fail: Selector Configuration Not Found" self.log.info(msg) return False,msg if((self.basic['output']['selectorStage'].lower() != 'true') & (self.basic['output']['selectorStage'].lower() != 'false')): msg = "JSON Validation Fail:: Selection is Not defined Correctly, it should be either enable or disable" self.log.info(msg) return False,msg if(str(type(self.advance['selector'])) != "<class 'dict'>"): msg = "JSON Validation Fail: Error: Syntax of Selector" self.log.info(msg) return False,msg if 'dataLocation' not in self.basic: msg = "JSON Validation Fail: Data Location Not Defined" self.log.info(msg) return False,msg if 'deployLocation' not in self.basic: msg = "JSON Validation Fail: Deploy Location Not Defined" self.log.info(msg) return False,msg if 'deployment' in self.basic: if(str(type(self.basic['deployment'])) != "<class 'str'>"): msg = "JSON Validation Fail: deployment Should be String and value should be either enable or disable" self.log.info(msg) return False,msg if(self.basic['deployment'] == 'enable'): if 'deployer' in self.advance: if(str(type(self.advance['deployer'])) != "<class 'dict'>"): msg = "JSON Validation Fail: deployer configuration should be nexted json object" self.log.info(msg) return False,msg else: msg = "JSON Validation Fail: deployer configuration is missing" self.log.info(msg) return False,msg except ValueError as e: print("Error"+str(e)) return False,e return True,'Good' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json from pathlib import Path from distutils.util import strtobool class code_configure(): def __init__(self): self.code_config = {} self.unsupported_algo = [] self.supported_model = {"classification":{"Logistic Regression": "LogisticRegression", "Naive Bayes": "GaussianNB", "Decision Tree": "DecisionTreeClassifier", "Random Forest": "RandomForestClassifier", "Support Vector Machine": "SVC", "K Nearest Neighbors": "KNeighborsClassifier", "Gradient Boosting": "GradientBoostingClassifier", "Extreme Gradient Boosting (XGBoost)":"XGBClassifier", "Light Gradient Boosting (LightGBM)": "LGBMClassifier","Categorical Boosting (CatBoost)": "CatBoostClassifier"}, "regression":{"Linear Regression": "LinearRegression", "Lasso": "Lasso", "Ridge": "Ridge", "Decision Tree": "DecisionTreeRegressor", "Random Forest": "RandomForestRegressor", "Extreme Gradient Boosting (XGBoost)": "XGBRegressor", "Light Gradient Boosting (LightGBM)": "LGBMRegressor","Categorical Boosting (CatBoost)": "CatBoostRegressor"},"timeSeriesForecasting":{"MLP": "MLP","LSTM":"LSTM"}} #task 11997 def __get_true_option(self, d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def __get_true_options(self, d): options = [] if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): options.append(k) return options def __get_scoring_criteria(self, criteria): mapping = {'Mean Squared Error':'MSE', 'Root Mean Squared Error':'RMSE','Mean Absolute Error':'MAE','R-Squared':'R2'} if criteria in mapping.keys(): return mapping[criteria] return criteria def __get_feature_selector(self, selector_config): feature_selector = [] if self.__get_true_option(selector_config['selectionMethod']) == 'featureSelection': feature_selector = self.__get_true_options(selector_config['featureSelection']) return feature_selector def __get_feature_reducer(self, selector_config): feature_reducer = "" if self.__get_true_option(selector_config['selectionMethod']) == 'featureEngineering': feature_reducer = self.__get_true_option(selector_config['featureEngineering'],'pca').lower() return feature_reducer def __getOptimizationParam(self, param_config): param_dict = {} param_dict['iterations'] = int(param_config['iterations']) param_dict['trainTestCVSplit'] = int(param_config['trainTestCVSplit']) param_dict['geneticparams'] = param_config['geneticparams'] return param_dict def add_model(self, model_name, config): if not self.unsupported_algo: self.code_config["algorithms"][model_name] = config.copy() def update_config(self, key, value): self.code_config[key] = value def save_config(self, file_path): if Path(file_path).is_dir(): file_path = Path(file_path)/'etc/code_config.json' with open(file_path, "w") as f: if not self.unsupported_algo: json.dump(self.code_config, f, indent=4) else: if 'ensemble' in self.unsupported_algo: json.dump({"Status":"Failure","msg":"Ensemble is not supported","error":"Ensemble is not supported"}, f) # keep error key elif 'text_features' in self.unsupported_algo: json.dump({"Status":"Failure","msg":"Text feature processing is not supported","error":"Text feature processing is not supported"}, f) # keep error key else: json.dump({"Status":"Failure","msg":f"Unsupported model {self.unsupported_algo}","error":f"Unsupported model {self.unsupported_algo}"}, f) # keep error key def __is_algo_supported(self, config): problem_type = self.__get_true_option(config['basic']['analysisType']) if problem_type not in self.supported_model.keys(): self.unsupported_algo = [problem_type] return False algos = config['basic']['algorithms'][problem_type] algos = self.__get_true_options(algos) self.unsupported_algo = [x for x in algos if x not in self.supported_model[problem_type].keys()] if self.unsupported_algo: return False return True def create_config(self, config): if isinstance(config, str): with open(config,'r') as f: config = json.load(f) problem_type = self.__get_true_option(config['basic']['analysisType']) self.code_config["problem_type"] = problem_type.lower() if not self.__is_algo_supported(config): return if 'ensemble' in config['advance']['mllearner_config']: if config['advance']['mllearner_config']['ensemble'] == 'enable': self.unsupported_algo = ['ensemble'] return self.code_config["modelName"] = config['basic']['modelName'] self.code_config["modelVersion"] = config['basic']['modelVersion'] if config['basic']['folderSettings']['fileType'].lower() == 'url': self.code_config["dataLocation"] = config['basic']['folderSettings']['labelDataFile'] else: self.code_config["dataLocation"] = config['basic']['dataLocation'] self.code_config["target_feature"] = config['basic']['targetFeature'] trainingfeatures = config['basic']['trainingFeatures'].split(',') datetimeFeature = list(map(str.strip, config['basic']['dateTimeFeature'].split(','))) for dtfeature in datetimeFeature: if dtfeature in trainingfeatures: trainingfeatures.remove(dtfeature) indexFeature = list(map(str.strip, config['basic']['indexFeature'].split(','))) for dtfeature in indexFeature: if dtfeature in trainingfeatures: trainingfeatures.remove(dtfeature) self.code_config["selected_features"] = trainingfeatures self.code_config["dateTimeFeature"] = datetimeFeature self.code_config["profiler"] = config['advance']['profiler'] self.code_config["feature_selector"]= self.__get_feature_selector(config['advance']['selector']) self.code_config["feature_reducer"]= self.__get_feature_reducer(config['advance']['selector']) self.code_config["corr_threshold"]= float(config['advance']['selector']['statisticalConfig'].get('correlationThresholdTarget',0.85)) self.code_config["var_threshold"]= float(config['advance']['selector']['statisticalConfig'].get('varianceThreshold',0.01)) self.code_config["pValueThreshold"]= float(config['advance']['selector']['statisticalConfig'].get('pValueThresholdTarget',0.04)) self.code_config["n_components"]= int(config['advance']['selector']['featureEngineering']['numberofComponents']) self.code_config["balancingMethod"] = config['advance']['categoryBalancingMethod'] self.code_config["test_ratio"] = int(config['advance']['testPercentage'])/100 #self.code_config["scoring_criteria"] = "accuracy" if self.code_config["problem_type"] in ['classification','regression']: self.code_config["algorithms"] = {} else: algo = self.__get_true_option(config['basic']['algorithms'][problem_type]) self.code_config["algorithms"] = {algo: config['advance'][problem_type]['modelParams'][algo]} #task 11997 self.code_config["scoring_criteria"] = self.__get_scoring_criteria(self.__get_true_option(config['basic']["scoringCriteria"][problem_type])) if problem_type.lower() == 'timeseriesforecasting': #task 11997 self.code_config["lag_order"] = self.code_config["algorithms"][algo]["lag_order"] self.code_config["noofforecasts"] = config["basic"]["noofforecasts"] self.code_config["target_feature"] = config['basic']['targetFeature'].split(',') self.code_config["optimization"] = config['advance']['mllearner_config']['optimizationMethod'] self.code_config["optimization_param"] = self.__getOptimizationParam(config['advance']['mllearner_config']['optimizationHyperParameter']) if __name__ == '__main__': codeConfigure = code_configure() codeConfigure.create_config("C:\\Users\\vashistah\\AppData\\Local\\HCLT\\AION\\config\\AION_1668151242.json") codeConfigure.save_config(r"C:\Users\vashistah\AppData\Local\HCLT\AION\target\AION_57_ts_1") ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json def get_true_option(d, default_value=None): if isinstance(d, dict): for k, v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def get_true_options(d): options = [] if isinstance(d, dict): for k, v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): options.append(k) return options def check_datetime(config): dateTime = config['basic']['dateTimeFeature'] if dateTime == '' or dateTime.lower()=='na': return False return True def check_dtype(d): flag= 1 for item in d: if item["type"].lower() != "text" and item["type"].lower() != "index": flag = 0 break return flag def check_text(d): #task 12627 flag= 0 for item in d: if item["type"].lower() == "text": flag = 1 break return flag def check_labelencoding(ftr_dict_list, target_ftr): for ftr_dict in ftr_dict_list: if ftr_dict['feature']!=target_ftr and ftr_dict['type'].lower()=='categorical' and ftr_dict['categoryEncoding'].lower()!='labelencoding': return False return True class timeseries(): def __init__(self,config): self.config=config #task 11997 if self.config['basic']['analysisType']['timeSeriesForecasting'].lower()=='true': self.problemType = 'timeSeriesForecasting' elif self.config['basic']['analysisType']['timeSeriesAnomalyDetection'].lower()=='true': self.problemType = 'timeSeriesAnomalyDetection' def validate_basic_config(self,status='pass',msg=None): #task 12627 date_time_status = check_datetime(self.config) text_status = check_text(self.config['advance']['profiler']['featureDict']) if not date_time_status and text_status: msg = 'For time series problem,\\n* One feature should be in datetime format\\n* Text feature not supported ' return 'error', msg elif not date_time_status: msg = 'For time series problem, one feature should be in datetime format' return 'error', msg elif text_status: msg = 'For time series problem, text feature not supported ' return 'error', msg selected_algos = get_true_options(self.config['basic']['algorithms'][self.problemType]) #task 11997 if isinstance(self.config['basic']['targetFeature'],str): targetFeature = list(self.config['basic']['targetFeature'].split(',')) if self.problemType=='timeSeriesForecasting': #task 11997 if len(targetFeature) > 1: if 'ARIMA' in selected_algos: status = 'error' msg = "ARIMA is not supported for multilabel (target) feature" return status, msg if "FBPROPHET" in selected_algos: status = 'error' msg = "FBPROPHET is not supported for multiLabel (target) feature" return status, msg if 'MLP' in selected_algos: status = 'error' msg = "MLP is not supported for multiLabel (target) feature" return status, msg if len(targetFeature) == 1 and 'VAR' in selected_algos: status = 'error' msg = "VAR is not supported for singleLabel (target) feature" return status, msg elif self.problemType=='timeSeriesAnomalyDetection': anomChecker = anomaly(self.config) status, msg = anomChecker.validate_basic_config() return status, msg class anomaly(): def __init__(self,config): self.config = config if self.config['basic']['analysisType']['anomalyDetection']=='': self.problemType = 'anomalyDetection' elif self.config['basic']['analysisType']['timeSeriesAnomalyDetection']: #task 11997 self.problemType = 'timeSeriesAnomalyDetection' def validate_basic_config(self,status='pass',msg=None): #task 12627 date_time_status = check_datetime(self.config) targetFeature = self.config['basic']['targetFeature'] if self.problemType=='anomalyDetection' and date_time_status: status = 'error' msg = 'Date feature detected. For anomaly detection on time series change problem type to Time Series Anomaly Detection or drop Date feature' return status, msg if targetFeature.lower()!= 'na' and targetFeature!= "" and self.config['basic']['inlierLabels'] == '': status = 'error' msg = 'Please provide inlier label in case of supervised anomaly detection' return status, msg class survival(): def __init__(self,config): self.config = config self.problemType= 'survivalAnalysis' def validate_basic_config(self): dateTimeStatus = check_datetime(self.config) labelencoding_status = check_labelencoding(self.config['advance']['profiler']['featureDict'], self.config['basic']['targetFeature']) if not dateTimeStatus and not labelencoding_status: msg = 'For survival analysis problem,\\n* One feature should be in datetime format\\n* Encoding of categorical features should be of label encoding ' return 'error', msg elif not dateTimeStatus: msg = 'One feature should be in datetime format for survival analysis problem. Please select it from model feature' return 'error', msg elif not labelencoding_status: msg = 'Categorical features are expected to be label encoded for survival analysis problem. Please select it from feature encoding' return 'error', msg else: return 'pass', " " class associationrule(): def __init__(self,config): self.config=config def validate_basic_config(self,status='pass', msg=None): if self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'].lower() == '' or self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'].lower() == 'na' or self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature'].lower() == '' or self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature'].lower() == 'na': return "error","Make sure to configure invoice feature and item feature" elif self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'] == self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature']: return "error","Make sure to invoice feature and item feature is configure correctly" else: return "pass", " " class documentsimilarity(): def __init__(self,config): self.config=config def validate_basic_config(self,status='pass', msg=None): flag = check_dtype(self.config['advance']['profiler']['featureDict']) if flag == 1: return "pass", " " else: msg="Make sure to change the feature type from Catgeory to Text and drop numerical features for document Similarity" return "error", msg def config_validate(path): with open(path, 'rb') as data_file: config = json.load(data_file) data_file.close() try: problem_type = get_true_option(config['basic']['analysisType']) status = 'pass' msg = '' if 'timeseries' in problem_type.lower(): #task 11997 obj = timeseries(config) elif problem_type.lower() == 'survivalanalysis': obj = survival(config) elif problem_type.lower() == 'anomalydetection': obj = anomaly(config) elif problem_type.lower() in ['similarityidentification','contextualsearch']: obj = documentsimilarity(config) elif problem_type.lower() == 'recommendersystem': if config['basic']['algorithms']['recommenderSystem']['AssociationRules-Apriori'].lower() == 'true': obj = associationrule(config) else: return 'pass',"" else: return 'pass',"" status,msg= obj.validate_basic_config() return(status,msg) except Exception as e: print(e) def start_check(config): return config_validate(config) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import logging import numpy as np import sys from pathlib import Path import nltk from nltk.tokenize import sent_tokenize from nltk import pos_tag from nltk import ngrams from nltk.corpus import wordnet from nltk import RegexpParser from textblob import TextBlob import spacy from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.base import BaseEstimator, TransformerMixin import urllib.request import zipfile import os from os.path import expanduser import platform from text import TextCleaning as text_cleaner from text.Embedding import extractFeatureUsingPreTrainedModel logEnabled = False spacy_nlp = None def ExtractFeatureCountVectors(ngram_range=(1, 1), max_df=1.0, min_df=1, max_features=None, binary=False): vectorizer = CountVectorizer(ngram_range = ngram_range, max_df = max_df, \ min_df = min_df, max_features = max_features, binary = binary) return vectorizer def ExtractFeatureTfIdfVectors(ngram_range=(1, 1), max_df=1.0, min_df=1, max_features=None, binary=False, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False): vectorizer = TfidfVectorizer(ngram_range = ngram_range, max_df = max_df, \ min_df = min_df, max_features = max_features, \ binary = binary, norm = norm, use_idf = use_idf, \ smooth_idf = smooth_idf, sublinear_tf = sublinear_tf) return vectorizer def GetPOSTags( inputText, getPOSTags_Lib='nltk'): global spacy_nlp tokens_postag_list = [] if (inputText == ""): __Log("debug", "{} function: Input text is not provided".format(sys._getframe().f_code.co_name)) else: if getPOSTags_Lib == 'spacy': if spacy_nlp == None: spacy_nlp = spacy.load('en_core_web_sm') doc = spacy_nlp(inputText) for token in doc: tokens_postag_list.append((token.text, token.tag_)) elif getPOSTags_Lib == 'textblob': doc = TextBlob(inputText) tokens_postag_list = doc.tags else: tokensList = WordTokenize(inputText) tokens_postag_list = pos_tag(tokensList) return tokens_postag_list def GetNGrams( inputText, ngramRange=(1,1)): ngramslist = [] for n in range(ngramRange[0],ngramRange[1]+1): nwordgrams = ngrams(inputText.split(), n) ngramslist.extend([' '.join(grams) for grams in nwordgrams]) return ngramslist def NamedEntityRecognition( inputText): global spacy_nlp neResultList = [] if (inputText == ""): __Log("debug", "{} function: Input text is not provided".format(sys._getframe().f_code.co_name)) else: if spacy_nlp == None: spacy_nlp = spacy.load('en_core_web_sm') doc = spacy_nlp(inputText) neResultList = [(X.text, X.label_) for X in doc.ents] return neResultList def KeywordsExtraction( inputText, ratio=0.2, words = None, scores=False, pos_filter=('NN', 'JJ'), lemmatize=False): keywordsList = [] if (inputText == ""): __Log("debug", "{} function: Input text is not provided".format(sys._getframe().f_code.co_name)) else: keywordsList = keywords(inputText, ratio = ratio, words = words, split=True, scores=scores, pos_filter=pos_filter, lemmatize=lemmatize) return keywordsList def __get_nodes(parent): nounList = [] verbList = [] for node in parent: if type(node) is nltk.Tree: if node.label() == "NP": subList = [] for item in node.leaves(): subList.append(item[0]) nounList.append((" ".join(subList))) elif node.label() == "VP": subList = [] for item in node.leaves(): subList.append(item[0]) verbList.append((" ".join(subList))) #verbList.append(node.leaves()[0][0]) __get_nodes(node) result = {'NP': nounList, 'VP': verbList} return result def ShallowParsing( inputText, lib='spacy'): tags = GetPOSTags(inputText, getPOSTags_Lib=lib) chunk_regex = r""" NBAR: {<DT>?<NN.*|JJ.*>*<NN.*>+} # Nouns and Adjectives, terminated with Nouns VBAR: {<RB.?>*<VB.?>*<TO>?<JJ>*<VB.?>+<VB>?} # Verbs and Verb Phrases NP: {<NBAR>} {<NBAR><IN><NBAR>} # Above, connected with in/of/etc... VP: {<VBAR>} {<VBAR><IN><VBAR>} # Above, connected with in/of/etc... """ rp = RegexpParser(chunk_regex) t = rp.parse(tags) return __get_nodes(t) def SyntacticAndEntityParsing(inputCorpus, featuresList=['POSTags','NGrams','NamedEntityRecognition','KeywordsExtraction','ShallowParsing'], posTagsLib='nltk', ngramRange=(1,1), ke_ratio=0.2, ke_words = None, ke_scores=False, ke_pos_filter=('NN', 'JJ'), ke_lemmatize=False): columnsList = ['Input'] columnsList.extend(featuresList) df = pd.DataFrame(columns=columnsList) df['Input'] = inputCorpus for feature in featuresList: if feature == 'POSTags': df[feature] = inputCorpus.apply(lambda x: GetPOSTags(x, posTagsLib)) if feature == 'NGrams': df[feature] = inputCorpus.apply(lambda x: GetNGrams(x, ngramRange)) if feature == 'NamedEntityRecognition': df[feature] = inputCorpus.apply(lambda x: NamedEntityRecognition(x)) if feature == 'KeywordsExtraction': df[feature] = inputCorpus.apply(lambda x: KeywordsExtraction(x, ratio=ke_ratio, words=ke_words, scores=ke_scores, pos_filter=ke_pos_filter, lemmatize=ke_lemmatize)) if feature == 'ShallowParsing': df[feature] = inputCorpus.apply(lambda x: ShallowParsing(x, lib=posTagsLib)) return df def __Log( logType="info", text=None): if logType.lower() == "exception": logging.exception( text) elif logEnabled: if logType.lower() == "info": logging.info( text) elif logType.lower() == "debug": logging.debug( text) def SentenceTokenize( inputText): return text_cleaner.WordTokenize(inputText) def WordTokenize( inputText, tokenizationLib = 'nltk'): return text_cleaner.WordTokenize(inputText, tokenizationLib) def Lemmatize( inputTokensList, lemmatizationLib = 'nltk'): return text_cleaner.Lemmatize(inputTokensList, lemmatizationLib) def Stemmize( inputTokensList): return text_cleaner.Stemmize(inputTokensList) def ToLowercase( inputText): resultText = "" if inputText is not None and inputText != "": resultText = inputText.lower() return resultText def ToUppercase( inputText): resultText = "" if inputText is not None and inputText != '': resultText = inputText.upper() return resultText def RemoveNoise( inputText, removeNoise_fHtmlDecode = True, removeNoise_fRemoveHyperLinks = True, removeNoise_fRemoveMentions = True, removeNoise_fRemoveHashtags = True, removeNoise_RemoveOrReplaceEmoji = 'remove', removeNoise_fUnicodeToAscii = True, removeNoise_fRemoveNonAscii = True): return text_cleaner.RemoveNoise(inputText, removeNoise_fHtmlDecode, removeNoise_fRemoveHyperLinks, removeNoise_fRemoveMentions, removeNoise_fRemoveHashtags, removeNoise_RemoveOrReplaceEmoji, removeNoise_fUnicodeToAscii, removeNoise_fRemoveNonAscii) def RemoveStopwords( inputTokensList, stopwordsRemovalLib='nltk', stopwordsList = None, extend_or_replace='extend'): return text_cleaner.RemoveStopwords(inputTokensList, stopwordsRemovalLib, stopwordsList, extend_or_replace) def RemoveNumericTokens( inputText, removeNumeric_fIncludeSpecialCharacters=True): return text_cleaner.RemoveNumericTokens(inputText, removeNumeric_fIncludeSpecialCharacters) def RemovePunctuation( inputText, fRemovePuncWithinTokens=False): return text_cleaner.RemovePunctuation(inputText, fRemovePuncWithinTokens) def CorrectSpelling( inputTokensList): return text_cleaner.CorrectSpelling(inputTokensList) def ReplaceAcronym( inputTokensList, acrDict=None): return text_cleaner.ReplaceAcronym(inputTokensList, acrDict) def ExpandContractions( inputText, expandContractions_googleNewsWordVectorPath=None): return text_cleaner.ExpandContractions(inputText, expandContractions_googleNewsWordVectorPath) def get_pretrained_model_path(): try: from appbe.dataPath import DATA_DIR modelsPath = Path(DATA_DIR)/'PreTrainedModels'/'TextProcessing' except: modelsPath = Path('aion')/'PreTrainedModels'/'TextProcessing' if not modelsPath.exists(): modelsPath.mkdir(parents=True, exist_ok=True) return modelsPath def checkAndDownloadPretrainedModel(preTrainedModel, embedding_size=300): models = {'glove':{50:'glove.6B.50d.w2vformat.txt',100:'glove.6B.100d.w2vformat.txt',200:'glove.6B.200d.w2vformat.txt',300:'glove.6B.300d.w2vformat.txt'}, 'fasttext':{300:'wiki-news-300d-1M.vec'}} supported_models = [x for y in models.values() for x in y.values()] embedding_sizes = {x:y.keys() for x,y in models.items()} preTrainedModel = preTrainedModel.lower() if preTrainedModel not in models.keys(): raise ValueError(f'model not supported: {preTrainedModel}') if embedding_size not in embedding_sizes[preTrainedModel]: raise ValueError(f"Embedding size '{embedding_size}' not supported for {preTrainedModel}") selected_model = models[preTrainedModel][embedding_size] modelsPath = get_pretrained_model_path() p = modelsPath.glob('**/*') modelsDownloaded = [x.name for x in p if x.name in supported_models] if selected_model not in modelsDownloaded: if preTrainedModel == "glove": try: local_file_path = modelsPath/f"glove.6B.{embedding_size}d.w2vformat.txt" file_test, header_test = urllib.request.urlretrieve(f'https://aion-pretrained-models.s3.ap-south-1.amazonaws.com/text/glove.6B.{embedding_size}d.w2vformat.txt', local_file_path) except Exception as e: raise ValueError("Error: unable to download glove pretrained model, please try again or download it manually and placed it at {}. ".format(modelsPath)+str(e)) elif preTrainedModel == "fasttext": try: local_file_path = modelsPath/"wiki-news-300d-1M.vec.zip" url = 'https://aion-pretrained-models.s3.ap-south-1.amazonaws.com/text/wiki-news-300d-1M.vec.zip' file_test, header_test = urllib.request.urlretrieve(url, local_file_path) with zipfile.ZipFile(local_file_path) as zip_ref: zip_ref.extractall(modelsPath) Path(local_file_path).unlink() except Exception as e: raise ValueError("Error: unable to download fastText pretrained model, please try again or download it manually and placed it at {}. ".format(location)+str(e)) return modelsPath/selected_model def load_pretrained(path): embeddings = {} word = '' with open(path, 'r', encoding="utf8") as f: header = f.readline() header = header.split(' ') vocab_size = int(header[0]) embed_size = int(header[1]) for i in range(vocab_size): data = f.readline().strip().split(' ') word = data[0] embeddings[word] = [float(x) for x in data[1:]] return embeddings class TextProcessing(BaseEstimator, TransformerMixin): def __init__(self, functionSequence = ['RemoveNoise','ExpandContractions','Normalize','ReplaceAcronym', 'CorrectSpelling','RemoveStopwords','RemovePunctuation','RemoveNumericTokens'], fRemoveNoise = True, fExpandContractions = False, fNormalize = True, fReplaceAcronym = False, fCorrectSpelling = False, fRemoveStopwords = True, fRemovePunctuation = True, fRemoveNumericTokens = True, removeNoise_fHtmlDecode = True, removeNoise_fRemoveHyperLinks = True, removeNoise_fRemoveMentions = True, removeNoise_fRemoveHashtags = True, removeNoise_RemoveOrReplaceEmoji = 'remove', removeNoise_fUnicodeToAscii = True, removeNoise_fRemoveNonAscii = True, tokenizationLib='nltk', normalizationMethod = 'Lemmatization', lemmatizationLib = 'nltk', acronymDict = None, stopwordsRemovalLib = 'nltk', stopwordsList = None, extend_or_replace_stopwordslist = 'extend', removeNumeric_fIncludeSpecialCharacters = True, fRemovePuncWithinTokens = False, data_path = None ): global logEnabled #logEnabled = EnableLogging self.functionSequence = functionSequence self.fRemoveNoise = fRemoveNoise self.fExpandContractions = fExpandContractions self.fNormalize = fNormalize self.fReplaceAcronym = fReplaceAcronym self.fCorrectSpelling = fCorrectSpelling self.fRemoveStopwords = fRemoveStopwords self.fRemovePunctuation = fRemovePunctuation self.fRemoveNumericTokens = fRemoveNumericTokens self.removeNoise_fHtmlDecode = removeNoise_fHtmlDecode self.removeNoise_fRemoveHyperLinks = removeNoise_fRemoveHyperLinks self.removeNoise_fRemoveMentions = removeNoise_fRemoveMentions self.removeNoise_fRemoveHashtags = removeNoise_fRemoveHashtags self.removeNoise_RemoveOrReplaceEmoji = removeNoise_RemoveOrReplaceEmoji self.removeNoise_fUnicodeToAscii = removeNoise_fUnicodeToAscii self.removeNoise_fRemoveNonAscii = removeNoise_fRemoveNonAscii self.tokenizationLib = tokenizationLib self.normalizationMethod = normalizationMethod self.lemmatizationLib = lemmatizationLib self.acronymDict = acronymDict self.stopwordsRemovalLib = stopwordsRemovalLib self.stopwordsList = stopwordsList self.extend_or_replace_stopwordslist = extend_or_replace_stopwordslist self.removeNumeric_fIncludeSpecialCharacters = removeNumeric_fIncludeSpecialCharacters self.fRemovePuncWithinTokens = fRemovePuncWithinTokens self.data_path = data_path self.fit_and_transformed_ = False def fit(self, x, y=None): return self def transform(self, x): x = map(lambda inputText: text_cleaner.cleanText(inputText, functionSequence = self.functionSequence, fRemoveNoise = self.fRemoveNoise, fExpandContractions = self.fExpandContractions, fNormalize = self.fNormalize, fReplaceAcronym = self.fReplaceAcronym, fCorrectSpelling = self.fCorrectSpelling, fRemoveStopwords = self.fRemoveStopwords, fRemovePunctuation = self.fRemovePunctuation, fRemoveNumericTokens = self.fRemoveNumericTokens, removeNoise_fHtmlDecode = self.removeNoise_fHtmlDecode, removeNoise_fRemoveHyperLinks = self.removeNoise_fRemoveHyperLinks, removeNoise_fRemoveMentions = self.removeNoise_fRemoveMentions , removeNoise_fRemoveHashtags = self.removeNoise_fRemoveHashtags, removeNoise_RemoveOrReplaceEmoji = self.removeNoise_RemoveOrReplaceEmoji, removeNoise_fUnicodeToAscii = self.removeNoise_fUnicodeToAscii, removeNoise_fRemoveNonAscii = self.removeNoise_fRemoveNonAscii, tokenizationLib = self.tokenizationLib, normalizationMethod = self.normalizationMethod, lemmatizationLib = self.lemmatizationLib, acronymDict = self.acronymDict, stopwordsRemovalLib = self.stopwordsRemovalLib, stopwordsList = self.stopwordsList, extend_or_replace_stopwordslist = self.extend_or_replace_stopwordslist, removeNumeric_fIncludeSpecialCharacters = self.removeNumeric_fIncludeSpecialCharacters, fRemovePuncWithinTokens = self.fRemovePuncWithinTokens), x) x = pd.Series(list(x)) if hasattr(self, 'fit_and_transformed_') and not self.fit_and_transformed_: self.fit_and_transformed_ = True if self.data_path and Path(self.data_path).exists(): x.to_csv(Path(self.data_path)/'text_cleaned.csv', index=False) return x def get_feature_names_out(self): return ['tokenize'] class wordEmbedding(BaseEstimator, TransformerMixin): def __init__(self, preTrainedModel, embeddingSize=300,external_model=None,external_model_type='binary'): self.number_of_features = 0 self.embeddingSize = embeddingSize self.preTrainedModel = preTrainedModel.lower() self.external_model=external_model self.external_model_type = external_model_type if self.preTrainedModel == "glove": self.preTrainedModelpath = f'glove.6B.{self.embeddingSize}d.w2vformat.txt' self.binary = False elif self.preTrainedModel == "fasttext": self.preTrainedModelpath = 'wiki-news-300d-1M.vec' self.binary = False else: raise ValueError(f'Model ({self.preTrainedModel}) not supported') def fit(self, x, y=None): return self def transform(self, x): if ((isinstance(self.external_model, pd.DataFrame) and not self.external_model.empty) or (not isinstance(self.external_model, pd.DataFrame) and self.external_model)): if self.preTrainedModel == "fasttext" and self.external_model_type == 'binary': print('Transforming using external binary') extracted = np.vstack([self.external_model.get_sentence_vector( sentense) for sentense in x]) else: print('Transforming using external vector') extracted = extractFeatureUsingPreTrainedModel(x, pretrainedModelPath=None, loaded_model=self.external_model, embed_size=300) else: print('Transforming using Vector') models_path = checkAndDownloadPretrainedModel(self.preTrainedModel, self.embeddingSize) extracted = extractFeatureUsingPreTrainedModel(x, models_path) self.number_of_features = extracted.shape[1] return extracted def get_feature_names_out(self): return [str(x) for x in range(self.number_of_features)] def get_feature_names(self): return self.get_feature_names_out() def getProcessedPOSTaggedData(pos_tagged_data): def get_wordnet_post(tag): if tag.startswith('V'): return wordnet.VERB elif tag.startswith('J'): return wordnet.ADJ elif tag.startswith('R'): return wordnet.ADV else: return wordnet.NOUN def process_pos_tagged_data(text): processed_text = [f"{t[0]}_{get_wordnet_post(t[1])}" for t in text] processed_text = " ".join(processed_text) return processed_text processed_pos_tagged_data = pos_tagged_data.apply(process_pos_tagged_data) return processed_pos_tagged_data class PosTagging(BaseEstimator, TransformerMixin): def __init__(self, posTagsLib, data_path): self.posTagsLib = posTagsLib self.fit_and_transformed_ = False self.data_path = data_path def fit(self, x, y=None): return self def transform(self, x): parsing_output = SyntacticAndEntityParsing(x, featuresList=['POSTags'], posTagsLib=self.posTagsLib) output = getProcessedPOSTaggedData(parsing_output['POSTags']) if not self.fit_and_transformed_: self.fit_and_transformed_ = True if self.data_path and Path(self.data_path).exists(): output.to_csv(Path(self.data_path)/'pos_tagged.csv', index=False) return output ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__)))) #from .eda import ExploreTextData ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import logging from distutils.util import strtobool import numpy as np import pandas as pd from text import TextProcessing from sklearn.preprocessing import FunctionTransformer from sklearn.base import BaseEstimator, TransformerMixin from pathlib import Path external_model = None external_model_type = None def get_one_true_option(d, default_value): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value class textProfiler(): def __init__(self): self.log = logging.getLogger('eion') self.embedder = None self.bert_embedder_size = 0 def textCleaning(self, textCorpus): textProcessor = TextProcessing.TextProcessing() textCorpus = textProcessor.transform(textCorpus) return(textCorpus) def sentense_encode(self, item): return self.model.encode(item,show_progress_bar=False) def get_embedding_size(self, model, config): if model in config.keys(): config = config[model] else: config = {} model = model.lower() if model == 'glove': size_map = {'default': 100, '50d': 50, '100d':100, '200d': 200, '300d':300} size_enabled = get_one_true_option(config, 'default') return size_map[size_enabled] elif model == 'fasttext': size_map = {'default': 300} size_enabled = get_one_true_option(config, 'default') return size_map[size_enabled] elif model == 'latentsemanticanalysis': size_map = {'default': 100, '50d': 50, '100d':100, '200d': 200, '300d':300,'500d':500,'700d':700,'1000d':1000} size_enabled = get_one_true_option(config, 'default') return size_map[size_enabled] elif model in ['tf_idf', 'countvectors']: return int(config.get('maxFeatures', 2000)) else: # for word2vec return 300 def cleaner(self, conf_json, pipeList, data_path=None): cleaning_kwargs = {} textCleaning = conf_json.get('textCleaning') self.log.info("Text Preprocessing config: ",textCleaning) cleaning_kwargs['fRemoveNoise'] = strtobool(textCleaning.get('removeNoise', 'True')) cleaning_kwargs['fNormalize'] = strtobool(textCleaning.get('normalize', 'True')) cleaning_kwargs['fReplaceAcronym'] = strtobool(textCleaning.get('replaceAcronym', 'False')) cleaning_kwargs['fCorrectSpelling'] = strtobool(textCleaning.get('correctSpelling', 'False')) cleaning_kwargs['fRemoveStopwords'] = strtobool(textCleaning.get('removeStopwords', 'True')) cleaning_kwargs['fRemovePunctuation'] = strtobool(textCleaning.get('removePunctuation', 'True')) cleaning_kwargs['fRemoveNumericTokens'] = strtobool(textCleaning.get('removeNumericTokens', 'True')) cleaning_kwargs['normalizationMethod'] = get_one_true_option(textCleaning.get('normalizeMethod'), 'lemmatization').capitalize() removeNoiseConfig = textCleaning.get('removeNoiseConfig') if type(removeNoiseConfig) is dict: cleaning_kwargs['removeNoise_fHtmlDecode'] = strtobool(removeNoiseConfig.get('decodeHTML', 'True')) cleaning_kwargs['removeNoise_fRemoveHyperLinks'] = strtobool(removeNoiseConfig.get('removeHyperLinks', 'True')) cleaning_kwargs['removeNoise_fRemoveMentions'] = strtobool(removeNoiseConfig.get('removeMentions', 'True')) cleaning_kwargs['removeNoise_fRemoveHashtags'] = strtobool(removeNoiseConfig.get('removeHashtags', 'True')) cleaning_kwargs['removeNoise_RemoveOrReplaceEmoji'] = 'remove' if strtobool(removeNoiseConfig.get('removeEmoji', 'True')) else 'replace' cleaning_kwargs['removeNoise_fUnicodeToAscii'] = strtobool(removeNoiseConfig.get('unicodeToAscii', 'True')) cleaning_kwargs['removeNoise_fRemoveNonAscii'] = strtobool(removeNoiseConfig.get('removeNonAscii', 'True')) acronymConfig = textCleaning.get('acronymConfig') if type(acronymConfig) is dict: cleaning_kwargs['acronymDict'] = acronymConfig.get('acronymDict', None) stopWordsConfig = textCleaning.get('stopWordsConfig') if type(stopWordsConfig) is dict: cleaning_kwargs['stopwordsList'] = stopWordsConfig.get('stopwordsList', '[]') if isinstance(cleaning_kwargs['stopwordsList'], str): if cleaning_kwargs['stopwordsList'] != '[]': cleaning_kwargs['stopwordsList'] = cleaning_kwargs['stopwordsList'][1:-1].split(',') else: cleaning_kwargs['stopwordsList'] = [] cleaning_kwargs['extend_or_replace_stopwordslist'] = 'replace' if strtobool(stopWordsConfig.get('replace', 'True')) else 'extend' removeNumericConfig = textCleaning.get('removeNumericConfig') if type(removeNumericConfig) is dict: cleaning_kwargs['removeNumeric_fIncludeSpecialCharacters'] = strtobool(removeNumericConfig.get('removeNumeric_IncludeSpecialCharacters', 'True')) removePunctuationConfig = textCleaning.get('removePunctuationConfig') if type(removePunctuationConfig) is dict: cleaning_kwargs['fRemovePuncWithinTokens'] = strtobool(removePunctuationConfig.get('removePuncWithinTokens', 'False')) cleaning_kwargs['fExpandContractions'] = strtobool(textCleaning.get('expandContractions', 'False')) libConfig = textCleaning.get('libConfig') if type(libConfig) is dict: cleaning_kwargs['tokenizationLib'] = get_one_true_option(libConfig.get('tokenizationLib'), 'nltk') cleaning_kwargs['lemmatizationLib'] = get_one_true_option(libConfig.get('lemmatizationLib'), 'nltk') cleaning_kwargs['stopwordsRemovalLib'] = get_one_true_option(libConfig.get('stopwordsRemovalLib'), 'nltk') if data_path: cleaning_kwargs['data_path'] = data_path textProcessor = TextProcessing.TextProcessing(**cleaning_kwargs) pipeList.append(("TextProcessing",textProcessor)) textFeatureExtraction = conf_json.get('textFeatureExtraction') if strtobool(textFeatureExtraction.get('pos_tags', 'False')): pos_tags_lib = get_one_true_option(textFeatureExtraction.get('pos_tags_lib'), 'nltk') posTagger = TextProcessing.PosTagging( pos_tags_lib, data_path) pipeList.append(("posTagger",posTagger)) return pipeList def embedding(self, conf_json, pipeList): ngram_min = 1 ngram_max = 1 textFeatureExtraction = conf_json.get('textFeatureExtraction') if strtobool(textFeatureExtraction.get('n_grams', 'False')): n_grams_config = textFeatureExtraction.get("n_grams_config") ngram_min = int(n_grams_config.get('min_n', 1)) ngram_max = int(n_grams_config.get('max_n', 1)) if (ngram_min < 1) or ngram_min > ngram_max: ngram_min = 1 ngram_max = 1 invalidNgramWarning = 'WARNING : invalid ngram config.\nUsing the default values min_n={}, max_n={}'.format(ngram_min, ngram_max) self.log.info(invalidNgramWarning) ngram_range_tuple = (ngram_min, ngram_max) textConversionMethod = conf_json.get('textConversionMethod') conversion_method = get_one_true_option(textConversionMethod, None) embedding_size_config = conf_json.get('embeddingSize', {}) embedding_size = self.get_embedding_size(conversion_method, embedding_size_config) if conversion_method.lower() == "countvectors": vectorizer = TextProcessing.ExtractFeatureCountVectors( ngram_range=ngram_range_tuple,max_features=embedding_size) pipeList.append(("vectorizer",vectorizer)) self.log.info('----------> Conversion Method: CountVectors') elif conversion_method.lower() in ["fasttext","glove"]: embedding_method = conversion_method wordEmbeddingVecotrizer = TextProcessing.wordEmbedding(embedding_method, embedding_size) pipeList.append(("vectorizer",wordEmbeddingVecotrizer)) self.log.info('----------> Conversion Method: '+str(conversion_method)) elif conversion_method.lower() == "openai": from text.openai_embedding import embedding as openai_embedder vectorizer = openai_embedder() pipeList.append(("vectorizer",vectorizer)) self.log.info('----------> Conversion Method: '+str(conversion_method)) elif conversion_method.lower() == "sentencetransformer_distilroberta": from sentence_transformers import SentenceTransformer embedding_pretrained = {'model':'sentence-transformers/msmarco-distilroberta-base-v2','size': 768} self.bert_embedder_size = embedding_pretrained['size'] self.model = SentenceTransformer(embedding_pretrained['model']) self.embedder = FunctionTransformer(self.sentense_encode, feature_names_out = self.sentence_transformer_output) pipeList.append(("vectorizer",self.embedder)) self.log.info('----------> Conversion Method: SentenceTransformer using msmarco_distilroberta') elif conversion_method.lower() == "sentencetransformer_minilm": from sentence_transformers import SentenceTransformer embedding_pretrained = {'model':'sentence-transformers/all-MiniLM-L6-v2','size': 384} self.bert_embedder_size = embedding_pretrained['size'] self.model = SentenceTransformer(embedding_pretrained['model']) self.embedder = FunctionTransformer(self.sentense_encode, feature_names_out = self.sentence_transformer_output) pipeList.append(("vectorizer",self.embedder)) self.log.info('----------> Conversion Method: SentenceTransformer using MiniLM-L6-v2') elif conversion_method.lower() == "sentencetransformer_mpnet": from sentence_transformers import SentenceTransformer embedding_pretrained = {'model':'sentence-transformers/all-mpnet-base-v2','size': 768} self.bert_embedder_size = embedding_pretrained['size'] self.model = SentenceTransformer(embedding_pretrained['model']) self.embedder = FunctionTransformer(self.sentense_encode, feature_names_out = self.sentence_transformer_output) pipeList.append(("vectorizer",self.embedder)) self.log.info('----------> Conversion Method: SentenceTransformer using mpnet-base-v2') elif conversion_method.lower() == 'latentsemanticanalysis': vectorizer = TextProcessing.ExtractFeatureTfIdfVectors(ngram_range=ngram_range_tuple) pipeList.append(("vectorizer",vectorizer)) self.log.info('----------> Conversion Method: latentsemanticanalysis') elif conversion_method.lower() == 'tf_idf': vectorizer = TextProcessing.ExtractFeatureTfIdfVectors(ngram_range=ngram_range_tuple,max_features=embedding_size) pipeList.append(("vectorizer",vectorizer)) self.log.info('----------> Conversion Method: TF_IDF') else: df1 = pd.DataFrame() #df1['tokenize'] = textCorpus self.log.info('----------> Conversion Method: '+str(conversion_method)) return pipeList def sentence_transformer_output(self, transformer, names=None): return [str(x) for x in range(self.bert_embedder_size)] class textCombine(TransformerMixin): def __init__(self): pass def fit(self, X, y=None): return self def transform(self, X): if X.shape[1] > 1: return np.array([" ".join(i) for i in X]) else: if isinstance(X, np.ndarray): return np.ndarray.flatten(X) else: return X def get_pretrained_model_path(): try: from appbe.dataPath import DATA_DIR modelsPath = Path(DATA_DIR)/'PreTrainedModels'/'TextProcessing' except: modelsPath = Path('aion')/'PreTrainedModels'/'TextProcessing' if not modelsPath.exists(): modelsPath.mkdir(parents=True, exist_ok=True) return modelsPath def set_pretrained_model(pipe): from text.Embedding import load_pretrained import importlib.util global external_model global external_model_type params = pipe.get_params() model_name = params.get('text_process__vectorizer__preTrainedModel', None) if model_name and model_name.lower() in ['fasttext','glove'] and not external_model: if model_name == 'fasttext' and importlib.util.find_spec('fasttext'): import fasttext import fasttext.util cwd = os.getcwd() os.chdir(get_pretrained_model_path()) fasttext.util.download_model('en', if_exists='ignore') external_model = fasttext.load_model('cc.en.300.bin') os.chdir(cwd) external_model_type = 'binary' print('loaded fasttext binary') else: model_path = TextProcessing.checkAndDownloadPretrainedModel(model_name) embed_size, external_model = load_pretrained(model_path) external_model_type = 'vector' print(f'loaded {model_name} vector') pipe.set_params(text_process__vectorizer__external_model = external_model) pipe.set_params(text_process__vectorizer__external_model_type = external_model_type) def reset_pretrained_model(pipe, clear_mem=True): global external_model global external_model_type params = pipe.get_params() is_external_model = params.get('text_process__vectorizer__external_model', None) if (isinstance(is_external_model, pd.DataFrame) and not is_external_model.empty) or is_external_model: pipe.set_params(text_process__vectorizer__external_model = None) pipe.set_params(text_process__vectorizer__external_model_type = None) if clear_mem: external_model = None def release_pretrained_model(): global external_model global external_model_type external_model = None external_model_type = None ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import pandas as pd from nltk.tokenize import word_tokenize # Private function def unitvec(vec): return vec / np.linalg.norm(vec) def __word_average(vectors, sent, vector_size,key_to_index): """ Compute average word vector for a single doc/sentence. """ try: mean = [] for word in sent: index = key_to_index.get( word, None) if index != None: mean.append( vectors[index] ) if len(mean): return unitvec(np.array(mean).mean(axis=0)) return np.zeros(vector_size) except: raise # Private function def __word_average_list(vectors, docs, embed_size,key_to_index): """ Compute average word vector for multiple docs, where docs had been tokenized. """ try: return np.vstack([__word_average(vectors, sent, embed_size,key_to_index) for sent in docs]) except: raise def load_pretrained(path): df = pd.read_csv(path, index_col=0,sep=' ',quotechar = ' ' , header=None, skiprows=1,encoding_errors= 'replace') return len(df.columns), df def get_model( df:pd.DataFrame): index_to_key = {k:v for k,v in enumerate(df.index)} key_to_index = {v:k for k,v in enumerate(df.index)} df = df.to_numpy() return df, index_to_key, key_to_index def extractFeatureUsingPreTrainedModel(inputCorpus, pretrainedModelPath=None, loaded_model=False,key_to_index={}, embed_size=300): """ Extract feature vector from input Corpus using pretrained Vector model(word2vec,fasttext, glove(converted to word2vec format) """ try: if inputCorpus is None: return None else: if not pretrainedModelPath and ((isinstance(loaded_model, pd.DataFrame) and loaded_model.empty) or (not isinstance(loaded_model, pd.DataFrame) and not loaded_model)): inputCorpusWordVectors = None else: if (isinstance(loaded_model, pd.DataFrame) and not loaded_model.empty) or loaded_model: pretrainedModel = loaded_model else: embed_size, pretrainedModel = load_pretrained(pretrainedModelPath) pretrainedModel, index_to_key,key_to_index = get_model( pretrainedModel) if len(pretrainedModel): input_docs_tokens_list = [word_tokenize(inputDoc) for inputDoc in inputCorpus] inputCorpusWordVectors = __word_average_list(pretrainedModel, input_docs_tokens_list,embed_size,key_to_index) else: inputCorpusWordVectors = None return inputCorpusWordVectors except: raise ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import openai import tiktoken import numpy as np import pandas as pd from pathlib import Path from openai.embeddings_utils import get_embedding from sklearn.base import BaseEstimator, TransformerMixin class embedding(BaseEstimator, TransformerMixin): def __init__(self, embedding_engine='Text-Embedding', embedding_ctx_size=8191, encoding_method='cl100k_base'): self.embedding_engine = embedding_engine self.embedding_ctx_size = embedding_ctx_size self.encoding_method = encoding_method self.number_of_features = 1536 def fit(self,X,y=None): return self def transform(self, X): setup_openai() X = map(lambda text: self.len_safe_get_embedding( text), X) return list(X) def split_large_text(self, large_text): encoding = tiktoken.get_encoding( self.encoding_method) tokenized_text = encoding.encode(large_text) chunks = [] current_chunk = [] current_length = 0 for token in tokenized_text: current_chunk.append(token) current_length += 1 if current_length >= self.embedding_ctx_size: chunks.append(encoding.decode(current_chunk).rstrip(' .,;')) current_chunk = [] current_length = 0 if current_chunk: chunks.append(encoding.decode(current_chunk).rstrip(' .,;')) return chunks def len_safe_get_embedding(self, text): chunk_embeddings = [] chunk_lens = [] for chunk in self.split_large_text(text): chunk_embeddings.append( get_embedding(chunk, engine=self.embedding_engine)) chunk_lens.append(len(chunk)) chunk_embeddings = np.average(chunk_embeddings, axis=0, weights=None) chunk_embeddings = chunk_embeddings / np.linalg.norm(chunk_embeddings) # normalizes length to 1 chunk_embeddings = chunk_embeddings.tolist() return chunk_embeddings def get_feature_names_out(self): return [str(x) for x in range(self.number_of_features)] def get_feature_names(self): return self.get_feature_names_out() """ Open AI initialization has to be done separately as follows: 1. During training read the parameters from user a. from config b. SQLite database c. From Json file """ class setup_openai(): def __init__( self, config=None): param_keys = ['api_type','api_key','api_base','api_version'] if isinstance(config, dict): valid_params = {x:y for x,y in config.items() if x in param_keys} self._update_params(valid_params) elif self._is_sqlite(): self._update_params( self._get_cred_from_sqlite()) elif ((Path(__file__).parent.parent/'etc')/'openai.json').exists(): with open(((Path(__file__).parent.parent/'etc')/'openai.json'), 'r') as f: import json params = json.load(f) valid_params = {x:y for x,y in params.items() if x in param_keys} self._update_params(valid_params) else: raise ValueError('Open AI credentials are not provided.') def _is_sqlite(self): try: from AION.appbe.sqliteUtility import sqlite_db from AION.appbe.dataPath import DATA_DIR db_dir = Path(DATA_DIR)/'sqlite' db_file = 'config.db' if (db_dir/db_file).exists(): sqlite_obj = sqlite_db(db_dir,db_file) if sqlite_obj.table_exists('openai'): return True return False except: return False def _get_cred_from_sqlite(self): from AION.appbe.sqliteUtility import sqlite_db from AION.appbe.dataPath import DATA_DIR db_dir = Path(DATA_DIR)/'sqlite' db_file = 'config.db' sqlite_obj = sqlite_db(db_dir,db_file) data = sqlite_obj.read_data('openai')[0] param_keys = ['api_type','api_key','api_base','api_version'] return dict((x,y) for x,y in zip(param_keys,data)) def _update_params(self, valid_params): for key, value in valid_params.items(): if key == 'api_type': openai.api_type = value elif key == 'api_key': openai.api_key = value elif key == 'api_base': openai.api_base = value elif key == 'api_version': openai.api_version = value ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import sys import logging from collections import Counter import spacy import numpy as np import pandas as pd import nltk from nltk.corpus import stopwords from nltk import pos_tag from nltk.tokenize import word_tokenize from nltk.stem.wordnet import WordNetLemmatizer from textblob import TextBlob from sklearn.feature_extraction.text import CountVectorizer ''' nltk.download("punkt") nltk.download("wordnet") ''' stopWords = stopwords.words("english") class ExploreTextData: def __init__(self, logEnabled=False): self.logEnabled = logEnabled def __Log(self, logType="info", text=None): if logType.lower() == "exception": logging.exception( text) elif self.logEnabled: if logType.lower() == "info": logging.info( text) elif logType.lower() == "debug": logging.debug( text) def Describe(self, inputCorpus): """ Generate descriptive statistics for length of documents. Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- dict Summary statistics of the Series or Dataframe provided. """ try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) stat = {} word_count = self.DocumentWordCount(inputCorpus) stat['count'] = float(len(word_count)) stat['mean'] = float(word_count.mean()) stat['std'] = float(word_count.std()) stat['max'] = float(word_count.max()) stat['min'] = float(word_count.min()) return pd.DataFrame.from_dict(stat, orient='index') except: self.__Log("exception", sys.exc_info()) raise def DocumentLength(self, inputCorpus): """ Calculate the length of each document in corpus Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- pandas.Series of {int} series of length of documents """ try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) return inputCorpus.str.len() except: self.__Log("exception", sys.exc_info()) raise def DocumentWordCount(self, inputCorpus): """ Calculate the number of words in each document in corpus Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- pandas.Series of {int} series of number of words in documents """ try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) return inputCorpus.str.split().map(lambda x: len(x)) except: self.__Log("exception", sys.exc_info()) raise def AverageWordLength(self, inputCorpus): """ Calculate the average length of words in each document in corpus Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- pandas.Series of {double} series of average length of words in documents """ try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) return inputCorpus.str.split()\ .apply(lambda x: [len(i) for i in x])\ .map(lambda x: np.mean(x)) except: self.__Log("exception", sys.exc_info()) raise def StopWordsCount(self, inputCorpus): """ Calculate the number of stopwords in each document in corpus Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- pandas.Series of {int} series of count of stopwords in documents """ try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) stopWordsCount = [] inputCorpus = list(inputCorpus) for doc in inputCorpus: count = 0 for word in doc.split(): if word in stopWords: count += 1 stopWordsCount.append(count) return pd.Series(stopWordsCount) except: self.__Log("exception", sys.exc_info()) raise def MostCommonWords(self, inputCorpus, num_of_words=40): """ get the most common words in corpus Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- Pandas.DataFrame{string, int} Dataframe with columns "most_common_words" and "freq" """ try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) new = inputCorpus.str.split() new = new.values.tolist() corpus = [word for i in new for word in i if word not in stopWords] counter = Counter(corpus) most = counter.most_common() x, y = [], [] for word, count in most[: num_of_words + 1]: x.append(word) y.append(count) return pd.DataFrame([x, y],index=['most_common_words', 'freq']).T except: self.__Log("exception", sys.exc_info()) raise def NullCount(self, inputCorpus): """ Calculate the number of null entries in corpus Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- int count of null entries in corpus """ try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) return pd.Series(inputCorpus.isnull().sum()) except: self.__Log("exception", sys.exc_info()) raise def TopNgram(self, inputCorpus, ngram, num_of_words=10): """ Get the top words from the ngrams Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences ngram: int ngram required num_of_words:int, optional numbers of words to be returned Returns ------- Pandas.DataFrame{string, int} Dataframe with columns "ngram_words" and "freq" """ try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) words = [] for doc in inputCorpus: word = [w for w in word_tokenize(doc) if (w not in stopWords)] words.append(" ".join(word)) vec = CountVectorizer(ngram_range=(ngram, ngram)).fit(words) bag_of_words = vec.transform(inputCorpus) sum_words = bag_of_words.sum(axis=0) words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()] words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)[:num_of_words] words = [] frequency = [] for word, freq in words_freq: words.append(word) frequency.append(freq) return pd.DataFrame([words, frequency],index=['ngram_words', 'freq']).T except: self.__Log("exception", sys.exc_info()) raise def Polarity(self, inputCorpus): """ Get the polarity of the text Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- pandas.Series {double} series of calculated polarity of the documents """ try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) return inputCorpus.apply(lambda x: TextBlob(x).sentiment.polarity) except: self.__Log("exception", sys.exc_info()) raise def ReadabilityScore(self, inputCorpus): """ Get the Readability Score of the text Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- pandas.Series {double} series of calculated Readability Score of the documents """ import textstat try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) if isinstance(inputCorpus, pd.Series): return pd.Series([textstat.flesch_reading_ease(text) for text in inputCorpus]) else: return [textstat.flesch_reading_ease(inputCorpus)] except: self.__Log("exception", sys.exc_info()) raise def TagEntityCount(self, inputCorpus): """ Calculate the frequency of each entity present in documents Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- Pandas.DataFrame{string, int} Dataframe with columns "entity" and "freq" """ def ner(text): doc = nlp(text) return [X.label_ for X in doc.ents] try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) nlp = spacy.load("en_core_web_sm") ent = inputCorpus.apply(lambda x: ner(x)) ent = [x for sub in ent for x in sub] counter = Counter(ent) count = counter.most_common() x, y = map(list, zip(*count)) return pd.DataFrame([x, y],index=['entity', 'freq']).T except: self.__Log("exception", sys.exc_info()) raise def MostCommonTokenPerEntity(self, inputCorpus, entity="GPE"): """ Get the frequency of most common words corresponding to the specified entity in documents Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences entity: string, optional name of the entity corresponding to which words are counted Returns ------- Pandas.DataFrame{string, int} Dataframe with columns "token" and "freq" """ def ner(text, ent): doc = nlp(text) return [X.text for X in doc.ents if X.label_ == ent] try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) nlp = spacy.load("en_core_web_sm") gpe = inputCorpus.apply(lambda x: ner(x, entity.upper())) gpe = [i for x in gpe for i in x] counter = Counter(gpe) x, y = map(list, zip(*counter.most_common(10))) return pd.DataFrame([x, y],index=['token', 'freq']).T except: self.__Log("exception", sys.exc_info()) raise def MostCommonPosTag(self, inputCorpus): """ Get the frequency of most common POS tag present in documents Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences Returns ------- Pandas.DataFrame{string, int} Dataframe with columns "postag" and "freq" """ def pos(text): pos = pos_tag(word_tokenize(text)) pos = list(map(list, zip(*pos)))[1] return pos try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) tags = inputCorpus.apply(lambda x: pos(x)) tags = [x for l in tags for x in l] counter = Counter(tags) x, y = list(map(list, zip(*counter.most_common(7)))) return pd.DataFrame([x, y],index=['postag', 'freq']).T except: self.__Log("exception", sys.exc_info()) raise def MostCommonWordsInPOSTag(self, inputCorpus, tag="NN"): """ Get the frequency of most common words related to specified POS tag present in documents Parameters ---------- inputCorpus: sequence of input documents where each document consists of paragraphs or sentences tag: string, optional POS tag corresponding to which words frequency will be calculated Returns ------- Pandas.DataFrame{string, int} Dataframe with columns "words" and "freq" """ def get_POSTag(text, tag): adj = [] pos = pos_tag(word_tokenize(text)) for word, tg in pos: if tg == tag: adj.append(word) return adj try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) words = inputCorpus.apply(lambda x: get_POSTag(x, tag.upper())) words = [x for l in words for x in l] counter = Counter(words) x = [] y = [] if len(counter): x, y = list(map(list, zip(*counter.most_common(7)))) return pd.DataFrame([x, y],index=['words', 'freq']).T except: self.__Log("exception", sys.exc_info()) raise def __preprocessData(self, inputCorpus): """ Prepare the data for topic modelling """ try: self.__Log("info", "Start of {} function".format(sys._getframe().f_code.co_name)) corpus = [] lem = WordNetLemmatizer() for doc in inputCorpus: words = [w for w in word_tokenize(doc) if (w not in stopWords)] words = [lem.lemmatize(w) for w in words if len(w) > 2] corpus.append(words) return corpus except: self.__Log("exception", sys.exc_info()) raise ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import re import string import sys import demoji #demoji.download_codes() import nltk import spacy from nltk.corpus import stopwords from bs4 import BeautifulSoup from text_unidecode import unidecode from textblob import TextBlob from spellchecker import SpellChecker from nltk import pos_tag from nltk.tokenize import word_tokenize from nltk.corpus import wordnet from nltk.stem.wordnet import WordNetLemmatizer from nltk.stem.porter import PorterStemmer from spacy.lang.en import English from collections import defaultdict import contractions spacy_nlp = None def WordTokenize( inputText, tokenizationLib = 'nltk'): tokenList = [] if inputText is not None and inputText != "": tokenizationLib = tokenizationLib.lower() if tokenizationLib == 'nltk': tokenList = word_tokenize(inputText) elif tokenizationLib == 'textblob': tbObj = TextBlob(inputText) tokenList = tbObj.words elif tokenizationLib == 'spacy': nlp = English() nlpDoc = nlp(inputText) for token in nlpDoc: tokenList.append(token.text) elif tokenizationLib == 'keras': from tensorflow.keras.preprocessing.text import text_to_word_sequence tokenList = text_to_word_sequence(inputText) else: tokenList = word_tokenize(inputText) return tokenList def SentenceTokenize( inputText): sentenceList = [] if inputText is not None and inputText != "": sentenceList = sent_tokenize(inputText) return sentenceList def Lemmatize(inputTokensList, lemmatizationLib = 'nltk'): global spacy_nlp lemmatized_list= [] lemmatizationLib = lemmatizationLib.lower() if (inputTokensList is not None) and (len(inputTokensList)!=0): if (lemmatizationLib == 'textblob'): inputText = " ".join(inputTokensList) sent = TextBlob(inputText) tag_dict = {"J": 'a', "N": 'n', "V": 'v', "R": 'r'} words_and_tags = [(w, tag_dict.get(pos[0], 'n')) for w, pos in sent.tags] lemmatized_list = [wd.lemmatize(tag) for wd, tag in words_and_tags] if (lemmatizationLib == 'spacy'): inputText = " ".join(inputTokensList) if spacy_nlp == None: spacy_nlp = spacy.load('en_core_web_sm') doc = spacy_nlp(inputText) for token in doc: if token.text != token.lemma_: if token.lemma_ != "-PRON-": lemmatized_list.append(token.lemma_) else: lemmatized_list.append(token.text) else: lemmatized_list.append(token.text) else: tag_map = defaultdict(lambda : wordnet.NOUN) tag_map['J'] = wordnet.ADJ tag_map['V'] = wordnet.VERB tag_map['R'] = wordnet.ADV wnLemmatizer = WordNetLemmatizer() token_tags = pos_tag(inputTokensList) lemmatized_list = [wnLemmatizer.lemmatize(token, tag_map[tag[0]]) for token, tag in token_tags] return lemmatized_list def Stemmize(inputTokensList): stemmedTokensList= [] if (inputTokensList is not None) and (len(inputTokensList)!=0): porterStemmer = PorterStemmer() stemmedTokensList = [porterStemmer.stem(token) for token in inputTokensList] return stemmedTokensList def ToLowercase(inputText): resultText = "" if inputText is not None and inputText != "": resultText = inputText.lower() return resultText def ToUppercase(inputText): resultText = "" if inputText is not None and inputText != '': resultText = inputText.upper() return resultText def RemoveNoise(inputText, removeNoise_fHtmlDecode = True, removeNoise_fRemoveHyperLinks = True, removeNoise_fRemoveMentions = True, removeNoise_fRemoveHashtags = True, removeNoise_RemoveOrReplaceEmoji = 'remove', removeNoise_fUnicodeToAscii = True, removeNoise_fRemoveNonAscii = True): if inputText is not None and inputText != "": if removeNoise_fHtmlDecode == True: inputText = BeautifulSoup(inputText, "html.parser").text if removeNoise_fRemoveHyperLinks == True: inputText = re.sub(r'https?:\/\/\S*', '', inputText, flags=re.MULTILINE) if removeNoise_fRemoveMentions == True: inputText = re.sub('[@]+\S+','', inputText) if removeNoise_fRemoveHashtags == True: inputText = re.sub('[#]+\S+','', inputText) if removeNoise_RemoveOrReplaceEmoji == 'remove': inputText = demoji.replace(inputText, "") elif removeNoise_RemoveOrReplaceEmoji == 'replace': inputText = demoji.replace_with_desc(inputText, " ") if removeNoise_fUnicodeToAscii == True: inputText = unidecode(inputText) if removeNoise_fRemoveNonAscii == True: inputText= re.sub(r'[^\x00-\x7F]+',' ', inputText) inputText = re.sub(r'\s+', ' ', inputText) inputText = inputText.strip() return inputText def RemoveStopwords(inputTokensList, stopwordsRemovalLib='nltk', stopwordsList = None, extend_or_replace='extend'): resultTokensList = [] if (inputTokensList is not None) and (len(inputTokensList)!=0): stopwordsRemovalLib= stopwordsRemovalLib.lower() if stopwordsRemovalLib == 'spacy': nlp = English() stopwordRemovalList = nlp.Defaults.stop_words else: stopwordRemovalList = set(stopwords.words('english')) if extend_or_replace == 'replace': if stopwordsList is not None: stopwordRemovalList = set(stopwordsList) else: if stopwordsList: stopwordRemovalList = stopwordRemovalList.union(set(stopwordsList)) resultTokensList = [word for word in inputTokensList if word not in stopwordRemovalList] return resultTokensList def RemoveNumericTokens(inputText, removeNumeric_fIncludeSpecialCharacters=True): resultText = "" if inputText is not None and inputText != "": if removeNumeric_fIncludeSpecialCharacters == True: #Remove tokens having numbers and punctuations resultText = re.sub(r'\b\d+[^a-zA-Z]*\d*\b',' ', inputText) else: #Remove only numeric tokens resultText = re.sub(r'\b\d+\b','', inputText) # convert consecutive whitespaces to single space in the results resultText = re.sub(r'\s+', ' ', resultText) return resultText def RemovePunctuation(inputText, fRemovePuncWithinTokens=False): resultText = "" if inputText is not None and len(inputText) != 0: if fRemovePuncWithinTokens == True: resultText = inputText.translate(str.maketrans("","", string.punctuation)) else: punctuationList = list(string.punctuation) tokensList = WordTokenize(inputText) resultTokensList = [word for word in tokensList if word not in punctuationList] resultText = " ".join(resultTokensList) resultText = re.sub(r'\s+', ' ', resultText) return resultText def CorrectSpelling(inputTokensList): correctedTokensList = [] if (inputTokensList is not None) and (len(inputTokensList)!=0): spell = SpellChecker() for word in inputTokensList: word = word.lower() if word not in spell: word = spell.correction(word) if word: correctedTokensList.append(word) return correctedTokensList def ReplaceAcronym(inputTokensList, acrDict=None): resultTokensList = [] if (inputTokensList is not None) and (len(inputTokensList)!=0): if ((acrDict is not None) and (len(acrDict) != 0)): acrDictLowercase = dict((key.lower(), value.lower()) for key, value in acrDict.items()) resultTokensList = [acrDictLowercase.get(token.lower(), token.lower()) for token in inputTokensList] else: resultTokensList = inputTokensList return resultTokensList def ExpandContractions(inputText): resultText = "" if inputText != '': resultText = contractions.fix(inputText) return resultText def cleanText( inputText, functionSequence = ['RemoveNoise','ExpandContractions','Normalize','ReplaceAcronym', 'CorrectSpelling','RemoveStopwords','RemovePunctuation','RemoveNumericTokens'], fRemoveNoise = True, fExpandContractions = False, fNormalize = True, fReplaceAcronym = False, fCorrectSpelling = False, fRemoveStopwords = True, fRemovePunctuation = True, fRemoveNumericTokens = True, removeNoise_fHtmlDecode = True, removeNoise_fRemoveHyperLinks = True, removeNoise_fRemoveMentions = True, removeNoise_fRemoveHashtags = True, removeNoise_RemoveOrReplaceEmoji = 'remove', removeNoise_fUnicodeToAscii = True, removeNoise_fRemoveNonAscii = True, tokenizationLib='nltk', normalizationMethod = 'Lemmatization', lemmatizationLib = 'nltk', acronymDict = None, stopwordsRemovalLib = 'nltk', stopwordsList = None, extend_or_replace_stopwordslist = 'extend', removeNumeric_fIncludeSpecialCharacters = True, fRemovePuncWithinTokens = False ): if inputText is not None and inputText != "": for function in functionSequence: if function == 'RemoveNoise': if (fRemoveNoise == True): inputText = RemoveNoise(inputText, removeNoise_fHtmlDecode, removeNoise_fRemoveHyperLinks, removeNoise_fRemoveMentions, removeNoise_fRemoveHashtags, removeNoise_RemoveOrReplaceEmoji, removeNoise_fUnicodeToAscii, removeNoise_fRemoveNonAscii) if function == 'ExpandContractions': if (fExpandContractions == True): inputText = ExpandContractions(inputText) if function == 'Normalize': if (fNormalize == True): inputTokens = WordTokenize(inputText, tokenizationLib) if (normalizationMethod == 'Stemming'): inputTokens = Stemmize(inputTokens) else: inputTokens = Lemmatize(inputTokens, lemmatizationLib) inputText = " ".join(inputTokens) if function == 'ReplaceAcronym': if fReplaceAcronym == True and (acronymDict is not None) and acronymDict != 'None': inputText = ToLowercase(inputText) inputTokens = WordTokenize(inputText, tokenizationLib) inputTokens= ReplaceAcronym(inputTokens, acronymDict) inputText = " ".join(inputTokens) if function == 'CorrectSpelling': if (fCorrectSpelling == True): try: inputTokens = WordTokenize(inputText, tokenizationLib) inputTokens = CorrectSpelling(inputTokens) inputText = " ".join(inputTokens) except Exception as e: print(e) pass if function == 'RemoveStopwords': if (fRemoveStopwords == True): inputText = ToLowercase(inputText) inputTokens = WordTokenize(inputText, tokenizationLib) inputTokens = RemoveStopwords(inputTokens, stopwordsRemovalLib, stopwordsList, extend_or_replace_stopwordslist) inputText = " ".join(inputTokens) if function == 'RemovePunctuation': if (fRemovePunctuation == True): inputText = RemovePunctuation(inputText, fRemovePuncWithinTokens) if function == 'RemoveNumericTokens': if (fRemoveNumericTokens == True): inputText = RemoveNumericTokens(inputText, removeNumeric_fIncludeSpecialCharacters) inputText = ToLowercase(inputText) return inputText ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json #Python sklearn & std libraries import numpy as np import pandas as pd from time_series.ts_arima_eion import eion_arima from time_series.aion_fbprophet import aion_fbprophet from time_series.timeseriesDLUnivariate import timeseriesDLUnivariate from time_series.timeseriesDLMultivariate import timeseriesDLMultivariate from time_series.tsDLMultiVrtInUniVrtOut import tsDLMultiVrtInUniVrtOut from statsmodels.tsa.vector_ar.vecm import coint_johansen from statsmodels.tsa.vector_ar.var_model import VAR from math import * from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_absolute_error from math import sqrt import logging import os import sys import time import pickle from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_squared_error from statsmodels.tsa.stattools import adfuller import pmdarima as pm from statsmodels.tsa.stattools import grangercausalitytests from statsmodels.stats.stattools import durbin_watson from time_series.ts_modelvalidation import timeseriesModelTests from sklearn.utils import check_array from time_series.tsStationarySeasonalityTest import tsStationarySeasonalityTest class timeseries(): def __init__(self,tsConfig,modelconfig,modelList,data,targetFeature,dateTimeFeature,modelName,trainPercentage,usecasename,version,deployLocation,scoreParam): self.tsConfig = tsConfig self.modelconfig = modelconfig self.modelList = modelList self.data = data self.data1=data self.pred_freq = '' self.additional_regressors='' self.trainPercentage = trainPercentage self.targetFeature = targetFeature self.dateTimeFeature = dateTimeFeature self.modelName=modelName self.usecasename=usecasename self.model_fit=None self.selectedColumns = '' self.version=version self.deployLocation=deployLocation self.dictDiffCount={} self.log = logging.getLogger('eion') self.scoreParam=str(scoreParam) try: ##For bug:12280 self.data.dropna(how='all',axis=1,inplace=True) except Exception as e: self.data.fillna(0) self.log.info("data empty feature process error info:, check any text column contain empty records. if yes, please remove the column and upload the data for time series forecasting. \n"+str(e)) def var_prediction(self,no_of_prediction): tdata = self.data.drop([self.dateTimeFeature], axis=1) tdata.index = self.data[self.dateTimeFeature] lag_order = self.model_fit.k_ar predictions = self.model_fit.forecast(tdata.values[-lag_order:],steps=no_of_prediction) predictions = predictions.round(2) col = self.targetFeature.split(",") pred = pd.DataFrame(index=range(0,len(predictions)),columns=col) for j in range(0,len(col)): for i in range(0, len(predictions)): pred.iloc[i][j] = predictions[i][j] predictions = pred pred=self.invertTransformation(tdata,self.targetFeature,predictions,self.dictDiffCount) return pred def save_dl_model(self,smodel,scaler_model): try: saved_model = self.usecasename+'_'+self.version filename = os.path.join(self.deployLocation,'model',saved_model) smodel.save(filename) if scaler_model != 'NA' and scaler_model != '': scaler_filename = os.path.join(self.deployLocation,'model',saved_model+'_scaler.pkl') with open(scaler_filename, 'wb') as f: pickle.dump(scaler_model,f) f.close() else: scaler_filename = 'NA' return filename,saved_model,scaler_filename except Exception as e: print(e) def save_model(self,smodel): try: saved_model = self.usecasename+'_'+self.version+'.sav' filename = os.path.join(self.deployLocation,'model',saved_model) with open(filename, 'wb') as f: pickle.dump(smodel,f) f.close() return filename,saved_model except Exception as e: print(e) def mean_absolute_percentage_error(self,y_true, y_pred): try: y_true, y_pred=np.array(y_true), np.array(y_pred) mape=np.mean(np.abs((y_true - y_pred) / y_true+sys.float_info.epsilon)) * 100 return mape except Exception as inst: self.log.info('------------- mean_absolute_percentage_error ---------------') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ## Fbprophet model def getfbprophetmodel(self,predicted_data_file,dataFolderLocation,tFeature): try: modelName='fbprophet' modelconfig = self.modelconfig['fbprophet'] self.targetFeature=tFeature[0] X_Train = pd.DataFrame(self.data[self.targetFeature]) try: # self.data[self.dateTimeFeature] = pd.to_datetime(self.data[self.dateTimeFeature],errors='coerce') ##For bug 13513 - If the datetime needs UTC timestamp process, except part will handle. try: #for non utc timestamp self.data[self.dateTimeFeature] = pd.to_datetime(self.data[self.dateTimeFeature],errors='coerce') except: #for utc timestamp self.data[self.dateTimeFeature] = pd.to_datetime(self.data[self.dateTimeFeature],errors='coerce',utc=True) self.data = self.data.dropna() except: pass aion_prophet_obj = aion_fbprophet(modelconfig,self.trainPercentage,self.data,self.targetFeature,self.dateTimeFeature) self.log.info('Status:- |... TimeSeries Algorithm applied: FBPROPHET') self.model_fit,mae,rmse_prophet,mse,mape,r2,pred_freq,additional_regressors,prophet_df_new = aion_prophet_obj.aion_probhet(X_Train,self.dateTimeFeature,predicted_data_file,dataFolderLocation) ## Added for additional scoring params if (self.scoreParam.lower() == "r2"): scoringparam_v=r2 self.log.info("fbprophet User selected scoring parameter is r2. r2 value: "+str(r2)) elif (self.scoreParam.lower() == "rmse"): scoringparam_v=rmse_prophet self.log.info("fbprophet User selected scoring parameter is RMSE. RMSE value: "+str(rmse_prophet)) elif (self.scoreParam.lower() == "mse"): scoringparam_v=mse self.log.info("fbprophet User selected scoring parameter is MSE. MSE value: "+str(mse)) elif (self.scoreParam.lower() == "mae"): scoringparam_v=mae self.log.info("fbprophet User selected scoring parameter is MAE. MAE value: "+str(mae)) else: scoringparam_v=rmse_prophet self.log.info('Status:- |... Score '+self.scoreParam.capitalize()+': '+str(round(scoringparam_v,2))) #task 11997 displaying user selected scoring parameter in status logs error_matrix = '"RMSE":"'+str(round(rmse_prophet,2))+'","MAPE":"'+str(round(mape,2))+'","R2":"'+str(round(r2,2))+'","MAE":"'+str(round(mae,2))+'","MSE":"'+str(round(mse,2))+'"' self.log.info("fbprophet all scoring parameter results: "+str(error_matrix)) scoredetails = '{"Model":"FBProphet ","Score":'+str(scoringparam_v)+',"Scoring Param": "'+str(self.scoreParam.lower())+'"}' self.selectedColumns = self.targetFeature+','+self.dateTimeFeature self.selectedColumns = self.selectedColumns.split(",") self.pred_freq = pred_freq self.additional_regressors=additional_regressors self.log.info('------------- End FBPROPHET Model -------------\n') return('Success',modelName.upper(),self.scoreParam.lower(),scoringparam_v,self.model_fit,self.selectedColumns,error_matrix,scoredetails,self.dictDiffCount,self.pred_freq,self.additional_regressors,prophet_df_new) except Exception as e: self.log.info("FBProphet operation failed. error: "+str(e)) return('Error',modelName.upper(),self.scoreParam.lower(),0,None,self.selectedColumns,'','{}',self.dictDiffCount,self.pred_freq,self.additional_regressors,pd.DataFrame()) ## Arima model def get_arima_values(self): try: tFeature = self.targetFeature.split(',') if(len(tFeature) == 1): model_name = 'arima' else: self.log.info("Note: ARIMA model is going to perform only on first feature of provided target features due to data not met the VAR model constraints") self.targetFeature=tFeature[0] sesonalityChecks=True stationaryChecks=False #start checking sessonality using ch test and ocsb self.log.info(self.data.head(5)) res = pm.arima.nsdiffs(self.data[self.targetFeature], m=355, max_D=5, test="ch") # 365 since daily self.log.info('-------> Seasonality checks: %f' % res) if res >=4: self.log.info("-----------> Data is following Seasonality ") self.log.info('Status:- |... Seasonality Check Done. Data is following Seasonality ') sesonalityChecks=True else: self.log.info("-----------> Data is not following Seasonality ") self.log.info('Status:- |... Seasonality Check Done. Data is not following Seasonality') sesonalityChecks=False # end checking sessonality using ch test and ocsb # start checking stationary data for time Series series=self.data[self.targetFeature] adf_test = pm.arima.ADFTest(alpha=0.05) resultSt = adfuller(self.data[self.targetFeature]) self.log.info('ADF Statistic: %f' % resultSt[0]) self.log.info('p-value: %f' % resultSt[1]) if resultSt[1]<= 0.05: stationaryChecks=True self.log.info("the data does not have a unit root and is stationary.") self.log.info('Status:- |... Stationary Check Done. Data is stationary') else: stationaryChecks=False self.log.info("the data has a unit root and is non-stationary.") self.log.info('Status:- |... Stationary Check Done. Data is non-stationary') # End of stationary checks self.log.info('\n------------- Start Arima Model -------------') self.log.info('-------> Top 5 Rows: ') self.log.info(self.data.head(5)) eion_arima_obj = eion_arima(self.modelconfig['arima'],self.trainPercentage,sesonalityChecks,stationaryChecks) return 'Success',eion_arima_obj except Exception as e: self.log.info('<!------------- Get ARIMA Values ---------------> '+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return 'Error',None def getEncDecLSTMMultVrtInUniVrtOut(self): try: self.log.info('Status:- |... TimeSeries Algorithm applied: Encoder Decoder LSTM') modelName='encoder_decoder_lstm_mvi_uvo' modelconfig = self.modelconfig['encoder_decoder_lstm_mvi_uvo'] df = self.data targetFeature = list(self.targetFeature.split(",")) try: # df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce') ##For bug 13513 - If the datetime needs UTC timestamp process, except part will handle. try: #for non utc timestamp df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce') except: #for utc timestamp df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce',utc=True) df = df.dropna() except: pass df = df.groupby(self.dateTimeFeature).mean() df = df.reset_index() tdata = df.drop([self.dateTimeFeature], axis=1) tdata.index = df[self.dateTimeFeature] #tdata = tdata[tdata.columns[tdata.columns.isin(targetFeature)]] #selectedColumns = self.targetFeature+','+self.dateTimeFeature #selectedColumns = selectedColumns.split(",") selectedColumns = tdata.columns df_predicted=None aion_dlts_obj = tsDLMultiVrtInUniVrtOut(modelconfig,self.trainPercentage,targetFeature,self.dateTimeFeature) status,mse,rmse,r2,mae,model,df_predicted,lag_order,scaler = aion_dlts_obj.lstm_encdec_mvin_uvout(tdata) if status.lower() == 'success': ## Added for additional scoring params if (self.scoreParam.lower() == "r2"): scoringparam_v=r2 self.log.info('Status:- |... Score R2(Avg) '+str(r2)) elif (self.scoreParam.lower() == "rmse"): scoringparam_v=rmse self.log.info("Status:- |... Score RMSE(Avg) "+str(rmse)) elif (self.scoreParam.lower() == "mse"): scoringparam_v=mse self.log.info("Status:- |... Score MSE(Avg) "+str(mse)) elif (self.scoreParam.lower() == "mae"): scoringparam_v=mae self.log.info("Status:- |... Score MAE(Avg) : "+str(mae)) else: scoringparam_v=rmse error_matrix = '"RMSE":"'+str(round(rmse,2))+'","MSE":"'+str(round(mse,2))+'"' error_matrix=error_matrix+',"R2":"'+str(round(r2,2))+'","MAE":"'+str(round(mae,2))+'"' self.log.info("LSTM Multivariant Input Univariate Output all scoring param results: "+str(error_matrix)) self.log.info('Status:- |... Score '+self.scoreParam.capitalize()+': '+str(round(scoringparam_v,2))) #task 11997 displaying user selected scoring parameter in status logs scoredetails = '{"Model":"LSTM Multivariant","Score":'+str(scoringparam_v)+',"Scoring Param": "'+str(self.scoreParam.lower())+'"}' else: return 'Error',modelName.upper(),self.scoreParam.lower(),'NA',None,selectedColumns,'','{}',pd.DataFrame(),lag_order,None except Exception as e: self.log.info("getEncDecLSTMMultVrtInUniVrtOut method error. Error msg: "+str(e)) return 'Error',modelName.upper(),self.scoreParam.lower(),'NA',None,selectedColumns,'','{}',pd.DataFrame(),lag_order,None return 'Success',modelName.upper(),self.scoreParam.lower(),scoringparam_v,model,selectedColumns,error_matrix,scoredetails,df_predicted,lag_order,scaler def getLSTMMultivariate(self): try: self.log.info('Status:- |... TimeSeries Algorithm applied: LSTM') modelName='lstm' modelconfig = self.modelconfig['lstm'] df = self.data targetFeature = list(self.targetFeature.split(",")) try: # df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce') ##For bug 13513 - If the datetime needs UTC timestamp process, except part will handle. try: #for non utc timestamp df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce') except: #for utc timestamp df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce',utc=True) df = df.dropna() except: pass df = df.groupby(self.dateTimeFeature).mean() df = df.reset_index() tdata = df.drop([self.dateTimeFeature], axis=1) tdata.index = df[self.dateTimeFeature] tdata = tdata[tdata.columns[tdata.columns.isin(targetFeature)]] selectedColumns = self.targetFeature+','+self.dateTimeFeature selectedColumns = selectedColumns.split(",") df_predicted=None aion_dlts_obj = timeseriesDLMultivariate(modelconfig,self.trainPercentage,targetFeature,self.dateTimeFeature) status,mse,rmse,r2,mae,model,df_predicted,lag_order,scaler = aion_dlts_obj.lstm_multivariate(tdata) if status.lower() == 'success': ## Added for additional scoring params if (self.scoreParam.lower() == "r2"): scoringparam_v=r2 self.log.info('Status:- |... Score R2(Avg) '+str(r2)) elif (self.scoreParam.lower() == "rmse"): scoringparam_v=rmse self.log.info("Status:- |... Score RMSE(Avg) "+str(rmse)) elif (self.scoreParam.lower() == "mse"): scoringparam_v=mse self.log.info("Status:- |... Score MSE(Avg) "+str(mse)) elif (self.scoreParam.lower() == "mae"): scoringparam_v=mae self.log.info("Status:- |... Score MAE(Avg) : "+str(mae)) else: scoringparam_v=rmse error_matrix = '"RMSE":"'+str(round(rmse,2))+'","MSE":"'+str(round(mse,2))+'"' error_matrix=error_matrix+',"R2":"'+str(round(r2,2))+'","MAE":"'+str(round(mae,2))+'"' self.log.info("LSTM Multivariant all scoring param results: "+str(error_matrix)) self.log.info('Status:- |... Score '+self.scoreParam.capitalize()+': '+str(round(scoringparam_v,2))) #task 11997 displaying user selected scoring parameter in status logs scoredetails = '{"Model":"LSTM Multivariant","Score":'+str(scoringparam_v)+',"Scoring Param": "'+str(self.scoreParam.lower())+'"}' else: return 'Error',modelName.upper(),self.scoreParam.lower(),'NA',None,selectedColumns,'','{}',pd.DataFrame(),lag_order,None except Exception as e: self.log.info("getLSTMMultivariate method error. Error msg: "+str(e)) return 'Error',modelName.upper(),self.scoreParam.lower(),'NA',None,selectedColumns,'','{}',pd.DataFrame(),lag_order,None return 'Success',modelName.upper(),self.scoreParam.lower(),scoringparam_v,model,selectedColumns,error_matrix,scoredetails,df_predicted,lag_order,scaler def getUniVarientLSTMModel(self): try: self.log.info('Status:- |... TimeSeries Algorithm applied: LSTM') modelName='lstm' lstmconfig = self.modelconfig['lstm'] df = self.data try: # df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce') ##For bug 13513 - If the datetime needs UTC timestamp process, except part will handle. try: #for non utc timestamp df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce') except: #for utc timestamp df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce',utc=True) df = df.dropna() except: pass tdata = df.drop([self.dateTimeFeature], axis=1) tdata.index = df[self.dateTimeFeature] tdata = pd.DataFrame(tdata[self.targetFeature]) selectedColumns = self.targetFeature+','+self.dateTimeFeature selectedColumns = selectedColumns.split(",") aion_dlts_obj = timeseriesDLUnivariate(lstmconfig,self.trainPercentage,self.targetFeature,self.dateTimeFeature,modelName) status,lstm_mse,lstm_rmse,r2,mae,lstm_model,df_predicted_lstm,lag_order,scaler = aion_dlts_obj.ts_lstm(tdata) if status.lower() == 'success': ## Added for additional scoring params if (self.scoreParam.lower() == "r2"): scoringparam_v=r2 self.log.info("LSTM Univariant User selected scoring parameter is r2. r2 value: "+str(r2)) elif (self.scoreParam.lower() == "rmse"): scoringparam_v=lstm_rmse self.log.info("LSTM Univariant User selected scoring parameter is RMSE. Rmse value: "+str(lstm_rmse)) elif (self.scoreParam.lower() == "mse"): scoringparam_v=lstm_mse self.log.info("LSTM Univariant User selected scoring parameter is MSE. Mse value: "+str(lstm_mse)) elif (self.scoreParam.lower() == "mae"): scoringparam_v=mae self.log.info("LSTM Univariant User selected scoring parameter is MAE. Mae value: "+str(mae)) else: scoringparam_v=lstm_rmse error_matrix = '"RMSE":"'+str(round(lstm_rmse,2))+'","MSE":"'+str(round(lstm_mse,2))+'"' error_matrix=error_matrix+',"R2":"'+str(round(r2,2))+'","MAE":"'+str(round(mae,2))+'"' self.log.info("LSTM Univariant, all scoring param results: "+str(error_matrix)) self.log.info('Status:- |... Score '+self.scoreParam.capitalize()+': '+str(round(scoringparam_v,2))) #task 11997 displaying user selected scoring parameter in status logs scoredetails = '{"Model":"LSTM Univariant","Score":'+str(scoringparam_v)+',"Scoring Param": "'+str(self.scoreParam.lower())+'"}' return 'Success',modelName.upper(),self.scoreParam.lower(),scoringparam_v,lstm_model,selectedColumns,error_matrix,scoredetails,df_predicted_lstm,lag_order,scaler else: return 'Error',modelName.upper(),self.scoreParam.lower(),0,None,selectedColumns,'','{}',pd.DataFrame(),0,None except Exception as inst: self.log.info('<!------------- LSTM Error ---------------> '+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return 'Error',modelName.upper(),self.scoreParam.lower(),0,None,selectedColumns,'','{}',pd.DataFrame(),0,None def getUniVarientMLPModel(self): try: self.log.info('Status:- |... TimeSeries Algorithm applied: MLP') modelName='mlp' lstmconfig = self.modelconfig['mlp'] df = self.data try: # df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce') ##For bug 13513 - If the datetime needs UTC timestamp process, except part will handle. try: #for non utc timestamp df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce') except: #for utc timestamp df[self.dateTimeFeature] = pd.to_datetime(df[self.dateTimeFeature],errors='coerce',utc=True) df = df.dropna() except: pass tdata = df.drop([self.dateTimeFeature], axis=1) tdata.index = df[self.dateTimeFeature] tdata = pd.DataFrame(tdata[self.targetFeature]) selectedColumns = self.targetFeature+','+self.dateTimeFeature selectedColumns = selectedColumns.split(",") aion_dlts_obj = timeseriesDLUnivariate(lstmconfig,self.trainPercentage,self.targetFeature,self.dateTimeFeature,modelName) mlp_mse,mlp_rmse,r2,mae,mlp_model,df_predicted_mlp,look_back,scaler = aion_dlts_obj.mlpDL(tdata) ## Added for additional scoring params if (self.scoreParam.lower() == "r2"): scoringparam_v=r2 self.log.info("MLP Univariant User selected scoring parameter is R2. R2 value: "+str(r2)) elif (self.scoreParam.lower() == "rmse"): scoringparam_v=mlp_rmse self.log.info("MLP Univariant User selected scoring parameter is RMSE. Rmse value: "+str(mlp_rmse)) elif (self.scoreParam.lower() == "mse"): scoringparam_v=mlp_mse self.log.info("MLP Univariant User selected scoring parameter is MSE. Mse value: "+str(mlp_mse)) elif (self.scoreParam.lower() == "mae"): scoringparam_v=mae self.log.info("MLP Univariant User selected scoring parameter is MAE. Mae value: "+str(mae)) else: scoringparam_v=mlp_rmse error_matrix = '"RMSE":"'+str(round(mlp_rmse,2))+'","MSE":"'+str(round(mlp_mse,2))+'"' error_matrix=error_matrix+',"R2":"'+str(round(r2,2))+'","MAE":"'+str(round(mae,2))+'"' self.log.info("MLP Univariant, all scoring param results: "+str(error_matrix)) self.log.info('Status:- |... Score '+self.scoreParam.capitalize()+': '+str(round(scoringparam_v,2))) #task 11997 displaying user selected scoring parameter in status logs scoredetails = '{"Model":"MLP","Score":'+str(scoringparam_v)+',"Scoring Param": "'+str(self.scoreParam.lower())+'"}' return 'Success',modelName.upper(),self.scoreParam.lower(),scoringparam_v,mlp_model,selectedColumns,error_matrix,scoredetails,df_predicted_mlp,look_back,scaler except Exception as inst: import traceback self.log.info("MLP Error in timeseries module: \n"+str(traceback.print_exc())) self.log.info('<!------------- MLP Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return 'Error',modelName.upper(),self.scoreParam.lower(),0,None,selectedColumns,'','{}',pd.DataFrame(),0,None def getARIMAmodel(self,predicted_data_file): try: modelName='arima' status,eion_arima_obj = self.get_arima_values() self.log.info('Status:- |... TimeSeries Algorithm applied: ARIMA') selected_feature_list = self.data[self.targetFeature].values selected_feature_list = selected_feature_list.astype('int32') self.log.info('-------> Target Feature First 5 Rows: ') self.log.info(self.data[self.targetFeature].head(5)) X_Train = pd.DataFrame(self.data[self.targetFeature]) try: # self.data[self.dateTimeFeature] = pd.to_datetime(self.data[self.dateTimeFeature],errors='coerce') ##For bug 13513 - If the datetime needs UTC timestamp process, except part will handle. try: #for non utc timestamp self.data[self.dateTimeFeature] = pd.to_datetime(self.data[self.dateTimeFeature],errors='coerce') except: #for utc timestamp self.data[self.dateTimeFeature] = pd.to_datetime(self.data[self.dateTimeFeature],errors='coerce',utc=True) self.data = self.data.dropna() except: pass if status.lower() == 'success': self.model_fit,mae,rmse_arima,mse,r2,aic_score,mape,valid,pred = eion_arima_obj.eion_arima(X_Train) ## Added for additional scoring params if (self.scoreParam.lower() == "r2"): scoringparam_v=r2 self.log.info("ARIMA Univariant User selected scoring parameter is r2. r2 value: "+str(r2)) elif (self.scoreParam.lower() == "rmse"): scoringparam_v=rmse_arima self.log.info("ARIMA Univariant User selected scoring parameter is RMSE. RMSE value: "+str(rmse_arima)) elif (self.scoreParam.lower() == "mse"): scoringparam_v=mse self.log.info("ARIMA Univariant User selected scoring parameter is MSE. MSE value: "+str(mse)) elif (self.scoreParam.lower() == "mae"): scoringparam_v=mae self.log.info("ARIMA Univariant User selected scoring parameter is MAE. RMSE value: "+str(rmse_arima)) else: scoringparam_v=rmse_arima error_matrix = '"RMSE":"'+str(round(rmse_arima,2))+'","MSE":"'+str(round(mse,2))+'"' error_matrix=error_matrix+',"R2":"'+str(round(r2,2))+'","MAE":"'+str(round(mae,2))+'"' self.log.info('Status:- |... Score '+self.scoreParam.capitalize()+': '+str(round(scoringparam_v,2))) #task 11997 displaying user selected scoring parameter in status logs self.log.info("ARIMA all scoring param results: "+str(error_matrix)) scoredetails = '{"Model":"ARIMA","Score":'+str(scoringparam_v)+',"Scoring Param": "'+str(self.scoreParam.lower())+'"}' df_pred = pd.DataFrame() df_pred[self.targetFeature+'_actual'] = valid[self.targetFeature] df_pred[self.targetFeature+'_pred'] = pred self.log.info('Status:- |... Score '+self.scoreParam.capitalize()+': '+str(round(scoringparam_v,2))) #task 11997 displaying user selected scoring parameter in status logs self.log.info("ARIMA AIC Score: "+str(round(aic_score,2))) selectedColumns = self.targetFeature+','+self.dateTimeFeature selectedColumns = selectedColumns.split(",") self.log.info('------------- End ARIMA Model -------------\n') return('Success',modelName.upper(),self.scoreParam.lower(),scoringparam_v,self.model_fit,selectedColumns,error_matrix,scoredetails,self.dictDiffCount,self.pred_freq,self.additional_regressors,rmse_arima,df_pred) else: return 'Error',modelName.upper(),self.scoreParam.lower(),0,None,selectedColumns,'','{}',self.dictDiffCount,self.pred_freq,self.additional_regressors,0,pd.DataFrame() except Exception as inst: self.log.info('<!------------- Arima Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return 'Error',modelName.upper(),self.scoreParam.lower(),0,None,selectedColumns,'','{}',self.dictDiffCount,self.pred_freq,self.additional_regressors,0,pd.DataFrame() ## VAR model fn def getVARmodel(self): from sklearn.metrics import r2_score modelName="var" if modelName.lower()=='var': try: self.log.info('-------> Top 5 Rows: ') self.log.info(self.data.head(5)) if(self.targetFeature != ''): self.selectedColumns = self.targetFeature self.selectedColumns = self.selectedColumns+','+self.dateTimeFeature self.selectedColumns = self.selectedColumns.split(",") self.data = self.data[self.selectedColumns] try: # self.data[self.dateTimeFeature] = pd.to_datetime(self.data[self.dateTimeFeature],errors='coerce') ##For bug 13513 - If the datetime needs UTC timestamp process, except part will handle. try: #for non utc timestamp self.data[self.dateTimeFeature] = pd.to_datetime(self.data[self.dateTimeFeature],errors='coerce') except: #for utc timestamp self.data[self.dateTimeFeature] = pd.to_datetime(self.data[self.dateTimeFeature],errors='coerce',utc=True) self.data = self.data.dropna() except: pass self.data = self.data.groupby(self.dateTimeFeature).mean() self.data = self.data.reset_index() tdata = self.data.drop([self.dateTimeFeature], axis=1) tdata.index = self.data[self.dateTimeFeature] cols = tdata.columns self.log.info('------------- Start VAR Model -------------') size = int(len(tdata) * (self.trainPercentage/100)) train = tdata.iloc[0:(int(size))] valid = tdata.iloc[(int(size)):(int(len(tdata))-1)] start = time.time() model = VAR(endog=train) self.log.info('Status:- |... TimeSeries Algorithm applied: VAR') self.log.info("\n-------------Selecting max lag order -----------") modelFitted = model.fit() lag_order = modelFitted.k_ar self.log.info('------->lag_order: '+str(lag_order)) executionTime=time.time() - start self.log.info('-------> Time: '+str(executionTime)+'\n') prediction = modelFitted.forecast(train.values[-lag_order:], steps=len(valid)) pred = pd.DataFrame(index=range(0,len(prediction)),columns=tdata.columns) for j in range(0,(len(tdata.columns))): for i in range(0, len(prediction)): pred.iloc[i][j] = prediction[i][j] self.log.info("\n--------------- Modal Validation Start ---------------") df_pred = pd.DataFrame() #check rmse error_matrix = '"FeaturesMatrix":[' valid = valid.reset_index() var_rmse = 0 mse_dict={} rmse_dict={} mae_dict={} r2_dict={} for i in tdata.columns: if(error_matrix != '"FeaturesMatrix":['): error_matrix = error_matrix+',' df_pred[i+'_actual'] = valid[i] df_pred[i+'_pred'] = pred[i] rmse_var = sqrt(mean_squared_error(valid[i],pred[i])) mse = mean_squared_error(valid[i],pred[i]) mae = mean_absolute_error(valid[i],pred[i]) mape=self.mean_absolute_percentage_error(valid[i],pred[i]) r2 = r2_score(valid[i],pred[i]) mse_dict[i]=mse rmse_dict[i]=rmse_var r2_dict[i]=r2 mae_dict[i]=mae var_rmse += round(rmse_var,2) error_matrix += '{"Features":"'+i+'","MSE":"'+str(round(mse,2))+'","MAPE":"'+str(round(mape,2))+'","RMSE":"'+str(round(rmse_var,2))+'","MAE":"'+str(round(mae,2))+'"}' self.log.info ("------->Feature: "+str(i)) self.log.info ("---------->MAE: "+str(mae)) self.log.info ("---------->MSE: "+str(mse)) self.log.info ("---------->RMSE :"+str(rmse_var)) self.log.info("------------->MAPE :"+str(mape)) self.log.info("------------->R2 :"+str(r2)) var_rmse = var_rmse/len(tdata.columns) self.log.info("--------------- Modal Validation End ---------------\n") pred=self.invertTransformation(train,self.targetFeature,pred,self.dictDiffCount) self.log.info("-------> Predictions on Test Data") self.log.info(pred.head(4)) model = VAR(endog=tdata) self.model_fit = model.fit() aic_score = self.model_fit.aic lag_order = self.model_fit.k_ar self.log.info('------->AIC Score: '+str(aic_score)) error_matrix += ']' scoredetails = '{"Model":"VAR","Score":'+str(aic_score)+'}' self.log.info('Status:- |... Score AIC: '+str(round(aic_score,2))) tsModelTestObj=timeseriesModelTests(self.data1,self.targetFeature,self.dateTimeFeature,0) lenTargetFeature=len(self.targetFeature) r2=list(r2_dict.values())[-1] rmse=list(rmse_dict.values())[-1] mse=list(mse_dict.values())[-1] mae=list(mae_dict.values())[-1] ## Added for additional scoring params if (self.scoreParam.lower() == "r2"): scoringparam_v=r2 self.log.info("VAR Univariant User selected scoring parameter is r2. r2 value: "+str(r2)) self.log.info('Status:- |... Score R2(Avg): '+str(round(r2,2))) elif (self.scoreParam.lower() == "rmse"): scoringparam_v=rmse self.log.info("VAR Univariant User selected scoring parameter is RMSE. RMSE value: "+str(rmse)) self.log.info('Status:- |... Score RMSE(Avg): '+str(round(rmse,2))) elif (self.scoreParam.lower() == "mse"): scoringparam_v=mse self.log.info("VAR Univariant User selected scoring parameter is MSE. MSE value: "+str(mse)) self.log.info('Status:- |... Score MSE(Avg): '+str(round(mse,2))) elif (self.scoreParam.lower() == "mae"): scoringparam_v=mae self.log.info("VAR Univariant User selected scoring parameter is MAE. MAE value: "+str(mae)) self.log.info('Status:- |... Score MAE(Avg): '+str(round(mae,2))) else: scoringparam_v=rmse self.log.info('Status:- |... Score RMSE(Avg): '+str(round(rmse,2))) error_matrix = '"RMSE":"'+str(round(rmse,2))+'","MSE":"'+str(round(mse,2))+'"' error_matrix=error_matrix+',"R2":"'+str(round(r2,2))+'","MAE":"'+str(round(mae,2))+'"' self.log.info("VAR, all scoring param results: "+str(error_matrix)) scoredetails = '{"Model":"VAR","Score":'+str(round(scoringparam_v,2))+',"Scoring Param": "'+str(self.scoreParam)+'"}' self.log.info('------------- Start VAR Model End-------------\n') #countDependantFeature=0 out = durbin_watson(tdata) for col, val in zip(tdata.columns, out): self.log.info(col +':'+str(round(val, 2))) if val>0.0 and val <1.5: self.log.info("There is a positive correlation") elif val>=1.5 and val <=2.5: self.log.info("Relationship is Normal ") else : self.log.info("There is a negative correlation") return('Success',modelName.upper(),self.scoreParam.lower(),round(scoringparam_v,2),self.model_fit,self.selectedColumns,error_matrix,scoredetails,df_pred,self.dictDiffCount,self.pred_freq,self.additional_regressors,lag_order) except Exception as inst: self.log.info('<!------------- Var model Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return 'Error',modelName.upper(),self.scoreParam.lower(),0,None,self.selectedColumns,'','{}',pd.DataFrame(),self.dictDiffCount,self.pred_freq,self.additional_regressors,0 ## Get best time series algorithm among selected algs def getbestmodel(self,rmse_prophet,rmse_arima,rmse_lstm,rmse_mlp,rmse_var): best_model='' ## For R2 score if (self.scoreParam.lower() == 'r2'): modelScore = [] modelScore.append(rmse_prophet) modelScore.append(rmse_arima) modelScore.append(rmse_lstm) modelScore.append(rmse_mlp) modelScore.append(rmse_var) if (max(modelScore) == rmse_arima): best_model='arima' self.log.info('Status:- |... TimeSeries Best Algorithm: ARIMA') return best_model elif (max(modelScore) == rmse_prophet): best_model='fbprophet' self.log.info('Status:- |... TimeSeries Best Algorithm: FBPROPHET') return best_model elif (max(modelScore) == rmse_lstm): best_model='lstm' self.log.info('Status:- |... TimeSeries Best Algorithm: LSTM') return best_model elif (max(modelScore) == rmse_mlp): best_model='mlp' self.log.info('Status:- |... TimeSeries Best Algorithm: MLP') return best_model elif (max(modelScore) == rmse_var): best_model='var' self.log.info('Status:- |... TimeSeries Best Algorithm: VAR') return best_model else: #'Both arima and fbprophet rmse are equal, so both models are performing equal. ## So, selecting arima as best one. best_model='arima' return best_model else: modelScore = [] modelScore.append(rmse_prophet) modelScore.append(rmse_arima) modelScore.append(rmse_lstm) modelScore.append(rmse_mlp) modelScore.append(rmse_var) if (min(modelScore) == rmse_arima and rmse_arima != 0xFFFF): best_model='arima' self.log.info('Status:- |... TimeSeries Best Algorithm: ARIMA') return best_model elif (min(modelScore) == rmse_prophet and rmse_prophet != 0xFFFF): best_model='fbprophet' self.log.info('Status:- |... TimeSeries Best Algorithm: FBPROPHET') return best_model elif (min(modelScore) == rmse_lstm and rmse_lstm != 0xFFFF): best_model='lstm' self.log.info('Status:- |... TimeSeries Best Algorithm: LSTM') return best_model elif (min(modelScore) == rmse_mlp and rmse_mlp != 0xFFFF): best_model='mlp' self.log.info('Status:- |... TimeSeries Best Algorithm: MLP') return best_model elif (min(modelScore) == rmse_var and rmse_var != 0xFFFF): best_model='var' self.log.info('Status:- |... TimeSeries Best Algorithm: VAR') return best_model else: #'Both arima and fbprophet rmse are equal, so both models are performing equal. ## So, selecting arima as best one. best_model='arima' return best_model ## Selecting best model algorithm def bestmodelProcess(self,modelNames,nfeatures,trained_data_file,tFeature,predicted_data_file,dataFolderLocation): try: best_model='' lag_order = 1 predict_var=None predict_arima=None predict_lstm=None predict_mlp=None predict_fbprophet=None modelNames = modelNames modelNames=[x.lower() for x in modelNames] inputFeature_len=nfeatures status = 'Success' if 'fbprophet' in modelNames: status,modelName_prophet,fbprophet,rmse_prophet,fp_model_fit,selectedColumns_prophet,error_matrix_prophet,scoredetails_prophet,dictDiffCount_prophet,pred_freq_prophet,additional_regressors_prophet,predict_fbprophet = self.getfbprophetmodel(predicted_data_file,dataFolderLocation,tFeature) if status.lower() == 'error': self.log.info('-------------> FBPROPHET RMSE Score: Error') if (self.scoreParam.lower() == 'r2'): rmse_prophet = -0xFFFF else: rmse_prophet = 0xFFFF else: self.log.info("-------------> FBPROPHET RMSE Score:\t"+str(round(rmse_prophet,2))) else: if (self.scoreParam.lower() == 'r2'): rmse_prophet = -0xFFFF else: rmse_prophet = 0xFFFF if 'arima' in modelNames: status,modelName,aic,rmse_arima,ar_model_fit,selectedColumns,error_matrix,scoredetails,dictDiffCount,pred_freq,additional_regressors,rmse_arima_act,predict_arima = self.getARIMAmodel(predicted_data_file) if status.lower() == 'error': self.log.info('-------------> ARIMA RMSE Score: Error') if (self.scoreParam.lower() == 'r2'): rmse_arima = -0xFFFF else: rmse_arima = 0xFFFF else: self.log.info('-------------> ARIMA RMSE Score:\t'+str(round(rmse_arima,2))) else: if (self.scoreParam.lower() == 'r2'): rmse_arima = -0xFFFF ## -65535 else: rmse_arima = 0xFFFF if 'lstm' in modelNames: if inputFeature_len == 1: status,modelName_lstm,score_type,rmse_lstm,lstm_model_fit,lstm_selectedColumns,error_matrix_lstm,scoredetails_lstm,predict_lstm,lag_order,lstm_scaler = self.getUniVarientLSTMModel() else: status,modelName_lstm,score_type,rmse_lstm,lstm_model_fit,lstm_selectedColumns,error_matrix_lstm,scoredetails_lstm,predict_lstm,lag_order,lstm_scaler = self.getLSTMMultivariate() if status.lower() == 'error': self.log.info('-------------> LSTM RMSE Score: Error') if (self.scoreParam.lower() == 'r2'): rmse_lstm = -0xFFFF else: rmse_lstm = 0xFFFF else: self.log.info('-------------> LSTM RMSE Score:\t'+str(round(rmse_lstm,2))) else: if (self.scoreParam.lower() == 'r2'): rmse_lstm = -0xFFFF else: rmse_lstm = 0xFFFF if 'mlp' in modelNames: status,modelName_mlp,score_type,rmse_mlp,mlp_model_fit,mlp_selectedColumns,error_matrix_mlp,scoredetails_mlp,predict_mlp,lag_order,mlp_scaler = self.getUniVarientMLPModel() if status.lower() == 'error': self.log.info('-------------> MLP Score: Error') if (self.scoreParam.lower() == 'r2'): rmse_mlp = -0xFFFF else: rmse_mlp = 0xFFFF else: self.log.info('-------------> MLP RMSE Score:\t'+str(round(rmse_mlp,2))) else: if (self.scoreParam.lower() == 'r2'): rmse_mlp = -0xFFFF else: rmse_mlp = 0xFFFF if 'var' in modelNames: status,modelName_var,score_var_type,rmse_var,var_model,var_selectedColumns,error_matrix_var,scoredetails_var,predict_var,dictDiffCount,pred_freq,additional_regressors,lag_order = self.getVARmodel() if status.lower() == 'error': self.log.info('-------------> VAR Score: Error') if (self.scoreParam.lower() == 'r2'): rmse_var = -0xFFFF else: rmse_var = 0xFFFF else: if (self.scoreParam.lower() == 'r2'): rmse_var = -0xFFFF else: rmse_var = 0xFFFF best_model = self.getbestmodel(rmse_prophet,rmse_arima,rmse_lstm,rmse_mlp,rmse_var) if (best_model.lower() == 'arima'): self.log.info('Best model is ARIMA based on metric '+str(self.scoreParam.lower())) predict_arima.to_csv(predicted_data_file) filename,saved_model = self.save_model(ar_model_fit) return best_model,modelName,aic,rmse_arima,ar_model_fit,selectedColumns,error_matrix,scoredetails,dictDiffCount,pred_freq,additional_regressors,filename,saved_model,lag_order,'NA' elif (best_model.lower() == 'fbprophet'): self.log.info('Best model is fbprophet based on metric '+str(self.scoreParam.lower())) predict_fbprophet.to_csv(predicted_data_file) filename,saved_model = self.save_model(fp_model_fit) return best_model,modelName_prophet,fbprophet,rmse_prophet,fp_model_fit,selectedColumns_prophet,error_matrix_prophet,scoredetails_prophet,dictDiffCount_prophet,pred_freq_prophet,additional_regressors_prophet,filename,saved_model,lag_order,'NA' elif (best_model.lower() == 'var'): self.log.info('Best model is VAR based on metric '+str(self.scoreParam.lower())) self.data.to_csv(trained_data_file) predict_var.to_csv(predicted_data_file) filename,saved_model = self.save_model(var_model) return best_model,modelName_var,score_var_type,rmse_var,var_model,var_selectedColumns,error_matrix_var,scoredetails_var,dictDiffCount,pred_freq,additional_regressors,filename,saved_model,lag_order,'NA' elif (best_model.lower() == 'lstm'): self.log.info('Best model is LSTM based on metric '+str(self.scoreParam.lower())) predict_lstm.to_csv(predicted_data_file) filename,saved_model,scaler_model = self.save_dl_model(lstm_model_fit,lstm_scaler) return best_model,modelName_lstm,score_type,rmse_lstm,lstm_model_fit,lstm_selectedColumns,error_matrix_lstm,scoredetails_lstm,self.dictDiffCount,self.pred_freq,self.additional_regressors,filename,saved_model,lag_order,scaler_model elif (best_model.lower() == 'mlp'): self.log.info('Best model is MLP based on metric '+str(self.scoreParam.lower())) predict_mlp.to_csv(predicted_data_file) filename,saved_model,scaler_model = self.save_dl_model(mlp_model_fit,mlp_scaler) return best_model,modelName_mlp,score_type,rmse_mlp,mlp_model_fit,mlp_selectedColumns,error_matrix_mlp,scoredetails_mlp,self.dictDiffCount,self.pred_freq,self.additional_regressors,filename,saved_model,lag_order,scaler_model else: pass except Exception as e: self.log.info('Issue in running multi time series algorithm selection process..Please check the config params') self.log.info('error: '+str(e)) #Method to determine seasonality and stationrity in the input data features. (Task:12622,12623) def seasonality_stationarity_test(self): ##The below part is to test stationarity and sessonality in the given time series data based on statsmodels lib. #self.data,self.targetFeature,self.dateTimeFeature self.log.info("<-------------- Time series stationarity and seasonality test Started...---------------->\n") ts_sstest=tsStationarySeasonalityTest(self.data,self.deployLocation) ## Time series Stationary check ## Currently stationarity check method set as Augmented dickey fuller, but kpss method also implemented. stationary_method='adfuller' if (isinstance(self.targetFeature,list)): target=self.targetFeature pass elif (isinstance(self.targetFeature,str)): target=list(self.targetFeature.split(',')) stats_model,n_lags,p_value,stationary_result,stationary_combined_res=ts_sstest.stationary_check(target,self.dateTimeFeature,stationary_method) ## Time series Seasonality check ##Seasonal model default set as additive seasonal_model="additive" df,decompose_result_mult,seasonality_result,seasonality_combined_res=ts_sstest.seasonal_check(target,self.dateTimeFeature,seasonal_model) self.log.info("<-------------- Time series stationarity and seasonality test completed.---------------->\n") return stationary_result,seasonality_result #Main timeseries function. def timeseries_learning(self,trained_data_file,predicted_data_file,dataFolderLocation): dataFolderLocation=dataFolderLocation lag_order = 1 # ##The below part is to test stationarity and sessonality in the given time series data based on statsmodels lib. stationary_result,seasonality_result=self.seasonality_stationarity_test() try : tFeature = self.targetFeature.split(',') lentFeature=len(tFeature) try: if lentFeature > 1: if any('timeseriesforecasting' in x.lower() for x in self.modelName): #task 11997 self.modelName.remove('timeseriesforecasting') if 'arima' in self.modelName: self.log.info('Status:- |... TimeSeries algorithm ARIMA not supported for multiple features') self.modelName.remove('arima') if 'fbprophet' in self.modelName: self.log.info('Status:- |... TimeSeries algorithm FBPROPHET not supported for multiple features') self.modelName.remove('fbprophet') if 'mlp' in self.modelName: self.log.info('Status:- |... TimeSeries algorithm MLP not supported for multiple features') self.modelName.remove('mlp') if len(self.modelName) == 0: self.log.info('--------> Default Set to VAR') self.modelName.append('var') if lentFeature == 1: if any('timeseriesforecasting' in x.lower() for x in self.modelName): #task 11997 self.modelName.remove('timeseriesforecasting') if 'var' in self.modelName: self.log.info('Status:- |... TimeSeries algorithm VAR not supported for single feature') self.modelName.remove('var') if len(self.modelName) == 0: self.log.info('--------> Default Set to ARIMA,FBProphet') self.modelName.append('arima') except Exception as e: self.log.info('input model name error: '+ str(e)) self.log.info("error in user selected model, may be wrong configuration, please check.") if (len(self.modelName) > 1): try: self.log.info('User selected models: '+str(self.modelName)) best_model,modelName,score_type,score,model_fit,selectedColumns,error_matrix,scoredetails,dictDiffCount,pred_freq,additional_regressors,filename,saved_model,lag_order,scaler_transformation = self.bestmodelProcess(self.modelName,lentFeature,trained_data_file,tFeature,predicted_data_file,dataFolderLocation) return best_model,modelName,score_type,score,model_fit,selectedColumns,error_matrix,scoredetails,dictDiffCount,pred_freq,additional_regressors,filename,saved_model,lag_order,scaler_transformation except Exception as e: self.log.info('multi model timeseries processing error '+str(e)) else: self.modelName = self.modelName[0] ## Normal arima ,var or fbprophet model call (user selects only one model at a time) if self.modelName.lower() == 'fbprophet': try: model_name='fbprophet' status,modelName,fbprophet,rmse_prophet,fp_model_fit,selectedColumns,error_matrix,scoredetails,dictDiffCount,pred_freq,additional_regressors,predict_output = self.getfbprophetmodel(predicted_data_file,dataFolderLocation,tFeature) if status.lower() == 'success': predict_output.to_csv(predicted_data_file) filename,saved_model = self.save_model(fp_model_fit) return 'self.modelName',modelName,fbprophet,rmse_prophet,fp_model_fit,selectedColumns,error_matrix,scoredetails,dictDiffCount,pred_freq,additional_regressors,filename,saved_model,lag_order,'NA' else: raise Exception('Exception during model training') except Exception as e: self.log.info('fbprophet error....') self.log.info(e) elif self.modelName.lower() == 'encoder_decoder_lstm_mvi_uvo': try: status,modelName_lstm,score_type,rmse_lstm,lstm_model_fit,lstm_selectedColumns,error_matrix_lstm,scoredetails_lstm,predict_lstm,lag_order,lstm_scaler = self.getEncDecLSTMMultVrtInUniVrtOut() if status.lower() == 'success': predict_lstm.to_csv(predicted_data_file) filename,saved_model,scaler_model = self.save_dl_model(lstm_model_fit,lstm_scaler) return self.modelName,modelName_lstm,score_type,rmse_lstm,lstm_model_fit,lstm_selectedColumns,error_matrix_lstm,scoredetails_lstm,self.dictDiffCount,self.pred_freq,self.additional_regressors,filename,saved_model,lag_order,scaler_model else: raise Exception('Exception during model training') except Exception as inst: self.log.info('<!------------- LSTM Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) elif self.modelName.lower() == 'lstm': try: if lentFeature == 1: status,modelName_lstm,score_type,rmse_lstm,lstm_model_fit,lstm_selectedColumns,error_matrix_lstm,scoredetails_lstm,predict_lstm,lag_order,lstm_scaler = self.getUniVarientLSTMModel() else: status,modelName_lstm,score_type,rmse_lstm,lstm_model_fit,lstm_selectedColumns,error_matrix_lstm,scoredetails_lstm,predict_lstm,lag_order,lstm_scaler = self.getLSTMMultivariate() if status.lower() == 'success': predict_lstm.to_csv(predicted_data_file) filename,saved_model,scaler_model = self.save_dl_model(lstm_model_fit,lstm_scaler) return self.modelName,modelName_lstm,score_type,rmse_lstm,lstm_model_fit,lstm_selectedColumns,error_matrix_lstm,scoredetails_lstm,self.dictDiffCount,self.pred_freq,self.additional_regressors,filename,saved_model,lag_order,scaler_model else: raise Exception('Exception during model training') except Exception as inst: self.log.info('<!------------- LSTM Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) elif self.modelName.lower() == 'mlp': try: status,modelName_mlp,score_type,rmse_mlp,mlp_model_fit,mlp_selectedColumns,error_matrix_mlp,scoredetails_mlp,predict_mlp,lag_order,mlp_scaler = self.getUniVarientMLPModel() if status.lower() == 'success': predict_mlp.to_csv(predicted_data_file) filename,saved_model,scaler_model = self.save_dl_model(mlp_model_fit,mlp_scaler) return self.modelName,modelName_mlp,score_type,rmse_mlp,mlp_model_fit,mlp_selectedColumns,error_matrix_mlp,scoredetails_mlp,self.dictDiffCount,self.pred_freq,self.additional_regressors,filename,saved_model,lag_order,scaler_model else: raise Exception('Exception during model training') except Exception as inst: self.log.info('<!------------- MLP Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) else: #task 12627 time series profiler removed if lentFeature>1: self.modelName='var' self.data.to_csv(trained_data_file) else: self.modelName='arima' if self.modelName.lower()=='var': tsModelTestObj=timeseriesModelTests(self.data,self.targetFeature,self.dateTimeFeature,0) self.data,self.dictDiffCount=tsModelTestObj.StatinaryChecks(self.dictDiffCount) #self.log.info('Status:- |... Stationary Check Done.') gtestResults,countVariables=tsModelTestObj.grangersCausationMatrix(self.data,tFeature) if countVariables >= (lentFeature*lentFeature)-(lentFeature) or ((lentFeature*lentFeature)-(lentFeature))/2 : coIntegrationVectors=tsModelTestObj.coIntegrationTest(self.data) if coIntegrationVectors<=lentFeature: self.log.info("There are statistically significant relationship in data ") self.log.info('Status:- |... Statistically Check Done. Statistically significant relations') else: self.log.info("There are no statistically significant relationship in data") self.log.info('Status:- |... Statistically Check Done. No statistically significant relations') else: self.modelName='arima' if self.modelName.lower()=='var': try: self.log.info('ARIMA, FBProphet cannot apply, Input data contains more than one feature, only VAR algorithm can apply, applying VAR by AION \n') status,modelName,aic,aic_score,model_fit,selectedColumns,error_matrix,scoredetails,predict_var,dictDiffCount,pred_freq,additional_regressors,lag_order = self.getVARmodel() if status.lower() == 'success': filename,saved_model = self.save_model(model_fit) predict_var.to_csv(predicted_data_file) return self.modelName,modelName,aic,aic_score,model_fit,selectedColumns,error_matrix,scoredetails,dictDiffCount,pred_freq,additional_regressors,filename,saved_model,lag_order,'NA' else: raise Exception('Exception during VAR model training') except Exception as inst: self.log.info('<!------------- Var model Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) if self.modelName.lower() == 'arima': try: status,modelName,aic,scoringparam_v,model_fit,selectedColumns,error_matrix,scoredetails,dictDiffCount,pred_freq,additional_regressors,rmse_arima_act,predict_output = self.getARIMAmodel(predicted_data_file) if status.lower() == 'success': predict_output.to_csv(predicted_data_file) filename,saved_model = self.save_model(model_fit) lag_order=0 return self.modelName,modelName,aic,scoringparam_v,model_fit,selectedColumns,error_matrix,scoredetails,dictDiffCount,pred_freq,additional_regressors,filename,saved_model,lag_order,'NA' else: raise Exception('Exception during model training') except Exception as inst: self.log.info('<!------------- Arima Error ---------------> '+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) except Exception as inst: self.log.info('<!------------- TimeSeries Learning Error ---------------> '+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def invertTransformation(self,Xtrain,targetFeature, preddf,dictDiffCount): try: dfforecast = preddf.copy() self.log.info(dfforecast.head(5)) columns =targetFeature.split(",") self.log.info(columns) self.log.info(dictDiffCount) for col in columns: if col in dictDiffCount: if dictDiffCount[col]==2: dfforecast[col] = (Xtrain[col].iloc[-1]-Xtrain[col].iloc[-2]) + dfforecast[col].cumsum() dfforecast[col] = Xtrain[col].iloc[-1] + dfforecast[col].cumsum() # Roll back 1st Diff return dfforecast except Exception as inst: self.log.info('<!------------- invertTransformation Error ---------------> '+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' # For timeseries pyramid pdaarima module from pmdarima.arima import auto_arima import pmdarima as pm import json #Python sklearn & std libraries import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_selection import VarianceThreshold from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_squared_error #from sklearn.metrics import mean_absolute_percentage_error from sklearn.linear_model import LinearRegression from math import sqrt import warnings # For serialization. #from sklearn.externals import joblib import pickle import os,sys # For ploting (mathlab) import matplotlib.pyplot as plt #Import eion config manager module import logging from sklearn import metrics from sklearn.metrics import accuracy_score import time import os import sys # Eion arima module class eion_arima (): #Constructor def __init__(self,configfile,testpercentage,sesonalityChecks,stationaryChecks): # eaobj - eion arima class object try: tsarima_params = configfile self.testpercentage = testpercentage self.start_p= int(tsarima_params['start_p']) self.start_q= int(tsarima_params['start_q']) self.max_p= int(tsarima_params['max_p']) self.max_q= int(tsarima_params['max_q']) self.max_d= int(tsarima_params['max_d']) self.max_order= int(tsarima_params['max_order']) self.start_Q= int(tsarima_params['start_Q']) self.max_P= int(tsarima_params['max_P']) self.max_D= int(tsarima_params['max_D']) self.max_Q= int(tsarima_params['max_Q']) self.m= int(tsarima_params['m']) self.start_P= int(tsarima_params['start_P']) self.seasonal= tsarima_params['seasonal'] #self.seasonal= sesonalityChecks self.stationary=stationaryChecks #print("self.seasonal: \n",self.seasonal) #print("self.stationary: \n",self.stationary) if self.seasonal and not self.seasonal.isspace(): if (self.seasonal.lower() == 'true'): self.seasonal=True elif (self.seasonal.lower() == 'false'): self.seasonal=False else: self.seasonal=True else: self.seasonal=True self.d= int(tsarima_params['d']) self.D= int(tsarima_params['D']) #self.trace= tsarima_params['trace'] self.error_action= tsarima_params['error_action'] self.suppress_warnings= tsarima_params['suppress_warnings'] self.stepwise= tsarima_params['stepwise'] #self.random= tsarima_params['random'] self.log = logging.getLogger('eion') except Exception as inst: self.log.info('<!------------- Arima INIT Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def mean_absolute_percentage_error(self,y_true, y_pred): try: y_true, y_pred=np.array(y_true), np.array(y_pred) return np.mean(np.abs((y_true - y_pred) / y_true+sys.float_info.epsilon)) * 100 except Exception as inst: self.log.info('<------------- mean_absolute_percentage_error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def eion_arima(self,train_data): try: start = time.time() auto_arima_stepwise_fit = pm.auto_arima(train_data, start_p=self.start_p, start_q=self.start_q,max_p=self.max_p, max_q=self.max_q,max_d=self.max_d,max_P=self.max_P,max_D=self.max_D,max_Q=self.max_Q,max_order=self.max_order, m=self.m,start_P=self.start_P,start_Q=self.start_Q, seasonal=self.seasonal,stationary=self.stationary,d=self.d, D=self.D,error_action=self.error_action,suppress_warnings=self.suppress_warnings,stepwise=self.stepwise) #auto_arima_stepwise_fit = pm.auto_arima(train_data, start_p=self.start_p, start_q=self.start_q,max_p=self.max_p, max_q=self.max_q,max_d=self.max_d,max_P=self.max_P,max_D=self.max_D,max_Q=self.max_Q,max_order=self.max_order, m=self.m,start_P=self.start_P,start_Q=self.start_Q, seasonal=True,stationary=True,d=self.d, D=self.D,error_action=self.error_action,suppress_warnings=self.suppress_warnings,random_state=20,stepwise=True) aic_score = auto_arima_stepwise_fit.aic() self.log.info('------->AIC Score: '+str(aic_score)) self.log.info('\n--------- Fit Summary --------------') self.log.info (auto_arima_stepwise_fit.summary()) self.log.info('--------- Fit Summary End--------------\n') self.log.info("\n--------------- Modal Validation Start ---------------") size = int(len(train_data) * (100 - self.testpercentage)/100) train = train_data.loc[0:size] valid = train_data.loc[size:len(train_data)] # valid_perc=((100-self.testpercentage)/100) # valid_perc=round(valid_perc, 1) # print("valid_perc: \n", valid_perc) self.log.info("------->Train Data Shape: "+str(train.shape)) self.log.info("------->Valid Data Shape"+str(valid.shape)) start1=len(train) end1=len(train_data) modelfit = auto_arima_stepwise_fit.fit(train) a_prediction = auto_arima_stepwise_fit.predict(valid.shape[0]) #a_prediction = auto_arima_stepwise_fit.predict(n_periods=len(valid)) #a_prediction = auto_arima_stepwise_fit.predict(start=start1,end=end1) #print("a_prediction: \n",a_prediction) #self.log.info(a_prediction) mae = metrics.mean_absolute_error(valid, a_prediction) self.log.info ("------->MAE: "+str(mae)) mape = self.mean_absolute_percentage_error(valid, a_prediction) #mape=np.mean(np.abs((valid - a_prediction) / valid)) * 100 self.log.info ("------->MAPE :"+str(mape)) #RMSE rmse = sqrt(mean_squared_error(valid,a_prediction)) mse = mean_squared_error(valid,a_prediction) self.log.info ("------->RMSE :"+str(rmse)) self.log.info ("------->MSE :"+str(mse)) from sklearn.metrics import r2_score r2 = r2_score(valid,a_prediction) ########### End #################### # now we have the model auto_arima_stepwise_fit.fit(train_data) self.log.info("------------- Validate Model End----------------\n") executionTime=time.time() - start self.log.info('-------> Time: '+str(executionTime)+'\n') return auto_arima_stepwise_fit,mae,rmse,mse,r2,aic_score,mape,valid,a_prediction except Exception as inst: self.log.info('<!------------- Arima Execute Error ---------------> '+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import os import numpy as np import numpy import pandas from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.layers import LSTM from sklearn.preprocessing import MinMaxScaler import logging import tensorflow as tf from tensorflow.keras.layers import Dropout import math import tensorflow as tf import keras_tuner #from keras_tuner.engine.hyperparameters import HyperParameters from keras_tuner.tuners import RandomSearch,BayesianOptimization ,Hyperband from sklearn.model_selection import train_test_split from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator import warnings warnings.simplefilter("ignore", UserWarning) class timeseriesDLMultivariate: def __init__(self,configfile,testpercentage,targetFeature,dateTimeFeature): self.look_back=None # self.df=df self.epochs=None self.batch_size=None self.hidden_layers=None self.optimizer=None self.activation_fn="relu" self.loss_fn=None self.first_layer=None self.dropout=None self.model_name=None self.dl_params = configfile # self.data=data self.targetFeature=targetFeature self.dateTimeFeature=dateTimeFeature self.testpercentage = float(testpercentage) self.log = logging.getLogger('eion') ##Added for ts hpt (TFSTask:7033) self.tuner_algorithm="" self.num_features=0 ##Get deep learning model hyperparameter from advanced config def getdlparams(self): val=self.dl_params self.log.info('-------> The given mlp/lstm timeseries algorithm parameters:>>') self.log.info(" "+str(val)) for k,v in val.items(): try: if (k == "tuner_algorithm"): self.tuner_algorithm=str(v) elif (k == "activation"): self.activation_fn=str(v) elif (k == "optimizer"): self.optimizer=str(v) elif (k == "loss"): self.loss_fn=str(v) elif (k == "first_layer"): if not isinstance(k,list): self.first_layer=str(v).split(',') else: self.first_layer=k elif (k == "lag_order"): if isinstance(k,list): k = ''.join(v) k=int(float(str(v))) else: self.look_back=int(float(str(v))) elif (k == "hidden_layers"): self.hidden_layers=int(v) elif (k == "dropout"): if not isinstance(k,list): self.dropout=str(v).split(',') else: self.dropout=k elif (k == "batch_size"): self.batch_size=int(v) elif (k == "epochs"): self.epochs=int(v) elif (k == "model_name"): self.model_name=str(v) except Exception as e: self.log.info('Exception occured in deeep learn param reading, setting up default params.') self.activation_fn="relu" self.optimizer="adam" self.loss_fn="mean_squared_error" self.first_layer=[8,512] self.hidden_layers=1 self.look_back=int(2) self.dropout=[0.1,0.5] self.batch_size=2 self.epochs=50 self.model_name="lstmmodel.h5" continue # Reshape the data to the required input shape of the LSTM model def create_dataset(self,X, y, n_steps): Xs, ys = [], [] for i in range(len(X) - n_steps): v = X.iloc[i:(i + n_steps)].values Xs.append(v) ys.append(y.iloc[i + n_steps]) return np.array(Xs), np.array(ys) ## Added function for hyperparam tuning (TFSTask:7033) def build_model(self,hp): n_features = len(self.targetFeature) try: loss=self.loss_fn optimizer=self.optimizer # self.getdlparams() try: if optimizer.lower() == "adam": optimizer=tensorflow.keras.optimizers.Adam elif(optimizer.lower() == "adadelta"): optimizer=tensorflow.keras.optimizers.experimental.Adadelta elif(optimizer.lower() == "nadam"): optimizer=tensorflow.keras.optimizers.experimental.Nadam elif(optimizer.lower() == "adagrad"): optimizer=tensorflow.keras.optimizers.experimental.Adagrad elif(optimizer.lower() == "adamax"): optimizer=tensorflow.keras.optimizers.experimental.Adamax elif(optimizer.lower() == "rmsprop"): optimizer=tensorflow.keras.optimizers.experimental.RMSprop elif(optimizer.lower() == "sgd"): optimizer=tensorflow.keras.optimizers.experimental.SGD else: optimizer=tensorflow.keras.optimizers.Adam except: optimizer=tf.keras.optimizers.Adam pass # look_back_min=int(self.look_back[0]) # look_back_max=int(self.look_back[1]) first_layer_min=round(int(self.first_layer[0])) first_layer_max=round(int(self.first_layer[1])) dropout_min=float(self.dropout[0]) dropout_max=float(self.dropout[1]) model=tf.keras.Sequential() try: model.add(LSTM(units=hp.Int('units',min_value=first_layer_min,max_value=first_layer_max,step=16),input_shape=(self.look_back,self.num_features))) except Exception as e: import traceback self.log.info("lstm build traceback: \n"+str(traceback.print_exc())) return model model.add(Dropout(hp.Float('Dropout_rate',min_value=dropout_min,max_value=dropout_max,step=0.1))) model.add(Dense(units=n_features)) model.compile(optimizer=optimizer(hp.Choice('learning_rate',values=[1e-1,1e-2,1e-3,1e-4])),loss=loss,metrics=[self.loss_fn]) except Exception as e: self.log.info(",Hyperparam tuning build_model err msg: \n"+ str(e)) return model ##Multivariate lstm prediction function (lstm model, train, prediction, metrics) def lstm_multivariate(self,df): try: self.getdlparams() n_features = len(self.targetFeature) self.num_features=n_features try: if (type(self.targetFeature) is list): pass else: self.targetFeature = list(self.targetFeature.split(",")) except: pass df_new = df[df.columns[df.columns.isin(self.targetFeature)]] scaler=MinMaxScaler() df_transformed=scaler.fit_transform(df_new) ## For hyperparam tuning below part is added.only for getting best model and best hyperparameters train_size = int(len(df) * 0.80) train_data, test_data = train_test_split(df, test_size=0.2, shuffle=False) self.hpt_train=train_data time_steps=self.look_back ## Just for initialization before hyperparameter tuning. tuner_alg=self.tuner_algorithm #The below create_dataset only for getting best model and best hyperparameters X_train, y_train = self.create_dataset(train_data, train_data, time_steps) X_test, y_test = self.create_dataset(test_data, test_data, time_steps) self.log.info("Hyperparameter tuning algorithm is given by user (AION->Advanced configuration -> timeSeriesForecasting->LSTM): \n"+str(tuner_alg)) try: ## Remove untitled_project dir in AION root folder created by previous tuner search run import shutil shutil.rmtree(r".\untitled_project") except: pass try: if (tuner_alg.lower()=="randomsearch"): tuner=RandomSearch(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=5,executions_per_trial=3) elif (tuner_alg.lower()=="bayesianoptimization"): tuner=BayesianOptimization(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=5,executions_per_trial=3) elif (tuner_alg.lower()=="hyperband"): tuner=Hyperband(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_epochs=50,factor=3) else: self.log.info("The given alg is not implemented. Using default hyperparam tuning algorithm: RandomSearch.\n") tuner=RandomSearch(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=5,executions_per_trial=3) from keras.callbacks import EarlyStopping stop_early = EarlyStopping(monitor='val_loss', patience=5) except Exception as e: self.log.info("The given alg have some issue, Using default hyperparam tuning algorithm: RandomSearch.\n") tuner=RandomSearch(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=5,executions_per_trial=3) self.log.info("tuner errmsg:\n"+str(e)) #hpt search for best params try: tuner.search(X_train, y_train,validation_data=(X_test, y_test),callbacks=[stop_early]) except: tuner.search(x=X_train,y=y_train,validation_split=0.2,callbacks=[stop_early]) # best_model = tuner.get_best_models(num_models=1)[0] # self.log.info("best_model.summary(): \n"+str(best_model.summary())) best_hps=tuner.get_best_hyperparameters(num_trials=1)[0] self.log.info("TS Multivariate LSTM best hyperparameter values:\n"+str(best_hps.values)) self.log.info("Activation fn:\n"+str(self.activation_fn)) # time_steps_best=best_hps.get('time_steps') n_input=self.look_back best_hmodel=tuner.hypermodel.build(best_hps) optimizer=self.optimizer self.first_layer=best_hps.get('units') self.dropout=best_hps.get('Dropout_rate') learning_rate=float(best_hps.get('learning_rate')) try: ##TFSTask:7033, Added below try block for time series hyperparam tuning, here, for any optimizer, best learning_rate is provided from best_hps. try: if optimizer.lower() == "adam": optimizer=tensorflow.keras.optimizers.Adam(learning_rate=learning_rate) elif(optimizer.lower() == "adadelta"): optimizer=tensorflow.keras.optimizers.experimental.Adadelta(learning_rate=learning_rate) elif(optimizer.lower() == "nadam"): optimizer=tensorflow.keras.optimizers.experimental.Nadam(learning_rate=learning_rate) elif(optimizer.lower() == "adagrad"): optimizer=tensorflow.keras.optimizers.experimental.Adagrad(learning_rate=learning_rate) elif(optimizer.lower() == "adamax"): optimizer=tensorflow.keras.optimizers.experimental.Adamax(learning_rate=learning_rate) elif(optimizer.lower() == "rmsprop"): optimizer=tensorflow.keras.optimizers.experimental.RMSprop(learning_rate=learning_rate) elif(optimizer.lower() == "sgd"): optimizer=tensorflow.keras.optimizers.experimental.SGD(learning_rate=learning_rate) else: optimizer=tensorflow.keras.optimizers.Adam(learning_rate=learning_rate) except: optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate) pass ##From best hyperparameter values, now creating multivariate time series model using time generator. t_lb=1 test_size=t_lb+1 train,test = train_test_split(df_transformed,test_size=0.2,shuffle=False) generatorTrain=TimeseriesGenerator(df_transformed,df_transformed,length=n_input,batch_size=self.batch_size) # generatorTest=TimeseriesGenerator(test,test,length=n_input,batch_size=self.batch_size) batch_0=generatorTrain[0] x,y=batch_0 epochs=int(self.epochs) ##Multivariate LSTM model try: from tensorflow.keras.layers import Dropout model=Sequential() model.add(LSTM(self.first_layer,activation=self.activation_fn,input_shape=(n_input,n_features))) model.add(Dropout(self.dropout)) model.add(Dense(n_features)) model.compile(optimizer=self.optimizer,loss=self.loss_fn) #model.fit(generatorTrain,epochs=epochs,batch_size=self.batch_size,shuffle=False) model.fit_generator(generatorTrain, epochs=epochs,shuffle=False, verbose=0) # lstm_mv_testScore_mse = model.evaluate(x, y, verbose=0) except Exception as e: self.log.info("multivariate model build error: error msg:: \n"+str(e)) return 'Error',0,0,0,0,None,pd.DataFrame(),0,None #predictions = model.predict_generator(generatorTest) except Exception as e: self.log.info("multivariate model build error: error msg:: \n"+str(e)) return 'Error',0,0,0,0,None,pd.DataFrame(),0,None try: predictions=[] future_pred_len=n_input #To get values for prediction,taking look_back steps of rows first_batch=test[-future_pred_len:] c_batch = first_batch.reshape((1,future_pred_len,n_features)) current_pred=None for i in range(len(test)): #get pred for firstbatch current_pred=model.predict_generator(c_batch)[0] predictions.append(current_pred) #remove first val c_batch_rmv_first=c_batch[:,1:,:] #update c_batch=np.append(c_batch_rmv_first,[[current_pred]],axis=1) prediction_actual=scaler.inverse_transform(predictions) test_data_actual=scaler.inverse_transform(test) mse=None rmse=None ## Creating dataframe for actual,predictions try: pred_cols=list() for i in range(len(self.targetFeature)): pred_cols.append(self.targetFeature[i]+'_pred') predictions = pd.DataFrame(prediction_actual, columns=pred_cols) actual = pd.DataFrame(test_data_actual, columns=self.targetFeature) actual.columns = [str(col) + '_actual' for col in df.columns] df_predicted=pd.concat([actual,predictions],axis=1) self.log.info("LSTM Multivariate prediction dataframe: \n"+str(df_predicted)) from math import sqrt from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error target=self.targetFeature mse_dict={} rmse_dict={} mae_dict={} r2_dict={} lstm_var = 0 for name in target: index = df.columns.get_loc(name) mse = mean_squared_error(test_data_actual[:,index],prediction_actual[:,index]) mse_dict[name]=mse rmse=sqrt(mse) rmse_dict[name]=rmse lstm_var = lstm_var+rmse self.log.info("Name of the target feature: "+str(name)) self.log.info("RMSE of the target feature: "+str(rmse)) r2 = r2_score(test_data_actual[:,index],prediction_actual[:,index]) r2_dict[name]=r2 mae = mean_absolute_error(test_data_actual[:,index],prediction_actual[:,index]) mae_dict[name]=mae ## For VAR comparison, send last target mse and rmse from above dict lstm_var = lstm_var/len(target) select_msekey=list(mse_dict.keys())[-1] l_mse=list(mse_dict.values())[-1] select_rmsekey=list(rmse_dict.keys())[-1] l_rmse=list(rmse_dict.values())[-1] select_r2key=list(r2_dict.keys())[-1] l_r2=list(r2_dict.values())[-1] select_maekey=list(mae_dict.keys())[-1] l_mae=list(mae_dict.values())[-1] self.log.info("Selected target feature of LSTM for best model selection: "+str(select_rmsekey)) self.log.info("lstm rmse: "+str(l_rmse)) self.log.info("lstm mse: "+str(l_mse)) self.log.info("lstm r2: "+str(l_r2)) self.log.info("lstm mae: "+str(l_mae)) except Exception as e: import traceback print(" traceback error:\n",traceback.print_exc()) self.log.info("prediction error traceback: \n"+str(traceback.print_exc())) except Exception as e: self.log.info("dataframe creation error. err.msg: "+str(e)) return 'Error',0,0,0,0,None,pd.DataFrame(),0,None return 'Success',round(l_mse,2),round(l_rmse,2),round(l_r2,2),round(l_mae,2),model,df_predicted,n_input,scaler # import os #predicted_file_name='lstm_prediction_df.csv' #predicted_file_path=os.path.join(self.dataFolderLocation,predicted_file_name) #df_predicted.to_csv(predicted_file_path) ##save model #model_path = os.path.join(self.dataFolderLocation,self.model_name) #self.log.info("mlp model saved at: "+str(model_path)) #model.save(model_path) except Exception as e: ## Just use below traceback print to get detailed error information. # import traceback # print(" traceback error 7:\n",traceback.print_exc()) ## Enable traceback for debugging self.log.info("dataframe creation error. err.msg: "+str(e)) return 'Error',0,0,0,0,None,pd.DataFrame(),0,None ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import sys import pandas as pd import numpy as np import numpy import pandas import math from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Input, Dense, TimeDistributed, LSTM, Dropout, RepeatVector from sklearn.preprocessing import MinMaxScaler import logging import tensorflow as tf import keras_tuner #from keras_tuner.engine.hyperparameters import HyperParameters from keras_tuner.tuners import RandomSearch,BayesianOptimization ,Hyperband from sklearn.model_selection import train_test_split from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator import warnings warnings.simplefilter("ignore", UserWarning) from sklearn.metrics import mean_absolute_percentage_error class tsDLMultiVrtInUniVrtOut: def __init__(self,configfile,testpercentage,targetFeature,dateTimeFeature): self.look_back=None self.look_forward=None # self.df=df self.epochs=None self.batch_size=None self.hidden_layers=None self.optimizer=None self.activation_fn="relu" self.loss_fn=None self.first_layer=None self.dropout=None self.model_name=None self.dl_params = configfile # self.data=data self.targetFeature=targetFeature self.dateTimeFeature=dateTimeFeature self.testpercentage = float(testpercentage) self.log = logging.getLogger('eion') ##Added for ts hpt (TFSTask:7033) self.tuner_algorithm="" self.num_features=0 ##Get deep learning model hyperparameter from advanced config def getdlparams(self): val=self.dl_params self.log.info('-------> The given mlp/lstm timeseries algorithm parameters:>>') self.log.info(" "+str(val)) for k,v in val.items(): try: if (k == "tuner_algorithm"): self.tuner_algorithm=str(v) elif (k == "activation"): if not isinstance(k,list): self.activation_fn=str(v).split(',') else: self.activation_fn=k elif (k == "optimizer"): self.optimizer=str(v) elif (k == "loss"): self.loss_fn=str(v) elif (k == "first_layer"): if not isinstance(k,list): self.first_layer=str(v).split(',') else: self.first_layer=k elif (k == "lag_order"): if isinstance(k,list): k = ''.join(v) k=int(float(str(v))) else: self.look_back=int(float(str(v))) elif (k == "forward_order"): if isinstance(k,list): k = ''.join(v) k=int(float(str(v))) else: self.look_forward=int(float(str(v))) elif (k == "hidden_layers"): self.hidden_layers=int(v) elif (k == "dropout"): if not isinstance(k,list): self.dropout=str(v).split(',') else: self.dropout=k elif (k == "batch_size"): self.batch_size=int(v) elif (k == "epochs"): self.epochs=int(v) elif (k == "model_name"): self.model_name=str(v) except Exception as e: self.log.info('Exception occured in deeep learn param reading, setting up default params.') self.activation_fn="relu" self.optimizer="adam" self.loss_fn="mean_squared_error" self.first_layer=[8,512] self.hidden_layers=1 self.look_back=int(2) self.dropout=[0.0,0.1,0.01] self.batch_size=2 self.epochs=50 self.model_name="lstmmodel.h5" continue # Reshape the data to the required input shape of the LSTM model def create_dataset(self,series, n_past, n_future, targetcolindx): X, y = list(), list() for window_start in range(len(series)): past_end = window_start + n_past future_end = past_end + n_future if future_end > len(series): break # slicing the past and future parts of the window past, future = series[window_start:past_end, :], series[past_end:future_end, targetcolindx] X.append(past) y.append(future) return np.array(X), np.array(y) #return X, y ## Added function for hyperparam tuning (TFSTask:7033) def build_model(self,hp): n_features = self.num_features try: loss=self.loss_fn optimizer=self.optimizer # self.getdlparams() try: if optimizer.lower() == "adam": optimizer=tensorflow.keras.optimizers.Adam elif(optimizer.lower() == "adadelta"): optimizer=tensorflow.keras.optimizers.experimental.Adadelta elif(optimizer.lower() == "nadam"): optimizer=tensorflow.keras.optimizers.experimental.Nadam elif(optimizer.lower() == "adagrad"): optimizer=tensorflow.keras.optimizers.experimental.Adagrad elif(optimizer.lower() == "adamax"): optimizer=tensorflow.keras.optimizers.experimental.Adamax elif(optimizer.lower() == "rmsprop"): optimizer=tensorflow.keras.optimizers.experimental.RMSprop elif(optimizer.lower() == "sgd"): optimizer=tensorflow.keras.optimizers.experimental.SGD else: optimizer=tensorflow.keras.optimizers.Adam except: optimizer=tf.keras.optimizers.Adam pass # look_back_min=int(self.look_back[0]) # look_back_max=int(self.look_back[1]) first_layer_min=round(int(self.first_layer[0])) first_layer_max=round(int(self.first_layer[1])) dropout_min=float(self.dropout[0]) dropout_max=float(self.dropout[1]) dropout_step=float(self.dropout[2]) #import pdb; pdb.set_trace() n_past= self.look_back n_future = self.look_back encoder_l = {} encoder_outputs = {} encoder_states = {} decoder_l = {} decoder_outputs = {} encoder_inputs = Input(shape=(n_past, n_features)) try: if(self.hidden_layers > 0): encoder_l[0] = LSTM(units=hp.Int('enc_input_unit',min_value=first_layer_min,max_value=first_layer_max,step=32), activation = hp.Choice(f'enc_input_activation', values = self.activation_fn), return_sequences = True, return_state=True) else: encoder_l[0] = LSTM(units=hp.Int('enc_input_unit',min_value=first_layer_min,max_value=first_layer_max,step=32), activation = hp.Choice(f'enc_input_activation', values = self.activation_fn), return_state=True) except Exception as e: import traceback self.log.info("lstm build traceback: \n"+str(traceback.print_exc())) model=tf.keras.Sequential() return model encoder_outputs[0] = encoder_l[0](encoder_inputs) encoder_states[0] = encoder_outputs[0][1:] if(self.hidden_layers > 0): for indx in range(self.hidden_layers): lindx = indx + 1 if lindx == self.hidden_layers: encoder_l[lindx] = LSTM(units=hp.Int(f'enc_lstm_units_{lindx}',min_value=first_layer_min,max_value=first_layer_max,step=32), dropout=hp.Float(f'enc_lstm_dropout_{lindx}',min_value=dropout_min,max_value=dropout_max,step=dropout_step), activation = hp.Choice(f'enc_lstm_activation_{lindx}', values = self.activation_fn), return_state=True) else: encoder_l[lindx] = LSTM(units=hp.Int(f'enc_lstm_units_{lindx}',min_value=first_layer_min,max_value=first_layer_max,step=32), dropout=hp.Float(f'enc_lstm_dropout_{lindx}',min_value=dropout_min,max_value=dropout_max,step=dropout_step), activation = hp.Choice(f'enc_lstm_activation_{lindx}', values = self.activation_fn), return_sequences = True, return_state=True) encoder_outputs[lindx] = encoder_l[lindx](encoder_outputs[indx][0]) encoder_states[lindx] = encoder_outputs[lindx][1:] decoder_inputs = RepeatVector(n_future)(encoder_outputs[self.hidden_layers][0]) else: decoder_inputs = RepeatVector(n_future)(encoder_outputs[0][0]) # if(self.hidden_layers > 0): decoder_l[0] = LSTM(encoder_states[0][0].get_shape()[1], activation = hp.Choice(f'dec_input_activation', values = self.activation_fn), return_sequences=True)(decoder_inputs,initial_state = encoder_states[0]) else: decoder_l[0] = LSTM(encoder_states[0][0].get_shape()[1], activation = hp.Choice(f'dec_input_activation', values = self.activation_fn), return_sequences=True)(decoder_inputs,initial_state = encoder_states[0]) if(self.hidden_layers > 0): for indx in range(self.hidden_layers): lindx = indx + 1 decoder_l[lindx] = LSTM(encoder_states[lindx][0].get_shape()[1], activation = hp.Choice(f'dec_lstm_activation_{lindx}', values = self.activation_fn), return_sequences=True)(decoder_l[indx],initial_state = encoder_states[lindx]) decoder_outputs[0] = TimeDistributed(tf.keras.layers.Dense(decoder_l[self.hidden_layers][0].get_shape()[1], activation = hp.Choice(f'dec_output_activation_1', values = self.activation_fn)))(decoder_l[self.hidden_layers]) decoder_outputs[1] = TimeDistributed(tf.keras.layers.Dense(1))(decoder_outputs[0]) else: # decoder_outputs[0] = TimeDistributed(tf.keras.layers.Dense(decoder_l[0][0].get_shape()[1]))(decoder_l[0]) # decoder_outputs[1] = LSTM(200, return_sequences=True)(decoder_outputs[0]) # decoder_outputs[2] = tf.keras.layers.Flatten()(decoder_outputs[1]) # decoder_outputs[3] = tf.keras.layers.Dense(1)(decoder_outputs[2]) decoder_outputs[0] = TimeDistributed(tf.keras.layers.Dense(decoder_l[0][0].get_shape()[1], activation = hp.Choice(f'dec_output_activation_1', values = self.activation_fn)))(decoder_l[0]) decoder_outputs[1] = TimeDistributed(tf.keras.layers.Dense(1))(decoder_outputs[0]) # model = tf.keras.models.Model(encoder_inputs,decoder_outputs[1]) self.log.info(model.summary()) model.compile(optimizer=optimizer(hp.Choice('learning_rate',values=[1e-1,1e-2,1e-3,1e-4])),loss=loss,metrics=[self.loss_fn]) except Exception as e: import traceback self.log.info(",Hyperparam tuning build_model err msg: \n"+ str(e)) self.log.info("Hyperparam tuning build_model err traceback: \n"+str(traceback.print_exc())) return model ##LSTM ecncoder decoder with multivariate input and univarite output prediction function (lstm model, train, prediction, metrics) def lstm_encdec_mvin_uvout(self,df): try: loss=self.loss_fn self.getdlparams() n_features = len(df.columns) self.num_features=n_features n_past= self.look_back n_future = self.look_back try: if (type(self.targetFeature) is list): pass else: self.targetFeature = list(self.targetFeature.split(",")) except: pass targetColIndx = [] for target in self.targetFeature: targetColIndx.append(df.columns.get_loc(target)) #if user doesnt applies any transformation, this will get applied scaler=MinMaxScaler() df_trnsf=scaler.fit_transform(df) train_data, test_data = train_test_split(df_trnsf, test_size=0.2, shuffle=False) tuner_alg=self.tuner_algorithm #The below create_dataset only for getting best model and best hyperparameters X_train, y_train = self.create_dataset(train_data, n_past, n_future, targetColIndx) X_test, y_test = self.create_dataset(test_data, n_past, n_future, targetColIndx) # X_train = X_train.reshape((X_train.shape[0], X_train.shape[1],n_features)) # y_train = y_train.reshape((y_train.shape[0], y_train.shape[1], 1)) self.log.info("Hyperparameter tuning algorithm is given by user (AION->Advanced configuration -> timeSeriesForecasting->LSTM): \n"+str(tuner_alg)) try: ## Remove untitled_project dir in AION root folder created by previous tuner search run import shutil shutil.rmtree(r".\untitled_project") except: pass try: if (tuner_alg.lower()=="randomsearch"): tuner=RandomSearch(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=1,executions_per_trial=3) elif (tuner_alg.lower()=="bayesianoptimization"): tuner=BayesianOptimization(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=5,executions_per_trial=3) elif (tuner_alg.lower()=="hyperband"): tuner=Hyperband(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_epochs=50,factor=3) else: self.log.info("The given alg is not implemented. Using default hyperparam tuning algorithm: RandomSearch.\n") tuner=RandomSearch(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=5,executions_per_trial=3) from keras.callbacks import EarlyStopping stop_early = EarlyStopping(monitor='val_loss', patience=5) except Exception as e: import traceback self.log.info("The given alg have some issue, Using default hyperparam tuning algorithm: RandomSearch.\n"+str(e)) tuner=RandomSearch(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=1,executions_per_trial=3) self.log.info("Started Exception default Random Search") #hpt search for best params try: self.log.info("First try: Tuner search started") tuner.search(X_train, y_train,validation_data=(X_test, y_test), callbacks=[stop_early]) self.log.info("First try: Tuner search ends") except Exception as e: self.log.info("Second try: Tuner search starts.\n"+str(e)) tuner.search(x=X_train,y=y_train,validation_split=0.2, callbacks=[stop_early]) self.log.info("Second try: Tuner search ends") # best_model = tuner.get_best_models(num_models=1)[0] #self.log.info("best_model.summary(): \n"+str(best_model.summary())) best_hps=tuner.get_best_hyperparameters(num_trials=1)[0] self.log.info("TS Multivariate LSTM best hyperparameter values:\n"+str(best_hps.values)) self.log.info("Activation fn:\n"+str(self.activation_fn)) n_input=self.look_back best_hmodel=tuner.hypermodel.build(best_hps) optimizer=self.optimizer learning_rate=float(best_hps.get('learning_rate')) try: ##TFSTask:7033, Added below try block for time series hyperparam tuning, here, for any optimizer, best learning_rate is provided from best_hps. try: if optimizer.lower() == "adam": optimizer=tensorflow.keras.optimizers.Adam(learning_rate=learning_rate) elif(optimizer.lower() == "adadelta"): optimizer=tensorflow.keras.optimizers.experimental.Adadelta(learning_rate=learning_rate) elif(optimizer.lower() == "nadam"): optimizer=tensorflow.keras.optimizers.experimental.Nadam(learning_rate=learning_rate) elif(optimizer.lower() == "adagrad"): optimizer=tensorflow.keras.optimizers.experimental.Adagrad(learning_rate=learning_rate) elif(optimizer.lower() == "adamax"): optimizer=tensorflow.keras.optimizers.experimental.Adamax(learning_rate=learning_rate) elif(optimizer.lower() == "rmsprop"): optimizer=tensorflow.keras.optimizers.experimental.RMSprop(learning_rate=learning_rate) elif(optimizer.lower() == "sgd"): optimizer=tensorflow.keras.optimizers.experimental.SGD(learning_rate=learning_rate) else: optimizer=tensorflow.keras.optimizers.Adam(learning_rate=learning_rate) except: optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate) pass ##From best hyperparameter values, now creating multivariate time series model using time generator. generatorTrain=TimeseriesGenerator(X_train, y_train, length=n_past, batch_size=self.batch_size) # generatorTest=TimeseriesGenerator(test,test,length=n_input,batch_size=self.batch_size) batch_0=generatorTrain[0] x,y=batch_0 epochs=int(self.epochs) ##Multivariate LSTM model try: encoder_l = {} encoder_outputs = {} encoder_states = {} decoder_l = {} decoder_outputs = {} enc_lstm_dropout = {} enc_input_unit = best_hps.get('enc_input_unit') enc_input_activation = best_hps.get('enc_input_activation') dec_input_activation = best_hps.get('dec_input_activation') dec_output_activation_1 = best_hps.get('dec_output_activation_1') enc_lstm_units = {} enc_lstm_activation = {} dec_lstm_activation = {} for indx in range(self.hidden_layers): lindx = indx + 1 enc_lstm_units[lindx] = best_hps.get('enc_lstm_units_'+str(lindx)) enc_lstm_activation[lindx] = best_hps.get('enc_lstm_activation_'+str(lindx)) dec_lstm_activation[lindx] = best_hps.get('dec_lstm_activation_'+str(lindx)) enc_lstm_dropout[lindx] = best_hps.get('enc_lstm_dropout_'+str(lindx)) encoder_inputs = Input(shape=(n_past, n_features)) if(self.hidden_layers > 0): encoder_l[0] = LSTM(enc_input_unit, activation = enc_input_activation, return_sequences = True, return_state=True) else: encoder_l[0] = LSTM(enc_input_unit, activation = enc_input_activation, return_state=True) encoder_outputs[0] = encoder_l[0](encoder_inputs) encoder_states[0] = encoder_outputs[0][1:] if(self.hidden_layers > 0): for indx in range(self.hidden_layers): lindx = indx + 1 if lindx == self.hidden_layers: encoder_l[lindx] = LSTM(enc_lstm_units[lindx], dropout = enc_lstm_dropout[lindx], activation = enc_lstm_activation[lindx], return_state=True) else: encoder_l[lindx] = LSTM(enc_lstm_units[lindx], dropout = enc_lstm_dropout[lindx], activation = enc_lstm_activation[lindx], return_sequences = True, return_state=True) encoder_outputs[lindx] = encoder_l[lindx](encoder_outputs[indx][0]) encoder_states[lindx] = encoder_outputs[lindx][1:] decoder_inputs = RepeatVector(n_future)(encoder_outputs[self.hidden_layers][0]) else: decoder_inputs = RepeatVector(n_future)(encoder_outputs[0][0]) # if(self.hidden_layers > 0): decoder_l[0] = LSTM(encoder_states[0][0].get_shape()[1], activation = dec_input_activation, return_sequences=True)(decoder_inputs,initial_state = encoder_states[0]) else: decoder_l[0] = LSTM(encoder_states[0][0].get_shape()[1], activation = dec_input_activation, return_sequences=True)(decoder_inputs,initial_state = encoder_states[0]) if(self.hidden_layers > 0): for indx in range(self.hidden_layers): lindx = indx + 1 decoder_l[lindx] = LSTM(encoder_states[lindx][0].get_shape()[1], activation = dec_lstm_activation[lindx], return_sequences=True)(decoder_l[indx],initial_state = encoder_states[lindx]) decoder_outputs[0] = TimeDistributed(tf.keras.layers.Dense(decoder_l[self.hidden_layers][0].get_shape()[1], activation = dec_output_activation_1))(decoder_l[self.hidden_layers]) decoder_outputs[1] = TimeDistributed(tf.keras.layers.Dense(1))(decoder_outputs[0]) else: decoder_outputs[0] = TimeDistributed(tf.keras.layers.Dense(decoder_l[0][0].get_shape()[1], activation = dec_output_activation_1))(decoder_l[0]) decoder_outputs[1] = TimeDistributed(tf.keras.layers.Dense(1))(decoder_outputs[0]) # model = tf.keras.models.Model(encoder_inputs,decoder_outputs[1]) self.log.info(model.summary()) self.log.info("loss="+self.loss_fn) model.compile(optimizer=optimizer,loss=self.loss_fn,metrics=[self.loss_fn]) #model.fit_generator(generatorTrain, epochs=epochs,shuffle=False, verbose=0) model.fit(X_train, y_train, batch_size=self.batch_size, epochs=epochs,shuffle=False, verbose=2) except Exception as e: import traceback self.log.info("multivariate model build error: error msg:: \n"+str(e)) return 'Error',0,0,0,0,None,pd.DataFrame(),0,None #predictions = model.predict_generator(generatorTest) except Exception as e: import traceback self.log.info("optimizer and timesereis generator build error: error msg:: \n"+str(e)) self.log.info("optimizer and timesereis generator build error traceback: \n"+str(traceback.print_exc())) return 'Error',0,0,0,0,None,pd.DataFrame(),0,None try: predictions=[] X_test, y_test = self.create_dataset(test_data, n_past, n_future, targetColIndx) predictions = model.predict(X_test) self.log.info(predictions) #convert the x test(includes target) to 2d as inverse transform accepts only 2d values xtestlen = len(X_test) xtest_2d = X_test.ravel().reshape(xtestlen * n_past, n_features) #inverse tranform of actual value xtest_2d = scaler.inverse_transform(xtest_2d) actual = xtest_2d[:, targetColIndx] #inverse tranform of predicted value prediction_1d = predictions.ravel() prediction_1d = prediction_1d.reshape(len(prediction_1d),1) self.log.info(prediction_1d) xtest_2d[:, targetColIndx] = prediction_1d xtest_2d = scaler.inverse_transform(xtest_2d) predictions = xtest_2d[:, targetColIndx] mse=None rmse=None ## Creating dataframe for actual,predictions try: pred_cols=list() actual_cols=list() for i in range(len(self.targetFeature)): pred_cols.append(self.targetFeature[i]+'_pred') actual_cols.append(self.targetFeature[i]+'_actual') predictions = pd.DataFrame(predictions.ravel(), columns=pred_cols) actual = pd.DataFrame(actual.ravel(), columns=actual_cols) df_predicted=pd.concat([actual,predictions],axis=1) self.log.info("LSTM Multivariate prediction dataframe: \n"+str(df_predicted)) from math import sqrt from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error target=self.targetFeature mse_dict={} rmse_dict={} mae_dict={} mape_dict={} r2_dict={} lstm_var = 0 self.log.info(actual.shape) self.log.info(actual) self.log.info(predictions.shape) self.log.info(predictions) mse = mean_squared_error(actual,predictions) mse_dict[self.targetFeature[0]]=mse rmse=sqrt(mse) rmse_dict[self.targetFeature[0]]=rmse lstm_var = lstm_var+rmse self.log.info("Name of the target feature: "+str(self.targetFeature)) self.log.info("RMSE of the target feature: "+str(rmse)) r2 = r2_score(actual,predictions) r2_dict[self.targetFeature[0]]=r2 mae = mean_absolute_error(actual,predictions) mae_dict[self.targetFeature[0]]=mae mape = mean_absolute_percentage_error(actual,predictions) mape_dict[self.targetFeature[0]]=mape ## For VAR comparison, send last target mse and rmse from above dict lstm_var = lstm_var/len(target) select_msekey=list(mse_dict.keys())[-1] l_mse=list(mse_dict.values())[-1] select_rmsekey=list(rmse_dict.keys())[-1] l_rmse=list(rmse_dict.values())[-1] select_r2key=list(r2_dict.keys())[-1] l_r2=list(r2_dict.values())[-1] select_maekey=list(mae_dict.keys())[-1] l_mae=list(mae_dict.values())[-1] l_mape=list(mape_dict.values())[-1] self.log.info("Selected target feature of LSTM for best model selection: "+str(select_rmsekey)) self.log.info("lstm rmse: "+str(l_rmse)) self.log.info("lstm mse: "+str(l_mse)) self.log.info("lstm r2: "+str(l_r2)) self.log.info("lstm mae: "+str(l_mae)) self.log.info("lstm mape: "+str(l_mape)) except Exception as e: import traceback self.log.info("prediction error traceback: \n"+str(traceback.print_exc())) except Exception as e: import traceback self.log.info("dataframe creation error. err.msg: "+str(e)) self.log.info("dataframe creation error traceback: \n"+str(traceback.print_exc())) return 'Error',0,0,0,0,None,pd.DataFrame(),0,None return 'Success',round(l_mse,2),round(l_rmse,2),round(l_r2,2),round(l_mae,2),model,df_predicted,n_input,scaler # import os #predicted_file_name='lstm_prediction_df.csv' #predicted_file_path=os.path.join(self.dataFolderLocation,predicted_file_name) #df_predicted.to_csv(predicted_file_path) ##save model #model_path = os.path.join(self.dataFolderLocation,self.model_name) #self.log.info("mlp model saved at: "+str(model_path)) #model.save(model_path) except Exception as e: import traceback ## Just use below traceback print to get detailed error information. # import traceback # print(" traceback error 7:\n",traceback.print_exc()) ## Enable traceback for debugging self.log.info("dataframe creation error. err.msg: "+str(e)) self.log.info("Final exception traceback: \n"+str(traceback.print_exc())) return 'Error',0,0,0,0,None,pd.DataFrame(),0,None ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json #Python sklearn & std libraries import numpy as np import pandas as pd from time_series.ts_arima_eion import eion_arima from statsmodels.tsa.vector_ar.vecm import coint_johansen from statsmodels.tsa.vector_ar.var_model import VAR from math import * from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_absolute_error from math import sqrt import logging import os import sys import time from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_squared_error from pandas import read_csv from statsmodels.tsa.stattools import adfuller import pmdarima as pm from statsmodels.tsa.stattools import grangercausalitytests from statsmodels.stats.stattools import durbin_watson from sklearn.utils import check_array class timeseriesModelTests(): def __init__(self,data,targetFeature,datetimeFeature,count): #self.tsConfig = tsConfig #self.modelconfig = modelconfig #self.modelList = modelList self.data = data self.targetFeature = targetFeature self.dateTimeFeature = datetimeFeature self.count=count self.log = logging.getLogger('eion') def StatinaryChecks(self,dictDiffCount): self.log.info("\n---------------Start Stationary Checks-----------") tFeature = self.targetFeature.split(',') tFeature.append(self.dateTimeFeature) self.data=self.data[tFeature] tFeature.remove(self.dateTimeFeature) lengthtFeature=len(tFeature) diffCount=0 try : for features in (tFeature): XSt = self.data[features] XSt=XSt.values resultSt = adfuller(XSt,autolag='AIC') stationaryFlag = False #print(resultSt) self.log.info('-------> Features: '+str(features)) self.log.info('----------> ADF Statistic: '+str(resultSt[0])) self.log.info('----------> p-value: %f' % resultSt[1]) if resultSt[1]<= 0.05: self.log.info("-------------> Converted As Stationary Data") stationaryFlag = True else: self.log.info("-------------> Stationary Conversion Required") stationaryFlag = False self.log.info('----------> Critical Values') for key, value in resultSt[4].items(): self.log.info('----------> '+str(key)+': '+str(value)) if stationaryFlag == False: self.data[features]=self.data[features].diff() self.data=self.data.dropna() dictDiffCount[features]=1 XStt = self.data[features] XStt=XStt.values resultStt = adfuller(XStt) if resultStt[1] > 0.05: self.data[features]=self.data[features].diff() self.data=self.data.dropna() dictDiffCount[features]=2 XSttt = self.data[features] XSttt=XSttt.values resultSttt = adfuller(XSttt) if resultSttt[1]<= 0.05: stationaryFlag = True else: stationaryFlag = True self.log.info("------------->"+str(dictDiffCount)) if stationaryFlag == True: self.log.info("----------> Equals to Stationary Data") else: self.log.info("----------> Not Equal To Stationary Data") self.log.info("-------> Stationary data diff()") self.log.info(dictDiffCount) self.log.info("---------------Start Stationary Checks Ends-----------\n") return self.data,dictDiffCount except Exception as inst: self.log.info('<!------------- Time Series Stationary Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def varTimeseriesModelTests(self,data): try : tFeature = self.targetFeature.split(',') self.log.info("\n--------- Start Granger Causality Test Results ------------") gtest=grangercausalitytests(data[tFeature], maxlag=15, addconst=True, verbose=True) self.log.info("-------> GrangerCausalitytest Results "+str(gtest.values())) self.log.info("--------- End Granger Causality Test Results ------------\n") return gtest except Exception as inst: self.log.info('<!------------- Time Series Granger Causality testTest Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def grangersCausationMatrix(self,data, variables, test='ssr_chi2test', verbose=False): try : countVariables=0 self.log.info(len(variables)) self.log.info("\n--------------Start GrangersCausationMatrix---------------") df = pd.DataFrame(np.zeros((len(variables), len(variables))), columns=variables, index=variables) for c in df.columns: for r in df.index: test_result = grangercausalitytests(data[[r, c]], maxlag=12, verbose=False) p_values = [round(test_result[i+1][0][test][1],4) for i in range(12)] if verbose: print(f'Y = {r}, X = {c}, P Values = {p_values}') min_p_value = np.min(p_values) df.loc[r, c] = min_p_value df.columns = [var + '_x' for var in variables] df.index = [var + '_y' for var in variables] self.log.info(df) for i in range(len(variables)): for j in range(len(variables)): if i!=j and df.iloc[i][j]<0.05 and df.iloc[i][j]<0.05: countVariables=countVariables+1 self.log.info("--------------End GrangersCausationMatrix---------------\n") return df,countVariables except Exception as inst: self.log.info('<!------------- Time Series grangersCausationMatrix Test Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return df,countVariables def coIntegrationTest(self,data): try : tdata = data.drop([self.dateTimeFeature], axis=1) tdata.index = data[self.dateTimeFeature] cols = tdata.columns self.log.info("\n-------------- Start of the Co Integration test ---------------") lenTargetFeature=len(self.targetFeature) countIntegrationFeature=0 N, l = tdata.shape jres = coint_johansen(tdata, 0, 1) trstat = jres.lr1 tsignf = jres.cvt for i in range(l): if trstat[i] > tsignf[i, 1]: r = i + 1 jres.r = r jres.evecr = jres.evec[:, :r] jres.r = r countIntegrationFeature=jres.r jres.evecr = jres.evec[:, :r] self.log.info('------->coint_johansen trace statistics: '+str(trstat)) self.log.info('------->coint_johansen critical values:') self.log.info(tsignf) self.log.info("------->There are "+str(countIntegrationFeature)+" Co-Integration vectors") self.log.info("-------------- End of the Co Integration test ---------------\n") return countIntegrationFeature except Exception as inst: self.log.info('<!------------- Time Series Co-Integration Test Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import numpy as np from statsmodels.tsa.stattools import adfuller from statsmodels.tsa.stattools import kpss from statsmodels.tsa.seasonal import seasonal_decompose import logging import os import warnings warnings.filterwarnings('ignore') ## Main class to find out seassonality and stationary in timeseries data. class tsStationarySeasonalityTest: def __init__(self,df,deployLocation): self.df=df self.deployLocation=deployLocation self.log = logging.getLogger('eion') ## to get the timeseries data stationary information def stationary_model(self,df,target_features,stationary_check_method): self.log.info("<------ Time Series stationary test started.....------------->\n") self.log.info("<------ Feature used:------------->\t"+str(target_features)) stationary_status=None if (stationary_check_method.lower()=='adfuller'): stats_model=adfuller(df[target_features]) # p_val=adf_result[1] statistic, p_value, n_lags, num_bservations,critical_values,info_criterion_best=stats_model[0],stats_model[1],stats_model[2],stats_model[3],stats_model[4],stats_model[5] ##Uncomment below logs when required. self.log.info("Adfuller test (time series stationary test) p_value: \t"+str(p_value)) # self.log.info("Adfuller test (time series stationary test) statistics: \t"+str(statistic)) # self.log.info("Adfuller test (time series stationary test) number of lags (time steps): \t"+str(n_lags)) # self.log.info("Adfuller test (time series stationary test) Critical values: \n") ##To display critical values # for key, value in stats_model[4].items(): # self.log.info(" \t"+str(key)+"\t"+str(value)) if (p_value>0.05): stationary_status="feature is non-stationary" self.log.info('Status:- |... '+str(target_features)+' is non stationary') elif(p_value<0.05): stationary_status="feature is stationary" self.log.info('Status:- |... '+str(target_features)+' is stationary') ##kpss is opposite to ADF in considering null hypothesis. In KPSS, if null hypothesis,then it is stationary as oppose to ADF. elif (stationary_check_method.lower()=='kpss'): from statsmodels.tsa.stattools import kpss stats_model = kpss(df[target_features]) statistic, p_value, n_lags, critical_values=stats_model[0],stats_model[1],stats_model[2],stats_model[3] self.log.info("kpss test (time series stationary test) p_value: \t"+str(p_value)) self.log.info("kpss test (time series stationary test) statistics: \t"+str(statistic)) self.log.info("kpss test (time series stationary test) number of lags (time steps): \t"+str(n_lags)) self.log.info("kpss test (time series stationary test) Critical values: \n") for key, value in stats_model[3].items(): self.log.info(" \t"+str(key)+"\t"+str(value)) ##In kpss, the stationary condition is opposite to Adafuller. if (p_value>0.05): self.log.info('Status:- |... '+str(target_features)+' is stationary') else: self.log.info('Status:- |... '+str(target_features)+' is non stationary') return stats_model,n_lags,p_value,stationary_status ## Get stationary details def stationary_check(self,target_features,time_col,method): df=self.df try: df[time_col]=pd.to_datetime(df[time_col]) except Exception as e: self.log.info("issue in datetime conversion...\n"+str(e)) df=df.set_index(time_col) try: stationary_check_method=method except: stationary_check_method='adfuller' if (len(target_features) == 1): try: if isinstance(target_features,list): target_features=''.join(target_features) elif isinstance(target_features,int): target_features=str(target_features) elif isinstance(target_features,str): pass except Exception as e: self.log.info("stationary check target feature error: \t"+str(e)) stationary_result={} stats_model,n_lags,p_value,stationary_status=self.stationary_model(df,target_features,stationary_check_method) stationary_result[target_features]=stationary_status elif(len(target_features) > 1): stationary_result={} for col in df.columns: # self.log.info("Multivariate feature for Stationary check:\t"+str(col)) stats_model,n_lags,p_value,stationary_status=self.stationary_model(df,col,stationary_check_method) stationary_result[col]=stationary_status else: self.log.info("TS Stationarity Test: Error in target feature, pls check.\n.") # self.log.info("Feature based stationarity_result:\n"+str(stationary_result)) # ## Stationary component for whole dataset stationary_combined_res=dict() # stats_model,n_lags,p_value,stationary_status=self.stationary_all_features(time_col,'adfuller') c_dict=[k for k,v in stationary_result.items() if 'non-stationary' in v] if (len(c_dict)>=1): stationary_combined_res['dataframe_stationarity']='Non-Stationary' self.log.info('Status:- |... Data is non stationarity') else: stationary_combined_res['dataframe_stationarity']='Stationary' # self.log.info("Stationarity information for whole dataset:\n"+str(stationary_combined_res)) self.log.info("Time series Stationarity test completed.\n") return stats_model,n_lags,p_value,stationary_result,stationary_combined_res #Get seasonality by using seasonal_decompose lib. def seasonality_model(self,target_features,df): self.log.info("<------ Time Series Seasonality test started.....------------->\n") self.log.info("<------ Feature used:------------->\n"+str(target_features)) seasonality_status=None try: try: stats_model = kpss(df[target_features]) statistic, p_value, n_lags, critical_values=stats_model[0],stats_model[1],stats_model[2],stats_model[3] except: n_lags=1 pass try: df_target=self.df[target_features] decompose_result_mult = seasonal_decompose(df_target,model='additive', extrapolate_trend='freq', period=n_lags) except Exception as e: self.log.info("Logging seasonality_model decompose_result_mult: \t"+str(e)) ##If additive model (type of seasonal component) failed, use multiplicative decompose_result_mult = seasonal_decompose(df_target,model='multiplicative', extrapolate_trend='freq', period=1) trend = decompose_result_mult.trend observed=decompose_result_mult.observed seasonal = decompose_result_mult.seasonal residual = decompose_result_mult.resid try: if isinstance(df_target, pd.Series): auto_correlation = df_target.autocorr(lag=n_lags) # self.log.info("seasonality test: auto_correlation value:\n"+str(auto_correlation)) elif isinstance(df_target, pd.DataFrame): df_target = df_target.squeeze() auto_correlation = df_target.autocorr(lag=n_lags) # self.log.info("seasonality test: auto_correlation value:\n"+str(auto_correlation)) except: pass self.log.info("<------------------ Time series Seasonality test result:------------------>") if (seasonal.sum()==0): seasonality_status="feature don't have seasonality (non seasonality)." self.log.info('Status:- |... '+str(target_features)+' does not have seasonality') self.log.info("<----- The model feature: "+str(target_features)+" does not have significant seasonality.----->\n") else: seasonality_status="feature has seasonality." self.log.info('Status:- |... '+str(target_features)+' have seasonality') ##Please use the below plot for GUI show (seasonality components) # decompose_result_mult.plot() df['observed'] = decompose_result_mult.observed df['residual'] = decompose_result_mult.resid df['seasonal'] = decompose_result_mult.seasonal df['trend'] = decompose_result_mult.trend df_name='timeseries_seasonality_check_'+f"{target_features}"+'.csv' dir_n = os.path.join(self.deployLocation,'data','seasonality') if not os.path.exists(dir_n): os.makedirs(dir_n) model_path=os.path.join(dir_n,df_name) self.log.info("Seasonality information saved as dataframe at:\t "+str(model_path)) ## Seasonal component for whole dataset df.to_csv(model_path) except Exception as e: self.log.info("Seasonality function exception: \t"+str(e)) return df,decompose_result_mult,seasonality_status ##Main function to check seasonlity in data def seasonal_check(self,target_features,time_col,seasonal_model): df=self.df # self.log.info("seasonal check started... \n") try: df[time_col]=pd.to_datetime(df[time_col]) except Exception as e: self.log.info("Issue in datetime conversion...\n"+str(e)) df=df.set_index(time_col) if (len(target_features)==1): try: if isinstance(target_features,list): target_features=''.join(target_features) elif isinstance(target_features,int): target_features=str(target_features) elif isinstance(target_features,str): pass except Exception as e: self.log.info("stationary check target feature error: \t"+str(e)) ## Seasonal component for individual feature based. seasonality_result=dict() df,decompose_result_mult,seasonality_status = self.seasonality_model(target_features,df) seasonality_result[target_features]=seasonality_status elif(len(target_features) > 1): seasonality_result=dict() self.log.info("TS seasonality Test: The problem type is time series Multivariate.") for col in df.columns: df,decompose_result_mult,seasonality_status = self.seasonality_model(col,df) seasonality_result[col]=seasonality_status else: self.log.info("TS seasonality Test: Error in target feature, pls check.\n.") # self.log.info("Feature based seasonality_result:\n"+str(seasonality_result)) # ## Seasonal component for whole dataset seasonality_combined_res=dict() c_dict=[k for k,v in seasonality_result.items() if 'non seasonality' in v] if (len(c_dict)>=1): seasonality_combined_res['dataframe_seasonality']='No Seasonal elements' else: seasonality_combined_res['dataframe_seasonality']='contains seasonal elements.' # self.log.info("Seasonality information for whole dataset:\n"+str(seasonality_combined_res)) self.log.info("Time series Seasonality test completed.\n") return df,decompose_result_mult,seasonality_result,seasonality_combined_res #Main fn for standalone test purpose if __name__=='__main__': print("Inside seasonality-stationary test main function...") print("Below code used for standalone test purpose.") # df=pd.read_csv(r"C:\AION_Works\Data\order_forecast_ts.csv") # print("df info: \n",df.info()) # df=df.drop('index',axis=1) # time_col="DateTime" # target='order1' # stationary_method='adfuller' # seasonal_model="additive" ## two models are available: 1.multiplicative, 2.additive # if (isinstance(target,list)): # pass # elif (isinstance(target,str)): # target=list(target.split(',')) # cls_ins=aion_ts_stationary_seassonality_test(df) # stats_model,n_lags,p_value=cls_ins.stationary_check(target,time_col,stationary_method) # df,decompose_result_mult=cls_ins.seasonal_check(target,time_col,seasonal_model) # print(" Time series stationary and seasonality check completed.") ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd # import os import tensorflow as tf import numpy as np from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split import math from sklearn.metrics import mean_squared_error from tensorflow.keras.layers import Dense from tensorflow.keras.layers import Dropout from tensorflow.keras import Sequential from tensorflow.keras.layers import LSTM import logging # import kerastuner import keras_tuner #from keras_tuner.engine.hyperparameters import HyperParameters from keras_tuner.tuners import RandomSearch,BayesianOptimization ,Hyperband import warnings warnings.simplefilter("ignore", UserWarning) # from keras.models import load_model # from tensorflow.keras.optimizers import SGD # from tensorflow.keras.utils import load_model from tensorflow.keras.models import load_model class timeseriesDLUnivariate: def __init__(self,configfile,testpercentage,targetFeature,dateTimeFeature,modelName): self.look_back=None #Preprocessed dataframe # self.df=df self.savedmodelname=None self.deploy_location=None self.epochs=None self.batch_size=None self.hidden_layers=None self.optimizer=None self.activation_fn=None self.loss_fn=None self.first_layer=None self.dropout=None self.model_name=None self.hpt_train=None ##Below is model type (MLP or lstm) self.model_type=modelName #self.dataFolderLocation=str(dataFolderLocation) ##Added for ts hpt self.tuner_algorithm="" self.dl_params = configfile # self.data=data self.targetFeature=targetFeature self.dateTimeFeature=dateTimeFeature self.testpercentage = testpercentage self.log = logging.getLogger('eion') #To extract dict key,values def extract_params(self,dict): self.dict=dict for k,v in self.dict.items(): return k,v ##Get deep learning model hyperparameter from advanced config def getdlparams(self): val=self.dl_params self.log.info('-------> The given mlp/lstm timeseries algorithm parameters:>>') self.log.info(" "+str(val)) for k,v in val.items(): try: if (k == "tuner_algorithm"): self.tuner_algorithm=str(v) elif (k == "activation"): self.activation_fn=str(v) elif (k == "optimizer"): self.optimizer=str(v) elif (k == "loss"): self.loss_fn=str(v) elif (k == "first_layer"): if not isinstance(k,list): self.first_layer=str(v).split(',') else: self.first_layer=k elif (k == "lag_order"): if isinstance(k,list): k = ''.join(v) k=int(float(str(v))) else: self.look_back=int(float(str(v))) elif (k == "hidden_layers"): self.hidden_layers=int(v) elif (k == "dropout"): if not isinstance(k,list): self.dropout=str(v).split(',') else: self.dropout=k elif (k == "batch_size"): self.batch_size=int(v) elif (k == "epochs"): self.epochs=int(v) elif (k == "model_name"): self.model_name=str(v) except Exception as e: self.log.info('Exception occured in deeep learn param reading, setting up default params.') self.activation_fn="relu" self.optimizer="adam" self.loss_fn="mean_squared_error" self.first_layer=[8,512] self.hidden_layers=1 self.look_back=int(2) self.dropout=[0.1,0.5] self.batch_size=2 self.epochs=50 self.model_name="lstmmodel.h5" continue ## Just use this if user need to create dataframe from input data. def createdf(self,df): target="" # splitting reframed to X and Y considering the first column to be out target featureX=reframed.drop(['var1(t)'],axis=1) X=df.drop([target],axis=1) Y=df[target] X_values=X.values Y_values=Y.values n_predict=len(Y_values) train_X,train_Y = X_values[:(X_values.shape[0]-n_predict),:],Y_values[:(X_values.shape[0]-n_predict)] test_X,test_Y = X_values[(X_values.shape[0]-n_predict):,:],Y_values[(X_values.shape[0]-n_predict):] #reshaping train and test to feed to LSTM train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1])) test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1])) return train_X,train_Y,test_X,test_Y # convert an array of values into a dataset matrix def numpydf(self,dataset, look_back): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) # x,y=numpy.array(dataX), numpy.array(dataY) return np.array(dataX), np.array(dataY) def model_save(self,model): import os.path savedmodelname=self.model_name path = os.path.join(self.deploy_location,savedmodelname) model.save(path) return (savedmodelname) ## MLP model buid def mlpDL(self,df): self.log.info("MLP timeseries learning starts.....") try: self.getdlparams() # look_back = self.look_back dataset = df.values dataset = dataset.astype('float32') ##The below Kwiatkowski-Phillips-Schmidt-Shin (kpss) statsmodel lib used for stationary check as well getting number of lags. ##number of lag calculated just for reference ,not used now. #Dont delete this, just use in future. from statsmodels.tsa.stattools import kpss statistic, p_value, n_lags, critical_values = kpss(df[self.targetFeature]) self.log.info("Based on kpss statsmodel, lag order (time steps to calculate next prediction) is: \t"+str(n_lags)) scaler = MinMaxScaler(feature_range=(0, 1)) dataset = scaler.fit_transform(dataset) # split into train and test sets train_size = int(len(dataset) * 0.80) test_size = len(dataset) - train_size train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:] self.hpt_train=train tuner_alg=self.tuner_algorithm try: ## Remove untitled_project dir in AION root folder created by previous tuner search run import shutil shutil.rmtree(r".\untitled_project") except: pass if (tuner_alg.lower()=="randomsearch"): tuner=RandomSearch(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=5,executions_per_trial=3) elif (tuner_alg.lower()=="bayesianoptimization"): tuner=BayesianOptimization(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=5,executions_per_trial=3) elif (tuner_alg.lower()=="hyperband"): tuner=Hyperband(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_epochs=50,factor=3) # tuner.search(X[...,np.new_axis],y,epochs=2,validation_data=(y[...,np.newaxis])) stop_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5) try: tuner.search(x=train,y=train,validation_data=(test,test),callbacks=[stop_early]) except: tuner.search(x=train,y=train,validation_split=0.2,callbacks=[stop_early]) # best_model=tuner.get_best_models(num_models=1)[0] best_hps=tuner.get_best_hyperparameters(num_trials=1)[0] best_first_layer=best_hps.get('units') best_dropout=best_hps.get('Dropout_rate') best_learning_rate=float(best_hps.get('learning_rate')) self.log.info("best hyperparameter values for mlp: \n"+str(best_hps.values)) look_back = 1 ## Because univariate problemtype trainX, trainY = self.numpydf(train, look_back) testX, testY = self.numpydf(test, look_back) best_hmodel=tuner.hypermodel.build(best_hps) ##Added for mlp issue,because tuner build also need to compile. try: best_hmodel.compile(loss=self.loss_fn, optimizer=self.optimizer) except: pass model_fit = best_hmodel.fit(trainX, trainY, epochs=self.epochs, batch_size=self.batch_size, verbose=2) val_acc_per_epoch = model_fit.history['loss'] best_epoch = val_acc_per_epoch.index(min(val_acc_per_epoch)) + 1 self.log.info("MLP best epochs value:\n"+str(best_epoch)) trainScore = best_hmodel.evaluate(trainX, trainY, verbose=0) testScore = best_hmodel.evaluate(testX, testY, verbose=0) #Scoring values for the model mse_eval=testScore try: #If mse_eval is list of values min_v=min(mse_eval) except: #If mse_eval is single value min_v=mse_eval rmse_eval = math.sqrt(min_v) # generate predictions for training trainPredict = best_hmodel.predict(trainX) #print(testX) testPredict = best_hmodel.predict(testX) #print(testPredict) # invert predictions, because we used mimanmax scaler trainY = scaler.inverse_transform([trainY]) trainPredict = scaler.inverse_transform(trainPredict) ## For test data testY = scaler.inverse_transform([testY]) testPredict = scaler.inverse_transform(testPredict) ## Creating dataframe for actual,predictions predictions = pd.DataFrame(testPredict, columns=[self.targetFeature+'_pred']) actual = pd.DataFrame(testY.T, columns=[self.targetFeature+'_actual']) df_predicted=pd.concat([actual,predictions],axis=1) #print(df_predicted) from math import sqrt from sklearn.metrics import mean_squared_error try: mse_mlp = mean_squared_error(testY.T,testPredict) rmse_mlp=sqrt(mse_mlp) self.log.info('mse_mlp: '+str(mse_mlp)) self.log.info('rmse_mlp: '+str(rmse_mlp)) from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error r2 = r2_score(testY.T,testPredict) mae = mean_absolute_error(testY.T,testPredict) self.log.info('r2_mlp: '+str(r2)) self.log.info('mae_mlp: '+str(mae)) except Exception as e: import traceback self.log.info("MLP dataframe creation error traceback: \n"+str(traceback.print_exc())) self.log.info(e) # df_predicted.to_csv('mlp_prediction.csv') except Exception as e: self.log.info("MLP timeseries model traceback error msg e: "+str(e)) self.log.info("MLP training successfully completed.\n") return mse_mlp,rmse_mlp,r2,mae,best_hmodel,df_predicted,look_back,scaler ## Added function for hyperparam tuning (TFSTask:7033) def build_model(self,hp): try: loss=self.loss_fn optimizer=self.optimizer try: if optimizer.lower() == "adam": optimizer=tf.keras.optimizers.Adam elif(optimizer.lower() == "adadelta"): optimizer=tf.keras.optimizers.experimental.Adadelta elif(optimizer.lower() == "nadam"): optimizer=tf.keras.optimizers.experimental.Nadam elif(optimizer.lower() == "adagrad"): optimizer=tf.keras.optimizers.experimental.Adagrad elif(optimizer.lower() == "adamax"): optimizer=tf.keras.optimizers.experimental.Adamax elif(optimizer.lower() == "rmsprop"): optimizer=tf.keras.optimizers.experimental.RMSprop elif(optimizer.lower() == "sgd"): optimizer=tf.keras.optimizers.experimental.SGD else: optimizer=tf.keras.optimizers.Adam except: optimizer=tf.keras.optimizers.Adam pass first_layer_min=round(int(self.first_layer[0])) first_layer_max=round(int(self.first_layer[1])) dropout_min=float(self.dropout[0]) dropout_max=float(self.dropout[1]) model=tf.keras.Sequential() if (self.model_type.lower() == 'lstm'): model.add(LSTM(units=hp.Int('units',min_value=first_layer_min,max_value=first_layer_max,step=16),input_shape=(self.look_back,self.hpt_train.shape[1]), activation=hp.Choice('dense_activation',values=['relu']))) elif (self.model_type.lower() == 'mlp'): # model.add(Dense(units=hp.Int('units',min_value=first_layer_min,max_value=first_layer_max,step=16),input_dim=(hp.Int('time_steps',min_value=look_back_min,max_value=look_back_max,step=1)), # activation='relu')) ##input_dim is 1 because mlp is for univariate. model.add(Dense(units=hp.Int('units',min_value=first_layer_min,max_value=first_layer_max,step=16),input_dim=(1),activation='relu')) model.add(Dropout(hp.Float('Dropout_rate',min_value=dropout_min,max_value=dropout_max,step=0.1))) model.add(Dense(units=1)) model.compile(optimizer=optimizer(hp.Choice('learning_rate',values=[1e-1,1e-2,1e-3,1e-4])),loss=loss,metrics=[loss]) except Exception as e: import traceback self.log.info("lstm errorbuild_model traceback: \n"+str(traceback.print_exc())) return model ##LSTM timeseries function call def ts_lstm(self,df): self.log.info("lstm network model learning starts.....\n") try: self.getdlparams() dataset = df.values dataset = dataset.astype('float32') ##The below Kwiatkowski-Phillips-Schmidt-Shin (kpss) statsmodel lib used for stationary check as well getting number of lags. ##number of lag calculated just for reference ,not used now. #Dont delete this, just use in future. from statsmodels.tsa.stattools import kpss statistic, p_value, n_lags, critical_values = kpss(df[self.targetFeature]) self.log.info("Based on kpss statsmodel, lag order (time steps to calculate next prediction) is: \t"+str(n_lags)) # normalize the dataset scaler = MinMaxScaler(feature_range=(0, 1)) dataset = scaler.fit_transform(dataset) # split into train and test sets train_size = int(len(dataset) * 0.80) test_size = len(dataset) - train_size train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:] self.hpt_train=train tuner_alg=self.tuner_algorithm try: ## Remove untitled_project dir in AION root folder created by previous tuner search run import shutil shutil.rmtree(r".\untitled_project") except: pass if (tuner_alg.lower()=="randomsearch"): tuner=RandomSearch(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=5,executions_per_trial=3) elif (tuner_alg.lower()=="bayesianoptimization"): tuner=BayesianOptimization(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_trials=5,executions_per_trial=3) elif (tuner_alg.lower()=="hyperband"): tuner=Hyperband(self.build_model,keras_tuner.Objective("val_loss", direction="min"),max_epochs=50,factor=3) # tuner.search(X[...,np.new_axis],y,epochs=2,validation_data=(y[...,np.newaxis])) from keras.callbacks import EarlyStopping stop_early = EarlyStopping(monitor='val_loss', patience=5) ##Need both x and y with same dimention. tuner.search(x=train,y=train,validation_split=0.2,callbacks=[stop_early]) # tuner.search(x=train,y=test,validation_data=(test,test),callbacks=[stop_early]) best_hps=tuner.get_best_hyperparameters(num_trials=1)[0] best_time_steps=self.look_back self.log.info("best lag order or lookback (time_steps) for LSTM: \n"+str(best_time_steps)) self.log.info("best hyperparameter values for LSTM: \n"+str(best_hps.values)) look_back = best_time_steps trainX, trainY = self.numpydf(train, look_back) testX, testY = self.numpydf(test, look_back) # reshape input to be [samples, time steps, features] trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1)) testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1)) #create and fit the LSTM network best_hmodel=tuner.hypermodel.build(best_hps) try: best_hmodel.compile(loss=self.loss_fn, optimizer=self.optimizer) except: pass model_fit = best_hmodel.fit(trainX, trainY, validation_split=0.2, epochs=self.epochs, batch_size=self.batch_size, verbose=2) val_acc_per_epoch = model_fit.history['loss'] best_epoch = val_acc_per_epoch.index(min(val_acc_per_epoch)) + 1 self.log.info("best epochs value:\n"+str(best_epoch)) # best_hmodel=tuner.hypermodel.build(best_hps) # best_hmodel.fit(x=trainX,y=trainY,validation_split=0.2,epochs=best_epoch) ##Using model_evaluate,calculate mse # mse_eval = model.evaluate(testX, testY, verbose=0) mse_eval = best_hmodel.evaluate(testX, testY, verbose=0) try: #If mse_eval is list of values min_v=min(mse_eval) except: #If mse_eval is single value min_v=mse_eval rmse_eval=math.sqrt(min_v) # self.log.info('LSTM mse:'+str(mse_eval)) # self.log.info('LSTM rmse:'+str(rmse_eval)) # lstm time series predictions trainPredict = best_hmodel.predict(trainX) testPredict = best_hmodel.predict(testX) # invert predictions, because we used mim=nmax scaler trainY = scaler.inverse_transform([trainY]) trainPredict = scaler.inverse_transform(trainPredict) testY = scaler.inverse_transform([testY]) testPredict = scaler.inverse_transform(testPredict) ## Creating dataframe for actual,predictions predictions = pd.DataFrame(testPredict, columns=[self.targetFeature+'_pred']) actual = pd.DataFrame(testY.T, columns=[self.targetFeature+'_actual']) df_predicted=pd.concat([actual,predictions],axis=1) from math import sqrt from sklearn.metrics import mean_squared_error try: mse_lstm=None mse_lstm = mean_squared_error(testY.T,testPredict) rmse_lstm=sqrt(mse_lstm) self.log.info("mse_lstm: "+str(mse_lstm)) self.log.info("rmse_lstm: "+str(rmse_lstm)) from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error r2 = r2_score(testY.T,testPredict) mae = mean_absolute_error(testY.T,testPredict) self.log.info('r2_lstm: '+str(r2)) self.log.info('mae_lstm: '+str(mae)) except Exception as e: self.log.info("lstm error loss fns"+str(e)) return 'Error',0,0,0,0,None,pd.DataFrame(),0,None except Exception as e: import traceback self.log.info("lstm training error traceback: \n"+str(traceback.print_exc())) return 'Error',0,0,0,0,None,pd.DataFrame(),0,None return 'Success',mse_lstm,rmse_lstm,r2,mae,best_hmodel,df_predicted,look_back,scaler if __name__ == '__main__': print('Inside timeseriesDLUnivariate main....\n') # tsdl_obj = timeseriesDLUnivariate() ## for testing purpose ''' df1= pd.read_csv(r"C:\aiontest\testPrograms\Data\energydemand.csv",encoding='utf-8', engine='python') dateTimeFeature = "utcTimeStamp" targetFeature="temperature" try: df1[dateTimeFeature] = pd.to_datetime(df1[dateTimeFeature]) #, format = '%d/%m/%Y %H.%M') except: pass tdata = df1.drop([dateTimeFeature], axis=1) tdata.index = df1[dateTimeFeature] tdata = pd.DataFrame(tdata[targetFeature]) cols = tdata.columns mse,rmse,model = tsdl_obj.mlpDL(tdata) lmse,lrmse,lstmmodel = tsdl_obj.ts_lstm(tdata) print("mlp mse: \n",mse) print("mlp rmse: \n",rmse) print("lstm mse: \n",lmse) print("lstm rmse: \n",lrmse) savedmodelname=tsdl_obj.model_save(lstmmodel) ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' # For timeseries pyramid pdaarima module import json #Python sklearn & std libraries import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_selection import VarianceThreshold from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error #from sklearn.metrics import mean_absolute_percentage_error from sklearn.linear_model import LinearRegression from math import sqrt import warnings # For serialization. #from sklearn.externals import joblib import pickle import os,sys # For ploting (mathlab) import matplotlib.pyplot as plt import plotly #Import eion config manager module import logging from sklearn import metrics from sklearn.metrics import accuracy_score import time import random import statsmodels.api as sm # prophet by Facebook # time series analysis #from statsmodels.tsa.seasonal import seasonal_decompose #from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from prophet.plot import plot_plotly,plot_components_plotly #import seaborn as sns from sklearn.model_selection import ParameterGrid import holidays #from prophet.diagnostics import performance_metrics #from prophet.diagnostics import cross_validation from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error import logging,sys from scipy.special import inv_boxcox from prophet.diagnostics import cross_validation #from sklearn.metrics import mean_absolute_percentage_error warnings.filterwarnings("ignore") # Aion Prophet module class aion_fbprophet (): #Constructor def __init__(self,configfile,testpercentage,data,targetFeature,dateTimeFeature): try: self.tsprophet_params = configfile self.data=data self.targetFeature=targetFeature self.dateTimeFeature=dateTimeFeature self.testpercentage = testpercentage self.log = logging.getLogger('eion') except Exception as inst: self.log.info('<!------------- Prophet INIT Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) #Find datetime column def get_datetime_col(self,data): df=data dt_col=[] categorical_features=[] discrete_features=[] # Here, I am checking each column type, whether it is object type or float or int. Then I am trying to convert the # Object type to datetime format using python pd.to_datetime() function. If the column converts , it is datetime format, else it is some other format (categorical or discrete) for col in df.columns: if (df[col].dtype == 'object' or df[col].dtype == 'datetime64[ns]' ): try: df[col] = pd.to_datetime(df[col]) dt_col.append(col) except ValueError: categorical_features.append(col) pass elif (df[col].dtype == 'float64' or 'int64' or 'int' or 'float64' or 'float'): #('int' or 'float' or 'int64' or 'float64')): #print("discrete features found..\n") discrete_features.append(col) else: pass #Uncomment to know the datetime, categorical and continuous cols # print ("Date time colms: dt_col: \n",dt_col) # print("categorical features: \n",categorical_features) # print("continuous features: \n",discrete_features) return dt_col def get_predict_frequency(self,df,datetime_col_name): #dt_col=pd.to_datetime(df[datetime_col_name], format='%m/%d/%Y %H:%M:%S') dt_col=pd.to_datetime(df[datetime_col_name]) #df['tvalue'] = df[datetime_col_name] df['time_diff'] = (df[datetime_col_name]-df[datetime_col_name].shift()).fillna(pd.Timedelta('0')) mean_diff_dt=df['time_diff'].mean() time_diff_secs=mean_diff_dt.total_seconds() time_sec_2_hr=((time_diff_secs/60)/60) pred_freq="" time_sec_2_hr=round(time_sec_2_hr) #For abbreviation ,refer https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases if (time_sec_2_hr < 1): pred_freq="min" else: if (time_sec_2_hr >= 24): if (time_sec_2_hr > 168): if(time_sec_2_hr > 696 or time_sec_2_hr < 744): # based on 29 days, to 31 days if(time_sec_2_hr > 8760): pred_freq="Y" else: pred_freq="M" else: pred_freq="W" else: pred_freq="D" else: pred_freq="H" pass return pred_freq #To extract dict key,values def extract_params(self,dict): self.dict=dict for k,v in self.dict.items(): return k,v def mean_absolute_percentage_error(self,y_true, y_pred): if (y_true.isin([0]).sum() > 0): y_true=y_true.mask(y_true==0).fillna(y_true.mean()) try: y_true, y_pred=np.array(y_true), np.array(y_pred) #return np.mean(np.abs((y_true - y_pred) / y_true+sys.float_info.epsilon)) * 100 return np.mean(np.abs((y_true - y_pred) / y_true)) * 100 except Exception as inst: self.log.info('<------------- mean_absolute_percentage_error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def regressor_list(self,regressorstr): lst = regressorstr.split (",") reg_list=[] for i in lst: reg_list.append(i) #print(reg_list) return reg_list # def get_regressors(self,reg): # print("get extra inputs for prophet...\n") def aion_probhet(self,train_data,datetime_col_name,predicted_data_file,dataFolderLocation): from prophet import Prophet #Getting prophet params #key,val = self.extract_params(self.tsprophet_params) val=self.tsprophet_params self.log.info('-------> The given prophet algorithm parameters:>>') self.log.info(" "+str(val)) changepoint_prior_scale=[] changepoint_range=[] mcmc_samples=[] interval_width=[] holidays_prior_scale=[] n_changepoints=[] uncertainty_samples=[] seasonality_prior_scale=[] seasonality_mode="" yearly_seasonality=None weekly_seasonality=None daily_seasonality=None additional_regressors="" holiday_country_name="" holiday_years=[] no_of_periods=0 pred_frequncy="" for k,v in val.items(): try: if (k == "seasonality_mode"): seasonality_mode=v elif (k == "changepoint_prior_scale"): changepoint_prior_scale=[float(i) for i in v.split(',')] elif (k == "changepoint_range"): changepoint_range=[float(i) for i in v.split(',')] elif (k == "yearly_seasonality"): if v.lower() == 'true': yearly_seasonality=True elif v.lower() == 'false': yearly_seasonality=False elif v.lower() == 'auto': yearly_seasonality=v else: yearly_seasonality=True elif (k == "weekly_seasonality"): if v.lower() == 'true': weekly_seasonality=True elif v.lower() == 'false': weekly_seasonality=False elif v.lower() == 'auto': weekly_seasonality=v else: weekly_seasonality=False #weekly_seasonality=v elif (k == "daily_seasonality"): if v.lower() == 'true': daily_seasonality=True elif v.lower() == 'false': daily_seasonality=False elif v.lower() == 'auto': daily_seasonality=v else: daily_seasonality=False elif (k == "mcmc_samples"): mcmc_samples=[float(i) for i in v.split(',')] elif (k == "interval_width"): interval_width=[float(i) for i in v.split(',')] elif (k == "holidays_prior_scale"): #holidays_prior_scale=float(v) holidays_prior_scale=[float(i) for i in v.split(',')] elif (k == "n_changepoints"): n_changepoints=[int(i) for i in v.split(',')] elif (k == "uncertainty_samples"): uncertainty_samples=[float(i) for i in v.split(',')] elif (k == "seasonality_prior_scale"): seasonality_prior_scale=[float(i) for i in v.split(',')] elif (k == "additional_regressors"): additional_regressors=str(v) elif (k == "holiday_country_name"): holiday_country_name=v elif (k == "holiday_years"): holiday_years=[int(i) for i in v.split(',')] elif (k == "no_of_periods"): no_of_periods=int(v) elif (k == "pred_frequncy"): pred_frequncy=v else: self.log.info("Invalid string.") except Exception: continue try: start = time.time() datetime_col_name=str(datetime_col_name) target_col=str(self.targetFeature) #extra_regressors=additional_regressors reg_list=self.regressor_list(additional_regressors) get_dtcol="" get_dtcol=self.get_datetime_col(self.data)[0] #get predict frequency for user data pred_freq= str(self.get_predict_frequency(self.data,datetime_col_name)) if (pred_frequncy): pred_frequncy=pred_frequncy else: #If user not defined predict_freq in aion config or GUI, our algorithm will find automatically by get_predict_frequency() method pred_frequncy=pred_freq self.log.info("Auto Predict frequency period (Hour-H/Day-D/Week-W/Month-M/Year-Y): \n"+str(pred_frequncy)) #For proper datetime format check. self.data[self.dateTimeFeature] = pd.to_datetime(self.data[self.dateTimeFeature]) filterd_df = self.data.filter([get_dtcol,target_col]) holiday = pd.DataFrame([]) holiday_specified=holidays.CountryHoliday(holiday_country_name,years=holiday_years) for date, name in sorted(holiday_specified.items()): holiday = holiday.append(pd.DataFrame({'ds': date, 'holiday': "Holidays"}, index=[0]), ignore_index=True) holiday['ds'] = pd.to_datetime(holiday['ds'], format='%Y-%m-%d %H:%M:%S', errors='ignore') filterd_df=filterd_df.rename(columns={self.dateTimeFeature:'ds',target_col:'y'}) #Set seasonality model try: if not seasonality_mode: self.log.info('empty input for seasonality_mode parameter in aion configuration file.Please check. Setting default mode: additive. \n') seasonality_mode=[] seasonality_mode=['additive'] multiplicative_s="multiplicative" additive_s="additive" else: seasonality_mode = seasonality_mode.split(',') len_seasonality_mode=len(seasonality_mode) except ValueError as e: self.log.info(e) params_grid = {'seasonality_mode':(seasonality_mode), 'changepoint_prior_scale':changepoint_prior_scale, 'changepoint_range': changepoint_range, 'yearly_seasonality': [yearly_seasonality], 'weekly_seasonality': [weekly_seasonality], 'daily_seasonality': [daily_seasonality], 'mcmc_samples': mcmc_samples, 'interval_width': interval_width, 'holidays_prior_scale':holidays_prior_scale, 'n_changepoints' : n_changepoints, 'uncertainty_samples': uncertainty_samples, 'seasonality_prior_scale': seasonality_prior_scale} grid = ParameterGrid(params_grid) p_cnt = 0 for p in grid: p_cnt = p_cnt+1 self.log.info("--------------- Total Possible prophet iterations: --------------- \n") self.log.info(p_cnt) self.log.info("\n--------------- Modal Validation Start ---------------") size = int(len(filterd_df) * (100 - self.testpercentage)/100) train = filterd_df.loc[0:size] valid = filterd_df.loc[size:len(filterd_df)] self.log.info("------->Train Data Shape: "+str(train.shape)) self.log.info("------->Valid Data Shape"+str(valid.shape)) X_train = train X_test = valid len_test=len(X_test) #For add_regressor,copy the add_regressor columns to use. if (additional_regressors): df1=pd.DataFrame() df1[additional_regressors]=self.data[additional_regressors] model_parameters_mape = pd.DataFrame(columns = ['MAPE','Parameters']) model_parameters_rmse = pd.DataFrame(columns = ['rmse','Parameters']) model_parameters_mse = pd.DataFrame(columns = ['mse','Parameters']) model_parameters_mae = pd.DataFrame(columns = ['MAE','Parameters']) model_parameters_r2 = pd.DataFrame(columns = ['r2','Parameters']) for P in grid: pred_forecast = pd.DataFrame() random.seed(0) train_model =Prophet(changepoint_prior_scale = P['changepoint_prior_scale'], seasonality_mode=P['seasonality_mode'], changepoint_range=P['changepoint_range'], holidays_prior_scale = P['holidays_prior_scale'], n_changepoints = P['n_changepoints'], mcmc_samples=P['mcmc_samples'], interval_width=P['interval_width'], uncertainty_samples=P['uncertainty_samples'], seasonality_prior_scale= P['seasonality_prior_scale'], holidays=holiday, weekly_seasonality=P['weekly_seasonality'], daily_seasonality = P['daily_seasonality'], yearly_seasonality = P['yearly_seasonality'] ) train_forecast=pd.DataFrame() try: train_model.fit(X_train) train_forecast = train_model.make_future_dataframe(periods=len_test, freq=pred_frequncy,include_history = False) train_forecast = train_model.predict(train_forecast) except ValueError as e: self.log.info(e) self.log.info ("------->Check mcmc_samples value in aion confiuration, either 0 (default) or defined value,e.g.mcmc_samples:'300' to be set.If no idea on value, set to default.\n") pred_forecast=train_forecast[['ds','yhat']] Actual=X_test len_act=len(Actual['y']) len_pred=len(pred_forecast['yhat']) MAPE = self.mean_absolute_percentage_error(Actual['y'],abs(pred_forecast['yhat'])) model_parameters_mape = model_parameters_mape.append({'MAPE':MAPE,'Parameters':p},ignore_index=True) #MAE MAE = mean_absolute_error(Actual['y'],abs(pred_forecast['yhat'])) rmse = sqrt(mean_squared_error(Actual['y'],abs(pred_forecast['yhat']))) mse = mean_squared_error(Actual['y'],abs(pred_forecast['yhat'])) r2 = r2_score(Actual['y'],abs(pred_forecast['yhat'])) # self.log.info ("------->Prophet RMSE :"+str(rmse)) # self.log.info ("------->Prophet MSE :"+str(mse)) # self.log.info ("------->Prophet MAE :"+str(MAE)) # self.log.info ("------->Prophet R2 :"+str(r2)) model_parameters_mape = model_parameters_mape.append({'MAPE':MAPE,'Parameters':p},ignore_index=True) model_parameters_rmse = model_parameters_rmse.append({'rmse':rmse,'Parameters':p},ignore_index=True) model_parameters_mse = model_parameters_mse.append({'mse':mse,'Parameters':p},ignore_index=True) model_parameters_mae = model_parameters_mae.append({'MAE':MAE,'Parameters':p},ignore_index=True) model_parameters_r2 = model_parameters_r2.append({'r2':r2,'Parameters':p},ignore_index=True) #end of for loop parameters_mape = model_parameters_mape.sort_values(by=['MAPE']) parameters_mape = parameters_mape.reset_index(drop=True) best_params_mape=parameters_mape['Parameters'][0] # print("Best Parameters on which the model has the least MAPE is: \n",best_params_mape) best_mape_score=parameters_mape['MAPE'].iloc[0] #self.log.info('------->Mean absolute percent error log: \n ') #self.log.info('------->best_mape_score: \n '+str(best_mape_score)) parameters_rmse = model_parameters_rmse.sort_values(by=['rmse']) parameters_rmse = parameters_rmse.reset_index(drop=True) best_params_rmse=parameters_rmse['Parameters'][0] best_rmse_score=parameters_rmse['rmse'].iloc[0] #self.log.info('------->Root Man Squared Error log (Prophet timeseries): \n ') #self.log.info('------->best_rmse_score ((Prophet timeseries)): \n '+str(best_rmse_score)) #mse parameters_mse = model_parameters_mse.sort_values(by=['mse']) parameters_mse = parameters_mse.reset_index(drop=True) best_params_mse = parameters_mse['Parameters'][0] best_mse_score=parameters_mse['mse'].iloc[0] #MAE parameters_mae = model_parameters_mae.sort_values(by=['MAE']) parameters_mae = parameters_mae.reset_index(drop=True) best_params_mae = parameters_mae['Parameters'][0] best_mae_score=parameters_mae['MAE'].iloc[0] # R2 score parameters_r2 = model_parameters_r2.sort_values(by=['r2']) parameters_r2 = parameters_r2.reset_index(drop=False) best_params_r2 = parameters_r2['Parameters'][0] best_r2_score=parameters_r2['r2'].iloc[0] #Final best prophet mse,rmse,mape scores # self.log.info ("------->Prophet RMSE :"+str(best_rmse_score)) # self.log.info ("------->Prophet MSE :"+str(best_mse_score)) # self.log.info ("------->Prophet MAE :"+str(best_mae_score)) # self.log.info ("------->Prophet R2 :"+str(best_r2_score)) #Extracting best model parameters for k,v in best_params_mape.items(): try: if (k == "changepoint_prior_scale"): changepoint_prior_scale=float(v) elif (k == "changepoint_range"): changepoint_range=float(v) elif (k == "daily_seasonality"): daily_seasonality=v elif (k == "holidays_prior_scale"): holidays_prior_scale=float(v) elif (k == "interval_width"): interval_width=float(v) elif (k == "mcmc_samples"): mcmc_samples=float(v) elif (k == "n_changepoints"): n_changepoints=int(v) elif (k == "seasonality_mode"): seasonality_mode=str(v) elif (k == "seasonality_prior_scale"): seasonality_prior_scale=int(v) elif (k == "uncertainty_samples"): uncertainty_samples=float(v) elif (k == "weekly_seasonality"): weekly_seasonality=v elif (k == "yearly_seasonality"): yearly_seasonality=v else: pass except Exception as e: self.log.info("\n prophet time series config param parsing error"+str(e)) #continue self.log.info("\n Best prophet model accuracy parameters.\n ") #Prophet model based on mape best params. best_prophet_model = Prophet(holidays=holiday, changepoint_prior_scale= changepoint_prior_scale, holidays_prior_scale = holidays_prior_scale, n_changepoints = n_changepoints, seasonality_mode = seasonality_mode, weekly_seasonality= weekly_seasonality, daily_seasonality = daily_seasonality, yearly_seasonality = yearly_seasonality, interval_width=interval_width, mcmc_samples=mcmc_samples, changepoint_range=changepoint_range) # If holiday not set using prophet model,we can add as below. # best_prophet_model.add_country_holidays(country_name=holiday_country_name) #prophet add_regressor ,adding additional influencer (regressor) features, but it different from multivariant model. if (additional_regressors): filterd_df[additional_regressors] = df1[additional_regressors] filterd_df.reset_index(drop=True) for v in reg_list: best_prophet_model=best_prophet_model.add_regressor(v) #best_prophet_model.fit(X_train) else: pass #Model prophet fit, it should be done before make_future_dataframe best_prophet_model.fit(filterd_df) future = best_prophet_model.make_future_dataframe(periods=no_of_periods, freq=pred_frequncy,include_history = False) if (additional_regressors): future[additional_regressors] = filterd_df[additional_regressors] future.reset_index(drop=True) future=future.dropna() else: pass #Final prediction forecast = best_prophet_model.predict(future) # forecast_df=forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']] # #Save forecast as csv file # forecast_df.to_csv(r"prophet_realtime_user_steps.csv",index = False, header=True) #Plot the predition and save in file forecast_plot = best_prophet_model.plot(forecast) imagefilename = os.path.join(dataFolderLocation,'log','img','prophet_fig.png') forecast_plot.savefig(imagefilename) #The below part is used to compare prophet predicted with actual value #For train data #Prophet model with train and test data, based on mape best params. best_prophet_model_new = Prophet(holidays=holiday, changepoint_prior_scale= changepoint_prior_scale, holidays_prior_scale = holidays_prior_scale, n_changepoints = n_changepoints, seasonality_mode = seasonality_mode, weekly_seasonality= weekly_seasonality, daily_seasonality = daily_seasonality, yearly_seasonality = yearly_seasonality, interval_width=interval_width, mcmc_samples=mcmc_samples, changepoint_range=changepoint_range) fp_forecast=pd.DataFrame() try: best_prophet_model_new.fit(X_train) fp_forecast = best_prophet_model_new.make_future_dataframe(periods=len_test, freq=pred_frequncy,include_history = False) fp_forecast = best_prophet_model_new.predict(fp_forecast) except ValueError as e: self.log.info(e) self.log.info ("------->Check mcmc_samples value in aion confiuration, either 0 (default) or defined value,e.g.mcmc_samples:'300' to be set.If no idea on value, set to default.\n") pred_forecast=fp_forecast[['ds','yhat']] pred_forecast['ds']=Actual['ds'].to_numpy() Actual.ds = pd.to_datetime(Actual.ds) pred_forecast.ds = pd.to_datetime(pred_forecast.ds) MAE = mean_absolute_error(Actual['y'],abs(pred_forecast['yhat'])) rmse = sqrt(mean_squared_error(Actual['y'],abs(pred_forecast['yhat']))) mse = mean_squared_error(Actual['y'],abs(pred_forecast['yhat'])) r2 = r2_score(Actual['y'],abs(pred_forecast['yhat'])) MAPE = self.mean_absolute_percentage_error(Actual['y'],abs(pred_forecast['yhat'])) #Final best prophet mse,rmse,mape scores self.log.info ("------->Prophet RMSE : "+str(rmse)) self.log.info ("------->Prophet MSE : "+str(mse)) self.log.info ("------->Prophet MAE : "+str(MAE)) self.log.info ("------->Prophet R2 : "+str(r2)) self.log.info("------->Prophet MAPE: "+str(MAPE)) #self.log.info(MAPE) #self.log.info('------->best_mape_score: \n '+str(best_mape_score)) prophet_df = pd.merge(Actual,pred_forecast, on=['ds'], how='left') cols = ['ds','y','yhat'] prophet_df_new = prophet_df[cols] prophet_df_new.dropna(inplace=True) actualfeature = target_col+'_actual' predictfeature = target_col+'_pred' prophet_df_new=prophet_df_new.rename(columns={'ds': 'datetime', 'y': actualfeature,'yhat': predictfeature}) #prophet_df_new.to_csv(predicted_data_file) #cv_results = cross_validation( model = best_prophet_model, initial = pd.to_timedelta(no_of_periods,unit=pred_frequncy), horizon = pd.to_timedelta(no_of_periods,unit=pred_frequncy)) #forecast_df=forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']] #Save forecast as csv file #forecast_df.to_csv(r"prophet_realtime_Output.csv",index = False, header=True) # self.log.info('------->Prophet time series forecast (last 7 prediction for user view): \n ') # self.log.info(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(7)) plot_prd=plot_plotly(best_prophet_model, forecast) imagefilename = os.path.join(dataFolderLocation,'log','img','1_ppm_plot') plotly.offline.plot(plot_prd, filename=imagefilename,auto_open = False) plot_prd_components=plot_components_plotly(best_prophet_model, forecast) imagefilename = os.path.join(dataFolderLocation,'log','img','2_ppm_plot') plotly.offline.plot(plot_prd_components, filename=imagefilename,auto_open = False) executionTime=(time.time() - start) self.log.info('-------> Time: '+str(executionTime)) return best_prophet_model,best_mae_score,best_rmse_score,best_mse_score,best_mape_score,best_r2_score,pred_frequncy,additional_regressors,prophet_df_new except Exception as inst: #print("********** aion_fbprophet exception ************* \n") self.log.info('<!------------- Prophet Execute Error ---------------> '+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator import numpy as np import pandas as pd from tensorflow.python.platform import googletest class GenericFeatureStatisticsGeneratorTest(googletest.TestCase): def setUp(self): self.gfsg = GenericFeatureStatisticsGenerator() def testProtoFromDataFrames(self): data = [[1, 'hi'], [2, 'hello'], [3, 'hi']] df = pd.DataFrame(data, columns=['testFeatureInt', 'testFeatureString']) dataframes = [{'table': df, 'name': 'testDataset'}] p = self.gfsg.ProtoFromDataFrames(dataframes) self.assertEqual(1, len(p.datasets)) test_data = p.datasets[0] self.assertEqual('testDataset', test_data.name) self.assertEqual(3, test_data.num_examples) self.assertEqual(2, len(test_data.features)) if test_data.features[0].name == 'testFeatureInt': numfeat = test_data.features[0] stringfeat = test_data.features[1] else: numfeat = test_data.features[1] stringfeat = test_data.features[0] self.assertEqual('testFeatureInt', numfeat.name) self.assertEqual(self.gfsg.fs_proto.INT, numfeat.type) self.assertEqual(1, numfeat.num_stats.min) self.assertEqual(3, numfeat.num_stats.max) self.assertEqual('testFeatureString', stringfeat.name) self.assertEqual(self.gfsg.fs_proto.STRING, stringfeat.type) self.assertEqual(2, stringfeat.string_stats.unique) def testNdarrayToEntry(self): arr = np.array([1.0, 2.0, None, float('nan'), 3.0], dtype=float) entry = self.gfsg.NdarrayToEntry(arr) self.assertEqual(2, entry['missing']) arr = np.array(['a', 'b', float('nan'), 'c'], dtype=str) entry = self.gfsg.NdarrayToEntry(arr) self.assertEqual(1, entry['missing']) def testNdarrayToEntryTimeTypes(self): arr = np.array( [np.datetime64('2005-02-25'), np.datetime64('2006-02-25')], dtype=np.datetime64) entry = self.gfsg.NdarrayToEntry(arr) self.assertEqual([1109289600000000000, 1140825600000000000], entry['vals']) arr = np.array( [np.datetime64('2009-01-01') - np.datetime64('2008-01-01')], dtype=np.timedelta64) entry = self.gfsg.NdarrayToEntry(arr) self.assertEqual([31622400000000000], entry['vals']) def testDTypeToType(self): self.assertEqual(self.gfsg.fs_proto.INT, self.gfsg.DtypeToType(np.dtype(np.int32))) # Boolean and time types treated as int self.assertEqual(self.gfsg.fs_proto.INT, self.gfsg.DtypeToType(np.dtype(np.bool))) self.assertEqual(self.gfsg.fs_proto.INT, self.gfsg.DtypeToType(np.dtype(np.datetime64))) self.assertEqual(self.gfsg.fs_proto.INT, self.gfsg.DtypeToType(np.dtype(np.timedelta64))) self.assertEqual(self.gfsg.fs_proto.FLOAT, self.gfsg.DtypeToType(np.dtype(np.float32))) self.assertEqual(self.gfsg.fs_proto.STRING, self.gfsg.DtypeToType(np.dtype(np.str))) # Unsupported types treated as string for now self.assertEqual(self.gfsg.fs_proto.STRING, self.gfsg.DtypeToType(np.dtype(np.void))) def testGetDatasetsProtoFromEntriesLists(self): entries = {} entries['testFeature'] = { 'vals': [1, 2, 3], 'counts': [1, 1, 1], 'missing': 0, 'type': self.gfsg.fs_proto.INT } datasets = [{'entries': entries, 'size': 3, 'name': 'testDataset'}] p = self.gfsg.GetDatasetsProto(datasets) self.assertEqual(1, len(p.datasets)) test_data = p.datasets[0] self.assertEqual('testDataset', test_data.name) self.assertEqual(3, test_data.num_examples) self.assertEqual(1, len(test_data.features)) numfeat = test_data.features[0] self.assertEqual('testFeature', numfeat.name) self.assertEqual(self.gfsg.fs_proto.INT, numfeat.type) self.assertEqual(1, numfeat.num_stats.min) self.assertEqual(3, numfeat.num_stats.max) hist = numfeat.num_stats.common_stats.num_values_histogram buckets = hist.buckets self.assertEqual(self.gfsg.histogram_proto.QUANTILES, hist.type) self.assertEqual(10, len(buckets)) self.assertEqual(1, buckets[0].low_value) self.assertEqual(1, buckets[0].high_value) self.assertEqual(.3, buckets[0].sample_count) self.assertEqual(1, buckets[9].low_value) self.assertEqual(1, buckets[9].high_value) self.assertEqual(.3, buckets[9].sample_count) def testGetDatasetsProtoSequenceExampleHistogram(self): entries = {} entries['testFeature'] = { 'vals': [1, 2, 2, 3], 'counts': [1, 2, 1], 'feat_lens': [1, 2, 1], 'missing': 0, 'type': self.gfsg.fs_proto.INT } datasets = [{'entries': entries, 'size': 3, 'name': 'testDataset'}] p = self.gfsg.GetDatasetsProto(datasets) hist = p.datasets[0].features[ 0].num_stats.common_stats.feature_list_length_histogram buckets = hist.buckets self.assertEqual(self.gfsg.histogram_proto.QUANTILES, hist.type) self.assertEqual(10, len(buckets)) self.assertEqual(1, buckets[0].low_value) self.assertEqual(1, buckets[0].high_value) self.assertEqual(.3, buckets[0].sample_count) self.assertEqual(1.8, buckets[9].low_value) self.assertEqual(2, buckets[9].high_value) self.assertEqual(.3, buckets[9].sample_count) def testGetDatasetsProtoWithWhitelist(self): entries = {} entries['testFeature'] = { 'vals': [1, 2, 3], 'counts': [1, 1, 1], 'missing': 0, 'type': self.gfsg.fs_proto.INT } entries['ignoreFeature'] = { 'vals': [5, 6], 'counts': [1, 1], 'missing': 1, 'type': self.gfsg.fs_proto.INT } datasets = [{'entries': entries, 'size': 3, 'name': 'testDataset'}] p = self.gfsg.GetDatasetsProto(datasets, features=['testFeature']) self.assertEqual(1, len(p.datasets)) test_data = p.datasets[0] self.assertEqual('testDataset', test_data.name) self.assertEqual(3, test_data.num_examples) self.assertEqual(1, len(test_data.features)) numfeat = test_data.features[0] self.assertEqual('testFeature', numfeat.name) self.assertEqual(1, numfeat.num_stats.min) def testGetDatasetsProtoWithMaxHistigramLevelsCount(self): # Selected entries' lengths make it easy to compute average length data = [['hi'], ['good'], ['hi'], ['hi'], ['a'], ['a']] df = pd.DataFrame(data, columns=['testFeatureString']) dataframes = [{'table': df, 'name': 'testDataset'}] # Getting proto from ProtoFromDataFrames instead of GetDatasetsProto # directly to avoid any hand written values ex: size of dataset. p = self.gfsg.ProtoFromDataFrames(dataframes, histogram_categorical_levels_count=2) self.assertEqual(1, len(p.datasets)) test_data = p.datasets[0] self.assertEqual('testDataset', test_data.name) self.assertEqual(6, test_data.num_examples) self.assertEqual(1, len(test_data.features)) numfeat = test_data.features[0] self.assertEqual('testFeatureString', numfeat.name) top_values = numfeat.string_stats.top_values self.assertEqual(3, top_values[0].frequency) self.assertEqual('hi', top_values[0].value) self.assertEqual(3, numfeat.string_stats.unique) self.assertEqual(2, numfeat.string_stats.avg_length) rank_hist = numfeat.string_stats.rank_histogram buckets = rank_hist.buckets self.assertEqual(2, len(buckets)) self.assertEqual('hi', buckets[0].label) self.assertEqual(3, buckets[0].sample_count) self.assertEqual('a', buckets[1].label) self.assertEqual(2, buckets[1].sample_count) if __name__ == '__main__': googletest.main() # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Code for generating the feature_statistics proto from generic data. The proto is used as input for the Overview visualization. """ from facets_overview.base_generic_feature_statistics_generator import BaseGenericFeatureStatisticsGenerator import facets_overview.feature_statistics_pb2 as fs class GenericFeatureStatisticsGenerator(BaseGenericFeatureStatisticsGenerator): """Generator of stats proto from generic data.""" def __init__(self): BaseGenericFeatureStatisticsGenerator.__init__( self, fs.FeatureNameStatistics, fs.DatasetFeatureStatisticsList, fs.Histogram) # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== from facets_overview.feature_statistics_generator import FeatureStatisticsGenerator import numpy as np import tensorflow as tf from tensorflow.python.platform import googletest class FeatureStatisticsGeneratorTest(googletest.TestCase): def setUp(self): self.fs = FeatureStatisticsGenerator() def testParseExampleInt(self): # Tests parsing examples of integers examples = [] for i in range(50): example = tf.train.Example() example.features.feature['num'].int64_list.value.append(i) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.features.feature, [], entries, i) self.assertEqual(1, len(entries)) self.assertIn('num', entries) info = entries['num'] self.assertEqual(0, info['missing']) self.assertEqual(self.fs.fs_proto.INT, info['type']) for i in range(len(examples)): self.assertEqual(1, info['counts'][i]) self.assertEqual(i, info['vals'][i]) def testParseExampleMissingValueList(self): # Tests parsing examples of integers examples = [] example = tf.train.Example() # pylint: disable=pointless-statement example.features.feature['str'] # pylint: enable=pointless-statement examples.append(example) example = tf.train.Example() example.features.feature['str'].bytes_list.value.append(b'test') examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.features.feature, [], entries, i) self.assertEqual(1, len(entries)) self.assertIn('str', entries) info = entries['str'] self.assertEqual(1, info['missing']) self.assertEqual(self.fs.fs_proto.STRING, info['type']) self.assertEqual(0, info['counts'][0]) self.assertEqual(1, info['counts'][1]) def _check_sequence_example_entries(self, entries, n_examples, n_features, feat_len=None): self.assertIn('num', entries) info = entries['num'] self.assertEqual(0, info['missing']) self.assertEqual(self.fs.fs_proto.INT, info['type']) for i in range(n_examples): self.assertEqual(n_features, info['counts'][i]) if feat_len is not None: self.assertEqual(feat_len, info['feat_lens'][i]) for i in range(n_examples * n_features): self.assertEqual(i, info['vals'][i]) if feat_len is None: self.assertEqual(0, len(info['feat_lens'])) def testParseExampleSequenceContext(self): # Tests parsing examples of integers in context field examples = [] for i in range(50): example = tf.train.SequenceExample() example.context.feature['num'].int64_list.value.append(i) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.context.feature, example.feature_lists.feature_list, entries, i) self._check_sequence_example_entries(entries, 50, 1) self.assertEqual(1, len(entries)) def testParseExampleSequenceFeatureList(self): examples = [] for i in range(50): example = tf.train.SequenceExample() feat = example.feature_lists.feature_list['num'].feature.add() feat.int64_list.value.append(i) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.context.feature, example.feature_lists.feature_list, entries, i) self._check_sequence_example_entries(entries, 50, 1, 1) def testParseExampleSequenceFeatureListMultipleEntriesInner(self): examples = [] for i in range(2): example = tf.train.SequenceExample() feat = example.feature_lists.feature_list['num'].feature.add() for j in range(25): feat.int64_list.value.append(i * 25 + j) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.context.feature, example.feature_lists.feature_list, entries, i) self._check_sequence_example_entries(entries, 2, 25, 1) def testParseExampleSequenceFeatureListMultipleEntriesOuter(self): # Tests parsing examples of integers in context field examples = [] for i in range(2): example = tf.train.SequenceExample() for j in range(25): feat = example.feature_lists.feature_list['num'].feature.add() feat.int64_list.value.append(i * 25 + j) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.context.feature, example.feature_lists.feature_list, entries, i) self._check_sequence_example_entries(entries, 2, 25, 25) def testVaryingCountsAndMissing(self): # Tests parsing examples of when some examples have missing features examples = [] for i in range(5): example = tf.train.Example() example.features.feature['other'].int64_list.value.append(0) for _ in range(i): example.features.feature['num'].int64_list.value.append(i) examples.append(example) example = tf.train.Example() example.features.feature['other'].int64_list.value.append(0) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.features.feature, [], entries, i) info = entries['num'] self.assertEqual(2, info['missing']) self.assertEqual(4, len(info['counts'])) for i in range(4): self.assertEqual(i + 1, info['counts'][i]) self.assertEqual(10, len(info['vals'])) def testParseExampleStringsAndFloats(self): # Tests parsing examples of string and float features examples = [] for i in range(50): example = tf.train.Example() example.features.feature['str'].bytes_list.value.append(b'hi') example.features.feature['float'].float_list.value.append(i) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.features.feature, [], entries, i) self.assertEqual(2, len(entries)) self.assertEqual(self.fs.fs_proto.FLOAT, entries['float']['type']) self.assertEqual(self.fs.fs_proto.STRING, entries['str']['type']) for i in range(len(examples)): self.assertEqual(1, entries['str']['counts'][i]) self.assertEqual(1, entries['float']['counts'][i]) self.assertEqual(i, entries['float']['vals'][i]) self.assertEqual('hi', entries['str']['vals'][i].decode( 'UTF-8', 'strict')) def testParseExamplesTypeMismatch(self): examples = [] example = tf.train.Example() example.features.feature['feat'].int64_list.value.append(0) examples.append(example) example = tf.train.Example() example.features.feature['feat'].bytes_list.value.append(b'str') examples.append(example) entries = {} self.fs._ParseExample(examples[0].features.feature, [], entries, 0) with self.assertRaises(TypeError): self.fs._ParseExample(examples[1].features.feature, [], entries, 1) def testGetDatasetsProtoFromEntriesLists(self): entries = {} entries['testFeature'] = { 'vals': [1, 2, 3], 'counts': [1, 1, 1], 'missing': 0, 'type': self.fs.fs_proto.INT } datasets = [{'entries': entries, 'size': 3, 'name': 'testDataset'}] p = self.fs.GetDatasetsProto(datasets) self.assertEqual(1, len(p.datasets)) test_data = p.datasets[0] self.assertEqual('testDataset', test_data.name) self.assertEqual(3, test_data.num_examples) self.assertEqual(1, len(test_data.features)) numfeat = test_data.features[0] self.assertEqual('testFeature', numfeat.name) self.assertEqual(self.fs.fs_proto.INT, numfeat.type) self.assertEqual(1, numfeat.num_stats.min) self.assertEqual(3, numfeat.num_stats.max) def testGetProtoNums(self): # Tests converting int examples into the feature stats proto examples = [] for i in range(50): example = tf.train.Example() example.features.feature['num'].int64_list.value.append(i) examples.append(example) example = tf.train.Example() example.features.feature['other'].int64_list.value.append(0) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.features.feature, [], entries, i) datasets = [{'entries': entries, 'size': len(examples), 'name': 'test'}] p = self.fs.GetDatasetsProto(datasets) self.assertEqual(1, len(p.datasets)) test_data = p.datasets[0] self.assertEqual('test', test_data.name) self.assertEqual(51, test_data.num_examples) numfeat = test_data.features[0] if ( test_data.features[0].name == 'num') else test_data.features[1] self.assertEqual('num', numfeat.name) self.assertEqual(self.fs.fs_proto.INT, numfeat.type) self.assertEqual(0, numfeat.num_stats.min) self.assertEqual(49, numfeat.num_stats.max) self.assertEqual(24.5, numfeat.num_stats.mean) self.assertEqual(24.5, numfeat.num_stats.median) self.assertEqual(1, numfeat.num_stats.num_zeros) self.assertAlmostEqual(14.430869689, numfeat.num_stats.std_dev, 4) self.assertEqual(1, numfeat.num_stats.common_stats.num_missing) self.assertEqual(50, numfeat.num_stats.common_stats.num_non_missing) self.assertEqual(1, numfeat.num_stats.common_stats.min_num_values) self.assertEqual(1, numfeat.num_stats.common_stats.max_num_values) self.assertAlmostEqual(1, numfeat.num_stats.common_stats.avg_num_values, 4) hist = numfeat.num_stats.common_stats.num_values_histogram buckets = hist.buckets self.assertEqual(self.fs.histogram_proto.QUANTILES, hist.type) self.assertEqual(10, len(buckets)) self.assertEqual(1, buckets[0].low_value) self.assertEqual(1, buckets[0].high_value) self.assertEqual(5, buckets[0].sample_count) self.assertEqual(1, buckets[9].low_value) self.assertEqual(1, buckets[9].high_value) self.assertEqual(5, buckets[9].sample_count) self.assertEqual(2, len(numfeat.num_stats.histograms)) buckets = numfeat.num_stats.histograms[0].buckets self.assertEqual(self.fs.histogram_proto.STANDARD, numfeat.num_stats.histograms[0].type) self.assertEqual(10, len(buckets)) self.assertEqual(0, buckets[0].low_value) self.assertEqual(4.9, buckets[0].high_value) self.assertEqual(5, buckets[0].sample_count) self.assertAlmostEqual(44.1, buckets[9].low_value) self.assertEqual(49, buckets[9].high_value) self.assertEqual(5, buckets[9].sample_count) buckets = numfeat.num_stats.histograms[1].buckets self.assertEqual(self.fs.histogram_proto.QUANTILES, numfeat.num_stats.histograms[1].type) self.assertEqual(10, len(buckets)) self.assertEqual(0, buckets[0].low_value) self.assertEqual(4.9, buckets[0].high_value) self.assertEqual(5, buckets[0].sample_count) self.assertAlmostEqual(44.1, buckets[9].low_value) self.assertEqual(49, buckets[9].high_value) self.assertEqual(5, buckets[9].sample_count) def testQuantiles(self): examples = [] for i in range(50): example = tf.train.Example() example.features.feature['num'].int64_list.value.append(i) examples.append(example) for i in range(50): example = tf.train.Example() example.features.feature['num'].int64_list.value.append(100) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.features.feature, [], entries, i) datasets = [{'entries': entries, 'size': len(examples), 'name': 'test'}] p = self.fs.GetDatasetsProto(datasets) numfeat = p.datasets[0].features[0] self.assertEqual(2, len(numfeat.num_stats.histograms)) self.assertEqual(self.fs.histogram_proto.QUANTILES, numfeat.num_stats.histograms[1].type) buckets = numfeat.num_stats.histograms[1].buckets self.assertEqual(10, len(buckets)) self.assertEqual(0, buckets[0].low_value) self.assertEqual(9.9, buckets[0].high_value) self.assertEqual(10, buckets[0].sample_count) self.assertEqual(100, buckets[9].low_value) self.assertEqual(100, buckets[9].high_value) self.assertEqual(10, buckets[9].sample_count) def testInfinityAndNan(self): examples = [] for i in range(50): example = tf.train.Example() example.features.feature['num'].float_list.value.append(i) examples.append(example) example = tf.train.Example() example.features.feature['num'].float_list.value.append(float('inf')) examples.append(example) example = tf.train.Example() example.features.feature['num'].float_list.value.append(float('-inf')) examples.append(example) example = tf.train.Example() example.features.feature['num'].float_list.value.append(float('nan')) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.features.feature, [], entries, i) datasets = [{'entries': entries, 'size': len(examples), 'name': 'test'}] p = self.fs.GetDatasetsProto(datasets) numfeat = p.datasets[0].features[0] self.assertEqual('num', numfeat.name) self.assertEqual(self.fs.fs_proto.FLOAT, numfeat.type) self.assertTrue(np.isnan(numfeat.num_stats.min)) self.assertTrue(np.isnan(numfeat.num_stats.max)) self.assertTrue(np.isnan(numfeat.num_stats.mean)) self.assertTrue(np.isnan(numfeat.num_stats.median)) self.assertEqual(1, numfeat.num_stats.num_zeros) self.assertTrue(np.isnan(numfeat.num_stats.std_dev)) self.assertEqual(53, numfeat.num_stats.common_stats.num_non_missing) hist = buckets = numfeat.num_stats.histograms[0] buckets = hist.buckets self.assertEqual(self.fs.histogram_proto.STANDARD, hist.type) self.assertEqual(1, hist.num_nan) self.assertEqual(10, len(buckets)) self.assertEqual(float('-inf'), buckets[0].low_value) self.assertEqual(4.9, buckets[0].high_value) self.assertEqual(6, buckets[0].sample_count) self.assertEqual(44.1, buckets[9].low_value) self.assertEqual(float('inf'), buckets[9].high_value) self.assertEqual(6, buckets[9].sample_count) def testInfinitysOnly(self): examples = [] example = tf.train.Example() example.features.feature['num'].float_list.value.append(float('inf')) examples.append(example) example = tf.train.Example() example.features.feature['num'].float_list.value.append(float('-inf')) examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.features.feature, [], entries, i) datasets = [{'entries': entries, 'size': len(examples), 'name': 'test'}] p = self.fs.GetDatasetsProto(datasets) numfeat = p.datasets[0].features[0] hist = buckets = numfeat.num_stats.histograms[0] buckets = hist.buckets self.assertEqual(self.fs.histogram_proto.STANDARD, hist.type) self.assertEqual(10, len(buckets)) self.assertEqual(float('-inf'), buckets[0].low_value) self.assertEqual(0.1, buckets[0].high_value) self.assertEqual(1, buckets[0].sample_count) self.assertEqual(0.9, buckets[9].low_value) self.assertEqual(float('inf'), buckets[9].high_value) self.assertEqual(1, buckets[9].sample_count) def testGetProtoStrings(self): # Tests converting string examples into the feature stats proto examples = [] for i in range(2): example = tf.train.Example() example.features.feature['str'].bytes_list.value.append(b'hello') examples.append(example) for i in range(3): example = tf.train.Example() example.features.feature['str'].bytes_list.value.append(b'hi') examples.append(example) example = tf.train.Example() example.features.feature['str'].bytes_list.value.append(b'hey') examples.append(example) entries = {} for i, example in enumerate(examples): self.fs._ParseExample(example.features.feature, [], entries, i) datasets = [{'entries': entries, 'size': len(examples), 'name': 'test'}] p = self.fs.GetDatasetsProto(datasets) self.assertEqual(1, len(p.datasets)) test_data = p.datasets[0] self.assertEqual('test', test_data.name) self.assertEqual(6, test_data.num_examples) strfeat = test_data.features[0] self.assertEqual('str', strfeat.name) self.assertEqual(self.fs.fs_proto.STRING, strfeat.type) self.assertEqual(3, strfeat.string_stats.unique) self.assertAlmostEqual(19 / 6.0, strfeat.string_stats.avg_length, 4) self.assertEqual(0, strfeat.string_stats.common_stats.num_missing) self.assertEqual(6, strfeat.string_stats.common_stats.num_non_missing) self.assertEqual(1, strfeat.string_stats.common_stats.min_num_values) self.assertEqual(1, strfeat.string_stats.common_stats.max_num_values) self.assertEqual(1, strfeat.string_stats.common_stats.avg_num_values) hist = strfeat.string_stats.common_stats.num_values_histogram buckets = hist.buckets self.assertEqual(self.fs.histogram_proto.QUANTILES, hist.type) self.assertEqual(10, len(buckets)) self.assertEqual(1, buckets[0].low_value) self.assertEqual(1, buckets[0].high_value) self.assertEqual(.6, buckets[0].sample_count) self.assertEqual(1, buckets[9].low_value) self.assertEqual(1, buckets[9].high_value) self.assertEqual(.6, buckets[9].sample_count) self.assertEqual(2, len(strfeat.string_stats.top_values)) self.assertEqual(3, strfeat.string_stats.top_values[0].frequency) self.assertEqual('hi', strfeat.string_stats.top_values[0].value) self.assertEqual(2, strfeat.string_stats.top_values[1].frequency) self.assertEqual('hello', strfeat.string_stats.top_values[1].value) buckets = strfeat.string_stats.rank_histogram.buckets self.assertEqual(3, len(buckets)) self.assertEqual(0, buckets[0].low_rank) self.assertEqual(0, buckets[0].high_rank) self.assertEqual(3, buckets[0].sample_count) self.assertEqual('hi', buckets[0].label) self.assertEqual(2, buckets[2].low_rank) self.assertEqual(2, buckets[2].high_rank) self.assertEqual(1, buckets[2].sample_count) self.assertEqual('hey', buckets[2].label) def testGetProtoMultipleDatasets(self): # Tests converting multiple datsets into the feature stats proto # including ensuring feature order is consistent in the protos. examples1 = [] for i in range(2): example = tf.train.Example() example.features.feature['str'].bytes_list.value.append(b'one') example.features.feature['num'].int64_list.value.append(0) examples1.append(example) examples2 = [] example = tf.train.Example() example.features.feature['num'].int64_list.value.append(1) example.features.feature['str'].bytes_list.value.append(b'two') examples2.append(example) entries1 = {} for i, example1 in enumerate(examples1): self.fs._ParseExample(example1.features.feature, [], entries1, i) entries2 = {} for i, example2 in enumerate(examples2): self.fs._ParseExample(example2.features.feature, [], entries2, i) datasets = [{ 'entries': entries1, 'size': len(examples1), 'name': 'test1' }, { 'entries': entries2, 'size': len(examples2), 'name': 'test2' }] p = self.fs.GetDatasetsProto(datasets) self.assertEqual(2, len(p.datasets)) test_data_1 = p.datasets[0] self.assertEqual('test1', test_data_1.name) self.assertEqual(2, test_data_1.num_examples) num_feat_index = 0 if test_data_1.features[0].name == 'num' else 1 self.assertEqual(0, test_data_1.features[num_feat_index].num_stats.max) test_data_2 = p.datasets[1] self.assertEqual('test2', test_data_2.name) self.assertEqual(1, test_data_2.num_examples) self.assertEqual(1, test_data_2.features[num_feat_index].num_stats.max) def testGetEntriesNoFiles(self): features, num_examples = self.fs._GetEntries(['test'], 10, lambda unused_path: []) self.assertEqual(0, num_examples) self.assertEqual({}, features) @staticmethod def get_example_iter(): def ex_iter(unused_filename): examples = [] for i in range(50): example = tf.train.Example() example.features.feature['num'].int64_list.value.append(i) examples.append(example.SerializeToString()) return examples return ex_iter def testGetEntries_one(self): features, num_examples = self.fs._GetEntries(['test'], 1, self.get_example_iter()) self.assertEqual(1, num_examples) self.assertTrue('num' in features) def testGetEntries_oneFile(self): unused_features, num_examples = self.fs._GetEntries(['test'], 1000, self.get_example_iter()) self.assertEqual(50, num_examples) def testGetEntries_twoFiles(self): unused_features, num_examples = self.fs._GetEntries(['test0', 'test1'], 1000, self.get_example_iter()) self.assertEqual(100, num_examples) def testGetEntries_stopInSecondFile(self): unused_features, num_examples = self.fs._GetEntries([ 'test@0', 'test@1', 'test@2', 'test@3', 'test@4', 'test@5', 'test@6', 'test@7', 'test@8', 'test@9' ], 75, self.get_example_iter()) self.assertEqual(75, num_examples) if __name__ == '__main__': googletest.main() # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Class for generating the feature_statistics proto. The proto is used as input for the Overview visualization. """ from facets_overview.base_feature_statistics_generator import BaseFeatureStatisticsGenerator import facets_overview.feature_statistics_pb2 as fs class FeatureStatisticsGenerator(BaseFeatureStatisticsGenerator): """Generator of stats proto from TF data.""" def __init__(self): BaseFeatureStatisticsGenerator.__init__(self, fs.FeatureNameStatistics, fs.DatasetFeatureStatisticsList, fs.Histogram) # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Base class for generating the feature_statistics proto from generic data. The proto is used as input for the Overview visualization. """ import numpy as np import pandas as pd import sys class BaseGenericFeatureStatisticsGenerator(object): """Base class for generator of stats proto from generic data.""" def __init__(self, fs_proto, datasets_proto, histogram_proto): self.fs_proto = fs_proto self.datasets_proto = datasets_proto self.histogram_proto = histogram_proto def ProtoFromDataFrames(self, dataframes, histogram_categorical_levels_count=None): """Creates a feature statistics proto from a set of pandas dataframes. Args: dataframes: A list of dicts describing tables for each dataset for the proto. Each entry contains a 'table' field of the dataframe of the data and a 'name' field to identify the dataset in the proto. histogram_categorical_levels_count: int, controls the maximum number of levels to display in histograms for categorical features. Useful to prevent codes/IDs features from bloating the stats object. Defaults to None. Returns: The feature statistics proto for the provided tables. """ datasets = [] for dataframe in dataframes: table = dataframe['table'] table_entries = {} for col in table: table_entries[col] = self.NdarrayToEntry(table[col]) datasets.append({ 'entries': table_entries, 'size': len(table), 'name': dataframe['name'] }) return self.GetDatasetsProto( datasets, histogram_categorical_levels_count=histogram_categorical_levels_count) def DtypeToType(self, dtype): """Converts a Numpy dtype to the FeatureNameStatistics.Type proto enum.""" if dtype.char in np.typecodes['AllFloat']: return self.fs_proto.FLOAT elif (dtype.char in np.typecodes['AllInteger'] or dtype == bool or np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)): return self.fs_proto.INT else: return self.fs_proto.STRING def DtypeToNumberConverter(self, dtype): """Converts a Numpy dtype to a converter method if applicable. The converter method takes in a numpy array of objects of the provided dtype and returns a numpy array of the numbers backing that object for statistical analysis. Returns None if no converter is necessary. Args: dtype: The numpy dtype to make a converter for. Returns: The converter method or None. """ if np.issubdtype(dtype, np.datetime64): def DatetimesToNumbers(dt_list): return np.array([pd.Timestamp(dt).value for dt in dt_list]) return DatetimesToNumbers elif np.issubdtype(dtype, np.timedelta64): def TimedetlasToNumbers(td_list): return np.array([pd.Timedelta(td).value for td in td_list]) return TimedetlasToNumbers else: return None def NdarrayToEntry(self, x): """Converts an ndarray to the Entry format.""" row_counts = [] for row in x: try: rc = np.count_nonzero(~np.isnan(row)) if rc != 0: row_counts.append(rc) except TypeError: try: row_counts.append(row.size) except AttributeError: row_counts.append(1) data_type = self.DtypeToType(x.dtype) converter = self.DtypeToNumberConverter(x.dtype) flattened = x.ravel() orig_size = len(flattened) # Remove all None and nan values and count how many were removed. flattened = flattened[flattened != np.array(None)] if converter: flattened = converter(flattened) if data_type == self.fs_proto.STRING: flattened_temp = [] for x in flattened: try: if str(x) != 'nan': flattened_temp.append(x) except UnicodeEncodeError: if x.encode('utf-8') != 'nan': flattened_temp.append(x) flattened = flattened_temp else: flattened = flattened[~np.isnan(flattened)].tolist() missing = orig_size - len(flattened) return { 'vals': flattened, 'counts': row_counts, 'missing': missing, 'type': data_type } def GetDatasetsProto(self, datasets, features=None, histogram_categorical_levels_count=None): """Generates the feature stats proto from dictionaries of feature values. Args: datasets: An array of dictionaries, one per dataset, each one containing: - 'entries': The dictionary of features in the dataset from the parsed examples. - 'size': The number of examples parsed for the dataset. - 'name': The name of the dataset. features: A list of strings that is a whitelist of feature names to create feature statistics for. If set to None then all features in the dataset are analyzed. Defaults to None. histogram_categorical_levels_count: int, controls the maximum number of levels to display in histograms for categorical features. Useful to prevent codes/IDs features from bloating the stats object. Defaults to None. Returns: The feature statistics proto for the provided datasets. """ features_seen = set() whitelist_features = set(features) if features else None all_datasets = self.datasets_proto() # TODO(jwexler): Add ability to generate weighted feature stats # if there is a specified weight feature in the dataset. # Initialize each dataset for dataset in datasets: all_datasets.datasets.add( name=dataset['name'], num_examples=dataset['size']) # This outer loop ensures that for each feature seen in any of the provided # datasets, we check the feature once against all datasets. for outer_dataset in datasets: for key, value in outer_dataset['entries'].items(): # If we have a feature whitelist and this feature is not in the # whitelist then do not process it. # If we have processed this feature already, no need to do it again. if ((whitelist_features and key not in whitelist_features) or key in features_seen): continue features_seen.add(key) # Default to type int if no type is found, so that the fact that all # values are missing from this feature can be displayed. feature_type = value['type'] if 'type' in value else self.fs_proto.INT # Process the found feature for each dataset. for j, dataset in enumerate(datasets): feat = all_datasets.datasets[j].features.add( type=feature_type, name=key.encode('utf-8')) value = dataset['entries'].get(key) has_data = value is not None and (value['vals'].size != 0 if isinstance( value['vals'], np.ndarray) else value['vals']) commonstats = None # For numeric features, calculate numeric statistics. if feat.type in (self.fs_proto.INT, self.fs_proto.FLOAT): featstats = feat.num_stats commonstats = featstats.common_stats if has_data: nums = value['vals'] featstats.std_dev = np.std(nums).item() featstats.mean = np.mean(nums).item() featstats.min = np.min(nums).item() featstats.max = np.max(nums).item() featstats.median = np.median(nums).item() featstats.num_zeros = len(nums) - np.count_nonzero(nums) nums = np.array(nums) num_nan = len(nums[np.isnan(nums)]) num_posinf = len(nums[np.isposinf(nums)]) num_neginf = len(nums[np.isneginf(nums)]) # Remove all non-finite (including NaN) values from the numeric # values in order to calculate histogram buckets/counts. The # inf values will be added back to the first and last buckets. nums = nums[np.isfinite(nums)] counts, buckets = np.histogram(nums) hist = featstats.histograms.add() hist.type = self.histogram_proto.STANDARD hist.num_nan = num_nan for bucket_count in range(len(counts)): bucket = hist.buckets.add( low_value=buckets[bucket_count], high_value=buckets[bucket_count + 1], sample_count=counts[bucket_count].item()) # Add any negative or positive infinities to the first and last # buckets in the histogram. if bucket_count == 0 and num_neginf > 0: bucket.low_value = float('-inf') bucket.sample_count += num_neginf elif bucket_count == len(counts) - 1 and num_posinf > 0: bucket.high_value = float('inf') bucket.sample_count += num_posinf if not hist.buckets: if num_neginf: hist.buckets.add( low_value=float('-inf'), high_value=float('-inf'), sample_count=num_neginf) if num_posinf: hist.buckets.add( low_value=float('inf'), high_value=float('inf'), sample_count=num_posinf) self._PopulateQuantilesHistogram(featstats.histograms.add(),nums.tolist()) elif feat.type == self.fs_proto.STRING: featstats = feat.string_stats commonstats = featstats.common_stats if has_data: strs = [] for item in value['vals']: strs.append(item if hasattr(item, '__len__') else item.encode('utf-8') if hasattr(item, 'encode') else str( item)) featstats.avg_length = np.mean(np.vectorize(len)(strs)) vals, counts = np.unique(strs, return_counts=True) featstats.unique = len(vals) sorted_vals = sorted(zip(counts, vals), reverse=True) sorted_vals = sorted_vals[:histogram_categorical_levels_count] for val_index, val in enumerate(sorted_vals): try: if (sys.version_info.major < 3 or isinstance(val[1], (bytes, bytearray))): printable_val = val[1].decode('UTF-8', 'strict') else: printable_val = val[1] except (UnicodeDecodeError, UnicodeEncodeError): printable_val = '__BYTES_VALUE__' bucket = featstats.rank_histogram.buckets.add( low_rank=val_index, high_rank=val_index, sample_count=(val[0].item()), label=printable_val) if val_index < 2: featstats.top_values.add( value=bucket.label, frequency=bucket.sample_count) # Add the common stats regardless of the feature type. if has_data: commonstats.num_missing = value['missing'] commonstats.num_non_missing = (all_datasets.datasets[j].num_examples - featstats.common_stats.num_missing) commonstats.min_num_values = int(np.min(value['counts']).astype(int)) commonstats.max_num_values = int(np.max(value['counts']).astype(int)) commonstats.avg_num_values = np.mean(value['counts']) if 'feat_lens' in value and value['feat_lens']: self._PopulateQuantilesHistogram( commonstats.feature_list_length_histogram, value['feat_lens']) self._PopulateQuantilesHistogram(commonstats.num_values_histogram, value['counts']) else: commonstats.num_non_missing = 0 commonstats.num_missing = all_datasets.datasets[j].num_examples return all_datasets def _PopulateQuantilesHistogram(self, hist, nums): """Fills in the histogram with quantile information from the provided array. Args: hist: A Histogram proto message to fill in. nums: A list of numbers to create a quantiles histogram from. """ if not nums: return num_quantile_buckets = 10 quantiles_to_get = [ x * 100 / num_quantile_buckets for x in range(num_quantile_buckets + 1) ] try: quantiles = np.percentile(nums, quantiles_to_get) except: quantiles = [0.0] hist.type = self.histogram_proto.QUANTILES quantiles_sample_count = float(len(nums)) / num_quantile_buckets for low, high in zip(quantiles, quantiles[1:]): hist.buckets.add( low_value=low, high_value=high, sample_count=quantiles_sample_count) # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Base class for generating the feature_statistics proto from TensorFlow data. The proto is used as input for the Overview visualization. """ from functools import partial from facets_overview.base_generic_feature_statistics_generator import BaseGenericFeatureStatisticsGenerator import tensorflow as tf # The feature name used to track sequence length when analyzing # tf.SequenceExamples. SEQUENCE_LENGTH_FEATURE_NAME = 'sequence length (derived feature)' class BaseFeatureStatisticsGenerator(BaseGenericFeatureStatisticsGenerator): """Base class for generator of stats proto from TF data.""" def __init__(self, fs_proto, datasets_proto, histogram_proto): BaseGenericFeatureStatisticsGenerator.__init__( self, fs_proto, datasets_proto, histogram_proto) def ProtoFromTfRecordFiles(self, files, max_entries=10000, features=None, is_sequence=False, iterator_options=None, histogram_categorical_levels_count=None): """Creates a feature statistics proto from a set of TFRecord files. Args: files: A list of dicts describing files for each dataset for the proto. Each entry contains a 'path' field with the path to the TFRecord file on disk and a 'name' field to identify the dataset in the proto. max_entries: The maximum number of examples to load from each dataset in order to create the proto. Defaults to 10000. features: A list of strings that is a whitelist of feature names to create feature statistics for. If set to None then all features in the dataset are analyzed. Defaults to None. is_sequence: True if the input data from 'tables' are tf.SequenceExamples, False if tf.Examples. Defaults to false. iterator_options: Options to pass to the iterator that reads the examples. Defaults to None. histogram_categorical_levels_count: int, controls the maximum number of levels to display in histograms for categorical features. Useful to prevent codes/IDs features from bloating the stats object. Defaults to None. Returns: The feature statistics proto for the provided files. """ datasets = [] for entry in files: entries, size = self._GetTfRecordEntries(entry['path'], max_entries, is_sequence, iterator_options) datasets.append({'entries': entries, 'size': size, 'name': entry['name']}) return self.GetDatasetsProto( datasets, features, histogram_categorical_levels_count) def _ParseExample(self, example_features, example_feature_lists, entries, index): """Parses data from an example, populating a dictionary of feature values. Args: example_features: A map of strings to tf.Features from the example. example_feature_lists: A map of strings to tf.FeatureLists from the example. entries: A dictionary of all features parsed thus far and arrays of their values. This is mutated by the function. index: The index of the example to parse from a list of examples. Raises: TypeError: Raises an exception when a feature has inconsistent types across examples. """ features_seen = set() for feature_list, is_feature in zip( [example_features, example_feature_lists], [True, False]): sequence_length = None for feature_name in feature_list: # If this feature has not been seen in previous examples, then # initialize its entry into the entries dictionary. if feature_name not in entries: entries[feature_name] = { 'vals': [], 'counts': [], 'feat_lens': [], 'missing': index } feature_entry = entries[feature_name] feature = feature_list[feature_name] value_type = None value_list = [] if is_feature: # If parsing a tf.Feature, extract the type and values simply. if feature.HasField('float_list'): value_list = feature.float_list.value value_type = self.fs_proto.FLOAT elif feature.HasField('bytes_list'): value_list = feature.bytes_list.value value_type = self.fs_proto.STRING elif feature.HasField('int64_list'): value_list = feature.int64_list.value value_type = self.fs_proto.INT else: # If parsing a tf.FeatureList, get the type and values by iterating # over all Features in the FeatureList. sequence_length = len(feature.feature) if sequence_length != 0 and feature.feature[0].HasField('float_list'): for feat in feature.feature: for value in feat.float_list.value: value_list.append(value) value_type = self.fs_proto.FLOAT elif sequence_length != 0 and feature.feature[0].HasField( 'bytes_list'): for feat in feature.feature: for value in feat.bytes_list.value: value_list.append(value) value_type = self.fs_proto.STRING elif sequence_length != 0 and feature.feature[0].HasField( 'int64_list'): for feat in feature.feature: for value in feat.int64_list.value: value_list.append(value) value_type = self.fs_proto.INT if value_type is not None: if 'type' not in feature_entry: feature_entry['type'] = value_type elif feature_entry['type'] != value_type: raise TypeError('type mismatch for feature ' + feature_name) feature_entry['counts'].append(len(value_list)) feature_entry['vals'].extend(value_list) if sequence_length is not None: feature_entry['feat_lens'].append(sequence_length) if value_list: features_seen.add(feature_name) # For all previously-seen features not found in this example, update the # feature's missing value. for f in entries: fv = entries[f] if f not in features_seen: fv['missing'] += 1 def _GetEntries(self, paths, max_entries, iterator_from_file, is_sequence=False): """Extracts examples into a dictionary of feature values. Args: paths: A list of the paths to the files to parse. max_entries: The maximum number of examples to load. iterator_from_file: A method that takes a file path string and returns an iterator to the examples in that file. is_sequence: True if the input data from 'iterator_from_file' are tf.SequenceExamples, False if tf.Examples. Defaults to false. Returns: A tuple with two elements: - A dictionary of all features parsed thus far and arrays of their values. - The number of examples parsed. """ entries = {} index = 0 for filepath in paths: reader = iterator_from_file(filepath) for record in reader: if is_sequence: sequence_example = tf.train.SequenceExample.FromString(record) self._ParseExample(sequence_example.context.feature, sequence_example.feature_lists.feature_list, entries, index) else: self._ParseExample( tf.train.Example.FromString(record).features.feature, [], entries, index) index += 1 if index == max_entries: return entries, index return entries, index def _GetTfRecordEntries(self, path, max_entries, is_sequence, iterator_options): """Extracts TFRecord examples into a dictionary of feature values. Args: path: The path to the TFRecord file(s). max_entries: The maximum number of examples to load. is_sequence: True if the input data from 'path' are tf.SequenceExamples, False if tf.Examples. Defaults to false. iterator_options: Options to pass to the iterator that reads the examples. Defaults to None. Returns: A tuple with two elements: - A dictionary of all features parsed thus far and arrays of their values. - The number of examples parsed. """ return self._GetEntries([path], max_entries, partial( tf.compat.v1.io.tf_record_iterator, options=iterator_options), is_sequence) # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # Generated by the protocol buffer compiler. 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filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='INT', index=0, number=0, options=None, type=None), _descriptor.EnumValueDescriptor( name='FLOAT', index=1, number=1, options=None, type=None), _descriptor.EnumValueDescriptor( name='STRING', index=2, number=2, options=None, type=None), _descriptor.EnumValueDescriptor( name='BYTES', index=3, number=3, options=None, type=None), ], containing_type=None, options=None, serialized_start=636, serialized_end=685, ) _sym_db.RegisterEnumDescriptor(_FEATURENAMESTATISTICS_TYPE) _HISTOGRAM_HISTOGRAMTYPE = _descriptor.EnumDescriptor( name='HistogramType', full_name='featureStatistics.Histogram.HistogramType', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='STANDARD', index=0, number=0, options=None, type=None), _descriptor.EnumValueDescriptor( name='QUANTILES', index=1, number=1, options=None, type=None), ], containing_type=None, options=None, serialized_start=2735, serialized_end=2779, ) _sym_db.RegisterEnumDescriptor(_HISTOGRAM_HISTOGRAMTYPE) _DATASETFEATURESTATISTICSLIST = _descriptor.Descriptor( name='DatasetFeatureStatisticsList', full_name='featureStatistics.DatasetFeatureStatisticsList', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='datasets', full_name='featureStatistics.DatasetFeatureStatisticsList.datasets', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=47, serialized_end=140, ) _DATASETFEATURESTATISTICS = _descriptor.Descriptor( name='DatasetFeatureStatistics', full_name='featureStatistics.DatasetFeatureStatistics', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='featureStatistics.DatasetFeatureStatistics.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='num_examples', full_name='featureStatistics.DatasetFeatureStatistics.num_examples', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='weighted_num_examples', full_name='featureStatistics.DatasetFeatureStatistics.weighted_num_examples', index=2, number=4, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='features', full_name='featureStatistics.DatasetFeatureStatistics.features', index=3, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=143, serialized_end=296, ) _FEATURENAMESTATISTICS = _descriptor.Descriptor( name='FeatureNameStatistics', full_name='featureStatistics.FeatureNameStatistics', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='featureStatistics.FeatureNameStatistics.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='type', full_name='featureStatistics.FeatureNameStatistics.type', index=1, number=2, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='num_stats', full_name='featureStatistics.FeatureNameStatistics.num_stats', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='string_stats', full_name='featureStatistics.FeatureNameStatistics.string_stats', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='bytes_stats', full_name='featureStatistics.FeatureNameStatistics.bytes_stats', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='custom_stats', full_name='featureStatistics.FeatureNameStatistics.custom_stats', index=5, number=6, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ _FEATURENAMESTATISTICS_TYPE, ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='stats', full_name='featureStatistics.FeatureNameStatistics.stats', index=0, containing_type=None, fields=[]), ], serialized_start=299, serialized_end=694, ) _WEIGHTEDCOMMONSTATISTICS = _descriptor.Descriptor( name='WeightedCommonStatistics', full_name='featureStatistics.WeightedCommonStatistics', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='num_non_missing', full_name='featureStatistics.WeightedCommonStatistics.num_non_missing', index=0, number=1, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='num_missing', full_name='featureStatistics.WeightedCommonStatistics.num_missing', index=1, number=2, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='avg_num_values', full_name='featureStatistics.WeightedCommonStatistics.avg_num_values', index=2, number=3, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='tot_num_values', full_name='featureStatistics.WeightedCommonStatistics.tot_num_values', index=3, number=4, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=696, serialized_end=816, ) _CUSTOMSTATISTIC = _descriptor.Descriptor( name='CustomStatistic', full_name='featureStatistics.CustomStatistic', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='featureStatistics.CustomStatistic.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='num', full_name='featureStatistics.CustomStatistic.num', index=1, number=2, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='str', full_name='featureStatistics.CustomStatistic.str', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='histogram', full_name='featureStatistics.CustomStatistic.histogram', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='val', full_name='featureStatistics.CustomStatistic.val', index=0, containing_type=None, fields=[]), ], serialized_start=818, serialized_end=937, ) _NUMERICSTATISTICS = _descriptor.Descriptor( name='NumericStatistics', full_name='featureStatistics.NumericStatistics', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='common_stats', full_name='featureStatistics.NumericStatistics.common_stats', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='mean', full_name='featureStatistics.NumericStatistics.mean', index=1, number=2, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='std_dev', full_name='featureStatistics.NumericStatistics.std_dev', index=2, number=3, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='num_zeros', full_name='featureStatistics.NumericStatistics.num_zeros', index=3, number=4, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='min', full_name='featureStatistics.NumericStatistics.min', index=4, number=5, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='median', full_name='featureStatistics.NumericStatistics.median', index=5, number=6, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='max', full_name='featureStatistics.NumericStatistics.max', index=6, number=7, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='histograms', full_name='featureStatistics.NumericStatistics.histograms', index=7, number=8, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='weighted_numeric_stats', full_name='featureStatistics.NumericStatistics.weighted_numeric_stats', index=8, number=9, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=940, serialized_end=1238, ) _STRINGSTATISTICS_FREQANDVALUE = _descriptor.Descriptor( name='FreqAndValue', full_name='featureStatistics.StringStatistics.FreqAndValue', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='deprecated_freq', full_name='featureStatistics.StringStatistics.FreqAndValue.deprecated_freq', index=0, number=1, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\030\001'))), _descriptor.FieldDescriptor( name='value', full_name='featureStatistics.StringStatistics.FreqAndValue.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='frequency', full_name='featureStatistics.StringStatistics.FreqAndValue.frequency', index=2, number=3, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1560, serialized_end=1637, ) _STRINGSTATISTICS = _descriptor.Descriptor( name='StringStatistics', full_name='featureStatistics.StringStatistics', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='common_stats', full_name='featureStatistics.StringStatistics.common_stats', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='unique', full_name='featureStatistics.StringStatistics.unique', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='top_values', full_name='featureStatistics.StringStatistics.top_values', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='avg_length', full_name='featureStatistics.StringStatistics.avg_length', index=3, number=4, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='rank_histogram', full_name='featureStatistics.StringStatistics.rank_histogram', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='weighted_string_stats', full_name='featureStatistics.StringStatistics.weighted_string_stats', index=5, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[_STRINGSTATISTICS_FREQANDVALUE, ], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1241, serialized_end=1637, ) _WEIGHTEDNUMERICSTATISTICS = _descriptor.Descriptor( name='WeightedNumericStatistics', full_name='featureStatistics.WeightedNumericStatistics', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='mean', full_name='featureStatistics.WeightedNumericStatistics.mean', index=0, number=1, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='std_dev', full_name='featureStatistics.WeightedNumericStatistics.std_dev', index=1, number=2, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='median', full_name='featureStatistics.WeightedNumericStatistics.median', index=2, number=3, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='histograms', full_name='featureStatistics.WeightedNumericStatistics.histograms', index=3, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1639, serialized_end=1763, ) _WEIGHTEDSTRINGSTATISTICS = _descriptor.Descriptor( name='WeightedStringStatistics', full_name='featureStatistics.WeightedStringStatistics', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='top_values', full_name='featureStatistics.WeightedStringStatistics.top_values', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='rank_histogram', full_name='featureStatistics.WeightedStringStatistics.rank_histogram', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1766, serialized_end=1920, ) _BYTESSTATISTICS = _descriptor.Descriptor( name='BytesStatistics', full_name='featureStatistics.BytesStatistics', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='common_stats', full_name='featureStatistics.BytesStatistics.common_stats', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='unique', full_name='featureStatistics.BytesStatistics.unique', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='avg_num_bytes', full_name='featureStatistics.BytesStatistics.avg_num_bytes', index=2, number=3, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='min_num_bytes', full_name='featureStatistics.BytesStatistics.min_num_bytes', index=3, number=4, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='max_num_bytes', full_name='featureStatistics.BytesStatistics.max_num_bytes', index=4, number=5, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1923, serialized_end=2084, ) _COMMONSTATISTICS = _descriptor.Descriptor( name='CommonStatistics', full_name='featureStatistics.CommonStatistics', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='num_non_missing', full_name='featureStatistics.CommonStatistics.num_non_missing', index=0, number=1, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='num_missing', full_name='featureStatistics.CommonStatistics.num_missing', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='min_num_values', full_name='featureStatistics.CommonStatistics.min_num_values', index=2, number=3, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='max_num_values', full_name='featureStatistics.CommonStatistics.max_num_values', index=3, number=4, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='avg_num_values', full_name='featureStatistics.CommonStatistics.avg_num_values', index=4, number=5, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='tot_num_values', full_name='featureStatistics.CommonStatistics.tot_num_values', index=5, number=8, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='num_values_histogram', full_name='featureStatistics.CommonStatistics.num_values_histogram', index=6, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='weighted_common_stats', full_name='featureStatistics.CommonStatistics.weighted_common_stats', index=7, number=7, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='feature_list_length_histogram', full_name='featureStatistics.CommonStatistics.feature_list_length_histogram', index=8, number=9, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2087, serialized_end=2452, ) _HISTOGRAM_BUCKET = _descriptor.Descriptor( name='Bucket', full_name='featureStatistics.Histogram.Bucket', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='low_value', full_name='featureStatistics.Histogram.Bucket.low_value', index=0, number=1, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='high_value', full_name='featureStatistics.Histogram.Bucket.high_value', index=1, number=2, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='deprecated_count', full_name='featureStatistics.Histogram.Bucket.deprecated_count', index=2, number=3, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\030\001'))), _descriptor.FieldDescriptor( name='sample_count', full_name='featureStatistics.Histogram.Bucket.sample_count', index=3, number=4, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2634, serialized_end=2733, ) _HISTOGRAM = _descriptor.Descriptor( name='Histogram', full_name='featureStatistics.Histogram', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='num_nan', full_name='featureStatistics.Histogram.num_nan', index=0, number=1, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='num_undefined', full_name='featureStatistics.Histogram.num_undefined', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='buckets', full_name='featureStatistics.Histogram.buckets', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='type', full_name='featureStatistics.Histogram.type', index=3, number=4, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='name', full_name='featureStatistics.Histogram.name', index=4, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[_HISTOGRAM_BUCKET, ], enum_types=[ _HISTOGRAM_HISTOGRAMTYPE, ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2455, serialized_end=2779, ) _RANKHISTOGRAM_BUCKET = _descriptor.Descriptor( name='Bucket', full_name='featureStatistics.RankHistogram.Bucket', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='low_rank', full_name='featureStatistics.RankHistogram.Bucket.low_rank', index=0, number=1, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='high_rank', full_name='featureStatistics.RankHistogram.Bucket.high_rank', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='deprecated_count', full_name='featureStatistics.RankHistogram.Bucket.deprecated_count', index=2, number=3, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\030\001'))), _descriptor.FieldDescriptor( name='label', full_name='featureStatistics.RankHistogram.Bucket.label', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='sample_count', full_name='featureStatistics.RankHistogram.Bucket.sample_count', index=4, number=5, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2871, serialized_end=2983, ) _RANKHISTOGRAM = _descriptor.Descriptor( name='RankHistogram', full_name='featureStatistics.RankHistogram', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='buckets', full_name='featureStatistics.RankHistogram.buckets', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='name', full_name='featureStatistics.RankHistogram.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[_RANKHISTOGRAM_BUCKET, ], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2782, serialized_end=2983, ) _DATASETFEATURESTATISTICSLIST.fields_by_name['datasets'].message_type = _DATASETFEATURESTATISTICS _DATASETFEATURESTATISTICS.fields_by_name['features'].message_type = _FEATURENAMESTATISTICS _FEATURENAMESTATISTICS.fields_by_name['type'].enum_type = _FEATURENAMESTATISTICS_TYPE _FEATURENAMESTATISTICS.fields_by_name['num_stats'].message_type = _NUMERICSTATISTICS _FEATURENAMESTATISTICS.fields_by_name['string_stats'].message_type = _STRINGSTATISTICS _FEATURENAMESTATISTICS.fields_by_name['bytes_stats'].message_type = _BYTESSTATISTICS _FEATURENAMESTATISTICS.fields_by_name['custom_stats'].message_type = _CUSTOMSTATISTIC _FEATURENAMESTATISTICS_TYPE.containing_type = _FEATURENAMESTATISTICS _FEATURENAMESTATISTICS.oneofs_by_name['stats'].fields.append( _FEATURENAMESTATISTICS.fields_by_name['num_stats']) _FEATURENAMESTATISTICS.fields_by_name['num_stats'].containing_oneof = _FEATURENAMESTATISTICS.oneofs_by_name['stats'] _FEATURENAMESTATISTICS.oneofs_by_name['stats'].fields.append( _FEATURENAMESTATISTICS.fields_by_name['string_stats']) _FEATURENAMESTATISTICS.fields_by_name['string_stats'].containing_oneof = _FEATURENAMESTATISTICS.oneofs_by_name['stats'] _FEATURENAMESTATISTICS.oneofs_by_name['stats'].fields.append( _FEATURENAMESTATISTICS.fields_by_name['bytes_stats']) _FEATURENAMESTATISTICS.fields_by_name['bytes_stats'].containing_oneof = _FEATURENAMESTATISTICS.oneofs_by_name['stats'] _CUSTOMSTATISTIC.fields_by_name['histogram'].message_type = _HISTOGRAM _CUSTOMSTATISTIC.oneofs_by_name['val'].fields.append( _CUSTOMSTATISTIC.fields_by_name['num']) _CUSTOMSTATISTIC.fields_by_name['num'].containing_oneof = _CUSTOMSTATISTIC.oneofs_by_name['val'] _CUSTOMSTATISTIC.oneofs_by_name['val'].fields.append( _CUSTOMSTATISTIC.fields_by_name['str']) _CUSTOMSTATISTIC.fields_by_name['str'].containing_oneof = _CUSTOMSTATISTIC.oneofs_by_name['val'] _CUSTOMSTATISTIC.oneofs_by_name['val'].fields.append( _CUSTOMSTATISTIC.fields_by_name['histogram']) _CUSTOMSTATISTIC.fields_by_name['histogram'].containing_oneof = _CUSTOMSTATISTIC.oneofs_by_name['val'] _NUMERICSTATISTICS.fields_by_name['common_stats'].message_type = _COMMONSTATISTICS _NUMERICSTATISTICS.fields_by_name['histograms'].message_type = _HISTOGRAM _NUMERICSTATISTICS.fields_by_name['weighted_numeric_stats'].message_type = _WEIGHTEDNUMERICSTATISTICS _STRINGSTATISTICS_FREQANDVALUE.containing_type = _STRINGSTATISTICS _STRINGSTATISTICS.fields_by_name['common_stats'].message_type = _COMMONSTATISTICS _STRINGSTATISTICS.fields_by_name['top_values'].message_type = _STRINGSTATISTICS_FREQANDVALUE _STRINGSTATISTICS.fields_by_name['rank_histogram'].message_type = _RANKHISTOGRAM _STRINGSTATISTICS.fields_by_name['weighted_string_stats'].message_type = _WEIGHTEDSTRINGSTATISTICS _WEIGHTEDNUMERICSTATISTICS.fields_by_name['histograms'].message_type = _HISTOGRAM _WEIGHTEDSTRINGSTATISTICS.fields_by_name['top_values'].message_type = _STRINGSTATISTICS_FREQANDVALUE _WEIGHTEDSTRINGSTATISTICS.fields_by_name['rank_histogram'].message_type = _RANKHISTOGRAM _BYTESSTATISTICS.fields_by_name['common_stats'].message_type = _COMMONSTATISTICS _COMMONSTATISTICS.fields_by_name['num_values_histogram'].message_type = _HISTOGRAM _COMMONSTATISTICS.fields_by_name['weighted_common_stats'].message_type = _WEIGHTEDCOMMONSTATISTICS _COMMONSTATISTICS.fields_by_name['feature_list_length_histogram'].message_type = _HISTOGRAM _HISTOGRAM_BUCKET.containing_type = _HISTOGRAM _HISTOGRAM.fields_by_name['buckets'].message_type = _HISTOGRAM_BUCKET _HISTOGRAM.fields_by_name['type'].enum_type = _HISTOGRAM_HISTOGRAMTYPE _HISTOGRAM_HISTOGRAMTYPE.containing_type = _HISTOGRAM _RANKHISTOGRAM_BUCKET.containing_type = _RANKHISTOGRAM _RANKHISTOGRAM.fields_by_name['buckets'].message_type = _RANKHISTOGRAM_BUCKET DESCRIPTOR.message_types_by_name['DatasetFeatureStatisticsList'] = _DATASETFEATURESTATISTICSLIST DESCRIPTOR.message_types_by_name['DatasetFeatureStatistics'] = _DATASETFEATURESTATISTICS DESCRIPTOR.message_types_by_name['FeatureNameStatistics'] = _FEATURENAMESTATISTICS DESCRIPTOR.message_types_by_name['WeightedCommonStatistics'] = _WEIGHTEDCOMMONSTATISTICS DESCRIPTOR.message_types_by_name['CustomStatistic'] = _CUSTOMSTATISTIC DESCRIPTOR.message_types_by_name['NumericStatistics'] = _NUMERICSTATISTICS DESCRIPTOR.message_types_by_name['StringStatistics'] = _STRINGSTATISTICS DESCRIPTOR.message_types_by_name['WeightedNumericStatistics'] = _WEIGHTEDNUMERICSTATISTICS DESCRIPTOR.message_types_by_name['WeightedStringStatistics'] = _WEIGHTEDSTRINGSTATISTICS DESCRIPTOR.message_types_by_name['BytesStatistics'] = _BYTESSTATISTICS DESCRIPTOR.message_types_by_name['CommonStatistics'] = _COMMONSTATISTICS DESCRIPTOR.message_types_by_name['Histogram'] = _HISTOGRAM DESCRIPTOR.message_types_by_name['RankHistogram'] = _RANKHISTOGRAM _sym_db.RegisterFileDescriptor(DESCRIPTOR) DatasetFeatureStatisticsList = _reflection.GeneratedProtocolMessageType('DatasetFeatureStatisticsList', (_message.Message,), dict( DESCRIPTOR = _DATASETFEATURESTATISTICSLIST, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.DatasetFeatureStatisticsList) )) _sym_db.RegisterMessage(DatasetFeatureStatisticsList) DatasetFeatureStatistics = _reflection.GeneratedProtocolMessageType('DatasetFeatureStatistics', (_message.Message,), dict( DESCRIPTOR = _DATASETFEATURESTATISTICS, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.DatasetFeatureStatistics) )) _sym_db.RegisterMessage(DatasetFeatureStatistics) FeatureNameStatistics = _reflection.GeneratedProtocolMessageType('FeatureNameStatistics', (_message.Message,), dict( DESCRIPTOR = _FEATURENAMESTATISTICS, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.FeatureNameStatistics) )) _sym_db.RegisterMessage(FeatureNameStatistics) WeightedCommonStatistics = _reflection.GeneratedProtocolMessageType('WeightedCommonStatistics', (_message.Message,), dict( DESCRIPTOR = _WEIGHTEDCOMMONSTATISTICS, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.WeightedCommonStatistics) )) _sym_db.RegisterMessage(WeightedCommonStatistics) CustomStatistic = _reflection.GeneratedProtocolMessageType('CustomStatistic', (_message.Message,), dict( DESCRIPTOR = _CUSTOMSTATISTIC, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.CustomStatistic) )) _sym_db.RegisterMessage(CustomStatistic) NumericStatistics = _reflection.GeneratedProtocolMessageType('NumericStatistics', (_message.Message,), dict( DESCRIPTOR = _NUMERICSTATISTICS, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.NumericStatistics) )) _sym_db.RegisterMessage(NumericStatistics) StringStatistics = _reflection.GeneratedProtocolMessageType('StringStatistics', (_message.Message,), dict( FreqAndValue = _reflection.GeneratedProtocolMessageType('FreqAndValue', (_message.Message,), dict( DESCRIPTOR = _STRINGSTATISTICS_FREQANDVALUE, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.StringStatistics.FreqAndValue) )) , DESCRIPTOR = _STRINGSTATISTICS, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.StringStatistics) )) _sym_db.RegisterMessage(StringStatistics) _sym_db.RegisterMessage(StringStatistics.FreqAndValue) WeightedNumericStatistics = _reflection.GeneratedProtocolMessageType('WeightedNumericStatistics', (_message.Message,), dict( DESCRIPTOR = _WEIGHTEDNUMERICSTATISTICS, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.WeightedNumericStatistics) )) _sym_db.RegisterMessage(WeightedNumericStatistics) WeightedStringStatistics = _reflection.GeneratedProtocolMessageType('WeightedStringStatistics', (_message.Message,), dict( DESCRIPTOR = _WEIGHTEDSTRINGSTATISTICS, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.WeightedStringStatistics) )) _sym_db.RegisterMessage(WeightedStringStatistics) BytesStatistics = _reflection.GeneratedProtocolMessageType('BytesStatistics', (_message.Message,), dict( DESCRIPTOR = _BYTESSTATISTICS, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.BytesStatistics) )) _sym_db.RegisterMessage(BytesStatistics) CommonStatistics = _reflection.GeneratedProtocolMessageType('CommonStatistics', (_message.Message,), dict( DESCRIPTOR = _COMMONSTATISTICS, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.CommonStatistics) )) _sym_db.RegisterMessage(CommonStatistics) Histogram = _reflection.GeneratedProtocolMessageType('Histogram', (_message.Message,), dict( Bucket = _reflection.GeneratedProtocolMessageType('Bucket', (_message.Message,), dict( DESCRIPTOR = _HISTOGRAM_BUCKET, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.Histogram.Bucket) )) , DESCRIPTOR = _HISTOGRAM, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.Histogram) )) _sym_db.RegisterMessage(Histogram) _sym_db.RegisterMessage(Histogram.Bucket) RankHistogram = _reflection.GeneratedProtocolMessageType('RankHistogram', (_message.Message,), dict( Bucket = _reflection.GeneratedProtocolMessageType('Bucket', (_message.Message,), dict( DESCRIPTOR = _RANKHISTOGRAM_BUCKET, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.RankHistogram.Bucket) )) , DESCRIPTOR = _RANKHISTOGRAM, __module__ = 'feature_statistics_pb2' # @@protoc_insertion_point(class_scope:featureStatistics.RankHistogram) )) _sym_db.RegisterMessage(RankHistogram) _sym_db.RegisterMessage(RankHistogram.Bucket) _STRINGSTATISTICS_FREQANDVALUE.fields_by_name['deprecated_freq'].has_options = True _STRINGSTATISTICS_FREQANDVALUE.fields_by_name['deprecated_freq']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\030\001')) _HISTOGRAM_BUCKET.fields_by_name['deprecated_count'].has_options = True _HISTOGRAM_BUCKET.fields_by_name['deprecated_count']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\030\001')) _RANKHISTOGRAM_BUCKET.fields_by_name['deprecated_count'].has_options = True _RANKHISTOGRAM_BUCKET.fields_by_name['deprecated_count']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\030\001')) # @@protoc_insertion_point(module_scope) #from BaseHTTPServer import BaseHTTPRequestHandler,HTTPServer from http.server import BaseHTTPRequestHandler,HTTPServer #from SocketServer import ThreadingMixIn from socketserver import ThreadingMixIn ''' from augustus.core.ModelLoader import ModelLoader from augustus.strict import modelLoader ''' import pandas as pd import os,sys from os.path import expanduser import platform import numpy as np import configparser import threading import subprocess import argparse import re import cgi from datetime import datetime import json import sys from datetime import datetime user_records = {} class LocalModelData(object): models = {} class HTTPRequestHandler(BaseHTTPRequestHandler): def do_POST(self): print("PYTHON ######## REQUEST ####### STARTED") if None != re.search('/AION/', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) data = self.rfile.read(length) model = self.path.split('/')[-2] operation = self.path.split('/')[-1] data = json.loads(data) dataStr = json.dumps(data) if operation.lower() == 'predict': predict_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),'AION','aion_predict.py') outputStr = subprocess.check_output([sys.executable,predict_path,dataStr]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() resp = outputStr elif operation.lower() == 'explain': predict_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),'AION','aion_xai.py') outputStr = subprocess.check_output([sys.executable,predict_path,'local',dataStr]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_ai_explanation:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() resp = outputStr resp=resp+"\n" resp=resp.encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(resp) else: print("python ==> else2") data = {} elif None != re.search('/AION/pattern_anomaly_predict', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) model = self.path.split('/')[-1] data = self.rfile.read(length) data = json.loads(data) anomaly = False remarks = '' clusterid = -1 home = expanduser("~") if platform.system() == 'Windows': configfilename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'datadetails.json') filename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'clickstream.json') clusterfilename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'stateClustering.csv') probfilename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'stateTransitionProbability.csv') else: configfilename = os.path.join(home,'HCLT','AION','target',model,'datadetails.json') filename = os.path.join(home,'HCLT','AION','target',model,'clickstream.json') clusterfilename = os.path.join(home,'HCLT','AION','target',model,'stateClustering.csv') probfilename = os.path.join(home,'HCLT','AION','target',model,'stateTransitionProbability.csv') dfclus = pd.read_csv(clusterfilename) dfprod = pd.read_csv(probfilename) f = open(configfilename, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) activity = configSettingsJson['activity'] sessionid = configSettingsJson['sessionid'] f = open(filename, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) groupswitching = configSettingsJson['groupswitching'] page_threshold = configSettingsJson['transitionprobability'] chain_count = configSettingsJson['transitionsequence'] chain_probability = configSettingsJson['sequencethreshold'] currentactivity = data[activity] if bool(user_records): sessionid = data[sessionid] print(sessionid,user_records['SessionID']) if sessionid != user_records['SessionID']: user_records['SessionID'] = sessionid prevactivity = '' user_records['probarry'] = [] user_records['prevclusterid'] = -1 user_records['NoOfClusterHopping'] = 0 user_records['pageclicks'] = 1 else: prevactivity = user_records['Activity'] user_records['Activity'] = currentactivity pageswitch = True if prevactivity == currentactivity or prevactivity == '': probability = 0 pageswitch = False remarks = '' else: user_records['pageclicks'] += 1 df1 = dfprod[(dfprod['State'] == prevactivity) & (dfprod['NextState'] == currentactivity)] if df1.empty: remarks = 'Anomaly Detected - User in unusual state' anomaly = True clusterid = -1 probability = 0 user_records['probarry'].append(probability) n=int(chain_count) num_list = user_records['probarry'][-n:] avg = sum(num_list)/len(num_list) for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] else: probability = df1['Probability'].iloc[0] user_records['probarry'].append(probability) n=int(chain_count) num_list = user_records['probarry'][-n:] davg = sum(num_list)/len(num_list) for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] remarks = '' if user_records['prevclusterid'] != -1: if probability == 0 and user_records['prevclusterid'] != clusterid: user_records['NoOfClusterHopping'] = user_records['NoOfClusterHopping']+1 if user_records['pageclicks'] == 1: remarks = 'Anomaly Detected - Frequent Cluster Hopping' anomaly = True else: remarks = 'Cluster Hopping Detected' user_records['pageclicks'] = 0 if user_records['NoOfClusterHopping'] > int(groupswitching) and anomaly == False: remarks = 'Anomaly Detected - Multiple Cluster Hopping' anomaly = True elif probability == 0: remarks = 'Anomaly Detected - Unusual State Transition Detected' anomaly = True elif probability <= float(page_threshold): remarks = 'Anomaly Detected - In-frequent State Transition Detected' anomaly = True else: #print(pageswitch) #print(probability) if pageswitch == True: if probability == 0: remarks = 'Anomaly Detected - Unusual State Transition Detected' anomaly = True elif probability <= float(page_threshold): remarks = 'Anomaly Detected - In-frequent State Transition Detected' anomaly = True else: remarks = '' if davg < float(chain_probability): if anomaly == False: remarks = 'Anomaly Detected - In-frequent Pattern Detected' anomaly = True else: user_records['SessionID'] = data[sessionid] user_records['Activity'] = data[activity] user_records['probability'] = 0 user_records['probarry'] = [] user_records['chainprobability'] = 0 user_records['prevclusterid'] = -1 user_records['NoOfClusterHopping'] = 0 user_records['pageclicks'] = 1 for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] user_records['prevclusterid'] = clusterid self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() resp = '{"status":"SUCCESS","data":{"Anomaly":"'+str(anomaly)+'","Remarks":"'+str(remarks)+'"}}' resp=resp+"\n" resp=resp.encode() self.wfile.write(resp) else: self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() resp = '{"Anomaly":"Error","Remarks":"'+str(Int)+'"}' resp=resp+"\n" resp=resp.encode() self.wfile.write(resp) elif None != re.search('/AION/pattern_anomaly_settings', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) data = self.rfile.read(length) #print(data) #keyList = list(data.keys()) #print(keyList[0]) model = self.path.split('/')[-1] #print(model) data = json.loads(data) #dataStr = json.dumps(data) groupswitching = data['groupswitching'] transitionprobability = data['transitionprobability'] transitionsequence = data['transitionsequence'] sequencethreshold = data['sequencethreshold'] home = expanduser("~") if platform.system() == 'Windows': filename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'clickstream.json') else: filename = os.path.join(home,'HCLT','AION','target',model,'clickstream.json') #print(filename) data = {} data['groupswitching'] = groupswitching data['transitionprobability'] = transitionprobability data['transitionsequence'] = transitionsequence data['sequencethreshold'] = sequencethreshold updatedConfig = json.dumps(data) with open(filename, "w") as fpWrite: fpWrite.write(updatedConfig) fpWrite.close() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() resp = '{"Status":"SUCCESS"}' resp=resp+"\n" resp=resp.encode() self.wfile.write(resp) else: print("python ==> else2") data = {} else: print("python ==> else1") self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() print("PYTHON ######## REQUEST ####### ENDED") return def do_GET(self): print("PYTHON ######## REQUEST ####### STARTED") if None != re.search('/AION/predict', self.path): self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() model = self.path.split('/')[-1] display_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),'target',model,'display.json') displaymsg = 'Data in JSON Format' if(os.path.isfile(display_path)): with open(display_path) as file: config = json.load(file) file.close() features = config['modelFeatures'] datajson={} for feature in features: datajson[feature] = 'Value' displaymsg = json.dumps(datajson) msg=""" URL:{url} RequestType: POST Content-Type=application/json Body: {displaymsg} """.format(url=self.path,displaymsg=displaymsg) self.wfile.write(msg.encode()) else: self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() return class ThreadedHTTPServer(ThreadingMixIn, HTTPServer): allow_reuse_address = True def shutdown(self): self.socket.close() HTTPServer.shutdown(self) class SimpleHttpServer(): def __init__(self, ip, port): self.server = ThreadedHTTPServer((ip,port), HTTPRequestHandler) def start(self): self.server_thread = threading.Thread(target=self.server.serve_forever) self.server_thread.daemon = True self.server_thread.start() def waitForThread(self): self.server_thread.join() def stop(self): self.server.shutdown() self.waitForThread() if __name__=='__main__': parser = argparse.ArgumentParser(description='HTTP Server') parser.add_argument('port', type=int, help='Listening port for HTTP Server') parser.add_argument('ip', help='HTTP Server IP') args = parser.parse_args() server = SimpleHttpServer(args.ip, args.port) #delete file #create file #write just msg as "started" print('HTTP Server Running...........') #file close server.start() server.waitForThread() #from BaseHTTPServer import BaseHTTPRequestHandler,HTTPServer from http.server import BaseHTTPRequestHandler,HTTPServer #from SocketServer import ThreadingMixIn from socketserver import ThreadingMixIn ''' from augustus.core.ModelLoader import ModelLoader from augustus.strict import modelLoader ''' import pandas as pd from datetime import datetime import os,sys from os.path import expanduser import platform import numpy as np import configparser import threading import subprocess import argparse import re import cgi import time from datetime import datetime import json import sys from datetime import datetime import sqlite3 from os.path import expanduser from pathlib import Path from io import BytesIO DEPLOY_DATABASE_PATH = os.path.join(os.path.join(os.path.dirname(__file__)),'database') targetPath = Path(DEPLOY_DATABASE_PATH) targetPath.mkdir(parents=True, exist_ok=True) modelVersion = 'run_1' version = 1 class sqlite_db(): def __init__(self, location, database_file=None): if not isinstance(location, Path): location = Path(location) if database_file: self.database_name = database_file else: self.database_name = location.stem + '.db' db_file = str(location/self.database_name) self.conn = sqlite3.connect(db_file) self.cursor = self.conn.cursor() self.tables = [] def table_exists(self, name): if name in self.tables: return True elif name: query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';" listOfTables = self.cursor.execute(query).fetchall() if len(listOfTables) > 0 : self.tables.append(name) return True return False def read(self, table_name,condition=''): if condition == '': return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn) else: return pd.read_sql_query(f"SELECT * FROM {table_name} WHERE {condition}", self.conn) def create_table(self,name, columns, dtypes): query = f'CREATE TABLE IF NOT EXISTS {name} (' for column, data_type in zip(columns, dtypes): query += f"'{column}' TEXT," query = query[:-1] query += ');' self.conn.execute(query) return True def update(self,table_name,updates,condition): update_query = f'UPDATE {table_name} SET {updates} WHERE {condition}' self.cursor.execute(update_query) self.conn.commit() return True def write(self,data, table_name): if not self.table_exists(table_name): self.create_table(table_name, data.columns, data.dtypes) tuple_data = list(data.itertuples(index=False, name=None)) insert_query = f'INSERT INTO {table_name} VALUES(' for i in range(len(data.columns)): insert_query += '?,' insert_query = insert_query[:-1] + ')' self.cursor.executemany(insert_query, tuple_data) self.conn.commit() return True def delete(self, name): pass def close(self): self.conn.close() user_records = {} class LocalModelData(object): models = {} class HTTPRequestHandler(BaseHTTPRequestHandler): def do_POST(self): print("PYTHON ######## REQUEST ####### STARTED") if None != re.search('/AION/', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) #data = cgi.parse_qs(self.rfile.read(length), keep_blank_values=1) data = self.rfile.read(length) model = self.path.split('/')[-2] operation = self.path.split('/')[-1] #data = json.loads(data) #dataStr = json.dumps(data) home = expanduser("~") dataStr = data sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db') model_path = os.path.join(os.path.dirname(__file__),modelVersion) DATA_FILE_PATH = os.path.join(os.path.dirname(__file__),'temp') Path(DATA_FILE_PATH).mkdir(parents=True, exist_ok=True) isdir = os.path.isdir(model_path) if isdir: if operation.lower() == 'predict': if not sqlite_dbObj.table_exists('servingDetails'): data = {'usecase':model,'noOfPredictCalls':0,'noOfDriftCalls':0} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.create_table('servingDetails',data.columns, data.dtypes) df2 = pd.read_json(BytesIO(dataStr), orient ='records') if not sqlite_dbObj.table_exists('prodData'): sqlite_dbObj.create_table('prodData',df2.columns, df2.dtypes) sqlite_dbObj.write(df2,'prodData') data = sqlite_dbObj.read('servingDetails',"usecase = '"+model+"'") if len(data) == 0: data = {'usecase':model,'noOfPredictCalls':1,'noOfDriftCalls':0} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.write(data,'servingDetails') else: noofPredictCalls = int(data['noOfPredictCalls'].iloc[0])+1 sqlite_dbObj.update('servingDetails',"noOfPredictCalls = '"+str(noofPredictCalls)+"'","usecase = '"+model+"'") predict_path = os.path.join(model_path,'aion_predict.py') outputStr = subprocess.check_output([sys.executable,predict_path,dataStr]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() resp = outputStr elif operation.lower() == 'monitoring': if not sqlite_dbObj.table_exists('monitoring'): data = {'usecase':model,'status':'No Drift','Msg':'No Input Drift Found','RecordTime':'Time','version':'1'} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.create_table('monitoring',data.columns, data.dtypes) trainingDataPath = os.path.join(os.path.dirname(__file__),modelVersion,'data','preprocesseddata.csv') data = sqlite_dbObj.read('prodData') filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') data.to_csv(dataFile, index=False) predict_path = os.path.join(model_path,'aion_ipdrift.py') inputJSON={'trainingDataLocation':trainingDataPath,'currentDataLocation':dataFile} outputStr = subprocess.check_output([sys.executable,predict_path,json.dumps(inputJSON)]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'drift:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() outputData = json.loads(outputStr) status = outputData['status'] if status == 'SUCCESS': Msg = str(outputData['data']) else: Msg = 'Error during drift analysis' now = datetime.now() # current date and time date_time = now.strftime("%m/%d/%Y, %H:%M:%S") data = {'usecase':model,'status':status,'Msg':Msg,'RecordTime':date_time,'version':version} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.write(data,'monitoring') resp = outputStr resp=resp+"\n" resp=resp.encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(resp) else: print("python ==> else2") data = {} elif None != re.search('/AION/pattern_anomaly_predict', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) model = self.path.split('/')[-1] data = self.rfile.read(length) data = json.loads(data) anomaly = False remarks = '' clusterid = -1 home = expanduser("~") if platform.system() == 'Windows': configfilename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'datadetails.json') filename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'clickstream.json') clusterfilename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'stateClustering.csv') probfilename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'stateTransitionProbability.csv') else: configfilename = os.path.join(home,'HCLT','AION','target',model,'datadetails.json') filename = os.path.join(home,'HCLT','AION','target',model,'clickstream.json') clusterfilename = os.path.join(home,'HCLT','AION','target',model,'stateClustering.csv') probfilename = os.path.join(home,'HCLT','AION','target',model,'stateTransitionProbability.csv') dfclus = pd.read_csv(clusterfilename) dfprod = pd.read_csv(probfilename) f = open(configfilename, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) activity = configSettingsJson['activity'] sessionid = configSettingsJson['sessionid'] f = open(filename, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) groupswitching = configSettingsJson['groupswitching'] page_threshold = configSettingsJson['transitionprobability'] chain_count = configSettingsJson['transitionsequence'] chain_probability = configSettingsJson['sequencethreshold'] currentactivity = data[activity] if bool(user_records): sessionid = data[sessionid] print(sessionid,user_records['SessionID']) if sessionid != user_records['SessionID']: user_records['SessionID'] = sessionid prevactivity = '' user_records['probarry'] = [] user_records['prevclusterid'] = -1 user_records['NoOfClusterHopping'] = 0 user_records['pageclicks'] = 1 else: prevactivity = user_records['Activity'] user_records['Activity'] = currentactivity pageswitch = True if prevactivity == currentactivity or prevactivity == '': probability = 0 pageswitch = False remarks = '' else: user_records['pageclicks'] += 1 df1 = dfprod[(dfprod['State'] == prevactivity) & (dfprod['NextState'] == currentactivity)] if df1.empty: remarks = 'Anomaly Detected - User in unusual state' anomaly = True clusterid = -1 probability = 0 user_records['probarry'].append(probability) n=int(chain_count) num_list = user_records['probarry'][-n:] avg = sum(num_list)/len(num_list) for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] else: probability = df1['Probability'].iloc[0] user_records['probarry'].append(probability) n=int(chain_count) num_list = user_records['probarry'][-n:] davg = sum(num_list)/len(num_list) for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] remarks = '' if user_records['prevclusterid'] != -1: if probability == 0 and user_records['prevclusterid'] != clusterid: user_records['NoOfClusterHopping'] = user_records['NoOfClusterHopping']+1 if user_records['pageclicks'] == 1: remarks = 'Anomaly Detected - Frequent Cluster Hopping' anomaly = True else: remarks = 'Cluster Hopping Detected' user_records['pageclicks'] = 0 if user_records['NoOfClusterHopping'] > int(groupswitching) and anomaly == False: remarks = 'Anomaly Detected - Multiple Cluster Hopping' anomaly = True elif probability == 0: remarks = 'Anomaly Detected - Unusual State Transition Detected' anomaly = True elif probability <= float(page_threshold): remarks = 'Anomaly Detected - In-frequent State Transition Detected' anomaly = True else: #print(pageswitch) #print(probability) if pageswitch == True: if probability == 0: remarks = 'Anomaly Detected - Unusual State Transition Detected' anomaly = True elif probability <= float(page_threshold): remarks = 'Anomaly Detected - In-frequent State Transition Detected' anomaly = True else: remarks = '' if davg < float(chain_probability): if anomaly == False: remarks = 'Anomaly Detected - In-frequent Pattern Detected' anomaly = True else: user_records['SessionID'] = data[sessionid] user_records['Activity'] = data[activity] user_records['probability'] = 0 user_records['probarry'] = [] user_records['chainprobability'] = 0 user_records['prevclusterid'] = -1 user_records['NoOfClusterHopping'] = 0 user_records['pageclicks'] = 1 for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] user_records['prevclusterid'] = clusterid self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() resp = '{"status":"SUCCESS","data":{"Anomaly":"'+str(anomaly)+'","Remarks":"'+str(remarks)+'"}}' resp=resp+"\n" resp=resp.encode() self.wfile.write(resp) else: self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() resp = '{"Anomaly":"Error","Remarks":"'+str(Int)+'"}' resp=resp+"\n" resp=resp.encode() self.wfile.write(resp) elif None != re.search('/AION/pattern_anomaly_settings', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) data = self.rfile.read(length) #print(data) #keyList = list(data.keys()) #print(keyList[0]) model = self.path.split('/')[-1] #print(model) data = json.loads(data) #dataStr = json.dumps(data) groupswitching = data['groupswitching'] transitionprobability = data['transitionprobability'] transitionsequence = data['transitionsequence'] sequencethreshold = data['sequencethreshold'] home = expanduser("~") if platform.system() == 'Windows': filename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'clickstream.json') else: filename = os.path.join(home,'HCLT','AION','target',model,'clickstream.json') #print(filename) data = {} data['groupswitching'] = groupswitching data['transitionprobability'] = transitionprobability data['transitionsequence'] = transitionsequence data['sequencethreshold'] = sequencethreshold updatedConfig = json.dumps(data) with open(filename, "w") as fpWrite: fpWrite.write(updatedConfig) fpWrite.close() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() resp = '{"Status":"SUCCESS"}' resp=resp+"\n" resp=resp.encode() self.wfile.write(resp) else: print("python ==> else2") data = {} else: print("python ==> else1") self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() print("PYTHON ######## REQUEST ####### ENDED") return def do_GET(self): print("PYTHON ######## REQUEST ####### STARTED") if None != re.search('/AION/', self.path) or None != re.search('/aion/', self.path): usecase = self.path.split('/')[-2] operation = self.path.split('/')[-1] if operation.lower() == 'metrices': sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db') if sqlite_dbObj.table_exists('servingDetails'): df1 = sqlite_dbObj.read('servingDetails',"usecase = '"+usecase+"'") else: df1 = pd.DataFrame() if sqlite_dbObj.table_exists('monitoring'): df2 = sqlite_dbObj.read('monitoring') else: df2 = pd.DataFrame() if sqlite_dbObj.table_exists('modeldetails'): df3 = sqlite_dbObj.read('modeldetails') else: df3 = pd.DataFrame() msg='<html>\n' msg+='<head>\n' msg+='<title>Model Metrices</title>\n' msg+='</head>\n' msg+="""<style>table, th, td { border: 1px solid black; border-collapse: collapse;}</style>""" msg+='<body>\n' msg+='<h2>Model Metrices - Deployed Version '+str(version)+'</h2>' msg+='<br/>\n' msg+='<table style="width:80%">\n' msg+="""<tr> <th>Model</th> <th>Version</th> <th>ScoreType</th> <th>Score</th> </tr """ for idx in reversed(df3.index): msg += "<tr>\n" msg += "<td>"+str(df3.usecase[idx])+"</td>\n" msg += "<td>"+str(df3.version[idx])+"</td>\n" msg += "<td>"+str(df3.scoreType[idx])+"</td>\n" msg += "<td>"+str(df3.score[idx])+"</td>\n" msg += "</tr>\n" msg += '</table>\n' msg += '<br/>\n' msg += '<br/>\n' msg+='<table style="width:50%">\n' msg+='<tr>\n' msg+='<td>No of Predictions</td>\n' if df1.shape[0] > 0: msg+='<td>'+str(df1['noOfPredictCalls'].iloc[0])+'</td>\n' else: msg+='<td>0</td>\n' msg+='</tr>\n' msg+='<tr>\n' msg+='<td>No of Ground Truth</td>\n' msg+='<td>0</td>\n' msg+='</tr>\n' msg += '</table>\n' msg += '<br/>\n' msg+='<table style="width:100%">\n' msg+="""<tr> <th>UseCase</th> <th>Version</th> <th>Status</th> <th>Message</th> <th>Time</th> </tr> """ for idx in reversed(df2.index): msg += "<tr>\n" msg += "<td>"+str(df2.usecase[idx])+"</td>\n" msg += "<td>"+str(df3.version[idx])+"</td>\n" msg += "<td>"+str(df2.status[idx])+"</td>\n" msg += "<td>"+str(df2.Msg[idx])+"</td>\n" msg += "<td>"+str(df2.RecordTime[idx])+"</td>\n" msg += "</tr>\n" msg += '</table>\n' msg += '</body>\n' msg += '</html>\n' self.send_response(200) self.send_response(200) self.send_header('Content-Type', 'text/html') self.end_headers() self.wfile.write(msg.encode()) else: self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() return class ThreadedHTTPServer(ThreadingMixIn, HTTPServer): allow_reuse_address = True def shutdown(self): self.socket.close() HTTPServer.shutdown(self) class file_status(): def __init__(self,file): self.files_status = {} self.initializeFileStatus(file) def initializeFileStatus(self, file): self.files_status = {'path': file, 'time':file.stat().st_mtime} def is_file_changed(self): if self.files_status['path'].stat().st_mtime > self.files_status['time']: self.files_status['time'] = self.files_status['path'].stat().st_mtime return True return False def run(self): while( True): time.sleep(30) if self.is_file_changed(): readRun() class SimpleHttpServer(): def __init__(self, ip, port,model_file_path): self.server = ThreadedHTTPServer((ip,port), HTTPRequestHandler) self.status_checker = file_status(model_file_path) def start(self): self.server_thread = threading.Thread(target=self.server.serve_forever) self.server_thread.daemon = True self.server_thread.start() self.status_thread = threading.Thread(target=self.status_checker.run) self.status_thread.start() def waitForThread(self): self.server_thread.join() def stop(self): self.server.shutdown() self.waitForThread() def readRun(boot=False): filename = os.path.join(os.path.join(os.path.dirname(__file__)),'run') f = open (filename, "r") data = json.loads(f.read()) global modelVersion global version modelVersion = 'run_'+str(data['version']) version = str(data['version']) sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db') dataa = {'usecase':data['usecase'],'version':data['version'],'scoreType':data['scoreType'],'score':data['score']} data = pd.DataFrame(dataa, index=[0]) if not sqlite_dbObj.table_exists('modeldetails'): sqlite_dbObj.create_table('modeldetails',data.columns, data.dtypes) rdata = sqlite_dbObj.read('modeldetails',"version = '"+dataa['version']+"'") if (rdata.shape[0]) == 0 or (not boot): sqlite_dbObj.write(data,'modeldetails') readRun(boot=True) if __name__=='__main__': filename = os.path.join(os.path.join(os.path.dirname(__file__)),'run') parser = argparse.ArgumentParser(description='HTTP Server') parser.add_argument('port', type=int, help='Listening port for HTTP Server') parser.add_argument('ip', help='HTTP Server IP') args = parser.parse_args() server = SimpleHttpServer(args.ip, args.port,Path(filename)) #delete file #create file #write just msg as "started" print('HTTP Server Running...........') #file close server.start() server.waitForThread() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' from AION_185 import aion_prediction from AION_185 import featureslist from AION_185 import aion_drift from AION_185 import aion_performance ''' #from BaseHTTPServer import BaseHTTPRequestHandler,HTTPServer from http.server import BaseHTTPRequestHandler,HTTPServer #from SocketServer import ThreadingMixIn from socketserver import ThreadingMixIn ''' from augustus.core.ModelLoader import ModelLoader from augustus.strict import modelLoader ''' import pandas as pd import os,sys from os.path import expanduser import platform import numpy as np import configparser import threading import subprocess import argparse import re import cgi from datetime import datetime import json import sys from datetime import datetime user_records = {} class LocalModelData(object): models = {} class HTTPRequestHandler(BaseHTTPRequestHandler): def do_POST(self): print("PYTHON ######## REQUEST ####### STARTED") if None != re.search('/AION/predict', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) #data = cgi.parse_qs(self.rfile.read(length), keep_blank_values=1) data = self.rfile.read(length) data = json.loads(data) dataStr = json.dumps(data) outputStr = aion_prediction.predict(dataStr) outputStr = outputStr.strip() resp = outputStr resp=resp+"\n" resp=resp.encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(resp) else: print("python ==> else2") data = {} elif None != re.search('/AION/features', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) outputStr = featureslist.getfeatures() outputStr = outputStr.strip() resp = outputStr resp=resp+"\n" resp=resp.encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(resp) else: print("python ==> else2") data = {} elif None != re.search('/AION/monitoring', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) data = self.rfile.read(length) model = self.path.split('/')[-1] data = json.loads(data) dataStr = json.dumps(data) outputStr = aion_drift.drift(dataStr) outputStr = outputStr.strip() resp = outputStr resp=resp+"\n" resp=resp.encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(resp) else: print("python ==> else2") data = {} elif None != re.search('/AION/performance', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) data = self.rfile.read(length) data = json.loads(data) dataStr = json.dumps(data) outputStr = aion_performance.drift(dataStr) outputStr = outputStr.strip() resp = outputStr resp=resp+"\n" resp=resp.encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(resp) else: print("python ==> else2") data = {} elif None != re.search('/AION/pattern_anomaly_predict', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) model = self.path.split('/')[-1] data = self.rfile.read(length) data = json.loads(data) anomaly = False remarks = '' clusterid = -1 home = expanduser("~") if platform.system() == 'Windows': configfilename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'datadetails.json') filename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'clickstream.json') clusterfilename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'stateClustering.csv') probfilename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'stateTransitionProbability.csv') else: configfilename = os.path.join(home,'HCLT','AION','target',model,'datadetails.json') filename = os.path.join(home,'HCLT','AION','target',model,'clickstream.json') clusterfilename = os.path.join(home,'HCLT','AION','target',model,'stateClustering.csv') probfilename = os.path.join(home,'HCLT','AION','target',model,'stateTransitionProbability.csv') dfclus = pd.read_csv(clusterfilename) dfprod = pd.read_csv(probfilename) f = open(configfilename, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) activity = configSettingsJson['activity'] sessionid = configSettingsJson['sessionid'] f = open(filename, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) groupswitching = configSettingsJson['groupswitching'] page_threshold = configSettingsJson['transitionprobability'] chain_count = configSettingsJson['transitionsequence'] chain_probability = configSettingsJson['sequencethreshold'] currentactivity = data[activity] if bool(user_records): sessionid = data[sessionid] print(sessionid,user_records['SessionID']) if sessionid != user_records['SessionID']: user_records['SessionID'] = sessionid prevactivity = '' user_records['probarry'] = [] user_records['prevclusterid'] = -1 user_records['NoOfClusterHopping'] = 0 user_records['pageclicks'] = 1 else: prevactivity = user_records['Activity'] user_records['Activity'] = currentactivity pageswitch = True if prevactivity == currentactivity or prevactivity == '': probability = 0 pageswitch = False remarks = '' else: user_records['pageclicks'] += 1 df1 = dfprod[(dfprod['State'] == prevactivity) & (dfprod['NextState'] == currentactivity)] if df1.empty: remarks = 'Anomaly Detected - User in unusual state' anomaly = True clusterid = -1 probability = 0 user_records['probarry'].append(probability) n=int(chain_count) num_list = user_records['probarry'][-n:] avg = sum(num_list)/len(num_list) for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] else: probability = df1['Probability'].iloc[0] user_records['probarry'].append(probability) n=int(chain_count) num_list = user_records['probarry'][-n:] davg = sum(num_list)/len(num_list) for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] remarks = '' if user_records['prevclusterid'] != -1: if probability == 0 and user_records['prevclusterid'] != clusterid: user_records['NoOfClusterHopping'] = user_records['NoOfClusterHopping']+1 if user_records['pageclicks'] == 1: remarks = 'Anomaly Detected - Frequent Cluster Hopping' anomaly = True else: remarks = 'Cluster Hopping Detected' user_records['pageclicks'] = 0 if user_records['NoOfClusterHopping'] > int(groupswitching) and anomaly == False: remarks = 'Anomaly Detected - Multiple Cluster Hopping' anomaly = True elif probability == 0: remarks = 'Anomaly Detected - Unusual State Transition Detected' anomaly = True elif probability <= float(page_threshold): remarks = 'Anomaly Detected - In-frequent State Transition Detected' anomaly = True else: #print(pageswitch) #print(probability) if pageswitch == True: if probability == 0: remarks = 'Anomaly Detected - Unusual State Transition Detected' anomaly = True elif probability <= float(page_threshold): remarks = 'Anomaly Detected - In-frequent State Transition Detected' anomaly = True else: remarks = '' if davg < float(chain_probability): if anomaly == False: remarks = 'Anomaly Detected - In-frequent Pattern Detected' anomaly = True else: user_records['SessionID'] = data[sessionid] user_records['Activity'] = data[activity] user_records['probability'] = 0 user_records['probarry'] = [] user_records['chainprobability'] = 0 user_records['prevclusterid'] = -1 user_records['NoOfClusterHopping'] = 0 user_records['pageclicks'] = 1 for index, row in dfclus.iterrows(): clusterlist = row["clusterlist"] if currentactivity in clusterlist: clusterid = row["clusterid"] user_records['prevclusterid'] = clusterid self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() resp = '{"status":"SUCCESS","data":{"Anomaly":"'+str(anomaly)+'","Remarks":"'+str(remarks)+'"}}' resp=resp+"\n" resp=resp.encode() self.wfile.write(resp) else: self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() resp = '{"Anomaly":"Error","Remarks":"'+str(Int)+'"}' resp=resp+"\n" resp=resp.encode() self.wfile.write(resp) elif None != re.search('/AION/pattern_anomaly_settings', self.path): ctype, pdict = cgi.parse_header(self.headers.get('content-type')) if ctype == 'application/json': length = int(self.headers.get('content-length')) data = self.rfile.read(length) #print(data) #keyList = list(data.keys()) #print(keyList[0]) model = self.path.split('/')[-1] #print(model) data = json.loads(data) #dataStr = json.dumps(data) groupswitching = data['groupswitching'] transitionprobability = data['transitionprobability'] transitionsequence = data['transitionsequence'] sequencethreshold = data['sequencethreshold'] home = expanduser("~") if platform.system() == 'Windows': filename = os.path.join(home,'AppData','Local','HCLT','AION','target',model,'clickstream.json') else: filename = os.path.join(home,'HCLT','AION','target',model,'clickstream.json') #print(filename) data = {} data['groupswitching'] = groupswitching data['transitionprobability'] = transitionprobability data['transitionsequence'] = transitionsequence data['sequencethreshold'] = sequencethreshold updatedConfig = json.dumps(data) with open(filename, "w") as fpWrite: fpWrite.write(updatedConfig) fpWrite.close() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() resp = '{"Status":"SUCCESS"}' resp=resp+"\n" resp=resp.encode() self.wfile.write(resp) else: print("python ==> else2") data = {} else: print("python ==> else1") self.send_response(403) self.send_header('Content-Type', 'application/json') self.end_headers() print("PYTHON ######## REQUEST ####### ENDED") return def do_GET(self): print("PYTHON ######## REQUEST ####### STARTED") if None != re.search('/AION/predict', self.path): self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() features = featureslist.getfeatures() displaymsg = 'Data in JSON Format' config = json.loads(features) features = config['features'] datajson={} for feature in features: if feature['Type'].lower() != 'target': datajson[feature['feature']] = 'Value' displaymsg = json.dumps(datajson) msg=""" URL:{url} RequestType: POST Content-Type=application/json Body: {displaymsg} """.format(url=self.path,displaymsg=displaymsg) self.wfile.write(msg.encode()) elif None != re.search('/AION/monitoring', self.path): self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() displaymsg='{"trainingDataLocation":"Reference Data File Path","currentDataLocation":"Latest Data File Path"}' msg=""" URL:{url} RequestType: POST Content-Type=application/json Body: {displaymsg}""".format(url=self.path,displaymsg=displaymsg) self.wfile.write(msg.encode()) elif None != re.search('/AION/features', self.path): outputStr = featureslist.getfeatures() outputStr = outputStr.strip() resp = outputStr resp=resp+"\n" resp=resp.encode() self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(resp) else: msg=""" URL for prediction: /AION/predict URL for features List: /AION/features URL for monitoring: /AION/monitoring URL for performance: /AION/performance""" self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write(msg.encode()) return class ThreadedHTTPServer(ThreadingMixIn, HTTPServer): allow_reuse_address = True def shutdown(self): self.socket.close() HTTPServer.shutdown(self) class SimpleHttpServer(): def __init__(self, ip, port): self.server = ThreadedHTTPServer((ip,port), HTTPRequestHandler) def start(self): self.server_thread = threading.Thread(target=self.server.serve_forever) self.server_thread.daemon = True self.server_thread.start() def waitForThread(self): self.server_thread.join() def stop(self): self.server.shutdown() self.waitForThread() if __name__=='__main__': parser = argparse.ArgumentParser(description='HTTP Server') parser.add_argument('port', type=int, help='Listening port for HTTP Server') parser.add_argument('ip', help='HTTP Server IP') args = parser.parse_args() server = SimpleHttpServer(args.ip, args.port) #delete file #create file #write just msg as "started" print('HTTP Server Running...........') #file close server.start() server.waitForThread() import sys import os import pickle import json import traceback import warnings warnings.filterwarnings("ignore") import numpy as np import pandas as pd import dask.dataframe as dd import scipy from pandas import json_normalize import dask.distributed from dask_ml.wrappers import ParallelPostFit class incBatchPredictor(): def __init__(self): self.home = os.path.dirname(os.path.abspath(__file__)) self.configPath = os.path.join(self.home, 'etc', 'config.json') self.configDict = {} self.incProfilerPath = '' self.incSelectorPath = '' self.modelPath = '' self.incProfiler = None self.incSelector = None self.model = None self.targetFeature = None self.trainingFeatures = None self.modelName = '' self.problemType = '' self.algorithm = '' self.dataLocation = "" self.nworkers = None self.npartitions = None self.threadsPerWorker = None def get_nworkers(self): return self.nworkers def get_npartitions(self): return self.npartitions def get_threadsPerWorker(self): return self.threadsPerWorker def readData(self,data): try: if os.path.splitext(data)[1] in [".tsv",".csv",".data"]: df = dd.read_csv(data, # sep=r'\s*,\s*', assume_missing=True, parse_dates=True, infer_datetime_format=True, sample=1000000, # dtype={'caliper': 'object', # 'timestamp': 'object'}, # dtype='object', na_values=['-','?'] ) df = df.repartition(self.npartitions) else: if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) for key in jsonData: if type(jsonData[key]) == str: try: x = eval(jsonData[key]) if type(x) == int: jsonData[key] = int(jsonData[key]) print("check inside ==int") if type(x) == float: jsonData[key] = float(jsonData[key]) except: pass df = json_normalize(jsonData) df = df.replace(r'^\s*$', np.NaN, regex=True) df = dd.from_pandas(df, npartitions=self.npartitions) except KeyError as e: output = {"status":"FAIL","message":str(e).strip('"')} print(json.dumps(output)) except Exception as e: output = {"status":"FAIL","message":str(e).strip('"')} print(json.dumps(output)) return df def readConfig(self): with open(self.configPath, 'r', encoding= 'utf8') as f: self.configDict = json.load(f) self.targetFeature = self.configDict['targetFeature'] self.trainingFeatures = self.configDict['trainingFeatures'] self.modelName = self.configDict["modelName"] self.modelVersion = self.configDict["modelVersion"] self.dataLocation = self.configDict["dataLocation"] self.deployLocation = self.configDict["deployLocation"] self.incProfilerPath = self.configDict["profilerLocation"] self.incSelectorPath = self.configDict["selectorLocation"] self.problemType = self.configDict['analysisType'] self.algorithm = self.configDict["algorithm"] self.modelPath = self.configDict["modelLocation"] self.scoringCriteria = self.configDict['scoringCriteria'] self.nworkers = int(self.configDict["n_workers"]) self.npartitions = int(self.configDict["npartitions"]) self.threadsPerWorker = int(self.configDict["threads_per_worker"]) def pickleLoad(self, file): if os.path.exists(file): with open(file, 'rb') as f: model = pickle.load(f) return model else: return None def loadSavedModels(self): self.incProfiler = self.pickleLoad(os.path.join(self.home, 'model',self.incProfilerPath)) if self.incSelectorPath != '': self.incSelector = self.pickleLoad(os.path.join(self.home, 'model',self.incSelectorPath)) self.model = self.pickleLoad(os.path.join(self.home, 'model',self.modelPath)) def dataFramePreProcess(self, df): df = df.replace(r'^\s*$', np.NaN, regex=True) df = df.replace('-', np.nan) df = df.replace('?', np.nan) return df def profiler(self, df): X = self.dataFramePreProcess(df) if 'self.targetFeature' in X: X = X.drop(self.targetFeature, axis=1) X = self.incProfiler.transform(X) if self.incSelectorPath != '': X = self.incSelector.transform(X.to_dask_array(lengths=True)) # X = dd.from_dask_array(X) return X def trainedModel(self,X): ParallelPostFit(estimator=self.model) # preds = self.model.predict(X) if self.algorithm=="Distributed Light Gradient Boosting (LightGBM)": X = X.to_dask_array(lengths=True) preds = self.model.predict(X).compute() return preds def apply_output_format(self,df,modeloutput): label_maping = None if self.problemType.lower() == 'regression': if not isinstance(modeloutput, np.ndarray): modeloutput = modeloutput.to_numpy() dask_arr = dd.from_array(modeloutput) dask_arr.name = 'prediction' df = df.merge(dask_arr.to_frame()) df['rounded_prediction'] = df['prediction'].round(2) elif self.problemType.lower() == 'classification': print("type: ", type(modeloutput)) if not isinstance(modeloutput, np.ndarray): modeloutput = modeloutput.to_numpy() dask_arr = dd.from_array(modeloutput) dask_arr.name = "prediction" df = df.merge(dask_arr.to_frame()) with open(self.deployLocation + "/etc/" + "label_mapping.json") as jsonfile: label_maping = json.load(jsonfile) df["prediction"] = df["prediction"].astype(int) df["prediction"] = df["prediction"].astype(str) df["prediction_label"] = df["prediction"].map(label_maping) if df["prediction_label"].dtype == None: df["prediction_label"] = df["prediction"] outputjson = df.compute().to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} return(json.dumps(outputjson)) def predict(self,data): try: self.readConfig() df = self.readData(data) dfOrg = df.copy() if len(self.configDict)!=0: self.loadSavedModels() df = self.profiler(df) modeloutput = self.trainedModel(df) # dfOrg = dfOrg[self.allFtrs] output = self.apply_output_format(dfOrg, modeloutput) else: pass except Exception as e: print(traceback.format_exc()) output = {"status":"FAIL","message":str(e).strip('"')} return output if __name__ == "__main__": incBPobj = incBatchPredictor() incBPobj.readConfig() nWorkers = incBPobj.get_nworkers() threads_per_worker = incBPobj.get_threadsPerWorker() cluster = dask.distributed.LocalCluster(n_workers=nWorkers, threads_per_worker=threads_per_worker) client = dask.distributed.Client(cluster) output = incBPobj.predict(sys.argv[1]) print("predictions:",output) client.close() cluster.close() # -*- coding: utf-8 -*- """ Created on Sat Sep 10 23:57:56 2022 @author: jayaramakrishnans """ import numpy as np import pandas as pd from secrets import token_bytes from ppxgboost import PaillierAPI as paillier from ppxgboost import BoosterParser as boostparser from ppxgboost import PPBooster as ppbooster from ppxgboost.PPBooster import MetaData from ppxgboost.PPKey import PPBoostKey # from ope.pyope.ope import OPE from pyope.ope import OPE import joblib import logging import os from flask import Flask,request,jsonify,render_template # import pickle from flask import Flask, request, jsonify import json import jsonpickle app = Flask(__name__) class server_ppxgboost: def __init__(self): # self.problemtype=problemtype self.confdata=None print("Inside server_ppxgboost_1\n") ## Loading config file def configload(self): print("Inside server_ppxgboost_1,configload\n") cwd = os.path.abspath(os.path.dirname(__file__)) file_name='config.json' try: config_file=os.path.normpath(os.path.join(cwd,'config',file_name)) except Exception as e: print("config path error. Error Msg: \n",e) with open(config_file, 'r') as file: data = json.load(file) model_name=str(data["model_name"]) # version=str(data["version"]) return model_name ## Load server xgboost model from ../model dir. def model_load( self,path): print("Inside server_ppxgboost_1,model_load\n") cwd = os.path.abspath(os.path.dirname(__file__)) file_name=path try: model_location=os.path.normpath(os.path.join(cwd,'model',file_name)) except Exception as e: print("Model path error. Error Msg: \n",e) # print(path) loaded_model = joblib.load(model_location) return loaded_model ## Generate Encrypted prediction fn def ppxgboostpredict_server(self,model,ppBoostKey,clientdata,min_max): xgboost_model = model meta_min_max = MetaData(min_max) p_trees, feature_set, min_max = boostparser.model_to_trees(xgboost_model, min_max) enc_trees = ppbooster.enc_xgboost_model(ppBoostKey, p_trees, MetaData(min_max)) enc_client_data=clientdata # enc_predictions = ppbooster.predict_binary(enc_trees, X_test) # should rename the function enc_predictions = ppbooster.predict_binary(p_trees, enc_client_data) return enc_predictions ## XGBoost wrapper for native model (native model to scikit learn xgboost model) def xgboostwrappertonative(self,wrappermodel): print("Inside server_ppxgboost_7,xgboostwrappertonative= \n",wrappermodel) nativemodel = wrappermodel.get_booster() return nativemodel def training_dataset_parser(self,train_data: pd.DataFrame): """ :param train_data: dataframe training data :return: minimum of the training dataset, and maximum of the training dataset. """ return {'min': np.min(pd.DataFrame.min(train_data)), 'max': np.max(pd.DataFrame.max(train_data))} ## Homomorphic secure main server cls_obj=server_ppxgboost() @app.route('/homomorphicprediction_server_api',methods=['GET','POST']) def main_server(): data = request.get_json(force=True) response_data = json.dumps(data) json_in= json.loads(response_data) values = json_in['values'] features=json_in['features'] ppBoostKey=jsonpickle.decode(json_in['key']) encrypted_clientdata=pd.DataFrame(values,columns =features) ## Create encrypted predition from model model=None min_max = {'min': 0, 'max': 1000} model_name = cls_obj.configload() # model_name=usecase_name model_location=model_name model_xgboost = cls_obj.model_load(model_location) try: ## For sklearn based xgboost model to native model model = cls_obj.xgboostwrappertonative(model_xgboost) except: ## For native xgboost,we dont need to get booster. model= model_xgboost ## FFor logging cwd = os.path.abspath(os.path.dirname(__file__)) # model_name=model_name file_name = model_name.rsplit('.', 1) file_name=file_name[0] file_name=file_name+".log" try: hm_log=os.path.normpath(os.path.join(cwd,'logs',file_name)) os.makedirs(os.path.dirname(hm_log), exist_ok=True) except Exception as e: print("Log path error. Error Msg: \n",e) logging.basicConfig(filename=hm_log,format='%(asctime)s %(message)s', filemode='w',level=logging.DEBUG) log = logging.getLogger('AION') log.info('Server binary class encryption service started...') print("Encrypted client data is ....\n") log.info("Encrypted client data is (received by server): \n"+str(encrypted_clientdata)) enc_predictions = cls_obj.ppxgboostpredict_server(model,ppBoostKey,encrypted_clientdata,min_max) log.info("server side encrypted prediction: \n"+str(enc_predictions)) ## Serialize the ppboost encrypted prediction by jsonpickle, normal pikle lib not working. enc_predictions_json = jsonpickle.encode(enc_predictions) # enc_predictions_json = enc_predictions.to_json() return enc_predictions_json if __name__ == '__main__': #app.run(debug=True) app.run(host="localhost", port=9000, debug=True) # -*- coding: utf-8 -*- """ Created on Sat Sep 10 23:57:56 2022 """ import numpy as np import pandas as pd from secrets import token_bytes from ppxgboost import PaillierAPI as paillier from ppxgboost import BoosterParser as boostparser from ppxgboost import PPBooster as ppbooster from ppxgboost.PPBooster import MetaData from ppxgboost.PPKey import PPBoostKey # from ope.pyope.ope import OPE from pyope.ope import OPE import joblib import logging import os from flask import Flask,request,jsonify,render_template # import pickle from flask import Flask, request, jsonify import json import jsonpickle import os.path import time import subprocess import sys from os.path import expanduser import ntpath import shutil import platform from pathlib import Path home = expanduser("~") if platform.system() == 'Windows': LOG_FILE_PATH = os.path.join(home,'AppData','Local','HCLT','AION','logs') else: LOG_FILE_PATH = os.path.join(home,'HCLT','AION','logs') app = Flask(__name__) class server_ppxgboost: def __init__(self): # self.problemtype=problemtype self.confdata=None ## Loading config file def configload(self): cwd = os.path.abspath(os.path.dirname(__file__)) file_name='config.json' try: config_file=os.path.normpath(os.path.join(cwd,'config',file_name)) except Exception as e: print("config path error. Error Msg: \n",e) with open(config_file, 'r') as file: data = json.load(file) model_name=str(data["model_name"]) # version=str(data["version"]) return model_name ## Load server xgboost model from ../model dir. def model_load( self,path): cwd = os.path.abspath(os.path.dirname(__file__)) file_name=path try: model_location=os.path.normpath(os.path.join(cwd,'model',file_name)) except Exception as e: print("Model path error. Error Msg: \n",e) # print(path) loaded_model = joblib.load(model_location) return loaded_model ## Generate Encrypted prediction fn def ppxgboostpredict_server(self,model,ppBoostKey,clientdata,min_max): xgboost_model = model meta_min_max = MetaData(min_max) p_trees, feature_set, min_max = boostparser.model_to_trees(xgboost_model, min_max) enc_trees = ppbooster.enc_xgboost_model(ppBoostKey, p_trees, MetaData(min_max)) enc_client_data=clientdata # enc_predictions = ppbooster.predict_binary(enc_trees, X_test) # should rename the function enc_predictions = ppbooster.predict_binary(p_trees, enc_client_data) return enc_predictions ## XGBoost wrapper for native model (native model to scikit learn xgboost model) def xgboostwrappertonative(self,wrappermodel): nativemodel = wrappermodel.get_booster() return nativemodel def training_dataset_parser(self,train_data: pd.DataFrame): """ :param train_data: dataframe training data :return: minimum of the training dataset, and maximum of the training dataset. """ return {'min': np.min(pd.DataFrame.min(train_data)), 'max': np.max(pd.DataFrame.max(train_data))} ## Homomorphic secure main server cls_obj=server_ppxgboost() @app.route('/homomorphicprediction_server_api',methods=['GET','POST']) def main_server(): data = request.get_json(force=True) response_data = json.dumps(data) json_in= json.loads(response_data) values = json_in['values'] features=json_in['features'] ppBoostKey=jsonpickle.decode(json_in['key']) encrypted_clientdata=pd.DataFrame(values,columns =features) ## Create encrypted predition from model model=None min_max = {'min': 0, 'max': 1000} model_name = cls_obj.configload() # model_name=usecase_name model_location=model_name model_xgboost = cls_obj.model_load(model_location) try: ## For sklearn based xgboost model to native model model = cls_obj.xgboostwrappertonative(model_xgboost) except: ## For native xgboost,we dont need to get booster. model= model_xgboost ## FFor logging cwd = os.path.abspath(os.path.dirname(__file__)) # model_name=model_name file_name = model_name.rsplit('.', 1) file_name=file_name[0] file_name=file_name+".log" try: hm_log=os.path.normpath(os.path.join(cwd,'logs',file_name)) os.makedirs(os.path.dirname(hm_log), exist_ok=True) except Exception as e: print("Log path error. Error Msg: \n",e) logging.basicConfig(filename=hm_log,format='%(asctime)s %(message)s', filemode='w',level=logging.DEBUG) log = logging.getLogger('AION') log.info('Server regression encryption service started...') print("Encrypted client data is ....\n") log.info("Encrypted client data is (received by server): \n"+str(encrypted_clientdata)) print("Client side encrypted data: \n",encrypted_clientdata) enc_predictions = cls_obj.ppxgboostpredict_server(model,ppBoostKey,encrypted_clientdata,min_max) log.info("server side encrypted prediction: \n"+str(enc_predictions)) ## Serialize the ppboost encrypted prediction by jsonpickle, normal pikle lib not working. enc_predictions_json = jsonpickle.encode(enc_predictions) # enc_predictions_json = enc_predictions.to_json() return enc_predictions_json if __name__ == '__main__': #app.run(debug=True) app.run(host="localhost", port=9000, debug=True) # -*- coding: utf-8 -*- """ Created on Sat Sep 10 23:57:56 2022 """ import numpy as np import sqlite3 import sys import pandas as pd from secrets import token_bytes from ppxgboost import PaillierAPI as paillier from ppxgboost import BoosterParser as boostparser from ppxgboost import PPBooster as ppbooster from ppxgboost.PPBooster import MetaData from ppxgboost.PPKey import PPBoostKey # from ope.pyope.ope import OPE from pyope.ope import OPE import joblib import logging import os from flask import Flask,request,jsonify,render_template # import pickle from flask import Flask, request, jsonify import json import jsonpickle import time from pathlib import Path app = Flask(__name__) class server_ppxgboost: def __init__(self): # self.problemtype=problemtype print("Inside server_ppxgboost_1,init\n") self.confdata=None ## Loading config file def configload(self): cwd = os.path.abspath(os.path.dirname(__file__)) file_name='secure_config.json' try: config_file=os.path.normpath(os.path.join(cwd,'etc',file_name)) except Exception as e: print("config path error. Error Msg: \n",e) with open(config_file, 'r') as file: data = json.load(file) model_name=str(data["model_name"]) # version=str(data["version"]) return model_name ## Load server xgboost model from ../model dir. def model_load( self,path): cwd = os.path.abspath(os.path.dirname(__file__)) file_name=path try: model_location=os.path.normpath(os.path.join(cwd,'model',file_name)) except Exception as e: print("Model path error. Error Msg: \n",e) # print(path) loaded_model = joblib.load(model_location) return loaded_model def create_connection(self,db_file): conn = None try: conn = sqlite3.connect(db_file) conn.execute('''CREATE TABLE IF NOT EXISTS modelinfo (key BLOB NOT NULL,encrypttree BLOB NOT NULL)''') except Exception as e: print(e) return conn def count_encrypt_model(self,conn): try: sql = "select count(*) from modelinfo" cur = conn.cursor() cur.execute(sql) cur_result = cur.fetchone() return cur_result[0] except Exception as e: print(e) def create_encryptmodel(self,conn,modeldetails): sql = ''' INSERT INTO modelinfo(key,encrypttree) VALUES(?,?) ''' cur = conn.cursor() cur.execute(sql, modeldetails) conn.commit() return cur.lastrowid def search_encryptmodel(self,conn,key): try: sql = "SELECT encrypttree FROM modelinfo where key='"+key+"'" cursor = conn.execute(sql) for row in cursor: return row[0] return '' except Exception as e: print(e) def ppxgboostpredict_server(self,model,ppBoostKey,clientdata,min_max,noofclasses): try: db_file = Path(__file__).parent/'logs'/'encryptedModels' conn = self.create_connection(db_file) enc_trees = self.search_encryptmodel(conn,jsonpickle.encode(ppBoostKey)) if enc_trees != '': enc_trees = jsonpickle.decode(enc_trees) else: if self.count_encrypt_model(conn) >= 5: outputjson = {"status":"ERROR","msg":"Maximum Number of Encrypted"} return json.dumps(outputjson) xgboost_model = model meta_min_max = MetaData(min_max) p_trees, feature_set, min_max = boostparser.model_to_trees(xgboost_model, min_max) enc_trees = ppbooster.enc_xgboost_model(ppBoostKey, p_trees, MetaData(min_max)) modelinfo = (jsonpickle.encode(ppBoostKey),jsonpickle.encode(enc_trees)) self.create_encryptmodel(conn,modelinfo) enc_client_data=clientdata # try: # num_classes = model.n_classes_ # except: # num_classes = noofclasses num_classes = noofclasses if num_classes == 0: nc_predictions = ppbooster.predict_binary(p_trees, enc_client_data) else: enc_predictions = ppbooster.predict_multiclass(enc_trees, num_classes, enc_client_data) enc_predictions_json = jsonpickle.encode(enc_predictions) outputjson = {"status":"SUCCESS","data":enc_predictions_json} return json.dumps(outputjson) except Exception as e: outputjson = {"status":"ERROR","msg":str(e)} return json.dumps(outputjson) ## XGBoost wrapper for native model (native model to scikit learn xgboost model) def xgboostwrappertonative(self,wrappermodel): nativemodel = wrappermodel.get_booster() try: noOfClasses = wrappermodel.n_classes_ except Exception as e: print(e) noOfClasses = 0 return nativemodel,noOfClasses def training_dataset_parser(self,train_data: pd.DataFrame): """ :param train_data: dataframe training data :return: minimum of the training dataset, and maximum of the training dataset. """ return {'min': np.min(pd.DataFrame.min(train_data)), 'max': np.max(pd.DataFrame.max(train_data))} ## Homomorphic secure main server cls_obj=server_ppxgboost() def spredict(data): try: json_in= json.loads(data) values = json_in['values'] features=json_in['features'] ppBoostKey=jsonpickle.decode(json_in['key']) encrypted_clientdata=pd.DataFrame(values,columns =features) model=None min_max = {'min': 0, 'max': 1000} model_name = cls_obj.configload() model_location=model_name model_xgboost = cls_obj.model_load(model_location) try: model,noofclasses = cls_obj.xgboostwrappertonative(model_xgboost) except Exception as e: print(e) model= model_xgboost noofclasses = 0 cwd = os.path.abspath(os.path.dirname(__file__)) # model_name=model_name file_name = model_name.rsplit('.', 1) file_name=file_name[0] file_name=file_name+".log" try: hm_log=os.path.normpath(os.path.join(cwd,'logs',file_name)) os.makedirs(os.path.dirname(hm_log), exist_ok=True) except Exception as e: print("Log path error. Error Msg: \n",e) logging.basicConfig(filename=hm_log,format='%(asctime)s %(message)s', filemode='w',level=logging.DEBUG) log = logging.getLogger('AION') log.info('Server multiclass classification encryption service started...') log.info("Encrypted client data is (received by server): \n"+str(encrypted_clientdata)) output = cls_obj.ppxgboostpredict_server(model,ppBoostKey,encrypted_clientdata,min_max,noofclasses) print("predictions:",output) print("Inside server_ppxgboost_8,output= \n",output) return output except Exception as e: outputjson = {"status":"ERROR","msg":str(e)} output = json.dumps(outputjson) print("predictions:",output) return output if __name__ == "__main__": output = spredict(sys.argv[1]) # -*- coding: utf-8 -*- # -*- coding: utf-8 -*- import pandas as pd import numpy as np import logging import os import sys from logging import INFO from script.heBinary import client_ppxgboost from script.aion_predict import selector from script.inputprofiler import inputprofiler ## Client main class for binary classification class aion_hebinaryclient: def __init__(self): self.confdata=None def configLoad(self,jsonfile): import json jsonfile=str(jsonfile) with open(jsonfile, 'r') as file: self.confdata = json.load(file) return self.confdata def dataload(self,datapath): df = pd.read_csv(datapath) ## Data preprocess in test dataset, In aion, aion profiler will handle it. # df =df[~df.isin([np.nan, np.inf, -np.inf]).any(1)] # df=df.reset_index(drop=True) profilerobj = inputprofiler() df = profilerobj.run(df) selectobj = selector() df = selectobj.run(df) return df # Start Flower server for n rounds of federated learning if __name__ == "__main__": classobj=aion_hebinaryclient() data_location=str(sys.argv[1]) df=classobj.dataload(data_location) # print("df: \n",df) aionhmcobj=client_ppxgboost(df) ppxgboost_pred=aionhmcobj.main_client() print("final decrypted prediction at client side: \n",ppxgboost_pred) # classobj.log.info("At client end, homomorphic prediction df: \n"+str(ppxgboost_pred)) # classobj.log.info("Aion homomorphic client encrypted prediction df: \n"+str(ppxgboost_pred)) # -*- coding: utf-8 -*- # -*- coding: utf-8 -*- import pandas as pd import numpy as np import logging import os import sys from logging import INFO from script.heMulticlass import client_ppxgboost from script.aion_predict import selector from script.inputprofiler import inputprofiler import argparse class aion_hemulticlient: def __init__(self): self.confdata=None def dataload(self,datapath): df = pd.read_csv(datapath) ## Data preprocess in test dataset, In aion, aion profiler will handle it. # df =df[~df.isin([np.nan, np.inf, -np.inf]).any(1)] # df=df.reset_index(drop=True) profilerobj = inputprofiler() df = profilerobj.run(df) selectobj = selector() df = selectobj.run(df) return df if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('-i', '--inputDataLocation', help='Input Data Path') parser.add_argument('-k', '--keyGenerate', help='True') parser.add_argument('-e', '--endPoint', help='Service End Point') args = parser.parse_args() if args.inputDataLocation: dataLocation=args.inputDataLocation if args.keyGenerate: keyGenerate = args.keyGenerate else: keyGenerate='False' print(keyGenerate) if args.endPoint: endPoint=args.endPoint else: raise('End Point Not Defined') classobj=aion_hemulticlient() df=classobj.dataload(dataLocation) aionhmcobj=client_ppxgboost(df,keyGenerate,endPoint) ppxgboost_pred=aionhmcobj.main_client() print("final decrypted prediction at client side: \n",ppxgboost_pred) # -*- coding: utf-8 -*- # -*- coding: utf-8 -*- import pandas as pd import numpy as np import logging import os import sys from logging import INFO from script.heRegression import client_ppxgboost from script.aion_predict import selector from script.inputprofiler import inputprofiler import argparse class aion_hemulticlient: def __init__(self): self.confdata=None def dataload(self,datapath): df = pd.read_csv(datapath) ## Data preprocess in test dataset, In aion, aion profiler will handle it. # df =df[~df.isin([np.nan, np.inf, -np.inf]).any(1)] # df=df.reset_index(drop=True) profilerobj = inputprofiler() df = profilerobj.run(df) selectobj = selector() df = selectobj.run(df) return df if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('-i', '--inputDataLocation', help='Input Data Path') parser.add_argument('-k', '--keyGenerate', help='True') parser.add_argument('-e', '--endPoint', help='Service End Point') args = parser.parse_args() if args.inputDataLocation: dataLocation=args.inputDataLocation if args.keyGenerate: keyGenerate = args.keyGenerate else: keyGenerate='False' print(keyGenerate) if args.endPoint: endPoint=args.endPoint else: raise('End Point Not Defined') classobj=aion_hemulticlient() df=classobj.dataload(dataLocation) aionhmcobj=client_ppxgboost(df,keyGenerate,endPoint) ppxgboost_pred=aionhmcobj.main_client() print("final decrypted prediction at client side: \n",ppxgboost_pred) # -*- coding: utf-8 -*- import pandas as pd from sklearn.model_selection import train_test_split import numpy as np from secrets import token_bytes from ppxgboost import PaillierAPI as paillier from ppxgboost import BoosterParser as boostparser from ppxgboost import PPBooster as ppbooster from ppxgboost.PPBooster import MetaData from ppxgboost.PPKey import PPBoostKey # from ope.pyope.ope import OPE from pyope.ope import OPE import sys sys.path.insert(0, '..') import logging from logging import INFO import pickle import requests import json # from json import JSONEncoder import jsonpickle import os ##Aion main client class for ppxgboost based encryption,decryption class client_ppxgboost: def __init__(self,data): self.data=data self.prediction=None ## For logging cwd = os.path.abspath(os.path.dirname(__file__)) # model_name=model_name file_name = "he_binaryclass" file_name=file_name+".log" try: hm_log=os.path.normpath(os.path.join(cwd,'logs',file_name)) os.makedirs(os.path.dirname(hm_log), exist_ok=True) except Exception as e: print("Log path error. Error Msg: \n",e) logging.basicConfig(filename=hm_log,format='%(asctime)s %(message)s', filemode='w',level=logging.DEBUG) self.log = logging.getLogger('AION') self.log.info('Client binary class classification homomorphic encryption service started...') ## Loading configuration parameters, Not used now. def configload(self): try: data=self.confdata usecase_name=str(data["usecase_name"]) version=str(data["version"]) problem_type=data["problem_type"] model_location=str(data["model_location"]) data_location=str(data["data_location"]) selected_feature=data["selected_feature"] if (type(selected_feature) is str): selected_feature=selected_feature.split(',') target_feature=data["target_feature"] client_encryption_accuracy=str(data["client_encryption_accuracy"]) test_size=int(data["test_size"]) test_size=test_size/100 except Exception as e: self.log.info("Reading server config file issue. Err.Msg: %s "+str(e)) return usecase_name,data_location,model_location,problem_type,version,selected_feature,target_feature,client_encryption_accuracy,test_size ## Load the model, Not used at client now. def model_load(self, path): loaded_model = pickle.load(open(path, 'rb')) return loaded_model #Generating secure key def generate_ppboostkey(self): public_key, private_key = paillier.he_key_gen() prf_key = token_bytes(16) OPE_key = token_bytes(16) encrypter = OPE(OPE_key) ppBoostKey = PPBoostKey(public_key, prf_key, encrypter) return public_key,private_key,prf_key,OPE_key,encrypter,ppBoostKey ## Binary client prediction (decrypted prediction) def ppxgboostbinary_predict(self,enc_predictions,private_key): dec = ppbooster.client_decrypt(private_key, enc_predictions) ##For binary classification res = ppbooster.client_decrypt_prediction_binary(private_key, enc_predictions) res_df=pd.DataFrame(res) return res # class ppkeyEncoder(JSONEncoder): # def default(self,o): # return o.__dict__ ## Function to connect secure server via flask restapi (send enc data and receive enc prediction.) def connect_xgboostserver(self,ppBoostKey,encrypted_xtest): url = 'http://localhost:9000//homomorphicprediction_server_api' enc_dict={} # df_list=[encrypted_xtest.columns.values.tolist()]+df.values.tolist() enc_dict['values']=encrypted_xtest.values.tolist() enc_dict['features']=encrypted_xtest.columns.values.tolist() enc_dict['key']= jsonpickle.encode(ppBoostKey) json_out=json.dumps(enc_dict,indent=4) headers = { 'content-type': "application/json", 'cache-control': "no-cache" } r = requests.post(url,data=json_out,headers=headers) enc_predictions_obj=jsonpickle.decode(r.content) return enc_predictions_obj ## Create PaillierAPI based encrypted user given data , here, testdata=userdata def generate_encrypted_testdata(self,prf_key,encrypter,testdata,min_max): feature_set_testdata=set(testdata.columns) ppbooster.enc_input_vector(prf_key, encrypter, feature_set_testdata, testdata, MetaData(min_max)) return testdata ## Create min and max of testdata df for pailler encryption,decryption def training_dataset_parser(self, client_data: pd.DataFrame): """ :param client_data: dataframe training data :return: minimum of the training dataset, and maximum of the training dataset. """ return {'min': np.min(pd.DataFrame.min(client_data)), 'max': np.max(pd.DataFrame.max(client_data))} ## Main client function call for enc data, send data to server, receive enc pred, finally decrypt prediction def main_client(self): self.log.info('Client actual data sample (displaying last 10 values) : \n'+str(self.data.tail(10))) print(" Client actual data sample (displaying last 10 values) : \n",self.data.tail(10)) public_key,private_key,prf_key,OPE_key,encrypter,ppBoostKey = self.generate_ppboostkey() min_max = self.training_dataset_parser(self.data) meta_min_max = MetaData(min_max) encrypted_testdata = self.generate_encrypted_testdata(prf_key,encrypter,self.data,min_max) # print("Sending encrypted client data to server....\n") print("\n Client side encrypted input data to server (displaying last 10 rows): \n",encrypted_testdata.tail(10)) self.log.info('Client side encrypted input data to server (displaying last 10 rows): \n'+str(encrypted_testdata.tail(10))) enc_predictions = self.connect_xgboostserver(ppBoostKey,encrypted_testdata) print("\n Encrypted prediction from server (displaying last 10 values.): \n",enc_predictions[-10:]) self.log.info('\n Encrypted prediction from server (displaying last 10 values.): \n'+str(enc_predictions[-10:])) ## Decrypted predition dec = self.ppxgboostbinary_predict(enc_predictions,private_key) # ppxgboost_pred=pd.DataFrame(list(zip(dec, predictions)),columns =['homomorphic_prediction', 'actual_prediction']) ppxgboost_pred=pd.DataFrame(dec,columns =['homomorphic_prediction']) self.log.info("final decrypted prediction at client side:: \n"+str(ppxgboost_pred)) return ppxgboost_pred ## For standalone testing if __name__ == '__main__': problemtype='Binary classification' data=None targetfeature=None ppxgboost_client_obj=client_ppxgboost(problemtype,data,targetfeature) ppxgboost_dec_predictions = ppxgboost_client_obj.main_client() # print("In main: ppxgboost_dec_predictions: \n",ppxgboost_dec_predictions) # -*- coding: utf-8 -*- import pandas as pd from sklearn.model_selection import train_test_split import numpy as np from secrets import token_bytes from ppxgboost import PaillierAPI as paillier from ppxgboost import BoosterParser as boostparser from ppxgboost import PPBooster as ppbooster from ppxgboost.PPBooster import MetaData from ppxgboost.PPKey import PPBoostKey # from ope.pyope.ope import OPE from pyope.ope import OPE import sys sys.path.insert(0, '..') import logging from logging import INFO import pickle import requests import json # from json import JSONEncoder import jsonpickle import os from pathlib import Path ##Aion main client class for ppxgboost based encryption,decryption class client_ppxgboost: def __init__(self,data,keyGenerate,endPoint): self.data=data self.keyGenerate = keyGenerate self.endPoint = endPoint self.prediction=None ## For logging clientDirectory = os.path.abspath(os.path.dirname(__file__)) # model_name=model_name file_name = "he_regression" file_name=file_name+".log" self.keydir=os.path.join(clientDirectory,'..','keys') os.makedirs(self.keydir, exist_ok=True) try: hm_log=os.path.normpath(os.path.join(clientDirectory,'logs',file_name)) os.makedirs(os.path.dirname(hm_log), exist_ok=True) except Exception as e: print("Log path error. Error Msg: \n",e) logging.basicConfig(filename=hm_log,format='%(asctime)s %(message)s', filemode='w',level=logging.DEBUG) self.log = logging.getLogger('AION') self.log.info('Client Regression homomorphic encryption service started...') ## Loading configuration parameters, Not used now. def configload(self): try: data=self.confdata usecase_name=str(data["usecase_name"]) version=str(data["version"]) problem_type=data["problem_type"] model_location=str(data["model_location"]) data_location=str(data["data_location"]) selected_feature=data["selected_feature"] if (type(selected_feature) is str): selected_feature=selected_feature.split(',') target_feature=data["target_feature"] client_encryption_accuracy=str(data["client_encryption_accuracy"]) test_size=int(data["test_size"]) test_size=test_size/100 except Exception as e: self.log.info("Reading server config file issue. Err.Msg: %s "+str(e)) return usecase_name,data_location,model_location,problem_type,version,selected_feature,target_feature,client_encryption_accuracy,test_size ## Load the model, Not used at client now. def model_load(self, path): loaded_model = pickle.load(open(path, 'rb')) return loaded_model #Generating secure key def generate_ppboostkey(self): try: public_key_file = Path(__file__).parent.parent/'keys'/'public.k' private_key_file = Path(__file__).parent.parent/'keys'/'private.k' prf_key_file = Path(__file__).parent.parent/'keys'/'prf.k' ope_key_file = Path(__file__).parent.parent/'keys'/'ope.k' encryptor_key_file = Path(__file__).parent.parent/'keys'/'encryptor.k' boostkey_key_file = Path(__file__).parent.parent/'keys'/'boostkey.k' if not boostkey_key_file.exists() or self.keyGenerate == 'True': public_key, private_key = paillier.he_key_gen() pub_file = open(public_key_file, 'w') pub_file.write(jsonpickle.encode(public_key)) pri_file = open(private_key_file, 'w') pri_file.write(jsonpickle.encode(private_key)) prf_key = token_bytes(16) OPE_key = token_bytes(16) prf_file = open(prf_key_file, 'w') prf_file.write(jsonpickle.encode(prf_key)) ope_file = open(ope_key_file, 'w') ope_file.write(jsonpickle.encode(OPE_key)) encrypter = OPE(OPE_key) enc_file = open(encryptor_key_file, 'w') enc_file.write(jsonpickle.encode(encrypter)) ppBoostKey = PPBoostKey(public_key, prf_key, encrypter) boost_file = open(boostkey_key_file, 'w') boost_file.write(jsonpickle.encode(ppBoostKey)) else: pub_file = open(public_key_file, 'r') public_key = jsonpickle.decode(pub_file.read()) pub_file.close() pri_file = open(private_key_file, 'r') private_key = jsonpickle.decode(pri_file.read()) pri_file.close() prf_file = open(prf_key_file, 'r') prf_key = jsonpickle.decode(prf_file.read()) prf_file.close() ope_file = open(ope_key_file, 'r') OPE_key = jsonpickle.decode(ope_file.read()) ope_file.close() enc_file = open(encryptor_key_file, 'r') encrypter = jsonpickle.decode(enc_file.read()) enc_file.close() boost_file = open(boostkey_key_file, 'r') ppBoostKey = jsonpickle.decode(boost_file.read()) boost_file.close() return public_key,private_key,prf_key,OPE_key,encrypter,ppBoostKey except Exception as e: print(e) def ppxgboostreg_predict(self,enc_predictions,private_key): dec = [] for p in enc_predictions: dec.append(paillier.decrypt(private_key, p)) dec_df=pd.DataFrame(dec) return dec # class ppkeyEncoder(JSONEncoder): # def default(self,o): # return o.__dict__ ## Function to connect secure server via flask restapi (send enc data and receive enc prediction.) def connect_xgboostserver(self,ppBoostKey,encrypted_xtest): url = self.endPoint enc_dict={} # df_list=[encrypted_xtest.columns.values.tolist()]+df.values.tolist() enc_dict['values']=encrypted_xtest.values.tolist() enc_dict['features']=encrypted_xtest.columns.values.tolist() enc_dict['key']= jsonpickle.encode(ppBoostKey) json_out=json.dumps(enc_dict,indent=4) headers = { 'content-type': "application/json", 'cache-control': "no-cache" } response = requests.post(url,auth=('admin','aion'),data=json_out,headers=headers) #print(response.content) outputStr=response.content outputStr = outputStr.decode('utf-8') outputStr = outputStr.strip() predict_dict = json.loads(str(outputStr)) if (predict_dict['status'] == 'SUCCESS'): data = predict_dict['data'] enc_predictions_ob=jsonpickle.decode(data) return enc_predictions_ob else: print('Error') ## Create PaillierAPI based encrypted user given data , here, testdata=userdata def generate_encrypted_testdata(self,prf_key,encrypter,testdata,min_max): feature_set_testdata=set(testdata.columns) ppbooster.enc_input_vector(prf_key, encrypter, feature_set_testdata, testdata, MetaData(min_max)) return testdata ## Create min and max of testdata df for pailler encryption,decryption def training_dataset_parser(self, client_data: pd.DataFrame): """ :param client_data: dataframe training data :return: minimum of the training dataset, and maximum of the training dataset. """ return {'min': np.min(pd.DataFrame.min(client_data)), 'max': np.max(pd.DataFrame.max(client_data))} ## Main client function call for enc data, send data to server, receive enc pred, finally decrypt prediction def main_client(self): self.log.info('Client actual data sample (displaying last 10 values) : \n'+str(self.data.tail(10))) print(" Client actual data sample (displaying last 10 values) : \n",self.data.tail(10)) public_key,private_key,prf_key,OPE_key,encrypter,ppBoostKey = self.generate_ppboostkey() min_max = self.training_dataset_parser(self.data) meta_min_max = MetaData(min_max) encrypted_testdata = self.generate_encrypted_testdata(prf_key,encrypter,self.data,min_max) # print("Sending encrypted client data to server....\n") print("\n Client side encrypted input data to server (displaying last 10 rows): \n",encrypted_testdata.tail(10)) self.log.info('Client side encrypted input data to server (displaying last 10 rows): \n'+str(encrypted_testdata.tail(10))) enc_predictions = self.connect_xgboostserver(ppBoostKey,encrypted_testdata) print("\n Encrypted prediction from server (displaying last 10 values.): \n",enc_predictions[-10:]) self.log.info('\n Encrypted prediction from server (displaying last 10 values.): \n'+str(enc_predictions[-10:])) ## Decrypted predition dec = self.ppxgboostreg_predict(enc_predictions,private_key) # ppxgboost_pred=pd.DataFrame(list(zip(dec, predictions)),columns =['homomorphic_prediction', 'actual_prediction']) ppxgboost_pred=pd.DataFrame(dec,columns =['homomorphic_prediction']) # print("final decrypted prediction at client side: \n",ppxgboost_pred) self.log.info("Final decrypted prediction at client side:: \n"+str(ppxgboost_pred)) return ppxgboost_pred ## For standalone testing if __name__ == '__main__': problemtype='regression' data=None targetfeature=None ppxgboost_client_obj=client_ppxgboost(problemtype,data,targetfeature) ppxgboost_dec_predictions = ppxgboost_client_obj.main_client() print("In main: ppxgboost_dec_predictions: \n",ppxgboost_dec_predictions) # -*- coding: utf-8 -*- import pandas as pd from sklearn.model_selection import train_test_split import numpy as np from secrets import token_bytes from ppxgboost import PaillierAPI as paillier from ppxgboost import BoosterParser as boostparser from ppxgboost import PPBooster as ppbooster from ppxgboost.PPBooster import MetaData from ppxgboost.PPKey import PPBoostKey # from ope.pyope.ope import OPE from pyope.ope import OPE import sys sys.path.insert(0, '..') import logging from logging import INFO import pickle import requests import json # from json import JSONEncoder import jsonpickle import os from pathlib import Path ##Aion main client class for ppxgboost based encryption,decryption class client_ppxgboost: def __init__(self,data,keyGenerate,endPoint): self.data=data self.keyGenerate = keyGenerate self.endPoint = endPoint self.prediction=None ## For logging clientDirectory = os.path.abspath(os.path.dirname(__file__)) # model_name=model_name file_name = "he_multiclass" file_name=file_name+".log" self.keydir=os.path.join(clientDirectory,'..','keys') os.makedirs(self.keydir, exist_ok=True) try: hm_log=os.path.normpath(os.path.join(clientDirectory,'logs',file_name)) os.makedirs(os.path.dirname(hm_log), exist_ok=True) except Exception as e: print("Log path error. Error Msg: \n",e) logging.basicConfig(filename=hm_log,format='%(asctime)s %(message)s', filemode='w',level=logging.DEBUG) self.log = logging.getLogger('AION') self.log.info('Client Multi class classification homomorphic encryption service started...') ## Loading configuration parameters, Not used now. def configload(self): try: data=self.confdata usecase_name=str(data["usecase_name"]) version=str(data["version"]) problem_type=data["problem_type"] model_location=str(data["model_location"]) data_location=str(data["data_location"]) selected_feature=data["selected_feature"] if (type(selected_feature) is str): selected_feature=selected_feature.split(',') target_feature=data["target_feature"] client_encryption_accuracy=str(data["client_encryption_accuracy"]) test_size=int(data["test_size"]) test_size=test_size/100 except Exception as e: self.log.info("Reading server config file issue. Err.Msg: %s "+str(e)) return usecase_name,data_location,model_location,problem_type,version,selected_feature,target_feature,client_encryption_accuracy,test_size ## Load the model, Not used at client now. def model_load(self, path): loaded_model = pickle.load(open(path, 'rb')) return loaded_model #Generating secure key def generate_ppboostkey(self): try: public_key_file = Path(__file__).parent.parent/'keys'/'public.k' private_key_file = Path(__file__).parent.parent/'keys'/'private.k' prf_key_file = Path(__file__).parent.parent/'keys'/'prf.k' ope_key_file = Path(__file__).parent.parent/'keys'/'ope.k' encryptor_key_file = Path(__file__).parent.parent/'keys'/'encryptor.k' boostkey_key_file = Path(__file__).parent.parent/'keys'/'boostkey.k' if not boostkey_key_file.exists() or self.keyGenerate == 'True': public_key, private_key = paillier.he_key_gen() pub_file = open(public_key_file, 'w') pub_file.write(jsonpickle.encode(public_key)) pri_file = open(private_key_file, 'w') pri_file.write(jsonpickle.encode(private_key)) prf_key = token_bytes(16) OPE_key = token_bytes(16) prf_file = open(prf_key_file, 'w') prf_file.write(jsonpickle.encode(prf_key)) ope_file = open(ope_key_file, 'w') ope_file.write(jsonpickle.encode(OPE_key)) encrypter = OPE(OPE_key) enc_file = open(encryptor_key_file, 'w') enc_file.write(jsonpickle.encode(encrypter)) ppBoostKey = PPBoostKey(public_key, prf_key, encrypter) boost_file = open(boostkey_key_file, 'w') boost_file.write(jsonpickle.encode(ppBoostKey)) else: pub_file = open(public_key_file, 'r') public_key = jsonpickle.decode(pub_file.read()) pub_file.close() pri_file = open(private_key_file, 'r') private_key = jsonpickle.decode(pri_file.read()) pri_file.close() prf_file = open(prf_key_file, 'r') prf_key = jsonpickle.decode(prf_file.read()) prf_file.close() ope_file = open(ope_key_file, 'r') OPE_key = jsonpickle.decode(ope_file.read()) ope_file.close() enc_file = open(encryptor_key_file, 'r') encrypter = jsonpickle.decode(enc_file.read()) enc_file.close() boost_file = open(boostkey_key_file, 'r') ppBoostKey = jsonpickle.decode(boost_file.read()) boost_file.close() return public_key,private_key,prf_key,OPE_key,encrypter,ppBoostKey except Exception as e: print(e) ## PPboost multiclass prediction fn def ppxgboostmulticlass_predict(self,enc_predictions,private_key): ##For binary classification # res = ppbooster.client_decrypt_prediction_binary(private_key, enc_predictions) ## For multiclass classification res = ppbooster.client_decrypt_prediction_multiclass(private_key, enc_predictions) return res # class ppkeyEncoder(JSONEncoder): # def default(self,o): # return o.__dict__ ## Function to connect secure server via flask restapi (send enc data and receive enc prediction.) def connect_xgboostserver(self,ppBoostKey,encrypted_xtest): url = self.endPoint enc_dict={} # df_list=[encrypted_xtest.columns.values.tolist()]+df.values.tolist() enc_dict['values']=encrypted_xtest.values.tolist() enc_dict['features']=encrypted_xtest.columns.values.tolist() enc_dict['key']= jsonpickle.encode(ppBoostKey) json_out=json.dumps(enc_dict,indent=4) headers = { 'content-type': "application/json", 'cache-control': "no-cache" } response = requests.post(url,auth=('admin','aion'),data=json_out,headers=headers) #print(response.content) outputStr=response.content outputStr = outputStr.decode('utf-8') outputStr = outputStr.strip() predict_dict = json.loads(str(outputStr)) if (predict_dict['status'] == 'SUCCESS'): data = predict_dict['data'] enc_predictions_ob=jsonpickle.decode(data) return enc_predictions_ob else: print('Error') ## Create PaillierAPI based encrypted user given data , here, testdata=userdata def generate_encrypted_testdata(self,prf_key,encrypter,testdata,min_max): feature_set_testdata=set(testdata.columns) ppbooster.enc_input_vector(prf_key, encrypter, feature_set_testdata, testdata, MetaData(min_max)) return testdata ## Create min and max of testdata df for pailler encryption,decryption def training_dataset_parser(self, client_data: pd.DataFrame): """ :param client_data: dataframe training data :return: minimum of the training dataset, and maximum of the training dataset. """ return {'min': np.min(pd.DataFrame.min(client_data)), 'max': np.max(pd.DataFrame.max(client_data))} ## Main client function call for enc data, send data to server, receive enc pred, finally decrypt prediction def main_client(self): self.log.info('Client actual data sample (displaying last 10 values) : \n'+str(self.data.tail(10))) #print(" Client actual data sample (displaying last 10 values) : \n",self.data.tail(10)) public_key,private_key,prf_key,OPE_key,encrypter,ppBoostKey = self.generate_ppboostkey() min_max = self.training_dataset_parser(self.data) meta_min_max = MetaData(min_max) #print('++++++++++++++++++++++++++++') encrypted_testdata = self.generate_encrypted_testdata(prf_key,encrypter,self.data,min_max) # print("Sending encrypted client data to server....\n") #print("\n Client side encrypted input data to server (displaying last 10 rows): \n",encrypted_testdata.tail(10)) self.log.info('Client side encrypted input data to server (displaying last 10 rows): \n'+str(encrypted_testdata.tail(10))) enc_predictions = self.connect_xgboostserver(ppBoostKey,encrypted_testdata) #print("\n Encrypted prediction from server (displaying last 10 values.): \n",enc_predictions[-10:]) #self.log.info('\n Encrypted prediction from server (displaying last 10 values.): \n'+str(enc_predictions[-10:])) ## Decrypted predition dec = self.ppxgboostmulticlass_predict(enc_predictions,private_key) # ppxgboost_pred=pd.DataFrame(list(zip(dec, predictions)),columns =['homomorphic_prediction', 'actual_prediction']) ppxgboost_pred=pd.DataFrame(dec,columns =['homomorphic_prediction']) self.log.info("final decrypted prediction at client side:: \n"+str(ppxgboost_pred)) return ppxgboost_pred ## For standalone testing if __name__ == '__main__': problemtype='Multi class classification' data=None targetfeature=None ppxgboost_client_obj=client_ppxgboost(problemtype,data,targetfeature) ppxgboost_dec_predictions = ppxgboost_client_obj.main_client() # print("In main: ppxgboost_dec_predictions: \n",ppxgboost_dec_predictions) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from script.inputprofiler import inputprofiler def preprocessing(data): profilerobj = inputprofiler() data = profilerobj.run(data) data = data.astype(np.float64) return(data) import matplotlib.pyplot as plt try: from sklearn.externals import joblib except: import joblib import os,sys import pandas as pd from alibi.explainers import ALE,plot_ale import io import json import urllib, base64 import numpy as np from scipy.stats import linregress from statistics import mean def get_ranked_values(explanation): ranked_shap_vals = [] for cls_idx in range(len(explanation.shap_values)): this_ranking = ( explanation.raw['importances'][str(cls_idx)]['ranked_effect'], explanation.raw['importances'][str(cls_idx)]['names'] ) ranked_shap_vals.append(this_ranking) return ranked_shap_vals def feature_importance_using_shap(model,X,featuresNames,classes,x_test,x_test_waterfall): from alibi.explainers import KernelShap import shap shap.initjs() if hasattr(model, "decision_function"): pred_fcn = model.decision_function elif hasattr(model, "predict_proba"): pred_fcn = model.predict_proba else: pred_fcn = model.predict try: svm_explainer = KernelShap(pred_fcn,feature_names=featuresNames) xtest = x_test[0].reshape(1, -1) svm_explainer.fit(X,n_background_samples=100) svm_explanation = svm_explainer.explain(xtest) try: idx = 0 instance = x_test[0][None, :] pred = model.predict(instance) class_idx = pred.item() if isinstance(svm_explainer.expected_value,np.ndarray): forceplot = shap.force_plot(svm_explainer.expected_value[class_idx],svm_explanation.shap_values[class_idx][idx,:],instance,feature_names=featuresNames,matplotlib=True,show=False) else: forceplot = shap.force_plot(svm_explainer.expected_value,svm_explanation.shap_values[0][idx,:],instance,feature_names=featuresNames,matplotlib=True,show=False) plt.tight_layout(pad = 0) image = io.BytesIO() plt.savefig(image, format='png') image.seek(0) string = base64.b64encode(image.read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) except Exception as inst: exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) image_64 = '' try: plt.clf() if isinstance(svm_explainer.expected_value,np.ndarray): r = shap.multioutput_decision_plot(svm_explainer.expected_value.tolist(), svm_explanation.shap_values, idx, feature_names=featuresNames, feature_order='importance', highlight=[class_idx], legend_labels=classes, return_objects=True, legend_location='lower right',show=False) else: expectedvalues = [svm_explainer.expected_value] r = shap.multioutput_decision_plot(expectedvalues, svm_explanation.shap_values, idx, feature_names=featuresNames, highlight = [0], return_objects=True, legend_labels=['Value'], feature_order='importance', show=False) plt.tight_layout(pad = 0) image = io.BytesIO() plt.savefig(image, format='png') image.seek(0) string = base64.b64encode(image.read()) image2_64 = 'data:image/png;base64,' + urllib.parse.quote(string) except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) image2_64 = '' except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) image2_64 = '' image_64 = '' try: plt.clf() x_test_waterfall = x_test_waterfall[featuresNames] explainer = shap.Explainer(model.predict, x_test_waterfall, feature_names=featuresNames) shap_values = explainer(x_test) r = shap.plots.waterfall(shap_values[0], show=False) image = io.BytesIO() plt.savefig(image, format='png', bbox_inches='tight') image.seek(0) string = base64.b64encode(image.read()) image3_64 = 'data:image/png;base64,' + urllib.parse.quote(string) except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) image3_64 = '' return (image_64, image2_64, image3_64) def feature_importance(xtrain,ytrain,xfeatures,yfeature,problemType): if problemType == 'classification': from sklearn.feature_selection import SelectFromModel from sklearn.ensemble import ExtraTreesClassifier selector = SelectFromModel(ExtraTreesClassifier()) selector.fit(xtrain,ytrain) values = selector.estimator_.feature_importances_ elif problemType == 'regression': from sklearn.feature_selection import SelectFromModel from sklearn.linear_model import Lasso selector = SelectFromModel(Lasso()) selector.fit(xtrain,ytrain) values = np.abs(selector.estimator_.coef_) labels = xtrain.columns.tolist() dft = pd.DataFrame() dft['labels'] = labels dft['values'] = values maxrecord = dft.iloc[dft['values'].argmax()] mostimportantfeature = maxrecord['labels'] f_imp = dft.to_json(orient='records') return(f_imp,mostimportantfeature) def get_trust_score(prdictfn,proba_fun,X_train,y_train): from alibi.confidence import TrustScore ts = TrustScore(k_filter=10,alpha=.05,filter_type='distance_knn',leaf_size=40,metric='euclidean',dist_filter_type='point') ts.fit(X_train, y_train, classes=3) y_pred = prdictfn(X_train) #y_prod = proba_fun(X_train) #probas = y_prod[range(len(y_pred)), y_pred] score, closest_class = ts.score(X_train, y_pred,k=2,dist_type='point') return(mean(score)) def getCounterFactuals(model,prdictfn,features,x_train,categories): from alibi.explainers import CounterFactualProto cat_vars_ord = {} categoryList=categories.keys().tolist() categoryCountList=categories.tolist() for i in range(0,len(categoryCountList)): cat_vars_ord[categoryList[i]] = categoryCountList[i] print(cat_vars_ord) X = x_train[0].reshape((1,) + x_train[0].shape) shape = X.shape print(shape) beta = .01 c_init = 1. c_steps = 5 max_iterations = 500 rng = (-1., 1.) # scale features between -1 and 1 feature_range = (x_train.min(axis=0), x_train.max(axis=0)) cf = CounterFactualProto(prdictfn,shape,cat_vars=cat_vars_ord) explanation = cf.explain(X) print(explanation) def getAnchorTabularofFirstRecord(predict_fn,features,X_train,X_test,labelMap, class_percent=None): threshold = 0.95 from alibi.explainers import AnchorTabular explainer = AnchorTabular(predict_fn, features) explainer.fit(X_train.values) X_test = X_test.values anchors = [] for idx in range(len(X_test)): prediction = explainer.predictor(X_test[idx].reshape(1, -1))[0] if len(labelMap) > 0: predictionstr = list(labelMap.keys())[list(labelMap.values()).index(prediction)] else: predictionstr = prediction explanation = explainer.explain(X_test[idx],threshold=threshold) if str(explanation.anchor) == '[]': if class_percent and class_percent.get(prediction, 0.0) > threshold: anchor = f"Cannot explain the prediction of this class ({predictionstr}) since there is no salient subset of features that is necessary for the prediction to hold. This behaviour is typical when the data is very imbalanced and is seen for the majority class in a classification problem." else: anchor = f'Can not get the explanation for {predictionstr}.' precision = explanation.precision[0] else: anchor = '%s' % (' AND '.join(explanation.anchor)) precision = explanation.precision coverage = explanation.coverage anchorjson = {} anchorjson['features'] = eval(str(features)) anchorjson['values'] = eval(str(list(X_test[idx]))) anchorjson['prediction'] = str(predictionstr) anchorjson['precision'] = str(round(precision,2)) anchorjson['anchor'] = anchor anchors.append(anchorjson) print(anchors) try: return(json.dumps(anchors)) except Exception as e: exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return(json.dumps({})) def ale_analysis(): displaypath = os.path.join(os.path.dirname(os.path.abspath(__file__)),"etc","display.json") with open(displaypath) as file: config = json.load(file) file.close() model = joblib.load(os.path.join(os.path.dirname(os.path.abspath(__file__)),"model",config['saved_model'])) predict_fn = lambda x: model.predict(x) predictproba_fn = lambda x: model.predict_proba(x) dathPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'data','postprocesseddata.csv.gz') dataFrame=pd.read_csv(dathPath,compression='gzip') #dataFrame = pd.read_csv(dathPath) testdathPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'data','predicteddata.csv.gz') testdataFrame=pd.read_csv(testdathPath,compression='gzip') #testdataFrame = pd.read_csv(testdathPath) features = config['modelFeatures']#['SepalWidthCm','PetalLengthCm'] targetfeature = config['targetFeature']#'Species' labelMap = config['labelMaps'] targetData = dataFrame[targetfeature] if config['problemType'].lower() == 'regression': X_train = dataFrame[features] X_test = testdataFrame.head(5) X_test = X_test[features] else: valueCount=targetData.value_counts() class_percent = (valueCount/ len(targetData)).to_dict() categoryList=valueCount.keys().tolist() class_names = categoryList X_train = dataFrame[features] X_test = testdataFrame.groupby('predict').first().reset_index() X_test = X_test[features] f_imp,m_imp_f = feature_importance(X_train,targetData,features,targetfeature,config['problemType'].lower()) if hasattr(model, "decision_function"): logit_fun_lr = model.decision_function try: logit_ale_lr = ALE(logit_fun_lr, feature_names=features, target_names=categoryList) logit_exp_lr = logit_ale_lr.explain(X_train.values) values = logit_exp_lr.ale_values feature = logit_exp_lr.feature_names feature_values = logit_exp_lr.feature_values lines= [] sentences = [] for x in range(0,len(feature)): f_value = feature_values[x] value = values[x] lines= [] for y in range(0,len(class_names)): line = [] for z in value: cordinate = z[y] line.append(cordinate) lines.append(line) line = lines[0] slope1, intercept1, r_value, p_value, std_err = linregress(f_value,line) line = lines[1] slope2, intercept2, r_value, p_value, std_err = linregress(f_value,line) xi = (intercept1-intercept2) / (slope2-slope1) xi = round(xi,2) lastvalues = {} i = 0 for line in lines: value = line[len(line)-1] lastvalues[class_names[i]] = value i = i+1 Keymax = max(lastvalues, key=lastvalues.get) Keymin = min(lastvalues, key=lastvalues.get) Keymaxclass = list(labelMap.keys())[list(labelMap.values()).index(Keymax)] Keyminclass = list(labelMap.keys())[list(labelMap.values()).index(Keymin)] sentense = '<b>Effect of '+str(feature[x])+'</b><br>For data samples having <b>'+str(feature[x])+'</b> >= <b>~'+str(xi)+'</b> ,there is a very high chance that they are of class <b>'+str(Keymaxclass)+'</b> '+targetfeature+'. For data samples having <b>'+str(feature[x])+'</b> < <b>~'+str(xi)+'</b> there is a very high change that they are of class <b>'+str(Keyminclass)+'</b> '+targetfeature+'.' sentences.append(sentense) except: sentense = '' sentences.append(sentense) xi = 0 elif hasattr(model, "predict_proba"): logit_fun_lr = model.predict_proba logit_ale_lr = ALE(logit_fun_lr, feature_names=features, target_names=categoryList) logit_exp_lr = logit_ale_lr.explain(X_train.values) values = logit_exp_lr.ale_values feature = logit_exp_lr.feature_names feature_values = logit_exp_lr.feature_values lines= [] sentences = [] sentense = 'Graphs gives a feature value how much more(less) probability does the model assign to each class relative to mean prediction. This also means that any increase in relative probability of one class must result into a decrease in probability of another class.' sentences.append(sentense) xi = 0 elif hasattr(model, "predict"): logit_fun_lr = model.predict logit_ale_lr = ALE(logit_fun_lr, feature_names=features, target_names=['Value']) logit_exp_lr = logit_ale_lr.explain(X_train.values) values = logit_exp_lr.ale_values feature = logit_exp_lr.feature_names feature_values = logit_exp_lr.feature_values lines= [] sentences = [] sentense = 'The ALE value corresponding to that feature value is difference to the mean effect of that feature. Put differently, the ALE value is the relative feature effect on the prediction at that feature value.' sentences.append(sentense) xi = 0 if (len(features)%2 ==0): n_cols = int(len(features)/2) else: n_cols = int(len(features)/2)+1 figheight = n_cols*3 try: plot_ale(logit_exp_lr,n_cols=2, fig_kw={'figwidth': 8, 'figheight': figheight}) plt.tight_layout(pad = 0) image = io.BytesIO() plt.savefig(image, format='png') image.seek(0) string = base64.b64encode(image.read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) except: image_64 = '' #score = get_trust_score(model.predict,proba_fun_lr,X_train.values,targetData.values) if config['problemType'].lower() == 'classification': anchorjson = getAnchorTabularofFirstRecord(predict_fn,features,X_train,X_test,labelMap,class_percent) else: anchorjson = getAnchorTabularofFirstRecord(predict_fn,features,X_train,X_test,labelMap) #anchors=[] #anchorjson = json.dumps(anchors) #feature_importance_using_shap(model,X_train.values,features,class_names) #getCounterFactuals(model,predictproba_fn,features,X_train.values,valueCount) output_json = {"status":"SUCCESS","data":{"data":image_64,"most_influencedfeature":m_imp_f,"interceptionpoint":xi,"sentences":sentences,"feature_importance":json.loads(f_imp),"anchorjson":json.loads(anchorjson)}} output_json = json.dumps(output_json) print("aion_ai_explanation:",output_json) return(output_json) def local_analysis(jsonData): jsonData = json.loads(jsonData) displaypath = os.path.join(os.path.dirname(os.path.abspath(__file__)),"etc","display.json") with open(displaypath) as file: config = json.load(file) file.close() model = joblib.load(os.path.join(os.path.dirname(os.path.abspath(__file__)),"model",config['saved_model'])) predict_fn = lambda x: model.predict(x) dathPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'data','postprocesseddata.csv.gz') dataFrame=pd.read_csv(dathPath,compression='gzip') testdathPath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data', 'predicteddata.csv.gz') testdataFrame = pd.read_csv(testdathPath, compression='gzip') features = config['modelFeatures']#['SepalWidthCm','PetalLengthCm'] targetfeature = config['targetFeature']#'Species' targetData = dataFrame[targetfeature] valueCount=targetData.value_counts() categoryList=valueCount.keys().tolist() class_names = categoryList #class_names = class_names.sort() X_train = dataFrame[features] from pandas import json_normalize df_test = json_normalize(jsonData) df_test = preprocessing(df_test) df_test = df_test[features] from alibi.explainers import AnchorTabular explainer = AnchorTabular(predict_fn, features) explainer.fit(X_train.values) df_test = df_test.values prediction = explainer.predictor(df_test.reshape(1, -1))[0] labelMap = config['labelMaps'] if len(labelMap) > 0: prediction = list(labelMap.keys())[list(labelMap.values()).index(prediction)] else: prediction = str(prediction) try: explanation = explainer.explain(df_test,threshold=0.85) if str(explanation.anchor) == '[]': anchor = 'NA' precision = str(round(explanation.precision[0],2)) else: anchor = '%s' % (' AND '.join(explanation.anchor)) precision = str(round(explanation.precision,2)) coverage = explanation.coverage except Exception as e: print(e) anchor = 'NA' precision = 0 coverage = 0 df_test_waterfall = testdataFrame forceplot,multidecisionplot,waterfallplot = feature_importance_using_shap(model,X_train.head(300).values,features,class_names,df_test,df_test_waterfall) output_json = {"status":"SUCCESS","data":{"anchor":anchor,"precision":precision,"coverage":coverage,"prediction":prediction,"forceplot":forceplot,"multidecisionplot":multidecisionplot,"waterfallplot":waterfallplot}} #print(output_json) output_json = json.dumps(output_json) print("aion_ai_explanation:",output_json) return(output_json) if __name__ == '__main__': analysis_type = sys.argv[1] if analysis_type.lower() == 'global': ale_analysis() if analysis_type.lower() == 'local': data = sys.argv[2] local_analysis(data) from script.inputprofiler import inputprofiler def preprocessing(data): profilerobj = inputprofiler() data = profilerobj.run(data) data = data.astype(np.float64) return(data) import matplotlib.pyplot as plt try: from sklearn.externals import joblib except: import joblib import os,sys import pandas as pd from alibi.explainers import ALE,plot_ale import io import json import urllib, base64 import numpy as np from scipy.stats import linregress from statistics import mean from tensorflow.keras.models import load_model from tensorflow.keras import backend as K import tensorflow as tf tf.compat.v1.disable_eager_execution() def recall_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return recall def precision_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + K.epsilon()) return precision def f1_m(y_true, y_pred): precision = precision_m(y_true, y_pred) recall = recall_m(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def rmse_m(y_true, y_pred): return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1)) def r_square(y_true, y_pred): SS_res = K.sum(K.square(y_true-y_pred)) SS_tot = K.sum(K.square(y_true-K.mean(y_true))) return (1 - SS_res/(SS_tot+K.epsilon())) def get_ranked_values(explanation): ranked_shap_vals = [] for cls_idx in range(len(explanation.shap_values)): this_ranking = ( explanation.raw['importances'][str(cls_idx)]['ranked_effect'], explanation.raw['importances'][str(cls_idx)]['names'] ) ranked_shap_vals.append(this_ranking) return ranked_shap_vals def feature_importance_using_shap(model,X,featuresNames,classes,x_test,problemType,modelname,x_test_waterfall): from alibi.explainers import KernelShap import shap shap.initjs() if hasattr(model, "decision_function") and problemType.lower() == 'classification': pred_fcn = model.decision_function elif hasattr(model, "predict_proba") and problemType.lower() == 'classification': pred_fcn = lambda x: model.predict_proba(np.expand_dims(x, axis=2)) else: if modelname == 'Neural Network': pred_fcn = lambda x: model.predict(x) else: pred_fcn = lambda x: model.predict(np.expand_dims(x, axis=2)) svm_explainer = KernelShap(pred_fcn,feature_names=featuresNames) xtest = x_test[0].reshape(1, -1) svm_explainer.fit(X,n_background_samples=100) svm_explanation = svm_explainer.explain(xtest) try: idx = 0 instance = x_test[0][None, :] if problemType.lower() == 'classification': if modelname == 'Neural Network': instance = x_test else: instance = np.expand_dims(x_test, axis=2) pred = np.argmax(model.predict(instance),axis=1) class_idx = pred.item() else: instance = np.expand_dims(x_test, axis=2) pred = model.predict(instance) class_idx = 0 if isinstance(svm_explainer.expected_value,np.ndarray): forceplot = shap.force_plot(svm_explainer.expected_value[class_idx],svm_explanation.shap_values[class_idx][idx,:],instance,feature_names=featuresNames,matplotlib=True,show=False) else: forceplot = shap.force_plot(svm_explainer.expected_value,svm_explanation.shap_values[0][idx,:],instance,feature_names=featuresNames,matplotlib=True,show=False) plt.tight_layout(pad = 0) image = io.BytesIO() plt.savefig(image, format='png') image.seek(0) string = base64.b64encode(image.read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) image_64 = '' try: plt.clf() if isinstance(svm_explainer.expected_value,np.ndarray): r = shap.multioutput_decision_plot(svm_explainer.expected_value.tolist(), svm_explanation.shap_values, idx, feature_names=featuresNames, feature_order='importance', highlight=[class_idx], legend_labels=classes, return_objects=True, legend_location='lower right',show=False) else: expectedvalues = [svm_explainer.expected_value] r = shap.multioutput_decision_plot(expectedvalues, svm_explanation.shap_values, idx, feature_names=featuresNames, highlight = [0], return_objects=True, legend_labels=['Value'], feature_order='importance', show=False) plt.tight_layout(pad = 0) image = io.BytesIO() plt.savefig(image, format='png') image.seek(0) string = base64.b64encode(image.read()) image2_64 = 'data:image/png;base64,' + urllib.parse.quote(string) except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) image2_64 = '' try: plt.clf() explainer = shap.DeepExplainer(model, X) shap_values = explainer.shap_values(x_test_waterfall.values) exp = shap.plots._waterfall.waterfall_legacy(explainer.expected_value[0].numpy(), shap_values[0][0],feature_names=featuresNames,show=False) image = io.BytesIO() plt.savefig(image, format='png', bbox_inches='tight') image.seek(0) string = base64.b64encode(image.read()) image3_64 = 'data:image/png;base64,' + urllib.parse.quote(string) except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) image3_64 = '' return(image_64,image2_64,image3_64) def feature_importance(xtrain,ytrain,xfeatures,yfeature,problemType): if problemType == 'classification': from sklearn.feature_selection import SelectFromModel from sklearn.ensemble import ExtraTreesClassifier selector = SelectFromModel(ExtraTreesClassifier()) selector.fit(xtrain,ytrain) values = selector.estimator_.feature_importances_ elif problemType == 'regression': from sklearn.feature_selection import SelectFromModel from sklearn.linear_model import Lasso selector = SelectFromModel(Lasso()) selector.fit(xtrain,ytrain) values = np.abs(selector.estimator_.coef_) labels = xtrain.columns.tolist() dft = pd.DataFrame() dft['labels'] = labels dft['values'] = values maxrecord = dft.iloc[dft['values'].argmax()] mostimportantfeature = maxrecord['labels'] f_imp = dft.to_json(orient='records') return(f_imp,mostimportantfeature) def get_trust_score(prdictfn,proba_fun,X_train,y_train): from alibi.confidence import TrustScore ts = TrustScore(k_filter=10,alpha=.05,filter_type='distance_knn',leaf_size=40,metric='euclidean',dist_filter_type='point') ts.fit(X_train, y_train, classes=3) y_pred = prdictfn(X_train) #y_prod = proba_fun(X_train) #probas = y_prod[range(len(y_pred)), y_pred] score, closest_class = ts.score(X_train, y_pred,k=2,dist_type='point') return(mean(score)) def getCounterFactuals(model,prdictfn,features,x_train,categories): from alibi.explainers import CounterFactualProto cat_vars_ord = {} categoryList=categories.keys().tolist() categoryCountList=categories.tolist() for i in range(0,len(categoryCountList)): cat_vars_ord[categoryList[i]] = categoryCountList[i] print(cat_vars_ord) X = x_train[0].reshape((1,) + x_train[0].shape) shape = X.shape print(shape) beta = .01 c_init = 1. c_steps = 5 max_iterations = 500 rng = (-1., 1.) # scale features between -1 and 1 feature_range = (x_train.min(axis=0), x_train.max(axis=0)) cf = CounterFactualProto(prdictfn,shape,cat_vars=cat_vars_ord) explanation = cf.explain(X) print(explanation) def getAnchorTabularofFirstRecord(predict_fn,features,X_train,X_test,labelMap, class_percent=None): threshold = 0.95 from alibi.explainers import AnchorTabular explainer = AnchorTabular(predict_fn, features) explainer.fit(X_train.values) X_test = X_test.values anchors = [] for idx in range(len(X_test)): prediction = explainer.predictor(X_test[idx].reshape(1, -1))[0] if isinstance(prediction,np.ndarray): prediction = prediction[0] if len(labelMap) > 0: predictionstr = list(labelMap.keys())[list(labelMap.values()).index(prediction)] else: predictionstr = str(prediction) try: explanation = explainer.explain(X_test[idx],threshold=threshold) if str(explanation.anchor) == '[]': if class_percent and class_percent.get(prediction, 0.0) > threshold: anchor = f"Cannot explain the prediction of this class ({predictionstr}) since there is no salient subset of features that is necessary for the prediction to hold. This behaviour is typical when the data is very imbalanced and is seen for the majority class in a classification problem." else: anchor = f'Can not get the explaination for {predictionstr}.' precision = explanation.precision[0] else: anchor = '%s' % (' AND '.join(explanation.anchor)) precision = explanation.precision coverage = explanation.coverage except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) anchor = 'Reason Not found' precision = 0 anchorjson = {} anchorjson['features'] = eval(str(features)) anchorjson['values'] = eval(str(list(X_test[idx]))) anchorjson['prediction'] = predictionstr anchorjson['precision'] = precision anchorjson['anchor'] = anchor anchors.append(anchorjson) return(json.dumps(anchors)) def ale_analysis(): displaypath = os.path.join(os.path.dirname(os.path.abspath(__file__)),"etc","display.json") with open(displaypath) as file: config = json.load(file) file.close() scoreParam = config['scoreParam'] filename_dl = os.path.join(os.path.dirname(os.path.abspath(__file__)),"model",config['saved_model']) if(scoreParam.lower() == 'rmse'): model = load_model(filename_dl,custom_objects={"rmse": rmse_m},compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[rmse_m]) elif(scoreParam.lower() == 'r2'): model = load_model(filename_dl,custom_objects={"r2": r_square},compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[r_square]) elif(scoreParam.lower() == 'recall'): model = load_model(filename_dl,custom_objects={"recall": recall_m},compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[recall_m]) elif(scoreParam.lower() == 'precision'): model = load_model(filename_dl,custom_objects={"precision": precision_m},compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[precision_m]) elif(scoreParam.lower() == 'roc_auc'): model = load_model(filename_dl,compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[tf.keras.metrics.AUC()]) elif(scoreParam.lower() == 'f1_score'): model = load_model(filename_dl,custom_objects={"f1_score": f1_m},compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[f1_m]) else: model = load_model(filename_dl) if config['modelname'] == 'Neural Network': predict_fn = lambda x: model.predict(x) else: predict_fn = lambda x: model.predict(np.expand_dims(x, axis=2)) predictproba_fn = lambda x: model.predict_proba(np.expand_dims(x, axis=2)) dathPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'data','postprocesseddata.csv.gz') dataFrame=pd.read_csv(dathPath,compression='gzip') testdathPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'data','predicteddata.csv.gz') testdataFrame=pd.read_csv(testdathPath,compression='gzip') features = config['modelFeatures']#['SepalWidthCm','PetalLengthCm'] targetfeature = config['targetFeature']#'Species' labelMap = config['labelMaps'] targetData = dataFrame[targetfeature] if config['problemType'].lower() == 'regression': X_train = dataFrame[features] X_test = testdataFrame.head(5) X_test = X_test[features] else: valueCount=targetData.value_counts() class_percent = (valueCount/ len(targetData)).to_dict() categoryList=valueCount.keys().tolist() class_names = categoryList X_train = dataFrame[features] X_test = testdataFrame.groupby('predict').first().reset_index() X_test = X_test[features] f_imp,m_imp_f = feature_importance(X_train,targetData,features,targetfeature,config['problemType'].lower()) if hasattr(model, "decision_function") and config['problemType'].lower() == 'classification': logit_fun_lr = model.decision_function try: logit_ale_lr = ALE(logit_fun_lr, feature_names=features, target_names=categoryList) logit_exp_lr = logit_ale_lr.explain(X_train.values) values = logit_exp_lr.ale_values feature = logit_exp_lr.feature_names feature_values = logit_exp_lr.feature_values lines= [] sentences = [] for x in range(0,len(feature)): f_value = feature_values[x] value = values[x] lines= [] for y in range(0,len(class_names)): line = [] for z in value: cordinate = z[y] line.append(cordinate) lines.append(line) line = lines[0] slope1, intercept1, r_value, p_value, std_err = linregress(f_value,line) line = lines[1] slope2, intercept2, r_value, p_value, std_err = linregress(f_value,line) xi = (intercept1-intercept2) / (slope2-slope1) xi = round(xi,2) lastvalues = {} i = 0 for line in lines: value = line[len(line)-1] lastvalues[class_names[i]] = value i = i+1 Keymax = max(lastvalues, key=lastvalues.get) Keymin = min(lastvalues, key=lastvalues.get) Keymaxclass = list(labelMap.keys())[list(labelMap.values()).index(Keymax)] Keyminclass = list(labelMap.keys())[list(labelMap.values()).index(Keymin)] sentense = '<b>Effect of '+str(feature[x])+'</b><br>For data samples having <b>'+str(feature[x])+'</b> >= <b>~'+str(xi)+'</b> ,there is a very high chance that they are of class <b>'+str(Keymaxclass)+'</b> '+targetfeature+'. For data samples having <b>'+str(feature[x])+'</b> < <b>~'+str(xi)+'</b> there is a very high change that they are of class <b>'+str(Keyminclass)+'</b> '+targetfeature+'.' sentences.append(sentense) except: sentense = '' sentences.append(sentense) xi = 0 elif hasattr(model, "predict_proba") and config['problemType'].lower() == 'classification': logit_fun_lr = lambda x: model.predict_proba(np.expand_dims(x, axis=2)) logit_ale_lr = ALE(logit_fun_lr, feature_names=features, target_names=categoryList) print(model.__class__) try: logit_exp_lr = logit_ale_lr.explain(X_train.values) except: X = np.expand_dims(X_train, axis=2) logit_exp_lr = logit_ale_lr.explain(X) values = logit_exp_lr.ale_values feature = logit_exp_lr.feature_names feature_values = logit_exp_lr.feature_values lines= [] sentences = [] sentense = 'Graphs gives a feature value how much more(less) probability does the model assign to each class relative to mean prediction. This also means that any increase in relative probability of one class must result into a decrease in probability of another class.' sentences.append(sentense) xi = 0 elif hasattr(model, "predict"): try: if config['modelname'] == 'Neural Network': logit_fun_lr = lambda x: model.predict(x) else: logit_fun_lr = lambda x: model.predict(np.expand_dims(x, axis=2)) logit_ale_lr = ALE(logit_fun_lr, feature_names=features, target_names=['Value']) logit_exp_lr = logit_ale_lr.explain(X_train.values) values = logit_exp_lr.ale_values feature = logit_exp_lr.feature_names feature_values = logit_exp_lr.feature_values lines= [] sentences = [] sentense = 'The ALE value corresponding to that feature value is difference to the mean effect of that feature. Put differently, the ALE value is the relative feature effect on the prediction at that feature value.' sentences.append(sentense) xi = 0 except: xi = 0 sentences = [] if (len(features)%2 ==0): n_cols = int(len(features)/2) else: n_cols = int(len(features)/2)+1 figheight = n_cols*3 try: plot_ale(logit_exp_lr,n_cols=2, fig_kw={'figwidth': 8, 'figheight': figheight}) plt.tight_layout(pad = 0) image = io.BytesIO() plt.savefig(image, format='png') image.seek(0) string = base64.b64encode(image.read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) except: image_64 = '' #score = get_trust_score(model.predict,proba_fun_lr,X_train.values,targetData.values) if config['problemType'].lower() == 'classification': anchorjson = getAnchorTabularofFirstRecord(predict_fn,features,X_train,X_test,labelMap,class_percent) else: anchorjson = getAnchorTabularofFirstRecord(predict_fn,features,X_train,X_test,labelMap) #anchors=[] #anchorjson = json.dumps(anchors) #feature_importance_using_shap(model,X_train.values,features,class_names) #getCounterFactuals(model,predictproba_fn,features,X_train.values,valueCount) output_json = {"status":"SUCCESS","data":{"data":image_64,"most_influencedfeature":m_imp_f,"interceptionpoint":xi,"sentences":sentences,"feature_importance":json.loads(f_imp),"anchorjson":json.loads(anchorjson)}} output_json = json.dumps(output_json) print("aion_ai_explanation:",output_json) return(output_json) def local_analysis(jsonData): jsonData = json.loads(jsonData) displaypath = os.path.join(os.path.dirname(os.path.abspath(__file__)),"etc","display.json") with open(displaypath) as file: config = json.load(file) file.close() scoreParam = config['scoreParam'] filename_dl = os.path.join(os.path.dirname(os.path.abspath(__file__)),"model",config['saved_model']) if(scoreParam.lower() == 'rmse'): model = load_model(filename_dl,custom_objects={"rmse": rmse_m},compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[rmse_m]) elif(scoreParam.lower() == 'r2'): model = load_model(filename_dl,custom_objects={"r2": r_square},compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[r_square]) elif(scoreParam.lower() == 'recall'): model = load_model(filename_dl,custom_objects={"recall": recall_m},compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[recall_m]) elif(scoreParam.lower() == 'precision'): model = load_model(filename_dl,custom_objects={"precision": precision_m},compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[precision_m]) elif(scoreParam.lower() == 'roc_auc'): model = load_model(filename_dl,compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[tf.keras.metrics.AUC()]) elif(scoreParam.lower() == 'f1_score'): model = load_model(filename_dl,custom_objects={"f1_score": f1_m},compile=False) model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[f1_m]) else: model = load_model(filename_dl) if config['modelname'] == 'Neural Network': predict_fn = lambda x: model.predict(x) else: predict_fn = lambda x: model.predict(np.expand_dims(x, axis=2)) dathPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'data','postprocesseddata.csv.gz') dataFrame=pd.read_csv(dathPath,compression='gzip') testdathPath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data', 'predicteddata.csv.gz') testdataFrame = pd.read_csv(testdathPath,compression='gzip') features = config['modelFeatures']#['SepalWidthCm','PetalLengthCm'] targetfeature = config['targetFeature']#'Species' targetData = dataFrame[targetfeature] valueCount=targetData.value_counts() categoryList=valueCount.keys().tolist() class_names = categoryList #class_names = class_names.sort() X_train = dataFrame[features] from pandas import json_normalize df_test = json_normalize(jsonData) df_test = preprocessing(df_test) df_test = df_test[features] from alibi.explainers import AnchorTabular explainer = AnchorTabular(predict_fn, features) explainer.fit(X_train.values) df_test = df_test.values prediction = explainer.predictor(df_test.reshape(1, -1))[0] if isinstance(prediction,np.ndarray): prediction = prediction[0] labelMap = config['labelMaps'] if len(labelMap) > 0: prediction = list(labelMap.keys())[list(labelMap.values()).index(prediction)] else: prediction = str(prediction) try: explanation = explainer.explain(df_test.reshape(1, -1),threshold=0.85) if str(explanation.anchor) == '[]': anchor = 'NA' precision = explanation.precision[0] else: anchor = '%s' % (' AND '.join(explanation.anchor)) precision = explanation.precision coverage = explanation.coverage except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) anchor = 'NA' precision = 0 coverage = 0 df_test_waterfall = testdataFrame forceplot,multidecisionplot,waterfallplot = feature_importance_using_shap(model,X_train.head(300).values,features,class_names,df_test,config['problemType'],config['modelname'],df_test_waterfall) output_json = {"status":"SUCCESS","data":{"anchor":anchor,"precision":precision,"coverage":coverage,"prediction":prediction,"forceplot":forceplot,"multidecisionplot":multidecisionplot,'waterfallplot':waterfallplot}} print(output_json) output_json = json.dumps(output_json) print("aion_ai_explanation:",output_json) return(output_json) if __name__ == '__main__': analysis_type = sys.argv[1] if analysis_type.lower() == 'global': ale_analysis() if analysis_type.lower() == 'local': data = sys.argv[2] #jsonData = json.loads(data) local_analysis(data) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import io import json import logging import pandas as pd import sys import numpy as np from pathlib import Path from word2number import w2n from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OrdinalEncoder from sklearn.preprocessing import OneHotEncoder from sklearn.impute import SimpleImputer, KNNImputer from sklearn.pipeline import Pipeline, FeatureUnion from sklearn.preprocessing import FunctionTransformer from sklearn.preprocessing import MinMaxScaler,StandardScaler from sklearn.preprocessing import PowerTransformer from sklearn.compose import ColumnTransformer from sklearn.base import TransformerMixin from sklearn.ensemble import IsolationForest from category_encoders import TargetEncoder import scipy try: import transformations.data_profiler_functions as cs except: import data_profiler_functions as cs if 'AION' in sys.modules: try: from appbe.app_config import DEBUG_ENABLED except: DEBUG_ENABLED = False else: DEBUG_ENABLED = False log_suffix = f'[{Path(__file__).stem}] ' class profiler(): def __init__(self, xtrain, ytrain=None, target=None, encode_target = False, config={}, keep_unprocessed=[],data_path=None,log=None): if not isinstance(xtrain, pd.DataFrame): raise ValueError(f'{log_suffix}supported data type is pandas.DataFrame but provide data is of {type(xtrain)} type') if xtrain.empty: raise ValueError(f'{log_suffix}Data frame is empty') if target and target in xtrain.columns: self.target = xtrain[target] xtrain.drop(target, axis=1, inplace=True) self.target_name = target elif ytrain: self.target = ytrain self.target_name = 'target' else: self.target = pd.Series() self.target_name = None self.data_path = data_path self.encode_target = encode_target self.label_encoder = None self.data = xtrain self.keep_unprocessed = keep_unprocessed self.colm_type = {} for colm, infer_type in zip(self.data.columns, self.data.dtypes): self.colm_type[colm] = infer_type self.numeric_feature = [] self.cat_feature = [] self.text_feature = [] self.wordToNumericFeatures = [] self.added_features = [] self.pipeline = [] self.dropped_features = {} self.train_features_type={} self.__update_type() self.config = config self.featureDict = config.get('featureDict', []) self.output_columns = [] self.feature_expender = [] self.text_to_num = {} self.force_numeric_conv = [] if log: self.log = log else: self.log = logging.getLogger('eion') self.type_conversion = {} def log_dataframe(self, msg=None): buffer = io.StringIO() self.data.info(buf=buffer) if msg: log_text = f'Data frame after {msg}:' else: log_text = 'Data frame:' log_text += '\n\t'+str(self.data.head(2)).replace('\n','\n\t') log_text += ('\n\t' + buffer.getvalue().replace('\n','\n\t')) self.log.info(log_text) def transform(self): if self.is_target_available(): if self.target_name: self.log.info(f"Target feature name: '{self.target_name}'") self.log.info(f"Target feature size: {len(self.target)}") else: self.log.info(f"Target feature not present") self.log_dataframe() print(self.data.info()) try: self.process() except Exception as e: self.log.error(e, exc_info=True) raise pipe = FeatureUnion(self.pipeline) try: if self.text_feature: from text.textProfiler import set_pretrained_model set_pretrained_model(pipe) conversion_method = self.get_conversion_method() process_data = pipe.fit_transform(self.data, y=self.target) # save for testing if DEBUG_ENABLED: if isinstance(process_data, scipy.sparse.spmatrix): process_data = process_data.toarray() df = pd.DataFrame(process_data) df.to_csv('debug_preprocessed.csv', index=False) if self.text_feature and conversion_method == 'latentsemanticanalysis': n_size = self.get_tf_idf_output_size( pipe) dimensions = self.get_tf_idf_dimensions() if n_size < dimensions or n_size > dimensions: dimensions = n_size from sklearn.decomposition import TruncatedSVD reducer = TruncatedSVD( n_components = dimensions) reduced_data = reducer.fit_transform( process_data[:,-n_size:]) text_process_idx = [t[0] for t in pipe.transformer_list].index('text_process') pipe.transformer_list[text_process_idx][1].steps.append(('feature_reducer',reducer)) if isinstance(process_data, scipy.sparse.spmatrix): process_data = process_data.toarray() process_data = np.concatenate((process_data[:,:-n_size], reduced_data), axis=1) last_step = self.feature_expender.pop() self.feature_expender.append({'feature_reducer':list(last_step.values())[0]}) except EOFError as e: if "Compressed file ended before the end-of-stream marker was reached" in str(e): raise EOFError('Pretrained model is not downloaded properly') self.update_output_features_names(pipe) if isinstance(process_data, scipy.sparse.spmatrix): process_data = process_data.toarray() df = pd.DataFrame(process_data, index=self.data.index, columns=self.output_columns) if self.is_target_available() and self.target_name: df[self.target_name] = self.target if self.keep_unprocessed: df[self.keep_unprocessed] = self.data[self.keep_unprocessed] self.log_numerical_fill() self.log_categorical_fill() self.log_normalization() return df, pipe, self.label_encoder def log_type_conversion(self): if self.log: self.log.info('----------- Inspecting Features -----------') self.log.info('----------- Type Conversion -----------') count = 0 for k, v in self.type_conversion.items(): if v[0] != v[1]: self.log.info(f'-------> {k} -> from {v[0]} to {v[1]} : {v[2]}') self.log.info('Status:- |... Feature inspection done') def check_config(self): removeDuplicate = self.config.get('removeDuplicate', False) self.config['removeDuplicate'] = cs.get_boolean(removeDuplicate) self.config['misValueRatio'] = float(self.config.get('misValueRatio', cs.default_config['misValueRatio'])) self.config['numericFeatureRatio'] = float(self.config.get('numericFeatureRatio', cs.default_config['numericFeatureRatio'])) self.config['categoryMaxLabel'] = int(self.config.get('categoryMaxLabel', cs.default_config['categoryMaxLabel'])) featureDict = self.config.get('featureDict', []) if isinstance(featureDict, dict): self.config['featureDict'] = [] if isinstance(featureDict, str): self.config['featureDict'] = [] def process(self): #remove duplicate not required at the time of prediction self.check_config() self.remove_constant_feature() self.remove_empty_feature(self.config['misValueRatio']) self.remove_index_features() self.dropna() if self.config['removeDuplicate']: self.drop_duplicate() #self.check_categorical_features() #self.string_to_numeric() self.process_target() self.train_features_type = {k:v for k,v in zip(self.data.columns, self.data.dtypes)} self.parse_process_step_config() self.process_drop_fillna() self.log_type_conversion() self.update_num_fill_dict() if DEBUG_ENABLED: print(self.num_fill_method_dict) self.update_cat_fill_dict() self.create_pipeline() self.text_pipeline(self.config) self.apply_outlier() if DEBUG_ENABLED: self.log.info(self.process_method) self.log.info(self.pipeline) def is_target_available(self): return (isinstance(self.target, pd.Series) and not self.target.empty) or len(self.target) def process_target(self, operation='encode', arg=None): if self.is_target_available(): # drop null values self.__update_index( self.target.notna(), 'target') if self.encode_target: self.label_encoder = LabelEncoder() self.target = self.label_encoder.fit_transform(self.target) return self.label_encoder return None def is_target_column(self, column): return column == self.target_name def fill_default_steps(self): num_fill_method = cs.get_one_true_option(self.config.get('numericalFillMethod',{})) normalization_method = cs.get_one_true_option(self.config.get('normalization',{}),'none') for colm in self.numeric_feature: if num_fill_method: self.fill_missing_value_method(colm, num_fill_method.lower()) if normalization_method: self.fill_normalizer_method(colm, normalization_method.lower()) cat_fill_method = cs.get_one_true_option(self.config.get('categoricalFillMethod',{})) cat_encode_method = cs.get_one_true_option(self.config.get('categoryEncoding',{})) for colm in self.cat_feature: if cat_fill_method: self.fill_missing_value_method(colm, cat_fill_method.lower()) if cat_encode_method: self.fill_encoder_value_method(colm, cat_encode_method.lower(), default=True) def parse_process_step_config(self): self.process_method = {} user_provided_data_type = {} for feat_conf in self.featureDict: colm = feat_conf.get('feature', '') if not self.is_target_column(colm): if colm in self.data.columns: user_provided_data_type[colm] = feat_conf['type'] if user_provided_data_type: self.update_user_provided_type(user_provided_data_type) self.fill_default_steps() for feat_conf in self.featureDict: colm = feat_conf.get('feature', '') if not self.is_target_column(colm): if colm in self.data.columns: if feat_conf.get('fillMethod', None): self.fill_missing_value_method(colm, feat_conf['fillMethod'].lower()) if feat_conf.get('categoryEncoding', None): self.fill_encoder_value_method(colm, feat_conf['categoryEncoding'].lower()) if feat_conf.get('normalization', None): self.fill_normalizer_method(colm, feat_conf['normalization'].lower()) if feat_conf.get('outlier', None): self.fill_outlier_method(colm, feat_conf['outlier'].lower()) if feat_conf.get('outlierOperation', None): self.fill_outlier_process(colm, feat_conf['outlierOperation'].lower()) def get_tf_idf_dimensions(self): dim = cs.get_one_true_option(self.config.get('embeddingSize',{}).get('TF_IDF',{}), 'default') return {'default': 300, '50d':50, '100d':100, '200d':200, '300d':300}[dim] def get_tf_idf_output_size(self, pipe): start_index = {} for feat_expender in self.feature_expender: if feat_expender: step_name = list(feat_expender.keys())[0] index = list(feat_expender.values())[0] for transformer_step in pipe.transformer_list: if transformer_step[1].steps[-1][0] in step_name: start_index[index] = {transformer_step[1].steps[-1][0]: transformer_step[1].steps[-1][1].get_feature_names_out()} if start_index: for key,value in start_index.items(): for k,v in value.items(): if k == 'vectorizer': return len(v) return 0 def update_output_features_names(self, pipe): columns = self.output_columns start_index = {} index_shifter = 0 for feat_expender in self.feature_expender: if feat_expender: step_name = list(feat_expender.keys())[0] for key,value in start_index.items(): for k,v in value.items(): index_shifter += len(v) index = list(feat_expender.values())[0] for transformer_step in pipe.transformer_list: if transformer_step[1].steps[-1][0] in step_name: start_index[index + index_shifter] = {transformer_step[1].steps[-1][0]: transformer_step[1].steps[-1][1].get_feature_names_out()} #print(start_index) if start_index: for key,value in start_index.items(): for k,v in value.items(): if k == 'vectorizer': v = [f'{x}_vect' for x in v] self.output_columns[key:key] = v self.added_features = [*self.added_features, *v] def text_pipeline(self, conf_json): if self.text_feature: from text.textProfiler import textProfiler from text.textProfiler import textCombine pipeList = [] text_pipe = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", self.text_feature) ], remainder="drop")), ("text_fillNa",SimpleImputer(strategy='constant', fill_value='')), ("merge_text_feature", textCombine())]) obj = textProfiler() pipeList = obj.textProfiler(conf_json, pipeList, self.data_path) last_step = "merge_text_feature" for pipe_elem in pipeList: text_pipe.steps.append((pipe_elem[0], pipe_elem[1])) last_step = pipe_elem[0] text_transformer = ('text_process', text_pipe) self.pipeline.append(text_transformer) self.feature_expender.append({last_step:len(self.output_columns)}) def create_pipeline(self): num_pipe = {} for k,v in self.num_fill_method_dict.items(): for k1,v1 in v.items(): if k1 and k1 != 'none': num_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_num_imputer(k)), (k1, self.get_num_scaler(k1)) ]) else: num_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_num_imputer(k)) ]) self.output_columns.extend(v1) cat_pipe = {} for k,v in self.cat_fill_method_dict.items(): for k1,v1 in v.items(): cat_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_cat_imputer(k)), (k1, self.get_cat_encoder(k1)) ]) if k1 not in ['onehotencoding']: self.output_columns.extend(v1) else: self.feature_expender.append({k1:len(self.output_columns)}) for key, pipe in num_pipe.items(): self.pipeline.append((key, pipe)) for key, pipe in cat_pipe.items(): self.pipeline.append((key, pipe)) "Drop: feature during training but replace with zero during prediction " def process_drop_fillna(self): drop_column = [] if 'numFill' in self.process_method.keys(): for col, method in self.process_method['numFill'].items(): if method == 'drop': self.process_method['numFill'][col] = 'zero' drop_column.append(col) if 'catFill' in self.process_method.keys(): for col, method in self.process_method['catFill'].items(): if method == 'drop': self.process_method['catFill'][col] = 'zero' drop_column.append(col) if drop_column: self.data.dropna(subset=drop_column, inplace=True) def update_num_fill_dict(self): self.num_fill_method_dict = {} if 'numFill' in self.process_method.keys(): for f in cs.supported_method['fillNa']['numeric']: self.num_fill_method_dict[f] = {} for en in cs.supported_method['normalization']: self.num_fill_method_dict[f][en] = [] for col in self.numeric_feature: numFillDict = self.process_method.get('numFill',{}) normalizationDict = self.process_method.get('normalization',{}) if f == numFillDict.get(col, '') and en == normalizationDict.get(col,''): self.num_fill_method_dict[f][en].append(col) if not self.num_fill_method_dict[f][en] : del self.num_fill_method_dict[f][en] if not self.num_fill_method_dict[f]: del self.num_fill_method_dict[f] def update_cat_fill_dict(self): self.cat_fill_method_dict = {} if 'catFill' in self.process_method.keys(): for f in cs.supported_method['fillNa']['categorical']: self.cat_fill_method_dict[f] = {} for en in cs.supported_method['categoryEncoding']: self.cat_fill_method_dict[f][en] = [] for col in self.cat_feature: catFillDict = self.process_method.get('catFill',{}) catEncoderDict = self.process_method.get('catEncoder',{}) if f == catFillDict.get(col, '') and en == catEncoderDict.get(col,''): self.cat_fill_method_dict[f][en].append(col) if not self.cat_fill_method_dict[f][en] : del self.cat_fill_method_dict[f][en] if not self.cat_fill_method_dict[f]: del self.cat_fill_method_dict[f] def __update_type(self): self.numeric_feature = list( set(self.data.select_dtypes(include='number').columns.tolist()) - set(self.keep_unprocessed)) self.cat_feature = list( set(self.data.select_dtypes(include='category').columns.tolist()) - set(self.keep_unprocessed)) self.text_feature = list( set(self.data.select_dtypes(include='object').columns.tolist()) - set(self.keep_unprocessed)) self.datetime_feature = list( set(self.data.select_dtypes(include='datetime').columns.tolist()) - set(self.keep_unprocessed)) def update_user_provided_type(self, data_types): allowed_types = ['numerical','categorical', 'text'] skipped_types = ['date','index'] type_mapping = {'numerical': np.dtype('float'), 'float': np.dtype('float'),'categorical': 'category', 'text':np.dtype('object'),'date':'datetime64[ns]','index': np.dtype('int64'),} mapped_type = {k:type_mapping[v] for k,v in data_types.items() if v in allowed_types} skipped_features = [k for k,v in data_types.items() if v in skipped_types] if skipped_features: self.keep_unprocessed.extend( skipped_features) self.keep_unprocessed = list(set(self.keep_unprocessed)) self.update_type(mapped_type, 'user provided data type') def get_type(self, as_list=False): if as_list: return [self.colm_type.values()] else: return self.colm_type def update_type(self, data_types={}, reason=''): invalid_features = [x for x in data_types.keys() if x not in self.data.columns] if invalid_features: valid_feat = list(set(data_types.keys()) - set(invalid_features)) valid_feat_type = {k:v for k,v in data_types if k in valid_feat} else: valid_feat_type = data_types for k,v in valid_feat_type.items(): if v != self.colm_type[k].name: try: self.data.astype({k:v}) self.colm_type.update({k:self.data[k].dtype}) self.type_conversion[k] = (self.colm_type[k] , v, 'Done', reason) except: self.type_conversion[k] = (self.colm_type[k] , v, 'Fail', reason) if v == np.dtype('float64') and self.colm_type[k].name == 'object': if self.check_numeric( k): self.data[ k] = pd.to_numeric(self.data[ k], errors='coerce') self.type_conversion[k] = (self.colm_type[k] , v, 'Done', reason) self.force_numeric_conv.append( k) else: raise ValueError(f"Can not convert '{k}' feature to 'numeric' as numeric values are less than {self.config['numericFeatureRatio'] * 100}%") self.data = self.data.astype(valid_feat_type) self.__update_type() def check_numeric(self, feature): col_values = self.data[feature].copy() col_values = pd.to_numeric(col_values, errors='coerce') if col_values.count() >= (self.config['numericFeatureRatio'] * len(col_values)): return True return False def string_to_numeric(self): def to_number(x): try: return w2n.word_to_num(x) except: return np.nan for col in self.text_feature: col_values = self.data[col].copy() col_values = pd.to_numeric(col_values, errors='coerce') if col_values.count() >= (self.config['numericFeatureRatio'] * len(col_values)): self.text_to_num[col] = 'float64' self.wordToNumericFeatures.append(col) if self.text_to_num: columns = list(self.text_to_num.keys()) self.data[columns] = self.data[columns].apply(lambda x: to_number(x), axis=1, result_type='broadcast') self.update_type(self.text_to_num) self.log.info('----------- Inspecting Features -----------') for col in self.text_feature: self.log.info(f'-------> Feature : {col}') if col in self.text_to_num: self.log.info('----------> Numeric Status :Yes') self.log.info('----------> Data Type Converting to numeric :Yes') else: self.log.info('----------> Numeric Status :No') self.log.info(f'\nStatus:- |... Feature inspection done for numeric data: {len(self.text_to_num)} feature(s) converted to numeric') self.log.info(f'\nStatus:- |... Feature word to numeric treatment done: {self.text_to_num}') self.log.info('----------- Inspecting Features End -----------') def check_categorical_features(self): num_data = self.data.select_dtypes(include='number') num_data_unique = num_data.nunique() num_to_cat_col = {} for i, value in enumerate(num_data_unique): if value < self.config['categoryMaxLabel']: num_to_cat_col[num_data_unique.index[i]] = 'category' if num_to_cat_col: self.update_type(num_to_cat_col, 'numerical to categorical') str_to_cat_col = {} str_data = self.data.select_dtypes(include='object') str_data_unique = str_data.nunique() for i, value in enumerate(str_data_unique): if value < self.config['categoryMaxLabel']: str_to_cat_col[str_data_unique.index[i]] = 'category' for colm in str_data.columns: if self.data[colm].str.len().max() < cs.default_config['str_to_cat_len_max']: str_to_cat_col[colm] = 'category' if str_to_cat_col: self.update_type(str_to_cat_col, 'text to categorical') def drop_features(self, features=[], reason='unspecified'): if isinstance(features, str): features = [features] feat_to_remove = [x for x in features if x in self.data.columns] if feat_to_remove: self.data.drop(feat_to_remove, axis=1, inplace=True) for feat in feat_to_remove: self.dropped_features[feat] = reason self.log_drop_feature(feat_to_remove, reason) self.__update_type() def __update_index(self, indices, reason=''): if isinstance(indices, (bool, pd.core.series.Series)) and len(indices) == len(self.data): if not indices.all(): self.data = self.data[indices] if self.is_target_available(): self.target = self.target[indices] self.log_update_index((indices == False).sum(), reason) def dropna(self): self.data.dropna(how='all',inplace=True) if self.is_target_available(): self.target = self.target[self.data.index] def drop_duplicate(self): index = self.data.duplicated(keep='first') self.__update_index( ~index, reason='duplicate') def log_drop_feature(self, columns, reason): self.log.info(f'---------- Dropping {reason} features ----------') self.log.info(f'\nStatus:- |... {reason} feature treatment done: {len(columns)} {reason} feature(s) found') self.log.info(f'-------> Drop Features: {columns}') self.log.info(f'Data Frame Shape After Dropping (Rows,Columns): {self.data.shape}') def log_update_index(self,count, reason): if count: if reason == 'target': self.log.info('-------> Null Target Rows Drop:') self.log.info(f'-------> Dropped rows count: {count}') elif reason == 'duplicate': self.log.info('-------> Duplicate Rows Drop:') self.log.info(f'-------> Dropped rows count: {count}') elif reason == 'outlier': self.log.info(f'-------> Dropped rows count: {count}') self.log.info('Status:- |... Outlier treatment done') self.log.info(f'-------> Data Frame Shape After Dropping samples(Rows,Columns): {self.data.shape}') def log_normalization(self): if self.process_method.get('normalization', None): self.log.info(f'\nStatus:- !... Normalization treatment done') for method in cs.supported_method['normalization']: cols = [] for col, m in self.process_method['normalization'].items(): if m == method: cols.append(col) if cols and method != 'none': self.log.info(f'Running {method} on features: {cols}') def log_numerical_fill(self): if self.process_method.get('numFill', None): self.log.info(f'\nStatus:- !... Fillna for numeric feature done') for method in cs.supported_method['fillNa']['numeric']: cols = [] for col, m in self.process_method['numFill'].items(): if m == method: cols.append(col) if cols: self.log.info(f'-------> Running {method} on features: {cols}') def log_categorical_fill(self): if self.process_method.get('catFill', None): self.log.info(f'\nStatus:- !... FillNa for categorical feature done') for method in cs.supported_method['fillNa']['categorical']: cols = [] for col, m in self.process_method['catFill'].items(): if m == method: cols.append(col) if cols: self.log.info(f'-------> Running {method} on features: {cols}') def remove_constant_feature(self): unique_values = self.data.nunique() constant_features = [] for i, value in enumerate(unique_values): if value == 1: constant_features.append(unique_values.index[i]) if constant_features: self.drop_features(constant_features, "constant") def remove_empty_feature(self, misval_ratio=1.0): missing_ratio = self.data.isnull().sum() / len(self.data) missing_ratio = {k:v for k,v in zip(self.data.columns, missing_ratio)} empty_features = [k for k,v in missing_ratio.items() if v > misval_ratio] if empty_features: self.drop_features(empty_features, "empty") def remove_index_features(self): index_feature = [] for feat in self.numeric_feature: if self.data[feat].nunique() == len(self.data): #if (self.data[feat].sum()- sum(self.data.index) == (self.data.iloc[0][feat]-self.data.index[0])*len(self.data)): # index feature can be time based count = (self.data[feat] - self.data[feat].shift() == 1).sum() if len(self.data) - count == 1: index_feature.append(feat) self.drop_features(index_feature, "index") def fill_missing_value_method(self, colm, method): if colm in self.numeric_feature: if method in cs.supported_method['fillNa']['numeric']: if 'numFill' not in self.process_method.keys(): self.process_method['numFill'] = {} if method == 'na' and self.process_method['numFill'].get(colm, None): pass # don't overwrite else: self.process_method['numFill'][colm] = method if colm in self.cat_feature: if method in cs.supported_method['fillNa']['categorical']: if 'catFill' not in self.process_method.keys(): self.process_method['catFill'] = {} if method == 'na' and self.process_method['catFill'].get(colm, None): pass else: self.process_method['catFill'][colm] = method def check_encoding_method(self, method, colm,default=False): if not self.is_target_available() and (method.lower() == list(cs.target_encoding_method_change.keys())[0]): method = cs.target_encoding_method_change[method.lower()] if default: self.log.info(f"Applying Label encoding instead of Target encoding on feature '{colm}' as target feature is not present") return method def fill_encoder_value_method(self,colm, method, default=False): if colm in self.cat_feature: if method.lower() in cs.supported_method['categoryEncoding']: if 'catEncoder' not in self.process_method.keys(): self.process_method['catEncoder'] = {} if method == 'na' and self.process_method['catEncoder'].get(colm, None): pass else: self.process_method['catEncoder'][colm] = self.check_encoding_method(method, colm,default) else: self.log.info(f"-------> categorical encoding method '{method}' is not supported. supported methods are {cs.supported_method['categoryEncoding']}") def fill_normalizer_method(self,colm, method): if colm in self.numeric_feature: if method in cs.supported_method['normalization']: if 'normalization' not in self.process_method.keys(): self.process_method['normalization'] = {} if (method == 'na' or method == 'none') and self.process_method['normalization'].get(colm, None): pass else: self.process_method['normalization'][colm] = method else: self.log.info(f"-------> Normalization method '{method}' is not supported. supported methods are {cs.supported_method['normalization']}") def apply_outlier(self): inlier_indice = np.array([True] * len(self.data)) if self.process_method.get('outlier', None): self.log.info('-------> Feature wise outlier detection:') for k,v in self.process_method['outlier'].items(): if k in self.numeric_feature: if v == 'iqr': index = cs.findiqrOutlier(self.data[k]) elif v == 'zscore': index = cs.findzscoreOutlier(self.data[k]) elif v == 'disable': index = None if k in self.process_method['outlierOperation'].keys(): if self.process_method['outlierOperation'][k] == 'dropdata': inlier_indice = np.logical_and(inlier_indice, index) elif self.process_method['outlierOperation'][k] == 'average': mean = self.data[k].mean() index = ~index self.data.loc[index,[k]] = mean self.log.info(f'-------> {k}: Replaced by Mean {mean}: total replacement {index.sum()}') elif self.process_method['outlierOperation'][k] == 'nochange' and v != 'disable': self.log.info(f'-------> Total outliers in "{k}": {(~index).sum()}') if self.config.get('outlierDetection',None): if self.config['outlierDetection'].get('IsolationForest','False') == 'True': if self.numeric_feature: index = cs.findiforestOutlier(self.data[self.numeric_feature]) inlier_indice = np.logical_and(inlier_indice, index) self.log.info(f'-------> Numeric feature based Outlier detection(IsolationForest):') if inlier_indice.sum() != len(self.data): self.__update_index(inlier_indice, 'outlier') def fill_outlier_method(self,colm, method): if colm in self.numeric_feature: if method in cs.supported_method['outlier_column_wise']: if 'outlier' not in self.process_method.keys(): self.process_method['outlier'] = {} if method not in ['Disable', 'na']: self.process_method['outlier'][colm] = method else: self.log.info(f"-------> outlier detection method '{method}' is not supported for column wise. supported methods are {cs.supported_method['outlier_column_wise']}") def fill_outlier_process(self,colm, method): if colm in self.numeric_feature: if method in cs.supported_method['outlierOperation']: if 'outlierOperation' not in self.process_method.keys(): self.process_method['outlierOperation'] = {} self.process_method['outlierOperation'][colm] = method else: self.log.info(f"-------> outlier process method '{method}' is not supported for column wise. supported methods are {cs.supported_method['outlierOperation']}") def get_cat_imputer(self,method): if method == 'mode': return SimpleImputer(strategy='most_frequent') elif method == 'zero': return SimpleImputer(strategy='constant', fill_value=0) def get_cat_encoder(self,method): if method == 'labelencoding': return OrdinalEncoder() elif method == 'onehotencoding': return OneHotEncoder(sparse=False,handle_unknown="ignore") elif method == 'targetencoding': if not self.is_target_available(): raise ValueError('Can not apply Target Encoding when target feature is not present') return TargetEncoder() def get_num_imputer(self,method): if method == 'mode': return SimpleImputer(strategy='most_frequent') elif method == 'mean': return SimpleImputer(strategy='mean') elif method == 'median': return SimpleImputer(strategy='median') elif method == 'knnimputer': return KNNImputer() elif method == 'zero': return SimpleImputer(strategy='constant', fill_value=0) def get_num_scaler(self,method): if method == 'minmax': return MinMaxScaler() elif method == 'standardscaler': return StandardScaler() elif method == 'lognormal': return PowerTransformer(method='yeo-johnson', standardize=False) def recommenderStartProfiler(self,modelFeatures): return cs.recommenderStartProfiler(self,modelFeatures) def folderPreprocessing(self,folderlocation,folderdetails,deployLocation): return cs.folderPreprocessing(self,folderlocation,folderdetails,deployLocation) def textSimilarityStartProfiler(self, doc_col_1, doc_col_2): return cs.textSimilarityStartProfiler(self, doc_col_1, doc_col_2) def get_conversion_method(self): return cs.get_one_true_option(self.config.get('textConversionMethod','')).lower() def set_features(features,profiler=None): return cs.set_features(features,profiler) import os import sys import numpy as np import scipy import pandas as pd from pathlib import Path default_config = { 'misValueRatio': '1.0', 'numericFeatureRatio': '1.0', 'categoryMaxLabel': '20', 'str_to_cat_len_max': 10 } target_encoding_method_change = {'targetencoding': 'labelencoding'} supported_method = { 'fillNa': { 'categorical' : ['mode','zero','na'], 'numeric' : ['median','mean','knnimputer','zero','drop','na'], }, 'categoryEncoding': ['labelencoding','targetencoding','onehotencoding','na','none'], 'normalization': ['standardscaler','minmax','lognormal', 'na','none'], 'outlier_column_wise': ['iqr','zscore', 'disable', 'na'], 'outlierOperation': ['dropdata', 'average', 'nochange'] } def findiqrOutlier(df): Q1 = df.quantile(0.25) Q3 = df.quantile(0.75) IQR = Q3 - Q1 index = ~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))) return index def findzscoreOutlier(df): z = np.abs(scipy.stats.zscore(df)) index = (z < 3) return index def findiforestOutlier(df): from sklearn.ensemble import IsolationForest isolation_forest = IsolationForest(n_estimators=100) isolation_forest.fit(df) y_pred_train = isolation_forest.predict(df) return y_pred_train == 1 def get_one_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def get_boolean(value): if (isinstance(value, str) and value.lower() == 'true') or (isinstance(value, bool) and value == True): return True else: return False def recommenderStartProfiler(self,modelFeatures): try: self.log.info('----------> FillNA:0') self.data = self.data.fillna(value=0) self.log.info('Status:- !... Missing value treatment done') self.log.info('----------> Remove Empty Row') self.data = self.data.dropna(axis=0,how='all') self.log.info('Status:- !... Empty feature treatment done') userId,itemId,rating = modelFeatures.split(',') self.data[itemId] = self.data[itemId].astype(np.int32) self.data[userId] = self.data[userId].astype(np.int32) self.data[rating] = self.data[rating].astype(np.float32) return self.data except Exception as inst: self.log.info("Error: dataProfiler failed "+str(inst)) return(self.data) def folderPreprocessing(self,folderlocation,folderdetails,deployLocation): try: dataset_directory = Path(folderlocation) dataset_csv_file = dataset_directory/folderdetails['label_csv_file_name'] tfrecord_directory = Path(deployLocation)/'Video_TFRecord' from savp import PreprocessSAVP import csv csvfile = open(dataset_csv_file, newline='') csv_reader = csv.DictReader(csvfile) PreprocessSAVP(dataset_directory,csv_reader,tfrecord_directory) dataColumns = list(self.data.columns) VideoProcessing = True return dataColumns,VideoProcessing,tfrecord_directory except Exception as inst: self.log.info("Error: dataProfiler failed "+str(inst)) def textSimilarityStartProfiler(self, doc_col_1, doc_col_2): import os try: features = [doc_col_1, doc_col_2] pipe = None dataColumns = list(self.data.columns) self.numofCols = self.data.shape[1] self.numOfRows = self.data.shape[0] from transformations.textProfiler import textProfiler self.log.info('-------> Execute Fill NA With Empty String') self.data = self.data.fillna(value=" ") self.log.info('Status:- |... Missing value treatment done') self.data[doc_col_1] = textProfiler().textCleaning(self.data[doc_col_1]) self.data[doc_col_2] = textProfiler().textCleaning(self.data[doc_col_2]) self.log.info('-------> Concatenate: ' + doc_col_1 + ' ' + doc_col_2) self.data['text'] = self.data[[doc_col_1, doc_col_2]].apply(lambda row: ' '.join(row.values.astype(str)), axis=1) from tensorflow.keras.preprocessing.text import Tokenizer pipe = Tokenizer() pipe.fit_on_texts(self.data['text'].values) self.log.info('-------> Tokenizer: Fit on Concatenate Field') self.log.info('Status:- |... Tokenizer the text') self.data[doc_col_1] = self.data[doc_col_1].astype(str) self.data[doc_col_1] = self.data[doc_col_1].astype(str) return (self.data, pipe, self.target_name, features) except Exception as inst: self.log.info("StartProfiler failed " + str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) def set_features(features,profiler=None): if profiler: features = [x for x in features if x not in profiler.added_features] return features + profiler.text_feature return features # -*- coding: utf-8 -*- import pandas as pd import numpy as np import logging import os import sys from flwr.common.logger import log from logging import INFO from flserver import flserver class aionfls: def __init__(self): self.confdata=None def configLoad(self,jsonfile): import json jsonfile=str(jsonfile) with open(jsonfile, 'r') as file: self.confdata = json.load(file) return self.confdata def dataload(self,datapath): df = pd.read_csv(datapath) #chunk_size=50000 ## Data preprocess in test dataset, In aion, aion profiler will handle it. df =df[~df.isin([np.nan, np.inf, -np.inf]).any(axis=1)] df=df.reset_index(drop=True) return df # Start Flower server for n rounds of federated learning if __name__ == "__main__": classobj=aionfls() json_file=sys.argv[1] confdata = classobj.configLoad(json_file) data_location = confdata["data_location"] # deploy_location=confdata['deploy_location'] cwd = os.path.abspath(os.path.dirname(__file__)) model_name=confdata['model_name'] version=str(confdata['version']) file_name=model_name+'_'+version+".log" try: fl_log=os.path.normpath(os.path.join(cwd,'logs',file_name)) except Exception as e: classobj.log.info("Log path error. Error Msg: \n",e) logging.basicConfig(filename=fl_log,format='%(asctime)s %(message)s', filemode='w',level=logging.DEBUG) classobj.log = logging.getLogger('AION') print("===============") print("flserver main function") print("===============") if (confdata['evaluation_required'].lower() == 'false'): df=None aionflobj=flserver(df,confdata) print("flserver condition is false") else: ## User selected option is True print("flserver condition is true") data_location = os.path.normpath(os.path.join(cwd, data_location)) # print("current data_location",data_location) df=classobj.dataload(data_location) aionflobj=flserver(df,confdata) status=aionflobj.runFLServer() classobj.log.info("Aion FL Server run Status: \n"+str(status)) import tensorflow as tf def dl_regression_model(input_shape, output_shape, optimizer, loss_func, act_func): inputs = tf.keras.Input(shape=(input_shape,)) x = tf.keras.layers.Dense(64, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(inputs) x = tf.keras.layers.Dense(32, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(16, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(8, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) outputs = tf.keras.layers.Dense(output_shape, kernel_initializer='he_normal', bias_initializer='zeros')(x) model = tf.keras.Model(inputs=inputs, outputs=outputs) model.compile(loss=loss_func , optimizer=optimizer, metrics=["mean_absolute_error", "mean_squared_error", ]) return model def dl_multiClass_classification(input_shape, output_shape, optimizer, loss_func, act_func, last_act_func): inputs = tf.keras.Input(shape=(input_shape,)) x = tf.keras.layers.Dense(64, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(inputs) x = tf.keras.layers.Dense(32, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(16, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(8, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) outputs = tf.keras.layers.Dense(output_shape, kernel_initializer='he_normal', bias_initializer='zeros', activation=last_act_func)(x) model = tf.keras.Model(inputs=inputs, outputs=outputs) model.compile(optimizer, loss_func, metrics=["accuracy"]) return model def dl_binary_classification(input_shape, output_shape, optimizer, loss_func, act_func, last_act_func): inputs = tf.keras.Input(shape=(input_shape,)) x = tf.keras.layers.Dense(64, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(inputs) x = tf.keras.layers.Dense(32, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(16, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(8, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) outputs = tf.keras.layers.Dense(output_shape, kernel_initializer='he_normal', bias_initializer='zeros', activation=last_act_func)(x) model = tf.keras.Model(inputs=inputs, outputs=outputs) model.compile(optimizer, loss_func, metrics=["accuracy"]) return model # -*- coding: utf-8 -*- """ Created on Wed Jun 15 14:36:11 2022 @author: @aionteam """ import flwr import flwr as fl import tensorflow as tf from typing import Any, Callable, Dict, List, Optional, Tuple import utils from sklearn.metrics import log_loss from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, mean_absolute_error,r2_score from typing import Dict import numpy as np import logging import os # import sys from flwr.common.logger import log from logging import INFO import pickle as pkl from flwr.server.client_proxy import ClientProxy import dl_model from sklearn.preprocessing import StandardScaler import pandas as pd ## Below import can be used when aion specific grpc communication used. # from aionflgrpcserver import aionflgrpcserver # Make TensorFlow logs less verbose os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" os.environ["GRPC_VERBOSITY"] = "debug" # """ AION Federated Learning Server. Geting weights from clients, aggregate the weights by FedAvg algorithm and update the client model.""" class flserver(): def __init__(self,df,confdata): self.log = logging.getLogger('AION') ## Below params will be used later self.confparams=confdata self.df=df self.fl_round=0 print("Inside flserver init func") ## Flower server number of rounds for fl model update (clients-server) def fit_round(self,rnd: int) -> Dict: """Send round number to client.""" self.fl_round=rnd log(INFO, "===========================") self.log.info("federated learning round: "+str(rnd)) log(INFO, "federated learning round: %s ",str(rnd)) log(INFO, "===========================") # print(f"federated learning round: {rnd}") return {"rnd": rnd} def fit_config(self,rnd: int) -> Dict: """Send round number to client.""" self.round_count = rnd log(INFO, "===========================") log(INFO, "Starting round %s ...",str(rnd)) log(INFO, "===========================") model_hyperparams = self.confparams["model_hyperparams"] batch_size = model_hyperparams["batch_size"] local_epochs = model_hyperparams["epochs"] config = { "batch_size": int(batch_size), # "local_epochs": 1 if rnd < 2 else 2, "local_epochs": int(local_epochs), "rnd": rnd, } return config def evaluate_config(self, rnd: int): model_hyperparams = self.confparams["model_hyperparams"] val_batch_size = model_hyperparams["batch_size"] # val_steps = 5 if rnd < 4 else 10 return {"val_batch_size": int(val_batch_size)} ## Loading configuration parameters def configload(self,confparams): try: data=confparams server_ip=str(data["server_IP"]) server_port=str(data["server_port"]) model_name=str(data["model_name"]) num_clients=int(data["min_available_clients"]) min_fit_clients=int(data["min_fit_clients"]) num_train_round=int(data["fl_round"]) data_location=str(data["data_location"]) model_params=data["model_hyperparams"] problem_type=data["problem_type"] server_address=f"{server_ip}:{server_port}" # model_location=str(data["model_store"]) model_version=str(data["version"]) selected_feature=data["selected_feature"] if (type(selected_feature) is str): selected_feature=selected_feature.split(',') target_feature=data["target_feature"] evaluation_required=data["evaluation_required"] self.log.info("Federated Learning <Server IP:Port> "+str(server_address)) except Exception as e: log(INFO, "Reading server config file issue. Err.Msg: %s ",str(e)) return server_address,model_name,num_clients,min_fit_clients,num_train_round,data_location,model_params,problem_type,model_version,selected_feature,target_feature,evaluation_required ## Save the final model def model_save(self,model,model_name,problem_type,version): cwd = os.path.abspath(os.path.dirname(__file__)) model_location=os.path.join(cwd, 'models') model_name=model_name version=str(version) model_name=self.confparams["model_name"] if (model_name.lower() == "deeplearning"): file_name = model_name + '_' +problem_type+'_'+version+ ".h5" else: file_name=file_name = model_name + '_' +problem_type+'_'+version+".sav" saved_model=os.path.normpath(os.path.join(model_location,file_name)) self.log.info("saved_model path: "+str(saved_model)) try: with open (saved_model,'wb') as f: pkl.dump(model,f) return True except Exception as e: self.log.info("fl server model save error. Error Msg: "+str(e)) return False ## Load the model, not used now. If user want to use aion trained model for evaluation at serverside, use this fn. def model_load(self, path): model_name=self.confparams["model_name"] if (model_name.lower() == "deeplearning"): loaded_model = tf.keras.models.load_model(path) else: loaded_model = pkl.load(open(path, 'rb')) return loaded_model # Fo normal ml models, def get_eval_fn, evaluate each round results with own dataset. It is optional, without this, fed server will aggregate (fedAvg) client weights and update results to clients without evaluate. def get_eval_fn(self,model,X,y,model_name,model_version): """Return an evaluation function for server-side evaluation.""" self.log.info("X_eval: \n"+str(X.shape)) self.log.info("y_eval: \n"+str(y.shape)) # scaler = StandardScaler() # X_scaled = scaler.fit_transform(X) # y = pd.get_dummies(y) # y_class = None def evaluate(server_round: int, parameters: fl.common.NDArrays, config: Dict[str, fl.common.Scalar],): # self.log.info("server side fedavg weights \n "+str(parameters)) try: problem_type=self.confparams["problem_type"] # if (self.model_name.lower() == 'logisticregression' ): # loss = log_loss(y, model.predict_proba(X)) # else: # loss = log_loss(y, model.predict(X)) if (problem_type.lower() == 'classification'): if (model_name.lower() == 'logisticregression' ): utils.set_model_params(model, parameters) loss = log_loss(y, model.predict_proba(X)) # loss = log_loss(y, model.predict_proba(X)) accuracy = model.score(X, y) log(INFO, "Server evaluation FL Round: %s processed Weights. -- Loss: %s, -- Accuracy: %s ",str(self.fl_round),str(loss), str(accuracy)) self.log.info("Accuracy: "+str(accuracy)) self.log.info("model coefficients: "+str(model.coef_)) self.log.info("model intercept: "+str(model.intercept_)) problem_type=self.confparams["problem_type"] self.model_save(model,model_name,problem_type,model_version) return loss, {"accuracy": accuracy} else: if (model_name.lower() == 'linearregression' ): print(model, type(model)) print(model.get_params) # rmse = mean_squared_error(y, model.predict(X), square=True) rmse = np.sqrt(mean_squared_error(y, model.predict(X))) mae = mean_absolute_error(y, model.predict(X)) r2=r2_score(y, model.predict(X)) loss = rmse mse=mean_squared_error(y, model.predict(X)) rmse = np.sqrt(mean_squared_error(y, model.predict(X))) mae = mean_absolute_error(y, model.predict(X)) r2=r2_score(y, model.predict(X)) loss = rmse results = { "mean_absolute_error": mae, "mean_squared_error": mse, "root_mean_squared_error": rmse, "r2":r2, } # accuracy=r2 log(INFO, "Server evaluation FL Round: %s processed Weights. -- Loss: %s, -- metrics: %s ",str(self.fl_round),str(rmse), str(results)) self.log.info("model coefficients: "+str(model.coef_)) self.log.info("model intercept: "+str(model.intercept_)) self.model_save(model,model_name,problem_type,model_version) # return loss, len(X), results return loss, results except Exception as e: log(INFO, "evaluate error msg: %s ",str(e)) return evaluate # for deep learn models, def get_eval_fn, evaluate each round results with own dataset. It is optional, without this, fed server will aggregate (fedAvg) client weights and update results to clients without evaluate. def get_eval_fn_dl(self, model,X,y,model_name,model_version): try: scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # y = pd.get_dummies(y) y_class = None def evaluate( server_round: int, weights: fl.common.NDArrays, config: Dict[str, fl.common.Scalar], ) -> Optional[Tuple[float, Dict[str, fl.common.Scalar]]]: # Update model with the latest parameters model.set_weights(weights) problem_type = self.confparams["problem_type"] self.model_save(model, model_name,problem_type, model_version) if model_name == 'deeplearning': if problem_type == 'regression': loss, mean_absolute_error, mean_squared_error = model.evaluate(X_scaled, y, verbose=1) y_pred = model.predict(X_scaled) from sklearn import metrics root_mean_squared_error = np.sqrt(metrics.mean_squared_error(y, y_pred)) log(INFO, "global model mean_absolute_error: %f ",mean_absolute_error) log(INFO, "global model mean_squared_error: %f ",mean_squared_error) log(INFO, "global model root_mean_squared_error: %f ",root_mean_squared_error) return loss, {"mean_absolute_error": mean_absolute_error, "mean_squared_error": mean_squared_error, "root_mean_squared_error": root_mean_squared_error} if problem_type == 'classification': y_class = pd.get_dummies(y) loss, accuracy = model.evaluate(X_scaled, y_class, verbose=1) log(INFO, "global model accuracy: %f ",round(accuracy * 100, 2)) log(INFO, "global model loss: %f ", round(loss, 2)) return loss, {"accuracy": accuracy} except Exception as e: log(INFO, "get_eval_fn_dl error: %s ",str(e)) return evaluate """ Below part is the aion specific grpc functions. To start the grpc server and client. Currently below modules are not used. """ # def callaiongrpcserver(self): # agrpcobj = aionflgrpcserver() # status=agrpcobj.startgrpcerver() # print("server grpc start status: \t",status) # return status # def stopaiongrpcserver(self): # agrpcobj = aionflgrpcserver() # status=agrpcobj.shutserver() # print("server grpc stop status: \t",status) # return status ## This function called from aionflmain.py, and run server. ## Getting flower fl strategy def get_strategy(self,min_fit_clients,num_clients,eval_fn,on_fit_config_fn,on_evaluate_config_fn): strategy = fl.server.strategy.FedAvg( min_fit_clients=min_fit_clients, min_available_clients=num_clients, evaluate_fn=eval_fn, on_fit_config_fn=on_fit_config_fn, on_evaluate_config_fn=on_evaluate_config_fn, # initial_parameters=fl.common.weights_to_parameters(model.get_weights()), ) return strategy def runFLServer(self): try: server_address,model_name,num_clients,min_fit_clients,num_train_round,data_location,model_params,problem_type,model_version,selected_feature,target_feature,evaluation_required = self.configload(self.confparams) df = self.df if (evaluation_required.lower() == 'true'): ## One more check for NaN,Inf occurance in dataframe df =df[~df.isin([np.nan, np.inf, -np.inf]).any(axis=1)] ## Remove index if passed. df=df.reset_index(drop=True) y=df[target_feature] X=df[selected_feature] if (problem_type.lower() == "classification"): if (model_name.lower() == "logisticregression"): #n_classes = df[target_feature].nunique() no_classes = len(df.groupby(target_feature).count()) no_features=len(selected_feature) self.log.info("no_classes: "+str(no_classes)) self.log.info("no_features: "+str(no_features)) modelName="logisticregression" try: model = LogisticRegression(**model_params, warm_start=True) except Exception as e: self.log.info("LR model error: \n"+str(e)) status=utils.setmodelName(modelName) utils.set_initial_params(model,no_classes,no_features) eval_fn=self.get_eval_fn(model,X,y,model_name,model_version) on_fit_config_fn=self.fit_round on_evaluate_config_fn=None min_fit_clients=2 strategy = self.get_strategy(min_fit_clients,num_clients,eval_fn,on_fit_config_fn,on_evaluate_config_fn) elif (model_name.lower() == "deeplearning"): # model_hyperparams = self.confparams["model_hyperparams"] optimizer = model_params["optimizer"] loss_func = model_params["losses"] act_func = model_params["activation"] last_act_func = model_params["last_activation"] input_shape = X.shape[1] # len(selected_feature) output_shape = len(y.value_counts()) model = None if output_shape == 2: if last_act_func == "sigmoid" and loss_func == "binary_crossentropy": model = dl_model.dl_binary_classification(input_shape, output_shape, optimizer, loss_func, act_func, last_act_func) elif last_act_func == "softmax" and loss_func == "categorical_crossentropy": model = dl_model.dl_binary_classification(input_shape, output_shape, optimizer, loss_func, act_func, last_act_func) else: model = dl_model.dl_multiClass_classification(input_shape, output_shape, optimizer, loss_func, act_func, last_act_func) print(model.summary()) eval_fn=self.get_eval_fn_dl(model,X,y,model_name,model_version) on_fit_config_fn=self.fit_config on_evaluate_config_fn=self.evaluate_config strategy = self.get_strategy(min_fit_clients,num_clients,eval_fn,on_fit_config_fn,on_evaluate_config_fn) elif(problem_type.lower() == "regression"): if (model_name.lower() == "linearregression"): model=LinearRegression(**model_params) status=utils.setmodelName(model_name) utils.set_initial_params_reg(model,X.shape[0],len(selected_feature)) # utils.set_initial_params_reg(model,X.shape[0],X.shape[1]) eval_fn=self.get_eval_fn(model,X,y,model_name,model_version) on_fit_config_fn=self.fit_round on_evaluate_config_fn=None min_fit_clients=2 strategy = self.get_strategy(min_fit_clients,num_clients,eval_fn,on_fit_config_fn,on_evaluate_config_fn) elif(model_name.lower() == "deeplearning"): input_shape = X.shape[1] # len(selected_feature) output_shape = len(y.value_counts()) optimizer = model_params["optimizer"] loss_func = model_params["losses"] act_func = model_params["activation"] model = None model = dl_model.dl_regression_model(input_shape, 1, optimizer, loss_func, act_func) eval_fn=self.get_eval_fn_dl(model,X,y,model_name,model_version) on_fit_config_fn=self.fit_config on_evaluate_config_fn=self.evaluate_config strategy = self.get_strategy(min_fit_clients,num_clients,eval_fn,on_fit_config_fn,on_evaluate_config_fn) elif (evaluation_required.lower() == 'false'): eval_fn=None if (model_name.lower() == "deeplearning"): # min_fit_clients =int( model_params["min_fit_clients"]) on_fit_config_fn=self.fit_config on_evaluate_config_fn=self.evaluate_config strategy = self.get_strategy(min_fit_clients,num_clients,eval_fn,on_fit_config_fn,on_evaluate_config_fn) else: min_fit_clients=0 on_fit_config_fn=self.fit_round on_evaluate_config_fn=None # strategy = self.get_strategy(min_fit_clients,num_clients,eval_fn,on_fit_config_fn,on_evaluate_config_fn) strategy = fl.server.strategy.FedAvg( min_available_clients=num_clients, eval_fn=None, on_fit_config_fn=self.fit_round,) else: log(INFO, "Please opt server evaluation as True or False in server configuration file.") log(INFO, "Federated learning Server started at @: %s ",str(server_address)) server_rnd=1 while (1): try: fl.server.start_server(server_address=server_address, strategy=strategy, config=fl.server.ServerConfig(num_rounds=num_train_round))# config={"num_rounds": num_train_round})#config=fl.server.ServerConfig(num_rounds=3) #,force_final_distributed_eval=True) except Exception as e: log(INFO, "Server exception: %s ",str(e)) log(INFO, "AION federated learning server completed for execution cycle: %s ",str(server_rnd)) # Evaluate the final trained model server_rnd+=1 log(INFO, "AION federated learning server execution successfully completed. Please check the log file for more information.") return True except Exception as e: self.log.info("AION Federated Learning Server run error. Error Msg: "+str(e)) log(INFO, "Server not executing, err.msg: %s ",str(e)) return False # Start Flower server for n rounds of federated learning # if __name__ == "__main__": # ''' Testing purpose code ''' # super_obj=flserver1() # json_file=sys.argv[1] # super_obj.log.info("User json_file: \n"+str(json_file)) # # configfile=None # server_address,model_name,num_clients,num_train_round,data_location,model_version,model_version,selected_feature,target_feature = super_obj.configload(super_obj.confparams) # df = pd.read_csv(data_location) # # df=super_obj.df # df =df[~df.isin([np.nan, np.inf, -np.inf]).any(1)] # df=df.reset_index(drop=True) # y=df[target_feature] # X = df.drop(target_feature, axis=1) # no_classes = len(df.groupby(target_feature).count()) # no_features=len(selected_feature.split(',')) # print("no_classes: \n",no_classes) # print("no_features: \n",no_features) # # num_classes = y_train.apply(pd.Series.nunique) # if (model_name.lower() == "logisticregression"): # modelName="logisticregression" # model = LogisticRegression(penalty="l2",max_iter=10, warm_start=True) # ## May be used in future (model load for server side eval) # # model=super_obj.model_load(model_location) # status=utils.setmodelName(modelName) # utils.set_initial_params(model,no_classes,no_features) # strategy = fl.server.strategy.FedAvg( # min_available_clients=num_clients, # eval_fn=super_obj.get_eval_fn(model,X,y), # on_fit_config_fn=super_obj.fit_round,) # # super_obj.log.info("Stating federated learning server.....\n") # log(INFO, "Stating AION federated learning server.....") # fl.server.start_server(server_address, strategy=strategy, config={"num_rounds": num_train_round}) # # super_obj.log.info("federated learning server execution completed.\n") # log(INFO, "AION federated learning server execution completed.....") from typing import Tuple, Union, List import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LinearRegression from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import SGDClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from flwr.common.logger import log from logging import INFO XY = Tuple[np.ndarray, np.ndarray] Dataset = Tuple[XY, XY] LogRegParams = Union[XY, Tuple[np.ndarray]] XYList = List[XY] modelUsed=None modelname=None def setmodelName(modelselected): try: modelname=str(modelselected) print("setmodelName ,given modelname: \n",modelname) if (modelname.lower() == 'logisticregression'): modelUsed=LogisticRegression() return True elif (modelname.lower() == "linearregression"): modelUsed = LinearRegression() return True elif (modelname.lower() == "sgdclassifier"): #from sklearn.linear_model import SGDClassifier modelUsed=SGDClassifier() return True elif (modelname.lower() == "knn"): modelUsed = KNeighborsClassifier() return True elif (modelname.lower() == "decisiontreeclassifier"): modelUsed = DecisionTreeClassifier() return True else: return False except Exception as e: log(INFO, "set fl model name fn issue: ",e) def get_model_parameters(model:modelUsed) -> LogRegParams: """Returns the paramters of a sklearn LogisticRegression model.""" model_name=model.__class__.__name__ if model.fit_intercept: params = (model.coef_, model.intercept_) else: params = (model.coef_,) return params def set_model_params( model:modelUsed, params: LogRegParams ) -> modelUsed: """Sets the parameters of a sklean LogisticRegression model.""" model.coef_ = params[0] model_name=model.__class__.__name__ try: if model.fit_intercept: model.intercept_ = params[1] except Exception as e: log(INFO, "set_model_params fn issue: ",e) pass return model def set_initial_params_reg(model,no_vals,no_features): """Sets initial parameters as zeros Required since model params are uninitialized until model.fit is called. But server asks for initial parameters from clients at launch. Refer to sklearn.linear_model.LogisticRegression documentation for more information. """ no_vals = no_vals n_features = no_features # model.classes_ = np.array([i for i in range(n_classes)]) model.coef_ = np.zeros( n_features,) model_name=model.__class__.__name__ try: if model.fit_intercept: # model.intercept_ = np.ones((no_vals,1)) model.intercept_ = np.zeros((no_vals,)) except Exception as e: log(INFO, "set_initial_params fn issue: ",e) pass def set_initial_params(model,no_classes,no_features): """Sets initial parameters as zeros Required since model params are uninitialized until model.fit is called. But server asks for initial parameters from clients at launch. Refer to sklearn.linear_model.LogisticRegression documentation for more information. """ n_classes = no_classes n_features = no_features model.classes_ = np.array([i for i in range(n_classes)]) model.coef_ = np.zeros((n_classes, n_features)) model_name=model.__class__.__name__ try: if model.fit_intercept: model.intercept_ = np.zeros((n_classes,)) except Exception as e: log(INFO, "set_initial_params fn issue: ",e) pass def shuffle(X: np.ndarray, y: np.ndarray) -> XY: """Shuffle X and y.""" rng = np.random.default_rng() idx = rng.permutation(len(X)) return X[idx], y[idx] def partition(X: np.ndarray, y: np.ndarray, num_partitions: int) -> XYList: """Split X and y into a number of partitions.""" return list( zip(np.array_split(X, num_partitions), np.array_split(y, num_partitions)) ) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import sys import os import pickle import json import timeit import warnings import traceback import logging from pathlib import Path warnings.filterwarnings("ignore") import numpy as np import pandas as pd import matplotlib.pyplot as plt from pandas import json_normalize import shutil from word2number import w2n from pytz import timezone import datetime from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score, accuracy_score, r2_score,mean_absolute_error, mean_squared_error, recall_score, precision_score, f1_score from river import stream class incBatchLearner(): def __init__(self): self.home = os.path.dirname(os.path.abspath(__file__)) self.configPath = os.path.join(self.home, 'production', 'Config.json') self.configDict = {} self.updConfigDict = None self.incFillPath = os.path.join(self.home,'production','profiler','incFill.pkl') self.incOutlierRemPath = os.path.join(self.home, 'production', 'profiler', 'incOutlierRem.pkl') self.incLabelMappingPath = os.path.join(self.home,'production', 'profiler' , 'incLabelMapping.pkl') self.incCatEncoderPath = os.path.join(self.home, 'production' , 'profiler', 'incCatEncoder.pkl') self.incScalerPath = os.path.join(self.home, 'production', 'profiler','incScaler.pkl') self.testPath = os.path.join(self.home, 'data', 'test.csv') self.modelName = '' self.incFill = None self.incLabelMapping = None self.incCatEncoder = None self.incScaler = None self.incOutlierRem = None self.model = None self.targetCol = None self.numFtrs = [] self.catFtrs = [] self.allFtrs = [] self.logFileName=os.path.join(self.home,'log','model_training_logs.log') filehandler = logging.FileHandler(self.logFileName, 'a','utf-8') formatter = logging.Formatter('%(message)s') filehandler.setFormatter(formatter) self.log = logging.getLogger('eion') self.log.propagate = False self.log.addHandler(filehandler) self.log.setLevel(logging.INFO) def readData(self, data, isTest = False): if not isTest: self.log.info('New Data Path: '+str(data)) else: self.log.info('Test Data Path: '+str(data)) startTime = timeit.default_timer() if os.path.splitext(data)[1] == ".tsv": df=pd.read_csv(data,encoding='utf-8',sep='\t') elif os.path.splitext(data)[1] == ".csv": df=pd.read_csv(data,encoding='utf-8') elif os.path.splitext(data)[1] == ".dat": df=pd.read_csv(data,encoding='utf-8') else: if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) df = json_normalize(jsonData) dataLoadTime = timeit.default_timer() - startTime self.log.info('\nData Load time(sec) :'+str(dataLoadTime)) self.log.info('\n First ten rows of new data') self.log.info(df.head(10)) self.log.info('Data Frame shape: '+str(df.shape)) df.rename(columns=lambda x:x.strip(), inplace=True) return df def readConfig(self): with open(self.configPath, 'r', encoding= 'utf8') as f: self.configDict = json.load(f) self.configDict['partialFit']+=1 self.log.info('************* Partial Fit '+str(self.configDict['partialFit'])+' *************** \n') msg = '-------> Execution Start Time: '+ datetime.datetime.now(timezone("Asia/Kolkata")).strftime('%Y-%m-%d %H:%M:%S' + ' IST') self.log.info(msg) self.targetCol = self.configDict['targetCol'] if 'numFtrs' in self.configDict: self.numFtrs = self.configDict['numFtrs'] if 'catFtrs' in self.configDict: self.catFtrs = self.configDict['catFtrs'] if 'allNumCols' in self.configDict: self.allNumCols = self.configDict['allNumCols'] if 'allCatCols' in self.configDict: self.allCatCols = self.configDict['allCatCols'] if 'encCols' in self.configDict: self.encCols = self.configDict['encCols'] if 'wordToNumCols' in self.configDict: self.wordToNumericCols = self.configDict['wordToNumCols'] self.emptyFtrs = self.configDict['emptyFtrs'] if 'encTarget' in self.configDict: self.encTarget = self.configDict['encTarget'] if 'noOfClasses' in self.configDict: self.allClasses = list(range(int(self.configDict['noOfClasses']))) self.misval_ratio = self.configDict['misval_ratio'] self.allFtrs = self.configDict['allFtrs'] self.modelName = self.configDict['modelName'] self.problemType = self.configDict['problemType'] self.modelPath = os.path.join(self.home, 'production', 'model', self.modelName+'.pkl') self.scoreParam = self.configDict['scoreParam'] self.score = self.configDict['score'] def pickleLoad(self, file, filename): if os.path.exists(file): with open(file, 'rb') as f: model = pickle.load(f) file_size = os.path.getsize(file) self.log.info(str(filename)+" size is :"+str(file_size)+"bytes") return model else: return None def s2n(self,value): try: x=eval(value) return x except: try: return w2n.word_to_num(value) except: return np.nan def convertWordToNumeric(self,dataframe,feature): try: dataframe[feature]=dataframe[feature].apply(lambda x: self.s2n(x)) return dataframe except Exception as inst: self.log.info("convertWordToNumeric Failed ===>"+str(inst)) return dataframe def pickleDump(self, model, path): if model is not None: with open(path, 'wb') as f: pickle.dump(model, f) def splitTrainTest(self,X,y): if self.problemType.lower() == 'regression': xtrain,xtest,ytrain,ytest=train_test_split(X,y,test_size=0.2,shuffle=True) else: try: xtrain,xtest,ytrain,ytest=train_test_split(X,y,stratify=y,test_size=0.2,shuffle=True) except: xtrain,xtest,ytrain,ytest=train_test_split(X,y,test_size=0.2,shuffle=True) return xtrain,xtest,ytrain,ytest def loadSavedModels(self): self.incFill = self.pickleLoad(self.incFillPath, 'Online Missing Value Filler') self.incLabelMapping = self.pickleLoad(self.incLabelMappingPath, 'Online Label Encoder') self.incCatEncoder = self.pickleLoad(self.incCatEncoderPath, 'Online Categorical Encoder') self.incScaler = self.pickleLoad(self.incScalerPath, 'Online Scaler') self.incOutlierRem = self.pickleLoad(self.incOutlierRemPath, 'Online Outlier Detector') self.model = self.pickleLoad(self.modelPath, str(os.path.basename(self.modelPath))[:-4]) self.log.info('\nData Profiler and ML models loaded in Memory') def saveModels(self): os.makedirs(os.path.join(self.home, 'production', 'profiler')) os.makedirs(os.path.join(self.home, 'production', 'model')) if type(self.configDict['num_fill']) == type({}) or type(self.configDict['cat_fill']) == type({}): self.pickleDump(self.incFill, self.incFillPath) self.pickleDump(self.incLabelMapping, self.incLabelMappingPath) self.pickleDump(self.incCatEncoder, self.incCatEncoderPath) self.pickleDump(self.incScaler, self.incScalerPath) self.pickleDump(self.incOutlierRem, self.incOutlierRemPath) self.pickleDump(self.model, self.modelPath) self.log.info('Models saved into production') def saveConfig(self): with open(self.configPath, 'w', encoding= 'utf8') as f: json.dump(self.updConfigDict, f, ensure_ascii=False) def apply_river_model(self, x, profModel, isTest): if not isTest: profModel.learn_one(x) return pd.Series(profModel.transform_one(x)) def apply_enc(self, x, isTest): if not isTest: y = x[self.encTarget] self.incCatEncoder.learn_one(x, y) return pd.Series(self.incCatEncoder.transform_one(x)) def apply_od_pipe(self, x): score = self.incOutlierRem.score_one(x) is_anomaly = self.incOutlierRem.classify(score) self.incOutlierRem.learn_one(x) return is_anomaly def dataFramePreProcess(self, df): df = df.replace('-', np.NaN) df = df.replace('?', np.NaN) df = df.replace(r'^\s*$', np.NaN, regex=True) columns = list(df.columns) if self.wordToNumericCols: for ftr in self.wordToNumericCols: if ftr in columns: self.log.info('Converting '+ftr+' to numeric type...') tempDataFrame=df.copy(deep=True) testDf = self.convertWordToNumeric(tempDataFrame,ftr) try: df[ftr]=testDf[ftr].astype(float) except: pass columns = list(df.columns) for empCol in self.emptyFtrs: if empCol in columns: df = df.drop(columns=[empCol]) columns = list(df.columns) self.log.info( 'Detecting Missing Values') nonNAArray=[] numOfRows = df.shape[0] for i in columns: numNa=df.loc[(pd.isna(df[i])),i ].shape[0] nonNAArray.append(tuple([i,numNa])) self.missingCols = [] self.emptyCols = [] for item in nonNAArray: numofMissingVals = item[1] if(numofMissingVals !=0): self.log.info('-------> Feature '+str(item[0])) self.log.info('----------> Number of Empty Rows '+str(numofMissingVals)) self.missingCols.append(item[0]) if(numofMissingVals >= numOfRows * self.misval_ratio): self.log.info('----------> Empty: Yes') self.log.info('----------> Permitted Rows: '+str(int(numOfRows * self.misval_ratio))) self.emptyCols.append(item[0]) if(len(self.missingCols) !=0): self.log.info( '----------- Detecting for Missing Values End -----------\n') else: self.log.info( '-------> Missing Value Features :Not Any') self.log.info( '----------- Detecting for Missing Values End -----------\n') return df def profiler(self, df, isTest=False): if not isTest: self.log.info('Starting profiling of New Training Data') else: self.log.info('Starting profiling of Testing Data') startTime = timeit.default_timer() df = self.dataFramePreProcess(df) if 'num_fill' in self.configDict: if self.configDict['num_fill'] == 'drop': df = df.dropna(axis = 0, subset=self.allNumCols) elif self.configDict['num_fill'] == 'zero': df[self.allNumCols] = df[self.allNumCols].fillna(value = 0.0) else: df[self.allNumCols]= df[self.allNumCols].apply(pd.to_numeric) df = df.astype(object).where(df.notna(), None) #river expects nan values to be None df[self.allNumCols]= df[self.allNumCols].apply(lambda row: self.apply_river_model(row.to_dict(), self.incFill['num_fill'], isTest), axis='columns') if not isTest: self.updConfigDict['num_fill'] = {col:self.incFill['num_fill'].stats[col].get() for col in self.allNumCols} if 'cat_fill' in self.configDict: if self.configDict['cat_fill'] == 'drop': df = df.dropna(axis = 0, subset=self.allCatCols) elif self.configDict['cat_fill'] == 'zero': df[self.allCatCols] = df[self.allCatCols].fillna(value = 0.0) else: df = df.astype(object).where(df.notna(), None) df[self.allCatCols]= df[self.allCatCols].apply(lambda row: self.apply_river_model(row.to_dict(),self.incFill['cat_fill'], isTest), axis='columns') if not isTest: self.updConfigDict['cat_fill'] = {col:self.incFill['cat_fill'].stats[col].get() for col in self.allCatCols} if not isTest: self.log.info('Missing value profiler model updated') if self.incLabelMapping: uq_classes = df[self.targetCol].unique() le_classes = list(self.incLabelMapping.classes_) uq_classes = [type(le_classes[0])(x) for x in uq_classes] unseen_classes = set(uq_classes) - set(le_classes) self.log.info('Existing classes: '+str(le_classes)) if len(unseen_classes)>0: self.log.info('New unseen classes: '+str(unseen_classes)) le_classes.extend(unseen_classes) from sklearn.preprocessing import LabelEncoder self.incLabelMapping = LabelEncoder() self.incLabelMapping.fit(le_classes) self.log.info(self.incLabelMapping.classes_) self.log.info('Label encoder refitted with new unseen classes') df[self.targetCol] = df[self.targetCol].apply(str) df[self.targetCol] = self.incLabelMapping.transform(df[self.targetCol]) if not isTest: self.log.info('Target column label encoding is done') if self.incCatEncoder: if self.problemType.lower() == 'regression': from sklearn.preprocessing import StandardScaler sc = StandardScaler() self.encTarget = 'scaledTarget' df['scaledTarget'] = sc.fit_transform(df[self.targetCol].to_numpy().reshape(-1,1)) transformed_data = df[self.encCols].apply(lambda row: self.apply_enc(row.to_dict(), isTest), axis='columns') if self.targetCol in transformed_data.columns: transformed_data.drop(self.targetCol, inplace=True, axis = 1) df[self.catFtrs] = transformed_data if not isTest: self.updConfigDict['catEnc'] = [] if len(self.catFtrs) == 1: col = self.catFtrs[0] self.configDict['catEnc'].append({col:self.incCatEncoder['TargetAgg'].state.to_dict()}) else: for i, col in enumerate(self.catFtrs): if i==0: no = '' else: no = str(i) self.configDict['catEnc'].append({col:self.incCatEncoder['TransformerUnion']['TargetAgg'+no].state.to_dict()}) self.log.info('Categorical encoding is done and profiler model updated') if self.incScaler: if not isTest: self.incScaler = self.incScaler.partial_fit(df[self.numFtrs]) self.log.info('Numerical features scaled and profiler model updated') df[self.numFtrs] = self.incScaler.transform(df[self.numFtrs]) if self.incOutlierRem and not isTest: df = df[df[self.numFtrs].apply(lambda x: False if self.apply_od_pipe(x.to_dict()) else True, axis=1)] df.reset_index(drop=True, inplace=True) self.log.info('Outliers removed and profiler model updated') if not isTest: self.log.info('Check config file in production folder for updated profiler values') profilerTime = timeit.default_timer() - startTime self.log.info('\nProfiling time(sec) :'+str(profilerTime)) return df def riverTrain(self, X, Y): trainStream = stream.iter_pandas(X, Y) for i, (xi, yi) in enumerate(trainStream): if yi!=None: self.model.learn_one(xi, yi) def riverEvaluate(self, xtest): testStream = stream.iter_pandas(xtest) preds = [] for xi,yi in testStream: pred = self.model.predict_one(xi) preds.append(pred) return preds def trainModel(self,df): startTime = timeit.default_timer() X = df[self.allFtrs] Y = df[self.targetCol] try: self.riverTrain(X,Y) trainTime = timeit.default_timer() - startTime self.log.info('\nModel Training time(sec) :'+str(trainTime)) self.log.info(self.modelName+' model updated') self.log.info('First fit model params are '+str(self.configDict['modelParams'])) except Exception as e: raise e def archiveModels(self): source = os.path.join(self.home, 'production') archivePath = os.path.join(self.home,'archives') if os.path.isdir(archivePath): NoOfArchives = sum(os.path.isdir(os.path.join(self.home,'archives',str(i))) for i in os.listdir(archivePath)) destination = os.path.join(self.home,'archives',str(NoOfArchives+1)) else: destination = os.path.join(archivePath,'1') if not os.path.exists(destination): os.makedirs(destination) allfiles = os.listdir(source) for f in allfiles: src_path = os.path.join(source, f) dst_path = os.path.join(destination, f) shutil.move(src_path, dst_path) self.log.info('Previous production models archived') def get_score(self,metric,actual,predict): if 'accuracy' in str(metric).lower(): score = accuracy_score(actual,predict) score = score*100 elif 'recall' in str(metric).lower(): score = recall_score(actual,predict,average='macro') score = score*100 elif 'precision' in str(metric).lower(): score = precision_score(actual,predict,average='macro') score = score*100 elif 'f1_score' in str(metric).lower(): score = f1_score(actual,predict, average='macro') score = score*100 elif 'roc_auc' in str(metric).lower(): try: score = roc_auc_score(actual,predict,average="macro") except: try: actual = pd.get_dummies(actual) predict = pd.get_dummies(predict) score = roc_auc_score(actual,predict, average='weighted', multi_class='ovr') except: score = 0 score = score*100 elif ('mse' in str(metric).lower()) or ('neg_mean_squared_error' in str(metric).lower()): score = mean_squared_error(actual,predict) elif ('rmse' in str(metric).lower()) or ('neg_root_mean_squared_error' in str(metric).lower()): score=mean_squared_error(actual,predict,squared=False) elif ('mae' in str(metric).lower()) or ('neg_mean_absolute_error' in str(metric).lower()): score=mean_absolute_error(actual,predict) elif 'r2' in str(metric).lower(): score=r2_score(actual,predict)*100 return round(score,2) def checkColumns(self, df): self.log.info('Checking DataColumns in new data') dfCols = list(df.columns) allCols = self.allFtrs.copy() allCols.append(self.targetCol) missingCols = [] for col in allCols: if col not in dfCols: missingCols.append(col) if len(missingCols)>0: raise Exception('DataFrame is missing columns: '+str(missingCols)) else: self.log.info('All required columns are present: '+str(list(dfCols)[:500])) def plotMetric(self): y = self.configDict['metricList'] fedrows = self.configDict['trainRowsList'] fig = plt.figure() ax = fig.gca() if self.configDict['problemType'] == 'classification': ax.set_yticks(np.arange(0, 110, 10)) plt.ylim(ymin=0) if self.configDict['problemType'] == 'regression': minMet = min(y) maxMet = max(y) plt.ylim(minMet - 10, maxMet+10) plt.plot(y) plt.ylabel(self.scoreParam) plt.xlabel('Partial Fits') plt.title(str(self.scoreParam)+' over training rows') if type(fedrows[0])!=type(''): fedrows = [str(x) for x in fedrows] x = list(range(len(fedrows))) for i in range(len(fedrows)): plt.annotate(fedrows[i], (x[i], y[i] + 5)) if self.configDict['problemType'] == 'classification': plt.annotate(round(y[i],1), (x[i], y[i] - 3)) plt.grid() plt.savefig(os.path.join(self.home, 'production','metric')) return def updateLearning(self,data): try: self.readConfig() self.updConfigDict = self.configDict.copy() df = self.readData(data) self.checkColumns(df) self.loadSavedModels() X = df[self.allFtrs] y = df[self.targetCol] xtrain,xtest,ytrain,ytest = self.splitTrainTest(X,y) dftrain = pd.concat((xtrain, ytrain), axis = 1) dftest = pd.concat((xtest, ytest), axis = 1) dftrain = self.profiler(dftrain) dftest = self.profiler(dftest, isTest = True) xtest = dftest[self.allFtrs] ytest = dftest[self.targetCol] self.trainModel(dftrain) preds = self.riverEvaluate(xtest) score = self.get_score(self.scoreParam, ytest, preds) self.updConfigDict['score'] = score self.log.info('Previous '+self.scoreParam+': '+str(self.configDict['score'])) self.log.info('Current '+self.scoreParam+': '+str(self.updConfigDict['score'])) self.configDict['trainRowsList'].append(self.configDict['trainRowsList'][-1]+xtrain.shape[0]) self.log.info('Number of data points trained on so far: '+str(self.configDict['trainRowsList'][-1])) self.configDict['metricList'].append(self.updConfigDict['score']) self.archiveModels() self.plotMetric() self.saveModels() self.saveConfig() msg = self.scoreParam+': Previous:'+str(self.configDict['score'])+' Current:'+ str(self.updConfigDict['score']) output = {"status":"SUCCESS","Msg":msg} self.log.info(str(output)) except Exception as e: print(traceback.format_exc()) self.log.info('Partial Fit Failed '+str(traceback.format_exc())) if self.updConfigDict != None: self.saveConfig() output = {"status":"FAIL","Msg":str(e).strip('"')} return json.dumps(output) if __name__ == "__main__": incBLObj = incBatchLearner() output = incBLObj.updateLearning(sys.argv[1]) print("aion_learner_status:",output) import sys import os import pickle import json import traceback import warnings warnings.filterwarnings("ignore") import numpy as np import pandas as pd import scipy from pandas import json_normalize from word2number import w2n from river import stream class incBatchPredictor(): def __init__(self): self.home = os.path.dirname(os.path.abspath(__file__)) self.configPath = os.path.join(self.home, 'production', 'Config.json') self.configDict = {} self.incFillPath = os.path.join(self.home,'production','profiler','incFill.pkl') self.incLabelMappingPath = os.path.join(self.home,'production', 'profiler' , 'incLabelMapping.pkl') self.incCatEncoderPath = os.path.join(self.home, 'production' , 'profiler', 'incCatEncoder.pkl') self.incScalerPath = os.path.join(self.home, 'production', 'profiler','incScaler.pkl') self.incFill = None self.incLabelMapping = None self.incCatEncoder = None self.incScaler = None self.model = None self.targetCol = None self.modelName = '' self.problemType = '' self.numFtrs = [] self.catFtrs = [] def readData(self, data): try: if os.path.splitext(data)[1] == ".tsv": df=pd.read_csv(data,encoding='utf-8',sep='\t') elif os.path.splitext(data)[1] == ".csv": df=pd.read_csv(data,encoding='utf-8') elif os.path.splitext(data)[1] == ".dat": df=pd.read_csv(data,encoding='utf-8') else: if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) df = json_normalize(jsonData) df.rename(columns=lambda x:x.strip(), inplace=True) return df except KeyError as e: output = {"status":"FAIL","message":str(e).strip('"')} print(json.dumps(output)) except Exception as e: output = {"status":"FAIL","message":str(e).strip('"')} print(json.dumps(output)) def readConfig(self): with open(self.configPath, 'r', encoding= 'utf8') as f: self.configDict = json.load(f) self.targetCol = self.configDict['targetCol'] if 'numFtrs' in self.configDict: self.numFtrs = self.configDict['numFtrs'] if 'catFtrs' in self.configDict: self.catFtrs = self.configDict['catFtrs'] if 'allNumCols' in self.configDict: self.allNumCols = self.configDict['allNumCols'] if 'allCatCols' in self.configDict: self.allCatCols = self.configDict['allCatCols'] if 'wordToNumCols' in self.configDict: self.wordToNumericCols = self.configDict['wordToNumCols'] self.emptyFtrs = self.configDict['emptyFtrs'] self.allFtrs = self.configDict['allFtrs'] self.modelName = self.configDict['modelName'] self.problemType = self.configDict['problemType'] self.modelPath = os.path.join(self.home, 'production', 'model', self.modelName+'.pkl') self.scoreParam = self.configDict['scoreParam'] self.score = self.configDict['score'] def pickleLoad(self, file): if os.path.exists(file): with open(file, 'rb') as f: model = pickle.load(f) return model else: return None def s2n(self,value): try: x=eval(value) return x except: try: return w2n.word_to_num(value) except: return np.nan def convertWordToNumeric(self,dataframe,feature): try: dataframe[feature]=dataframe[feature].apply(lambda x: self.s2n(x)) return dataframe except Exception as inst: self.log.info("convertWordToNumeric Failed ===>"+str(inst)) return dataframe def loadSavedModels(self): self.incFill = self.pickleLoad(self.incFillPath) self.incLabelMapping = self.pickleLoad(self.incLabelMappingPath) self.incCatEncoder = self.pickleLoad(self.incCatEncoderPath) self.incScaler = self.pickleLoad(self.incScalerPath) self.model = self.pickleLoad(self.modelPath) def apply_river_model(self, x, profModel): print(profModel.imputers) return pd.Series(profModel.transform_one(x)) def apply_enc(self, x): return pd.Series(self.incCatEncoder.transform_one(x)) def dataFramePreProcess(self, df): df = df.replace(r'^\s*$', np.NaN, regex=True) df = df.replace('-', np.nan) df = df.replace('?', np.nan) columns = list(df.columns) if self.wordToNumericCols: for ftr in self.wordToNumericCols: if ftr in columns: tempDataFrame=df.copy(deep=True) testDf = self.convertWordToNumeric(tempDataFrame,ftr) try: df[ftr]=testDf[ftr].astype(float) except: pass columns = list(df.columns) for empCol in self.emptyFtrs: if empCol in columns: df = df.drop(columns=[empCol]) return df def profiler(self, df): df = df[self.allFtrs] df = self.dataFramePreProcess(df) if 'num_fill' in self.configDict: if self.configDict['num_fill'] == 'drop': df = df.dropna(axis = 0, subset=self.allNumCols) elif self.configDict['num_fill'] == 'zero': df[self.numFtrs] = df[self.numFtrs].fillna(value = 0.0) else: for x in self.numFtrs: if x == self.targetCol: continue df[x] = df[x].fillna(value = self.configDict['num_fill'][x]) if 'cat_fill' in self.configDict: if self.configDict['cat_fill'] == 'drop': df = df.dropna(axis = 0, subset=self.allCatCols) elif self.configDict['cat_fill'] == 'zero': df[self.catFtrs] = df[self.catFtrs].fillna(value = 0.0) else: for x in self.catFtrs: if x == self.targetCol: continue df[x] = df[x].fillna(value = self.configDict['cat_fill'][x]) if self.incCatEncoder: transformed_data = df[self.catFtrs].apply(lambda row: self.apply_enc(row.to_dict()), axis='columns') df[self.catFtrs] = transformed_data if self.incScaler: df[self.numFtrs] = self.incScaler.transform(df[self.numFtrs]) return df def trainedModel(self,X): testStream = stream.iter_pandas(X) preds = [] if self.problemType.lower() == 'regression': for xi,yi in testStream: try: pred = self.model.predict_proba_one(xi) preds.append(pred) except: pred = self.model.predict_one(xi) preds.append(pred) preds = pd.DataFrame(preds) return preds elif self.problemType.lower() == 'classification': for xi,yi in testStream: try: pred = self.model.predict_proba_one(xi) preds.append(pred) except: continue out = pd.DataFrame(preds) return out def apply_output_format(self,df,modeloutput): if self.problemType.lower() == 'regression': df['prediction'] = modeloutput[0] df['prediction'] = df['prediction'].round(2) elif self.problemType.lower() == 'classification': modeloutput = round(modeloutput,2) if modeloutput.shape[1] == 1: df['prediction'] = modeloutput df['prediction'] = df['prediction'].astype(int) else: try: predCol = modeloutput.idxmax(axis=1) df['prediction'] = predCol.astype(int) df['prediction'] = self.incLabelMapping.inverse_transform(df['prediction']) except: df['prediction'] = modeloutput.idxmax(axis=1) df['probability'] = modeloutput.max(axis=1).round(2) modeloutput.columns = modeloutput.columns.astype(int) modeloutput.columns = self.incLabelMapping.inverse_transform(list(modeloutput.columns)) df['remarks'] = modeloutput.apply(lambda x: x.to_json(), axis=1) outputjson = df.to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} return(json.dumps(outputjson)) def predict(self,data): try: df = self.readData(data) dfOrg = df.copy() self.readConfig() if len(self.configDict)!=0: self.loadSavedModels() df = self.profiler(df) modeloutput = self.trainedModel(df) dfOrg = dfOrg[self.allFtrs] output = self.apply_output_format(dfOrg, modeloutput) else: pass except Exception as e: print(traceback.format_exc()) output = {"status":"FAIL","message":str(e).strip('"')} return output if __name__ == "__main__": incBPobj = incBatchPredictor() output = incBPobj.predict(sys.argv[1]) print("predictions:",output) import tensorflow as tf def dl_regression_model(input_shape, output_shape, optimizer, loss_func, act_func): inputs = tf.keras.Input(shape=(input_shape,)) x = tf.keras.layers.Dense(64, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(inputs) x = tf.keras.layers.Dense(32, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(16, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(8, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) outputs = tf.keras.layers.Dense(output_shape, kernel_initializer='he_normal', bias_initializer='zeros')(x) model = tf.keras.Model(inputs=inputs, outputs=outputs) model.compile(loss=loss_func , optimizer=optimizer, metrics=["mean_absolute_error", "mean_squared_error", ]) return model def dl_multiClass_classification(input_shape, output_shape, optimizer, loss_func, act_func, last_act_func): inputs = tf.keras.Input(shape=(input_shape,)) x = tf.keras.layers.Dense(64, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(inputs) x = tf.keras.layers.Dense(32, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(16, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(8, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) outputs = tf.keras.layers.Dense(output_shape, kernel_initializer='he_normal', bias_initializer='zeros', activation=last_act_func)(x) model = tf.keras.Model(inputs=inputs, outputs=outputs) model.compile(optimizer, loss_func, metrics=["accuracy"]) return model def dl_binary_classification(input_shape, output_shape, optimizer, loss_func, act_func, last_act_func): inputs = tf.keras.Input(shape=(input_shape,)) x = tf.keras.layers.Dense(64, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(inputs) x = tf.keras.layers.Dense(32, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(16, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) x = tf.keras.layers.Dense(8, kernel_initializer='he_normal', bias_initializer='zeros', activation=act_func)(x) outputs = tf.keras.layers.Dense(output_shape, kernel_initializer='he_normal', bias_initializer='zeros', activation=last_act_func)(x) model = tf.keras.Model(inputs=inputs, outputs=outputs) model.compile(optimizer, loss_func, metrics=["accuracy"]) return model # -*- coding: utf-8 -*- """ Created on Wed May 25 21:16:54 2022 @author: @aionteam """ import tensorflow as tf import warnings import flwr as flower import numpy as np import pandas as pd import os from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, mean_absolute_error,r2_score from sklearn.model_selection import train_test_split from sklearn.metrics import log_loss import utils import logging from flwr.common.logger import log from logging import INFO import time import pickle as pkl import json import sys import random import string from sklearn.preprocessing import StandardScaler import dl_model from sklearn import metrics ## Below import can be used when aion specific grpc communication used. # from aiongrpcclient import aiongrpcclient os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" os.environ["GRPC_VERBOSITY"] = "debug" logger = logging.getLogger('AION') """ The below aion fl client is for sklearn process""" class aionflc(flower.client.NumPyClient): def __init__(self,model,num_rounds,model_name,version,wait_time,client_id,num_records,model_overwrite,problem_type,X_train, X_test, y_train, y_test): self.count=0 self.num_rounds=round(num_rounds) self.model_name=model_name self.version=version self.wait_time=int(wait_time) self.client_id=client_id self.num_records=num_records self.model_overwrite=model_overwrite self.model=model self.problem_type=problem_type self.X_train, self.X_test, self.y_train, self.y_test=X_train, X_test, y_train, y_test # """ The below part not used now. In future, for our own grpc communication, this module will be used.Call this function where we want. Need to modify aiongrpcproto.proto according our requirement.""" # def callaiongrpcclient(self): # clientins = aiongrpcclient() # status=clientins.startgrpcclient() # return status #Save the final model def model_save(self,model): ##Locate standard model dir to save model cwd = os.path.abspath(os.path.dirname(__file__)) model_location=os.path.join(cwd, 'models') try: os.makedirs(model_location) except FileExistsError as fe: # here,model_location already exists pass model_name=self.model_name ## Saving model if (self.model_overwrite.lower() == 'false'): version=str(self.count) if (model_name.lower() == "deeplearning"): file_name=model_name+'_'+self.problem_type+'_'+version+".h5" saved_model=os.path.normpath(os.path.join(model_location,file_name)) log(INFO, "flclient saved_model path: %s ",str(saved_model)) try: model.save(saved_model) return True except Exception as e: logger.info("model save error. Err.Msg: "+str(e)) return False else: file_name=model_name+'_'+self.problem_type+'_'+version+".sav" saved_model=os.path.normpath(os.path.join(model_location,file_name)) log(INFO, "flclient saved_model path: %s ",str(saved_model)) try: with open (saved_model,'wb') as f: pkl.dump(model,f) return True except Exception as e: logger.info("model save error. Err.Msg: "+str(e)) return False elif (self.model_overwrite.lower() == 'true'): version=str(self.version) if (model_name.lower() == "deeplearning"): file_name=model_name+'_'+self.problem_type+'_'+version+".h5" saved_model=os.path.normpath(os.path.join(model_location,file_name)) log(INFO, "flclient saved_model path: %s ",str(saved_model)) try: model.save(saved_model) return True except Exception as e: logger.info("model save error. Err.Msg: "+str(e)) return False else: file_name=model_name+'_'+self.problem_type+'_'+version+".sav" saved_model=os.path.normpath(os.path.join(model_location,file_name)) log(INFO, "flclient saved_model path: %s ",str(saved_model)) try: with open (saved_model,'wb') as f: pkl.dump(model,f) return True except Exception as e: logger.info("model save error. Err.Msg: "+str(e)) return False else: ##Write own user instruction pass def get_parameters(self, config): return utils.get_model_parameters(self.model) def get_properties(self,model,time_out): """Return the current client properties.""" client_info={'client_id':self.client_id} time_out=100 return client_info,model,time_out def fit(self, parameters, config): utils.set_model_params(self.model, parameters) with warnings.catch_warnings(): warnings.simplefilter("ignore") num_partitions=round(self.num_rounds) # num_partitions=round(5) xtrain=np.array_split(self.X_train, num_partitions)[self.count] ytrain=np.array_split(self.y_train, num_partitions)[self.count] self.model.fit(xtrain, ytrain) time.sleep(self.wait_time) self.count+=1 print("-- Received Weights from Server.") print(f"\n Training finished for FL round: {config['rnd']}.\n") logger.info("-- Received Weights from Server. ") logger.info("Training finished for FL round: "+str(config['rnd'])+" -- Received Weights from Server") model_param=utils.get_model_parameters(self.model) model_param=list(model_param) return model_param, len(self.X_train),{} # def evaluate(self, parameters, config): # utils.set_model_params(self.model, parameters) # print("******** Test_1 ****************** \n") # if (self.problem_type.lower() == 'classification'): # if (self.model_name.lower() == 'logisticregression' ): # loss = log_loss(self.y_test, self.model.predict_proba(self.X_test)) # print("******** Test_1a ****************** \n") # else: # if (self.model_name.lower() == 'linearregression' ): # print("******** Test_1b ****************** \n") # # loss = log_loss(self.y_test, self.model.predict(self.X_test)) # rmse = np.sqrt(mean_squared_error(self.y_test, self.model.predict(self.X_test))) # mae = mean_absolute_error(self.y_test, self.model.predict(self.X_test)) # r2=r2_score(self.y_test, self.model.predict(self.X_test)) # loss = rmse # # accuracy=r2 # print(f"{self.client_id} Sending weights -- data processed {self.num_records}, -- Loss: {(rmse)}. -- r2: {r2}. ") # logger.info(str(self.client_id)+" Sending weights -- data processed "+str(self.num_records)+".-- Loss: "+str(rmse)+". -- r2: "+str(r2)) # logger.info("FL Client model intercept: "+str(model.intercept_)) # logger.info("FL Client model coefficients: "+str(model.coef_)) # self.model_save(self.model) # return loss, len(self.X_test), {"r2": r2} # print("******** Test_1c ****************** \n") # print("******** Test_2 ****************** \n") # accuracy = self.model.score(self.X_test, self.y_test) # print(f"{self.client_id} Sending weights -- data processed {self.num_records}, -- Loss: {(loss)}. -- accuracy: {accuracy}. ") # logger.info(str(self.client_id)+" Sending weights -- data processed "+str(self.num_records)+".-- Loss: "+str(loss)+". -- accuracy: "+str(accuracy)) # logger.info("FL Client model intercept: "+str(model.intercept_)) # logger.info("FL Client model coefficients: "+str(model.coef_)) # self.model_save(self.model) # return loss, len(self.X_test), {"accuracy": accuracy} def evaluate(self, parameters, config): utils.set_model_params(self.model, parameters) if (self.problem_type.lower() == 'classification'): if (self.model_name.lower() == 'logisticregression' ): loss = log_loss(self.y_test, self.model.predict_proba(self.X_test)) accuracy = self.model.score(self.X_test, self.y_test) print(f"{self.client_id} Sending weights -- data processed {self.num_records}, -- Loss: {(loss)}. -- accuracy: {accuracy}. ") logger.info(str(self.client_id)+" Sending weights -- data processed "+str(self.num_records)+".-- Loss: "+str(loss)+". -- accuracy: "+str(accuracy)) logger.info("FL Client model intercept: "+str(model.intercept_)) logger.info("FL Client model coefficients: "+str(model.coef_)) self.model_save(self.model) return loss, len(self.X_test), {"accuracy": accuracy} elif (self.problem_type.lower() == 'regression'): if (self.model_name.lower() == 'linearregression' ): # loss = log_loss(self.y_test, self.model.predict(self.X_test)) mse=mean_squared_error(self.y_test, self.model.predict(self.X_test)) rmse = np.sqrt(mean_squared_error(self.y_test, self.model.predict(self.X_test))) mae = mean_absolute_error(self.y_test, self.model.predict(self.X_test)) r2=r2_score(self.y_test, self.model.predict(self.X_test)) loss = rmse results = { "mean_absolute_error": mae, "mean_squared_error": mse, "root_mean_squared_error": rmse, "r2":r2, } print(f"{self.client_id} Sending weights -- data processed {self.num_records}, -- Loss: {(rmse)}. -- metrics: {results}. ") logger.info(str(self.client_id)+" Sending weights -- data processed "+str(self.num_records)+".-- Loss: "+str(rmse)+". -- metrics: "+str(results)) logger.info("FL Client model intercept: "+str(self.model.intercept_)) logger.info("FL Client model coefficients: "+str(self.model.coef_)) self.model_save(self.model) return loss, len(self.X_test), results """ The below aion fl client is for deep learning process. Why different client for sklearn and deeplearn ?: Because, flower calling the client object and process all functions (get_parameters,fit and evaluate) internally. So, user space we cannot combine both (sklearn n dl) using if..else. """ class aionflc_dl(flower.client.NumPyClient): def __init__(self,model,num_rounds,model_name,version,wait_time,client_id,num_records,model_overwrite,problem_type,X_train, X_test, y_train, y_test,model_params): self.count=0 self.num_rounds=round(num_rounds) self.model_name=model_name self.version=version self.wait_time=int(wait_time) self.client_id=client_id self.num_records=num_records self.model_overwrite=model_overwrite self.model=model self.problem_type=problem_type self.X_train, self.X_test, self.y_train, self.y_test=X_train, X_test, y_train, y_test self.model_params=model_params # """ The below part not used now. In future, for our own grpc communication, this module will be used.Call this function where we want. Need to modify aiongrpcproto.proto according our requirement.""" # def callaiongrpcclient(self): # clientins = aiongrpcclient() # status=clientins.startgrpcclient() # return status #Save the final model def model_save(self,model): ##Locate standard model dir to save model cwd = os.path.abspath(os.path.dirname(__file__)) model_location=os.path.join(cwd, 'models') try: os.makedirs(model_location) except FileExistsError as fe: # here,model_location already exists pass model_name=self.model_name # version=self.version ## Saving model if (self.model_overwrite.lower() == 'false'): version=str(self.count) if (model_name.lower() == "deeplearning"): file_name=model_name+'_'+self.problem_type+'_'+version+".h5" saved_model=os.path.normpath(os.path.join(model_location,file_name)) log(INFO, "flclient saved_model path: %s ",str(saved_model)) try: model.save(saved_model) return True except Exception as e: logger.info("model save error. Err.Msg: "+str(e)) return False else: file_name=model_name+'_'+self.problem_type+'_'+version+".sav" saved_model=os.path.normpath(os.path.join(model_location,file_name)) log(INFO, "flclient saved_model path: %s ",str(saved_model)) try: with open (saved_model,'wb') as f: pkl.dump(model,f) return True except Exception as e: logger.info("model save error. Err.Msg: "+str(e)) return False elif (self.model_overwrite.lower() == 'true'): version=str(self.version) if (model_name.lower() == "deeplearning"): file_name=model_name+'_'+self.problem_type+'_'+version+".h5" saved_model=os.path.normpath(os.path.join(model_location,file_name)) log(INFO, "flclient saved_model path: %s ",str(saved_model)) try: model.save(saved_model) return True except Exception as e: logger.info("model save error. Err.Msg: "+str(e)) return False else: file_name=model_name+'_'+self.problem_type+'_'+version+".sav" saved_model=os.path.normpath(os.path.join(model_location,file_name)) log(INFO, "flclient saved_model path: %s ",str(saved_model)) try: with open (saved_model,'wb') as f: pkl.dump(model,f) return True except Exception as e: logger.info("model save error. Err.Msg: "+str(e)) return False else: ##Write own user instruction pass def get_parameters(self, config): """Get parameters of the local model.""" return self.model.get_weights() def get_properties(self,model,time_out): """Return the current client properties.""" client_info={'client_id':self.client_id} time_out=100 return client_info,model,time_out def fit(self, parameters, config): """Train parameters on the locally held training set.""" # Update local model parameters self.model.set_weights(parameters) num_partitions=(self.num_rounds) # num_partitions=round(5) xtrain=np.array_split(self.X_train, num_partitions)[self.count] ytrain=np.array_split(self.y_train, num_partitions)[self.count] # y_train = np_utils.to_categorical(y_train, num_classes) # y_test = np_utils.to_categorical(y_test, num_classes) # Get hyperparameters for this round batch_size: int = int(self.model_params["batch_size"]) epochs: int = int(self.model_params["epochs"]) # round: int = config["rnd"] # self.round_id = round log(INFO, "===========================") log(INFO, "Start training model on local client %s round %i", self.client_id, config['rnd']) time.sleep(self.wait_time) self.count+=1 # Train the model using hyperparameters from config history = self.model.fit( xtrain, ytrain, batch_size, epochs, shuffle=False, # validation_split=0.1, validation_data=(self.X_test, self.y_test), verbose=1 ) # Return updated model parameters and results parameters_prime = self.model.get_weights() num_examples_train = len(self.X_train) model_name = self.model_name problem_type = self.problem_type if model_name == "deeplearning": if problem_type == "classification": acc = self.model.history.history['val_accuracy'] log(INFO, "Validated accuracy at the end of current round of client %s : %.2f %%", self.client_id, acc[-1]*100) log(INFO, "Finished training model on local client %s", self.client_id) results = { "loss": history.history["loss"][0], "accuracy": history.history["accuracy"][0], "val_loss": history.history["val_loss"][0], "val_accuracy": history.history["val_accuracy"][0], } if problem_type == "regression": mean_absolute_error = history.history['mean_absolute_error'][0] mean_squared_error = history.history['mean_squared_error'][0] y_pred = self.model.predict(self.X_test) from sklearn import metrics root_mean_squared_error = np.sqrt(metrics.mean_squared_error(self.y_test, y_pred)) log(INFO, "Mean Absolute Error at the end of current round of client %s : %f", self.client_id, mean_absolute_error) log(INFO, "Mean Squared Error at the end of current round of client %s : %f", self.client_id, mean_squared_error) log(INFO, "Root Mean Squared Error at the end of current round of client %s : %f", self.client_id, root_mean_squared_error) log(INFO, "Finished training model on local client %s", self.client_id) results = { "mean_absolute_error": mean_absolute_error, "mean_squared_error": mean_squared_error, "root_mean_squared_error": root_mean_squared_error, } return parameters_prime, num_examples_train, results def evaluate(self, parameters, config): """Evaluate parameters on the locally held test set.""" # Update local model with global parameters self.model.set_weights(parameters) num_partitions=(self.num_rounds) # Get config values # batch_size: int = config["val_batch_size"] batch_size: int = int(self.model_params["batch_size"]) steps: int = np.ceil(len(self.X_test)/batch_size) num_examples_test = len(self.X_test) log(INFO, "Run for only %i steps", steps) # Evaluate global model parameters on the local test data and return results model_name = self.model_name problem_type = self.problem_type self.model_save(self.model) if model_name == "deeplearning": if problem_type == "classification": loss, accuracy = self.model.evaluate(self.X_test, self.y_test,verbose=0) log(INFO, "Client %s : Accuracy %.2f %%", self.client_id, accuracy*100) log(INFO, "Client %s : Loss %.4f ", self.client_id, loss) return loss, num_examples_test, {"accuracy": accuracy} if problem_type == "regression": loss, mean_absolute_error, mean_squared_error = self.model.evaluate(self.X_test, self.y_test, steps=steps,verbose=1) y_pred = self.model.predict(self.X_test) root_mean_squared_error = np.sqrt(metrics.mean_squared_error(self.y_test, y_pred)) log(INFO, "Client %s : mean_absolute_error %f ", self.client_id, mean_absolute_error) log(INFO, "Client %s : mean_squared_error %f ", self.client_id, mean_squared_error) log(INFO, "Client %s : root_mean_squared_error %f ", self.client_id, root_mean_squared_error) return loss, num_examples_test, {"mean_absolute_error": mean_absolute_error, "mean_squared_error": mean_squared_error, "root_mean_squared_error": root_mean_squared_error} def randclientid(s,c): c=string.ascii_uppercase + string.digits return ''.join(random.choice(c) for x in range(s)) ## Loading input data def dataLoad(jsonfile): with open(jsonfile, 'r') as file: data = json.load(file) server_ip=str(data["server_IP"]) server_port=str(data["server_port"]) model_name=str(data["model_name"]) problem_type=str(data["problem_type"]) data_location=str(data["data_location"]) # deploy_location=str(data["deploy_location"]) model_params=data["model_hyperparams"] train_size=int(data["train_size"]) model_version=str(data["version"]) selected_feature=data["selected_feature"] if (type(selected_feature) is str): selected_feature=selected_feature.split(',') model_overwrite=data['model_overwrite'] target_feature=data["target_feature"] num_records=int(data['num_records_per_round']) wait_time=data['wait_time'] server_address=server_ip+':'+server_port # server_address=f"{server_ip}:{server_port}" return server_address,model_name,problem_type,data_location,model_params,model_version,selected_feature,target_feature,train_size,num_records,wait_time,model_overwrite # def getfilepath() """ Main aion federated learning client function call. """ if __name__ == "__main__": ##Client random id gen. rand_id=randclientid(9, "ABC1234567890") client_id='flclient-'+str(rand_id) try: json_file=sys.argv[1] except Exception as e: # sys.stdout.write("Please provide input configuration file. example: < python.exe 'fedclient\aionflc.py' 'fedclient\config.json' > ") log(INFO, "Please provide input configuration file. example: <python.exe 'fedclient\aionflc.py' 'fedclient\config.json'> \n") server_address,model_name,problem_type,data_location,model_params,model_version,selected_feature,target_feature,train_size,num_records,wait_time,model_overwrite = dataLoad(json_file) file_name=model_name+'_'+model_version+".log" cwd = os.path.abspath(os.path.dirname(__file__)) log_location = os.path.join(cwd, 'logs') try: os.makedirs(log_location) except FileExistsError as fe: # here,log_location already exists pass try: logobj = logging.getLogger('AION') fl_log=os.path.normpath(os.path.join(log_location,file_name)) log(INFO, "flclient log file path: %s ",str(fl_log)) logging.basicConfig(filename=fl_log,format='%(asctime)s %(message)s', filemode='w',level=logging.DEBUG) except Exception as e: log(INFO, "logging error. Error Msg: %s ",str(e)) pass ## default data location ~data\inputfile.csv data_location = os.path.normpath(os.path.join(cwd, data_location)) df = pd.read_csv(data_location) df =df[~df.isin([np.nan, np.inf, -np.inf]).any(axis=1)] df=df.reset_index(drop=True) y=df[target_feature] # X = df.drop(target_feature, axis=1) # # print("selected_feature: \n",selected_feature) X=df[selected_feature] input_shape = X.shape[1] # len(selected_feature) output_shape = len(y.value_counts()) test_size=(100-train_size)/100 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size) no_classes = len(df.groupby(target_feature).count()) no_features=len(selected_feature) ## Pass the train data. (X_train, y_train) = utils.partition(X_train, y_train, 1)[0] scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # y_train = pd.get_dummies(y_train) # y_test = pd.get_dummies(y_test) y_train_dl = pd.get_dummies(y_train, sparse=True) y_test_dl = pd.get_dummies(y_test, sparse=True) if (problem_type.lower() == "classification"): if (model_name.lower() == "logisticregression"): #n_classes = df[target_feature].nunique() no_classes = len(df.groupby(target_feature).count()) no_features=len(selected_feature) logger.info("no_classes: "+str(no_classes)) logger.info("no_features: "+str(no_features)) modelName="logisticregression" model = None model = LogisticRegression(**model_params, warm_start=True) try: status=utils.setmodelName(model_name) utils.set_initial_params(model,no_classes,no_features) except Exception as e: print("util error: \n",e) num_rounds=round(len(df)/num_records) log(INFO, "Federated learning Client connecting to Server @: %s ",str(server_address)) try: flower.client.start_numpy_client(server_address=server_address, client=aionflc(model,num_rounds,model_name,model_version,wait_time,client_id,num_records,model_overwrite,problem_type,X_train_scaled, X_test_scaled, y_train, y_test)) except Exception as e: logger.info("AION FL Client instance error: \n"+str(e)) log(INFO, "AION federated learning Client %s execution completed.",str(client_id)) elif (model_name.lower() == "deeplearning"): optimizer = model_params["optimizer"] loss_func = model_params["losses"] act_func = model_params["activation"] last_act_func = model_params["last_activation"] input_shape = X.shape[1] # len(selected_feature) output_shape = len(y.value_counts()) print(f"input_shape:{input_shape}, output_shape:{output_shape}.") model = None if output_shape == 2: if last_act_func == "sigmoid" and loss_func == "binary_crossentropy": model = dl_model.dl_binary_classification(input_shape, output_shape, optimizer, loss_func, act_func, last_act_func) elif last_act_func == "softmax" and loss_func == "categorical_crossentropy": model = dl_model.dl_binary_classification(input_shape, output_shape, optimizer, loss_func, act_func, last_act_func) else: model = dl_model.dl_multiClass_classification(input_shape, output_shape, optimizer, loss_func, act_func, last_act_func) print(model.summary()) # status=utils.setmodelName(modelName) # utils.set_initial_params(model,no_classes,no_features) num_rounds=round(len(df)/num_records) log(INFO, "Federated learning Client connecting to Server @: %s ",str(server_address)) try: flower.client.start_numpy_client(server_address=server_address, client=aionflc_dl(model,num_rounds,model_name,model_version,wait_time,client_id,num_records,model_overwrite,problem_type,X_train_scaled, X_test_scaled, y_train_dl, y_test_dl,model_params)) except Exception as e: logger.info("AION FL Client instance error: \n"+str(e)) log(INFO, "AION federated learning Client %s execution completed.",str(client_id)) logger.info("AION federated learning Client execution completed."+str(client_id)) elif(problem_type.lower() == "regression"): if (model_name.lower() == "linearregression"): # model=LinearRegression(**model_params,warm_start=True) if model_params['fit_intercept'] == 'True': model_params['fit_intercept'] = True else: model_params['fit_intercept'] = False if model_params['copy_X'] == 'True': model_params['copy_X'] = True else: model_params['copy_X'] = False if model_params['positive'] == 'True': model_params['positive'] = True else: model_params['positive'] = False model=LinearRegression(**model_params) status=utils.setmodelName(model_name) utils.set_initial_params_reg(model,X_train.shape[0],X_train.shape[1]) num_rounds=round(len(df)/num_records) log(INFO, "Federated learning Client connecting to Server @: %s ",str(server_address)) try: flower.client.start_numpy_client(server_address=server_address, client=aionflc(model,num_rounds,model_name,model_version,wait_time,client_id,num_records,model_overwrite,problem_type,X_train_scaled, X_test_scaled, y_train, y_test)) except Exception as e: logger.info("AION FL Client instance error: \n"+str(e)) log(INFO, "AION federated learning Client %s execution completed.",str(client_id)) elif(model_name.lower() == "deeplearning"): input_shape = X.shape[1] # len(selected_feature) output_shape = len(y.value_counts()) optimizer = model_params["optimizer"] loss_func = model_params["losses"] act_func = model_params["activation"] model = None model = dl_model.dl_regression_model(input_shape, 1, optimizer, loss_func, act_func) num_rounds=round(len(df)/num_records) log(INFO, "Federated learning Client connecting to Server @: %s ",str(server_address)) try: flower.client.start_numpy_client(server_address=server_address, client=aionflc_dl(model,num_rounds,model_name,model_version,wait_time,client_id,num_records,model_overwrite,problem_type,X_train_scaled, X_test_scaled, y_train, y_test,model_params)) except Exception as e: logger.info("AION FL Client instance error: \n"+str(e)) log(INFO, "AION federated learning Client %s execution completed.",str(client_id)) from typing import Tuple, Union, List import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LinearRegression from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import SGDClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from flwr.common.logger import log from logging import INFO XY = Tuple[np.ndarray, np.ndarray] Dataset = Tuple[XY, XY] LogRegParams = Union[XY, Tuple[np.ndarray]] XYList = List[XY] modelUsed=None modelname=None def setmodelName(modelselected): try: modelname=str(modelselected) print("setmodelName ,given modelname: \n",modelname) if (modelname.lower() == 'logisticregression'): modelUsed=LogisticRegression() return True elif (modelname.lower() == "linearregression"): modelUsed = LinearRegression() return True elif (modelname.lower() == "sgdclassifier"): #from sklearn.linear_model import SGDClassifier modelUsed=SGDClassifier() return True elif (modelname.lower() == "knn"): modelUsed = KNeighborsClassifier() return True elif (modelname.lower() == "decisiontreeclassifier"): modelUsed = DecisionTreeClassifier() return True else: return False except Exception as e: log(INFO, "set fl model name fn issue: ",e) def get_model_parameters(model:modelUsed) -> LogRegParams: """Returns the paramters of a sklearn LogisticRegression model.""" model_name=model.__class__.__name__ if model.fit_intercept: params = (model.coef_, model.intercept_) else: params = (model.coef_,) return params def set_model_params( model:modelUsed, params: LogRegParams ) -> modelUsed: """Sets the parameters of a sklean LogisticRegression model.""" model.coef_ = params[0] model_name=model.__class__.__name__ try: if model.fit_intercept: model.intercept_ = params[1] except Exception as e: log(INFO, "set_model_params fn issue: ",e) pass return model def set_initial_params_reg(model,no_vals,no_features): """Sets initial parameters as zeros Required since model params are uninitialized until model.fit is called. But server asks for initial parameters from clients at launch. Refer to sklearn.linear_model.LogisticRegression documentation for more information. """ no_vals = no_vals n_features = no_features # model.classes_ = np.array([i for i in range(n_classes)]) model.coef_ = np.zeros( n_features,) model_name=model.__class__.__name__ try: if model.fit_intercept: # model.intercept_ = np.ones((no_vals,1)) model.intercept_ = np.zeros((no_vals,)) except Exception as e: log(INFO, "set_initial_params fn issue: ",e) pass def set_initial_params(model,no_classes,no_features): """Sets initial parameters as zeros Required since model params are uninitialized until model.fit is called. But server asks for initial parameters from clients at launch. Refer to sklearn.linear_model.LogisticRegression documentation for more information. """ n_classes = no_classes n_features = no_features model.classes_ = np.array([i for i in range(n_classes)]) model.coef_ = np.zeros((n_classes, n_features)) model_name=model.__class__.__name__ try: if model.fit_intercept: model.intercept_ = np.zeros((n_classes,)) except Exception as e: log(INFO, "set_initial_params fn issue: ",e) pass def shuffle(X: np.ndarray, y: np.ndarray) -> XY: """Shuffle X and y.""" rng = np.random.default_rng() idx = rng.permutation(len(X)) return X[idx], y[idx] def partition(X: np.ndarray, y: np.ndarray, num_partitions: int) -> XYList: """Split X and y into a number of partitions.""" return list( zip(np.array_split(X, num_partitions), np.array_split(y, num_partitions)) ) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from hyperopt import fmin, tpe, hp, STATUS_OK, Trials import numpy as np import logging import sys import os class parametersDefine(): def __init__(self): self.paramDict = None self.log = logging.getLogger('eion') def getParamSpaceSize(self,paramDict): size=1 if(len(paramDict)==0): return 0 for keys in paramDict.keys(): size=size*len(paramDict[keys]) return size def paramDefine(self, paramSpace, method): paramDict = {} for j in list(paramSpace.keys()): inp = paramSpace[j] try: isLog = False isLin = False isRan = False isList = False isString = False try: # check if functions are given as input and reassign paramspace v = paramSpace[j] if 'logspace' in paramSpace[j]: paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "") isLog = True elif 'linspace' in paramSpace[j]: paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "") isLin = True elif 'range' in paramSpace[j]: paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "") isRan = True elif 'list' in paramSpace[j]: paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "") isList = True elif '[' and ']' in paramSpace[j]: paramSpace[j] = v.split('[')[1].split(']')[0].replace(" ", "") isList = True x = paramSpace[j].split(',') except Exception as e: if isinstance(paramSpace[j], (int, float)): paramSpace[j] = str(paramSpace[j]) x = [] x.append(paramSpace[j]) str_arg = paramSpace[j] # check if arguments are string try: test = eval(x[0]) except: isString = True if isString: paramDict.update({j: hp.choice(j, x)} if method == 'bayesopt' else {j: x}) else: res = eval(str_arg) if isLin: y = eval('np.linspace' + str(res)) paramDict.update({j: hp.uniform(j, eval(x[0]), eval(x[1]))} if method == 'bayesopt' else {j: y}) elif isLog: y = eval('np.logspace' + str(res)) paramDict.update( {j: hp.uniform(j, 10 ** eval(x[0]), 10 ** eval(x[1]))} if method == 'bayesopt' else {j: y}) elif isRan: y = eval('np.arange' + str(res)) paramDict.update({j: hp.choice(j, y)} if method == 'bayesopt' else {j: y}) # check datatype of argument elif isinstance(eval(x[0]), bool): y = list(map(lambda i: eval(i), x)) paramDict.update({j: hp.choice(j, eval(str(y)))} if method == 'bayesopt' else {j: y}) elif isinstance(eval(x[0]), float): res = eval(str_arg) if len(str_arg.split(',')) == 3 and not isList: y = eval('np.linspace' + str(res)) #print(y) paramDict.update({j: hp.uniform(j, eval(x[0]), eval(x[1]))} if method == 'bayesopt' else {j: y}) else: y = list(res) if isinstance(res, tuple) else [res] paramDict.update({j: hp.choice(j, y)} if method == 'bayesopt' else {j: y}) else: res = eval(str_arg) if len(str_arg.split(',')) == 3 and not isList: y = eval('np.linspace' +str(res)) if eval(x[2]) >= eval(x[1]) else eval('np.arange'+str(res)) else: y = list(res) if isinstance(res, tuple) else [res] paramDict.update({j: hp.choice(j, y)} if method == 'bayesopt' else {j: y}) except Exception as inst: self.log.info('\n-----> Parameter parsing failed!!!.' + str(inst)) self.log.info("The entered parameter is invalid: {"+ j +':'+ inp+'}') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) raise return paramDict ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import warnings warnings.filterwarnings('ignore') import logging import sklearn from sklearn.neighbors import NearestNeighbors from sklearn.cluster import KMeans from sklearn.cluster import DBSCAN from random import sample from numpy.random import uniform import numpy as np import math import pickle import os from math import isnan from sklearn.preprocessing import binarize from sklearn.preprocessing import LabelEncoder from sklearn.metrics import davies_bouldin_score from utils.file_ops import save_csv_compressed from sklearn.metrics import silhouette_score try: from sklearn.metrics import calinski_harabasz_score as calinski_harabaz_score except: from sklearn.metrics import calinski_harabaz_score import pandas as pd from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report from sklearn.metrics import roc_curve, auc from sklearn.metrics import roc_auc_score from sklearn.metrics import matthews_corrcoef from sklearn.metrics import brier_score_loss from sklearn.preprocessing import LabelBinarizer from sklearn.model_selection import train_test_split from sklearn.decomposition import LatentDirichletAllocation from learner.classificationModel import ClassifierModel from learner.regressionModel import RegressionModel from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error,make_scorer from sklearn.metrics import mean_squared_error from sklearn.metrics import RocCurveDisplay, auc, roc_curve import matplotlib.pyplot as plt #print("1") #from alibi.explainers import ALE,plot_ale #pd.set_option('display.max_columns', 10) #pd.set_option('display.width', None) def get_prediction( model, loaded_model, xtrain, xtest=None): train_prob = None test_prob = None predictedData = [] if xtest.empty: is_xtest = False else: is_xtest = True if model.lower() == 'lda': if is_xtest: predictedData = loaded_model.transform(xtest).argmax(axis=1) trainPredictedData = loaded_model.transform(xtrain) elif model.lower() == 'dbscan': if is_xtest: predictedData = loaded_model.fit_predict(xtest) predictedData = loaded_model.labels_ trainPredictedData = loaded_model.fit_predict(xtrain) trainPredictedData = loaded_model.labels_ elif model == 'Neural Architecture Search': train_prob = estimator.predict(xtrain) if train_prob.shape[1] == 1: train_prob = np.hstack(( 1-train_prob, train_prob)) trainPredictedData = np.argmax(train_prob, axis=1) if is_xtest: test_prob = estimator.predict(xtest) if test_prob.shape[1] == 1: test_prob = np.hstack(( 1-test_prob, test_prob)) predictedData = np.argmax(test_prob, axis=1) elif model in ['Deep Q Network','Dueling Deep Q Network']: from tf_agents.trajectories import time_step from tensorflow import constant q, _ = loaded_model(np.array(xtrain), step_type=constant([time_step.StepType.FIRST] * np.array(xtrain).shape[0]), training=False) train_prob = q.numpy() if train_prob.shape[1] == 1: train_prob = np.hstack(( 1-train_prob, train_prob)) trainPredictedData = np.argmax(train_prob, axis=1) predictedData = np.argmax(test_prob, axis=1) if is_xtest: q,_ = loaded_model(np.array(xtest), step_type=constant([time_step.StepType.FIRST] * np.array(xtest).shape[0]), training=False) test_prob = q.numpy() if test_prob.shape[1] == 1: test_prob = np.hstack(( 1-test_prob, test_prob)) predictedData = np.argmax(test_prob, axis=1) else: if is_xtest: predictedData = loaded_model.predict(xtest) trainPredictedData = loaded_model.predict(xtrain) if hasattr(loaded_model, 'predict_proba'): train_prob = loaded_model.predict_proba(xtrain) if is_xtest: test_prob = loaded_model.predict_proba(xtest) return trainPredictedData, predictedData, train_prob, test_prob class machinelearning(object): def __init__(self): self.features=[] self.log = logging.getLogger('eion') self.plots = [] def cluster_tendency(self,featureData): self.log.info("\n------------- Cluster Tendency Check -------------") d = featureData.shape[1] n = len(featureData) m = int(0.1 * n) nbrs = NearestNeighbors(n_neighbors=1).fit(featureData.values) rand_X = sample(range(0, n, 1), m) ujd = [] wjd = [] for j in range(0, m): u_dist, _ = nbrs.kneighbors(uniform(np.amin(featureData,axis=0),np.amax(featureData,axis=0),d).reshape(1, -1), 2, return_distance=True) ujd.append(u_dist[0][1]) if isinstance(featureData.iloc[rand_X[j]].values, pd.core.arrays.sparse.array.SparseArray): featureData_reshaped = np.asarray(featureData.iloc[rand_X[j]].values).reshape(1, -1) else: featureData_reshaped = featureData.iloc[rand_X[j]].values.reshape(1, -1) w_dist, _ = nbrs.kneighbors(featureData_reshaped, 2, return_distance=True) wjd.append(w_dist[0][1]) try: clusetTendency = sum(ujd) / (sum(ujd) + sum(wjd)) except: clusetTendency = 0 if isnan(clusetTendency): clusetTendency = 0 self.log.info("-------> Cluster Tendency value using Hopkins Statistic: "+str(clusetTendency)) self.log.info("------------- Cluster Tendency Check End-------------\n") return (clusetTendency) def calculateNumberofCluster(self,featureData): self.log.info("\n------------- Calculate Number of Cluster -------------") Sum_of_squared_distances = [] K = range(1,15) for k in K: km = KMeans(n_clusters=k) km = km.fit(featureData) Sum_of_squared_distances.append(km.inertia_) x1, y1 = 1, Sum_of_squared_distances[0] x2, y2 = 15, Sum_of_squared_distances[len(Sum_of_squared_distances)-1] distances = [] for inertia in range(len(Sum_of_squared_distances)): x0 = inertia+2 y0 = Sum_of_squared_distances[inertia] numerator = abs((y2-y1)*x0 - (x2-x1)*y0 + x2*y1 - y2*x1) denominator = math.sqrt((y2 - y1)**2 + (x2 - x1)**2) distances.append(numerator/denominator) n_clusters=distances.index(max(distances)) + 2 self.log.info("-------> n_clusters: "+str(n_clusters-1)) self.log.info("------------- Calculate Number of Cluster End-------------\n") return(n_clusters-1) def getclusterMatrix(self,featureData,targetData): silhouetteAvg = silhouette_score(featureData,targetData) self.log.info("-------> SilHouette_Avg: "+str(silhouetteAvg)) daviesBouldinScore=davies_bouldin_score(featureData, targetData) self.log.info("-------> DaviesBouldinScore: "+str(daviesBouldinScore)) calinskiHarabazScore=calinski_harabaz_score(featureData,targetData) self.log.info("-------> CalinskiHarabazScore: "+str(calinskiHarabazScore)) matrix = '"SilHouette_Avg":'+str(silhouetteAvg)+',"DaviesBouldinScore":'+str(daviesBouldinScore)+',"CalinskiHarabazScore":'+str(calinskiHarabazScore) return(matrix) def get_regression_matrix(self,targetData,predictedData): try: r2score=r2_score(targetData, predictedData) self.log.info('-------> R2_score :'+str(r2score)) except Exception as e: self.log.info('\n--------- r2_score ',str(e)) r2score = 0 try: meanabsoluteerror=(mean_absolute_error(targetData, predictedData)) self.log.info('-------> MAE :'+str(meanabsoluteerror)) except Exception as e: self.log.info('\n---------Error: meanabsoluteerror ',str(e)) meanabsoluteerror = 0 try: meanssquatederror=mean_squared_error(targetData, predictedData) self.log.info('-------> MSE :'+str(meanssquatederror)) except Exception as e: self.log.info('\n---------Error: meanssquatederror ',str(e)) meanssquatederror = 0 try: rootmeanssquatederror=mean_squared_error(targetData, predictedData,squared=False) self.log.info('-------> RMSE :'+str(rootmeanssquatederror)) except Exception as e: self.log.info('\n---------Error: rootmeanssquatederror ',str(e)) rootmeanssquatederror = 0 try: normalised_rmse_percentage = (rootmeanssquatederror/ ( np.max(targetData) - np.min(targetData) )) * 100 self.log.info('-------> Normalised RMSE percentage :'+str(normalised_rmse_percentage)) except Exception as e: self.log.info('\n---------Error: Normalised RMSE percentage ',str(e)) normalised_rmse_percentage = -1 try: targetArray, predictedArray = np.array(targetData), np.array(predictedData) try: EPSILON = 1e-10 meanpercentageerror=np.mean(np.abs((targetArray - predictedArray) / (targetArray+EPSILON)))*100 except ZeroDivisionError: meanpercentageerror = 0 self.log.info('-------> MAPE :'+str(meanpercentageerror)) except Exception as e: self.log.info('\n---------Error: meanpercentageerror ',str(e)) meanpercentageerror = 0 matrix = '"MAE":'+str(round(meanabsoluteerror,2))+',"R2Score":'+str(round(r2score,2))+',"MSE":'+str(round(meanssquatederror,2))+',"MAPE":'+str(round(meanpercentageerror,2))+',"RMSE":'+str(round(rootmeanssquatederror,2))+',"Normalised RMSE(%)":'+str(round(normalised_rmse_percentage,2)) return matrix def getClassificationPerformaceMatrix(self,le_trainY,predictedData,prob,labelMaps): setOfyTrue = set(le_trainY) unqClassLst = list(setOfyTrue) if len(unqClassLst) <= 20: if str(labelMaps) != '{}': inv_mapping_dict = {v: k for k, v in labelMaps.items()} unqClassLst2 = (pd.Series(unqClassLst)).map(inv_mapping_dict) unqClassLst2 = list(unqClassLst2) else: unqClassLst2 = unqClassLst indexName = [] columnName = [] targetnames=[] for item in unqClassLst2: indexName.append("act:"+str(item)) columnName.append("pre:"+str(item)) targetnames.append(str(item)) matrixconfusion = pd.DataFrame(confusion_matrix(le_trainY,predictedData, labels = unqClassLst),index = indexName, columns = columnName) pd.set_option('display.max_columns',len(targetnames)+2) self.log.info('-------> Confusion Matrix: ') self.log.info(matrixconfusion) pd.reset_option('display.max_columns') classificationreport = pd.DataFrame(classification_report(le_trainY, predictedData, labels = unqClassLst,target_names=targetnames,output_dict=True)).transpose() self.log.info('-------> Classification Report: ') self.log.info(classificationreport) matrixconfusion = matrixconfusion.to_json(orient='index') classificationreport = classificationreport.to_json(orient='index') else: #bugid: 14540 self.log.info('-------> As the number of class is more than 20, skipping the creation of confusion_matrix and classification Report') return "" lb = LabelBinarizer() lb.fit(le_trainY) transformTarget= lb.transform(le_trainY) if transformTarget.shape[-1] == 1: transformTarget = le_trainY prob = np.delete( prob, 0, 1) rocaucscore = roc_auc_score(transformTarget,prob,average="macro") brier_score = None mcc_score = matthews_corrcoef(le_trainY,predictedData) if len(unqClassLst) > 2: brier_score = np.mean(np.sum(np.square(prob - transformTarget), axis=1)) else: brier_score = brier_score_loss(transformTarget,prob) self.log.info('-------> ROC AUC SCORE :'+str(rocaucscore)) self.log.info(f'-------> Matthews correlation coefficient SCORE : {mcc_score}') self.log.info(f'-------> BRIER SCORE : {brier_score}') matrix = f'"ConfusionMatrix": {matrixconfusion},"ClassificationReport": {classificationreport},"ROC_AUC_SCORE": {rocaucscore},"MCC_SCORE": {mcc_score},"BRIER_SCORE": {brier_score}' return(matrix) def split_into_train_test_data(self,featureData,targetData,testPercentage,modelType='classification'): ''' if cvSplit == None: ''' self.log.info('\n-------------- Test Train Split ----------------') if testPercentage == 0: xtrain=featureData ytrain=targetData xtest=featureData ytest=targetData else: testSize=testPercentage/100 if modelType == 'regression': self.log.info('-------> Split Type: Random Split') xtrain,xtest,ytrain,ytest=train_test_split(featureData,targetData,test_size=testSize,shuffle=True) else: try: self.log.info('-------> Split Type: Stratify Split') xtrain,xtest,ytrain,ytest=train_test_split(featureData,targetData,stratify=targetData,test_size=testSize,shuffle=True) except: self.log.info('-------> Split Type: Random Split') xtrain,xtest,ytrain,ytest=train_test_split(featureData,targetData,test_size=testSize,shuffle=True) self.log.info('Status:- !... Train / test split done: '+str(100-testPercentage)+'% train,'+str(testPercentage)+'% test') self.log.info('-------> Train Data Shape: '+str(xtrain.shape)+' ---------->') self.log.info('-------> Test Data Shape: '+str(xtest.shape)+' ---------->') self.log.info('-------------- Test Train Split End ----------------\n') ''' else: xtrain=featureData ytrain=targetData xtest=featureData ytest=targetData ''' return(xtrain,ytrain,xtest,ytest) def checkForClassBalancing(self,targetData): imbalancedCount=0 valueCount=targetData.value_counts() self.log.info("---------- Checking for Class Imbalance on Train Data---------") self.log.info("-------> Categories and Count:") self.log.info(valueCount) categoryList=valueCount.keys().tolist() categoryCountList=valueCount.tolist() for i in range(0,len(categoryCountList)): if float(categoryCountList[i])<=float(0.5*max(categoryCountList)): self.log.info("-------> Found Imbalanced class: '"+str(categoryList[i])+"' Count: "+str(categoryCountList[i])) imbalancedCount=imbalancedCount+1 if imbalancedCount == 0: self.log.info("-------> Status: Balanced") self.log.info('Status:- |... Check for Data balancing done: Balanced') else: self.log.info("-------> Status: Unbalanced") self.log.info('Status:- |... Check for Data balancing done: Unbalanced') self.log.info("---------- Checking for Class Imbalance on Train Data End---------") return(imbalancedCount) def ExecuteClassBalancing(self,featureData,targetData,balancingMethod): from imblearn.over_sampling import SMOTE from imblearn.under_sampling import TomekLinks from collections import Counter self.log.info('\n------------ Balancing Start --------------') if balancingMethod.lower() == "oversample": self.log.info("-------> Method: SMOTE OverSampling Technique") k=1 seed=100 try: oversample = SMOTE(sampling_strategy='auto', k_neighbors=k, random_state=seed) balfeatureData, baltargetData = oversample.fit_resample(featureData, targetData) self.log.info(baltargetData.value_counts()) except Exception as inst: self.log.info("\n!!!!!!!!! OverSampling Fails "+str(inst)+" !!!!!!!!!!!!!!\n") balfeatureData = featureData baltargetData = targetData elif balancingMethod.lower() == "undersample": self.log.info("-------> Method: Tomelinks UnderSampling Technique") tLinks = TomekLinks() balfeatureData, baltargetData= tLinks.fit_resample(featureData, targetData) #Added for checking balancing act by the algorithm. counter = Counter(baltargetData) self.log.info("Class counter:\t"+str(baltargetData.value_counts())) max_class = max(counter,key=counter.get) max_value = max(counter.values()) self.log.info("Max samples: "+str(max_value)+ " in the class: "+str(max_class)) for k,v in counter.items(): if v < (max_value*98/100): self.log.info("Undersampling is not able to do perfect data balancing.") self.log.info("The method is used to identify the desired samples of data from the majority class that is having the lowest Euclidean distance with the minority class data. Downsampling may not balance the class after applying this method.\n") self.log.info(baltargetData.value_counts()) else: balfeatureData = featureData baltargetData = targetData self.log.info("-------> Method: Balancing Not Applied") self.log.info('-------> Memory Usage by Training DataFrame After Class Balancing '+str(featureData.memory_usage(deep=True).sum())) self.log.info('Status:- |... Data balancing done: '+str(balancingMethod)) self.log.info('------------ Balancing End --------------\n') return(balfeatureData,baltargetData) def combine_text_features(self,dataFrame,dataColumns): column_merge_flag = False merge_columns = [] if(len(dataColumns) > 1): dataFrame['combined'] = dataFrame[dataColumns].apply(lambda row: ' '.join(row.values.astype(str)), axis=1) merge_columns = dataColumns features = ['combined'] column_merge_flag = True self.log.info("After Text Concatenation") self.log.info(dataFrame['combined'].head(10)) self.log.info("List of Combined Columns ---> "+ str(dataColumns) +"\n") else: features = dataColumns return(dataFrame,features,column_merge_flag,merge_columns) ''' def create_preprocessing_pipeline(self,X): textDataProfilerObj=textDataProfiler() tfidfVector = TfidfVectorizer(tokenizer = textDataProfilerObj.textTokenizer) pipe = Pipeline([("cleaner", TextCleaner()),('vectorizer', tfidfVector)]) vectors=pipe.fit(X) transformedVector=pipe.transform(X) return(pipe,transformedVector) ''' def get_topics(self, model, feature_names, no_top_words): topicDict = {} for topic_idx, topic in enumerate(model.components_): wordDict = {} topicProb = [(feature_names[i],topic[i]/topic.sum()) for i in topic.argsort()[:-no_top_words - 1:-1]] for word, prob in topicProb: if word.endswith('_vect'): word = word[:-len('_vect')] wordDict[word] = prob topicDict[ topic_idx] = wordDict return topicDict def transform_target_feature(self,dataFrame,targetColumn): targetDataType=dataFrame[targetColumn].dtypes pandasNumericDtypes=['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] labelMapping= {} if targetDataType not in pandasNumericDtypes: le = LabelEncoder() le.fit(dataFrame[targetColumn]) le_trainY = le.transform(dataFrame[targetColumn]) labelMapping = dict(zip(le.classes_, le.transform(le.classes_))) self.log.info(" \n encoded Values of predicator column ===>"+str(labelMapping)) else: le_trainY = dataFrame[targetColumn] return le_trainY,labelMapping def setScoreParams(self,scoreParam,modelType,categoryCountList): if modelType == 'classification' or modelType == 'TextClassification': allowedmatrix = ['accuracy','recall','f1_score','precision','roc_auc'] if(scoreParam.lower() not in allowedmatrix): scoreParam = 'accuracy' elif scoreParam.lower() == 'none': scoreParam = 'accuracy' elif scoreParam.lower() == "recall": if len(categoryCountList) > 2: scoreParam = make_scorer(sklearn.metrics.recall_score, average = 'weighted') else: scoreParam = make_scorer(sklearn.metrics.recall_score) elif scoreParam.lower() == "precision" : if len(categoryCountList) > 2: scoreParam = make_scorer(sklearn.metrics.precision_score, average = 'weighted') else: scoreParam = make_scorer(sklearn.metrics.precision_score) elif scoreParam.lower() == "f1_score" : if len(categoryCountList) > 2: scoreParam = make_scorer(sklearn.metrics.f1_score, average = 'weighted') else: scoreParam = make_scorer(sklearn.metrics.f1_score) elif scoreParam.lower() == "roc_auc" : if len(categoryCountList) > 2: scoreParam = make_scorer(sklearn.metrics.roc_auc_score,needs_proba=True,multi_class='ovr',average='weighted') else: scoreParam = make_scorer(sklearn.metrics.roc_auc_score) else: scoreParam = scoreParam else: allowedmatrix = ['mse','r2','rmse','mae'] if(scoreParam.lower() not in allowedmatrix): scoreParam = 'neg_mean_squared_error' elif scoreParam.lower() == 'none': scoreParam = 'neg_mean_squared_error' elif scoreParam.lower() == 'mse': scoreParam = 'neg_mean_squared_error' elif scoreParam.lower() == 'rmse': #scoreParam = make_scorer(sklearn.metrics.mean_squared_error, squared = False) scoreParam='neg_root_mean_squared_error' elif scoreParam.lower() == 'mae': scoreParam = 'neg_mean_absolute_error' elif scoreParam.lower() == 'r2': scoreParam = 'r2' else: scoreParam = scoreParam #self.log.info('Status:- !... Scoring parameters selected') self.log.info("-------> Scoring parameter: "+str(scoreParam)) return(scoreParam) def getbestfeatureModel(self,modelType,scoreParam,score1,score2,model1,model2,threshold1,pscore1,rscore1,threshold2,pscore2,rscore2,featuresset1,featureset2): best_feature_model = featuresset1 self.log.info('\n ---------- ML Summary ------------') if modelType.lower() == "classification": if(threshold1 == -1 and threshold2 == -1): if score1> score2: self.log.info('-------> Best Features: '+str(featuresset1)) self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featuresset1 else: self.log.info('-------> Best Features:'+str(featureset2)) self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featureset2 elif(threshold1 == -1): self.log.info('-------> Best Features: '+str(featureset2)) self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featureset2 elif(threshold1 == -2): self.log.info('-------> Best Features: '+str(featuresset1)) self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model =featuresset1 else: if pscore1 == pscore2: if rscore1 > rscore2: self.log.info('-------> Best Features: '+str(featuresset1)) self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featuresset1 else: self.log.info('-------> Best Features: '+str(featureset2)) self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featureset2 elif rscore1 == rscore2: if pscore1 > pscore2: self.log.info('-------> Best Features: '+str(featuresset1)) self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featuresset1 else: self.log.info('-------> Best Features: '+str(featureset2)) self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featureset2 elif modelType.lower() == "regression": if scoreParam == "r2" or scoreParam == "explained_variance": if score1> score2 : self.log.info('-------> Best Features: '+str(featuresset1)) self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featuresset1 else: self.log.info('-------> Best Features: '+str(featureset2)) self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featureset2 else: if score1< score2 : self.log.info('-------> Best Features: '+str(featuresset1)) self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featuresset1 else: self.log.info('-------> Best Features: '+str(featureset2)) self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = featureset2 self.log.info('---------- ML Summary End ------------\n') return(best_feature_model) def startLearning(self,mlconfig,modelType,modelParams,modelList,scoreParam,targetColumn,dataFrame,xtrain,ytrain,xtest,ytest,categoryCountList,topFeatures,modelFeatures,allFeatures,targetType,deployLocation,iterName,iterVersion,trained_data_file,predicted_data_file,labelMaps,featuresBasedOn,code_configure,featureEngineeringSelector,modelEvaluationConfig,imageFolderLocation): model = 'None' params = 'None' score = 0xFFFF estimator = None model_tried = '' threshold = -1 pscore = -1 rscore = -1 topics = {} if(targetColumn != ''): targetData = dataFrame[targetColumn] datacolumns=list(dataFrame.columns) if targetColumn in datacolumns: datacolumns.remove(targetColumn) if(modelType != 'clustering') and (modelType != 'TopicModelling'): scoreParam = self.setScoreParams(scoreParam,modelType,categoryCountList) if len(topFeatures) > 0: self.log.info('\n-------------- Training ML: Top/StatisticalBased Features Start --------------') modelbasedon = 'StatisticalBased' if featureEngineeringSelector.lower() == 'true': self.log.info('Status:- |... Algorithm analysis based on feature engineering based feature selection started') modelbasedon = 'DimensionalityReduction' else: self.log.info('Status:- |... Algorithm analysis based on statistical based feature selection started') model_type1,model1,params1, score1, estimator1,model_tried1,xtrain1,ytrain1,xtest1,ytest1,threshold1,pscore1,rscore1,method,topics=self.startLearnerModule(mlconfig,modelType,modelParams,modelList,scoreParam,targetColumn,dataFrame,xtrain,ytrain,xtest,ytest,categoryCountList,topFeatures,targetType,deployLocation,iterName,iterVersion,trained_data_file,labelMaps,featuresBasedOn, modelbasedon,code_configure,modelEvaluationConfig) if model_tried != '': model_tried += ',' model_tried += model_tried1 topFeaturesStatus = True if featureEngineeringSelector.lower() == 'true': self.log.info('Status:- |... Algorithm analysis based on feature engineering based feature selection completed') else: self.log.info('Status:- |... Algorithm analysis for statistical based feature completed') self.log.info('-------------- Training ML: Top/StatisticalBased Features End --------------\n') else: topFeaturesStatus = False if len(modelFeatures) > 0: self.log.info('\n-------------- Training ML: Models Based Selected Features Start --------------') self.log.info('Status:- |... Algorithm analysis based on model based feature selection started') model_type2,model2,params2, score2, estimator2,model_tried2,xtrain2,ytrain2,xtest2,ytest2,threshold2,pscore2,rscore2,method,topics=self.startLearnerModule(mlconfig,modelType,modelParams,modelList,scoreParam,targetColumn,dataFrame,xtrain,ytrain,xtest,ytest,categoryCountList,modelFeatures,targetType,deployLocation,iterName,iterVersion,trained_data_file,labelMaps,featuresBasedOn, "ModelBased",code_configure,modelEvaluationConfig) #model_tried2['Features'] = 'ModelBased' if model_tried != '': model_tried += ',' model_tried += model_tried2 modelFeaturesStatus = True self.log.info('Status:- |... Algorithm analysis for model based selected features completed') self.log.info('-------------- Training ML: Models Based Selected Features End --------------\n') else: modelFeaturesStatus = False if len(allFeatures) > 0: self.log.info('Status:- |... Algorithm analysis based on all features Start') model_type3,model3,params3, score3, estimator3,model_tried3,xtrain3,ytrain3,xtest3,ytest3,threshold3,pscore3,rscore3,method,topics=self.startLearnerModule(mlconfig,modelType,modelParams,modelList,scoreParam,targetColumn,dataFrame,xtrain,ytrain,xtest,ytest,categoryCountList,allFeatures,targetType,deployLocation,iterName,iterVersion,trained_data_file,labelMaps,featuresBasedOn, "AllFeatures",code_configure,modelEvaluationConfig) #model_tried3['Features'] = 'AllFeatures' allFeaturesStatus = True if model_tried != '': model_tried += ',' model_tried += model_tried3 self.log.info('Status:- |... Algorithm analysis based all features completed') else: allFeaturesStatus = False #print(topFeaturesStatus,modelFeaturesStatus,allFeaturesStatus) if topFeaturesStatus: if modelFeaturesStatus: best_feature_model = self.getbestfeatureModel(modelType,scoreParam,score1,score2,model1,model2,threshold1,pscore1,rscore1,threshold2,pscore2,rscore2,'StatisticalBased','ModelBased') if best_feature_model == 'StatisticalBased' and allFeaturesStatus: best_feature_model = self.getbestfeatureModel(modelType,scoreParam,score1,score3,model1,model3,threshold1,pscore1,rscore1,threshold3,pscore3,rscore3,'StatisticalBased','AllFeatures') if best_feature_model == 'ModelBased' and allFeaturesStatus: best_feature_model = self.getbestfeatureModel(modelType,scoreParam,score2,score3,model2,model3,threshold2,pscore2,rscore2,threshold3,pscore3,rscore3,'ModelBased','AllFeatures') elif allFeaturesStatus: best_feature_model = self.getbestfeatureModel(modelType,scoreParam,score1,score3,model1,model3,threshold1,pscore1,rscore1,threshold3,pscore3,rscore3,'StatisticalBased','AllFeatures') else: best_feature_model = 'StatisticalBased' if featureEngineeringSelector.lower() == 'true': best_feature_model = 'DimensionalityReduction' else: if modelFeaturesStatus and allFeaturesStatus: best_feature_model = self.getbestfeatureModel(modelType,scoreParam,score2,score3,model2,model3,threshold2,pscore2,rscore2,threshold3,pscore3,rscore3,'ModelBased','AllFeatures') elif modelFeaturesStatus: best_feature_model = 'ModelBased' elif allFeaturesStatus: best_feature_model = 'AllFeatures' if (best_feature_model == 'StatisticalBased' or best_feature_model == 'DimensionalityReduction'): model_type = model_type1 model = model1 params = params1 score = score1 estimator = estimator1 #model_tried = model_tried1 xtrain = xtrain1 ytrain = ytrain1 xtest = xtest1 ytest = ytest1 features = topFeatures threshold = threshold1 pscore = pscore1 rscore = rscore1 elif (best_feature_model == 'AllFeatures'): model_type = model_type3 model = model3 params = params3 score = score3 estimator = estimator3 #model_tried = model_tried3 xtrain = xtrain3 ytrain = ytrain3 xtest = xtest3 ytest = ytest3 features = allFeatures threshold = threshold3 pscore = pscore3 rscore = rscore3 else: model_type = model_type2 model = model2 params = params2 score = score2 estimator = estimator2 #model_tried = model_tried2 xtrain = xtrain2 ytrain = ytrain2 xtest = xtest2 ytest = ytest2 threshold = threshold2 pscore = pscore2 rscore = rscore2 features = modelFeatures if score != 'NA': self.log.info('Status:- |... Final Best Algorithm selected: '+model+' having score='+str(round(score,2))+' based on '+best_feature_model+' feature selection') filename = os.path.join(deployLocation,'model',iterName+'_'+iterVersion+'.sav') saved_model = iterName+'_'+iterVersion+'.sav' if model == 'Neural Architecture Search': loaded_model = estimator try: estimator.save(filename, save_format="tf") except Exception: filename = os.path.join(deployLocation,'model','autoKerasModel.h5') estimator.save(filename) saved_model = 'autoKerasModel.h5' else: pickle.dump(estimator, open(filename, 'wb')) loaded_model = pickle.load(open(filename, 'rb')) if not xtest.empty: df_test = xtest.copy() else: df_test = xtrain.copy() if threshold == -1: if model.lower() == 'lda': predictedData = loaded_model.transform(xtest).argmax(axis=1) trainPredictedData = loaded_model.transform(xtrain) elif model.lower() == 'dbscan': predictedData = loaded_model.fit_predict(xtest) predictedData = loaded_model.labels_ trainPredictedData = loaded_model.fit_predict(xtrain) trainPredictedData = loaded_model.labels_ elif model == 'Neural Architecture Search': test_prob = estimator.predict(xtest) train_prob = estimator.predict(xtrain) if train_prob.shape[1] == 1: train_prob = np.hstack(( 1-train_prob, train_prob)) test_prob = np.hstack(( 1-test_prob, test_prob)) predictedData = np.argmax(test_prob, axis=1) trainPredictedData = np.argmax(train_prob, axis=1) elif model in ['Deep Q Network','Dueling Deep Q Network']: from tf_agents.trajectories import time_step from tensorflow import constant from sklearn.preprocessing import MinMaxScaler q, _ = loaded_model(np.array(xtest), step_type=constant([time_step.StepType.FIRST] * np.array(xtest).shape[0]), training=False) test_prob = MinMaxScaler().fit_transform( q.numpy()) q, _ = loaded_model(np.array(xtrain), step_type=constant([time_step.StepType.FIRST] * np.array(xtrain).shape[0]), training=False) train_prob = MinMaxScaler().fit_transform( q.numpy()) predictedData = np.argmax(test_prob, axis=1) trainPredictedData = np.argmax(train_prob, axis=1) elif modelType == 'clustering': if not xtest.empty: predictedData = loaded_model.predict(xtest) trainPredictedData = loaded_model.predict(xtrain) else: if not xtest.empty: predictedData = loaded_model.predict(xtest) trainPredictedData = loaded_model.predict(xtrain) if hasattr(loaded_model, 'predict_proba'): train_prob = loaded_model.predict_proba(xtrain) if not xtest.empty: test_prob = loaded_model.predict_proba(xtest) else: self.log.info("-------> Threshold :"+str(threshold)) if not xtest.empty: #bug 12437 if 'predict_proba' in dir(loaded_model): test_prob = loaded_model.predict_proba(xtest) predictedData = binarize(test_prob[:,1].reshape(-1, 1),threshold=threshold) else: raise Exception('--------- Loaded model does not support predict_proba ---------\n') train_prob = loaded_model.predict_proba(xtrain) trainPredictedData = binarize(train_prob[:,1].reshape(-1, 1),threshold=threshold) matrix = '' try: if(model_type == 'Classification'): self.log.info('\n--------- Performance Matrix with Train Data ---------') train_matrix = self.getClassificationPerformaceMatrix(ytrain,trainPredictedData,train_prob,labelMaps) self.log.info('--------- Performance Matrix with Train Data End ---------\n') if not xtest.empty: self.log.info('\n--------- Performance Matrix with Test Data ---------') performancematrix = self.getClassificationPerformaceMatrix(ytest,predictedData,test_prob,labelMaps) df_test['actual'] = ytest df_test['predict'] = predictedData self.log.info('--------- Performance Matrix with Test Data End ---------\n') matrix = performancematrix if hasattr( loaded_model, 'predict_proba'): predictedData_fit = loaded_model.predict_proba(xtest) elif model == 'Neural Architecture Search': predictedData_fit = estimator.predict(xtest) elif model in ['Deep Q Network','Dueling Deep Q Network']: from tf_agents.trajectories import time_step from tensorflow import constant q, _ = loaded_model(np.array(xtest), step_type=constant([time_step.StepType.FIRST] * np.array(xtest).shape[0]), training=False) predictedData_fit = q.numpy() else: predictedData_fit = loaded_model.predict(xtest) if predictedData_fit.shape[1] == 1: predictedData_fit = np.hstack((1 - predictedData_fit, predictedData_fit)) self.auc_roccurve(ytest,predictedData_fit,labelMaps,imageFolderLocation) else: df_test['actual'] = ytrain df_test['predict'] = trainPredictedData elif(model_type == 'Regression'): self.log.info('\n--------- Performance Matrix with Train Data ---------') train_matrix = self.get_regression_matrix(ytrain, trainPredictedData) self.log.info('--------- Performance Matrix with Train Data End ---------\n') if not xtest.empty: self.log.info('\n--------- Performance Matrix with Test Data ---------') matrix = self.get_regression_matrix(ytest, predictedData) df_test['actual'] = ytest df_test['predict'] = predictedData self.log.info('--------- Performance Matrix with Test Data End ---------\n') else: df_test['actual'] = ytrain df_test['predict'] = trainPredictedData elif(model_type == 'Clustering'): self.log.info('\n--------- Performance Matrix with Train Data ---------') train_matrix = self.getclusterMatrix(xtrain,trainPredictedData) self.log.info('--------- Performance Matrix with Train Data End ---------\n') self.log.info('\n--------- Performance Matrix with Test Data ---------') performacematrix = self.getclusterMatrix(xtest,predictedData) df_test['predict'] = predictedData self.log.info('--------- Performance Matrix with Test Data End ---------\n') matrix = performacematrix elif(model_type.lower() == 'topicmodelling'): self.log.info('\n--------- Performance Matrix with Train Data ---------') train_matrix = "" self.log.info('--------- Performance Matrix with Train Data End ---------\n') self.log.info('\n--------- Performance Matrix with Test Data ---------') performacematrix = "" df_test['predict'] = predictedData self.log.info('--------- Performance Matrix with Test Data End ---------\n') matrix = performacematrix except Exception as Inst: self.log.info('--------- Error Performance Matrix ---------\n') self.log.info(str(Inst)) df_test['predict'] = predictedData matrix = "" train_matrix = "" self.log.info('--------- Performance Matrix with Test Data End ---------\n') save_csv_compressed(df_test, predicted_data_file, encoding='utf-8') return 'Success',model_type,model,saved_model,matrix,train_matrix,xtrain.shape,model_tried,score,filename,features,threshold,pscore,rscore,method,estimator,xtrain,ytrain,xtest,ytest,topics,params def auc_roccurve(self,y_true,y_score,classee,imageFolderLocation): from keras.utils import to_categorical from sklearn.preprocessing import label_binarize import re n_classes = len(classee) y_true = to_categorical(y_true,num_classes = n_classes) fpr ={} tpr={} roc_auc={} class_names = list(classee.keys()) typeofclass = list(classee.values()) n_class = len(typeofclass) for i in range(n_classes): fpr[i],tpr[i],_ = roc_curve(y_true[:,i], y_score[:,i]) roc_auc[i]= auc(fpr[i],tpr[i]) plt.figure() plt.plot(fpr[i],tpr[i],label=f'{class_names[i]} (AUC = {roc_auc[i]:.2f})') plt.plot([0,1],[0,1], linestyle='--') plt.xlabel('False positive rate') plt.ylabel('True positive rate') plt.title(f'{class_names[i]} ROC Curve') plt.legend() img_location = os.path.join(imageFolderLocation,str(i)+'_roc.png') #15092 plt.savefig(img_location) def startLearnerModule(self,mlconfig,modelType,modelParams,modelList,scoreParam,targetColumn,dataFrame,xtrain,ytrain,xtest,ytest,categoryCountList,topFeatures,targetType,deployLocation,iterName,iterVersion,trained_data_file,labelMaps,featuresBasedOn, modelFeatureBased,code_configure,modelEvaluationConfig): matrix = '' threshold = -1 pscore = -1 rscore = -1 method = mlconfig['optimizationMethod'] method = method.lower() geneticParam = '' topics = {} optimizationHyperParameter = mlconfig['optimizationHyperParameter'] cvSplit = optimizationHyperParameter['trainTestCVSplit'] nIter = int(optimizationHyperParameter['iterations']) if(method.lower() == 'genetic'): geneticParam = optimizationHyperParameter['geneticparams'] scoreParam = scoreParam if 'thresholdTunning' in mlconfig: thresholdTunning = mlconfig['thresholdTunning'] else: thresholdTunning = 'NA' if len(topFeatures) !=0: self.features=topFeatures else: datacolumns=list(xtrain.columns) if targetColumn in datacolumns: datacolumns.remove(targetColumn) self.features =datacolumns self.log.info(f'-------> Number of Features Used For Training the Model: {len(self.features)}') features_names = str(self.features) if len(features_names) > 500: features_names = ','.join(self.features[:2]) + ', ..... ,' + ','.join(self.features[-2:]) self.log.info(f'-------> Features Used For Training the Model: {features_names}') xtrain = xtrain[self.features] if not xtest.empty: xtest = xtest[self.features] if cvSplit == "": cvSplit =None else: cvSplit =int(cvSplit) if modelType == 'classification': model_type = "Classification" MakeFP0 = False MakeFN0 = False if(len(categoryCountList) == 2): self.log.info("\n -------------- Check for FP or FN -------------- ") self.log.info("-------> Binary Classification") if(thresholdTunning.lower() == 'fp0'): self.log.info("-------> Threshold Tuning: False Positive") MakeFP0 = True elif(thresholdTunning.lower() == 'fn0'): self.log.info("-------> Threshold Tuning: False Negative") MakeFN0 = True if MakeFP0 == False and MakeFN0 == False: self.log.info("-------> Threshold Tuning: Not Any") self.log.info("-------------- Check for FP or FN End-------------- \n") elif(len(categoryCountList) > 2): #bug 12438 self.log.info("\n -------------- Check for FP or FN -------------- ") self.log.info("-------> Multiclass Classification") if(thresholdTunning.lower() == 'fp0' or thresholdTunning.lower() == 'fn0'): self.log.info("-------> Threshold Tuning: Not supported") else: self.log.info("-------> Threshold Tuning: Not Any") self.log.info("-------------- Check for FP or FN End-------------- \n") objClf = ClassifierModel(modelList, modelParams, scoreParam, cvSplit, nIter,geneticParam, xtrain,ytrain,xtest,ytest,method,modelType,MakeFP0,MakeFN0,deployLocation) model, params, score, estimator,model_tried,threshold,pscore,rscore = objClf.classModelling( modelFeatureBased,code_configure) elif modelType == 'regression': model_type = "Regression" objClf = RegressionModel(modelList, modelParams, scoreParam, cvSplit, nIter,geneticParam, xtrain,ytrain,xtest,ytest,method,deployLocation) model,params,score,estimator,model_tried = objClf.regressionModelling(modelFeatureBased,code_configure) elif modelType =='clustering': model_type = 'Clustering' print(modelList) if 'KMeans' in modelList: clustendency = self.cluster_tendency(xtrain) model='KMeans' model_tried = '{"Model":"KMeans","Score":"NA"}' kmeanmodelparams=modelParams['KMeans'] n_clusters = kmeanmodelparams['n_clusters'] if n_clusters == None or n_clusters == 0 or n_clusters == '': n_clusters = self.calculateNumberofCluster(xtrain) kmeanmodelparams['n_clusters'] = n_clusters kmeans=KMeans(n_clusters=n_clusters) targetData=kmeans.fit_predict(xtrain) self.log.info('Status:- |... ML Algorithm applied: KMeans') self.log.info('\n------------ Centers Points Start------------') values = kmeans.cluster_centers_.squeeze() #print(values) centers = pd.DataFrame(kmeans.cluster_centers_,columns= xtrain.columns) filename = os.path.join(deployLocation,'centers.csv') centers.to_csv(filename) labels = kmeans.labels_ i=0 for value_row in values: j=0 self.log.info('------->Label: '+str(i)) for value in value_row: self.log.info('---------->Feature: "'+str(self.features[j])+'" Center Point: '+str(value)) j = j+1 i = i+1 self.log.info('------------ Centers Points Start------------\n') score='NA' scoreParam=None params=kmeanmodelparams estimator=kmeans if 'DBSCAN' in modelList: DBSCAN_ModelParams=modelParams['DBSCAN'] db = DBSCAN(eps=DBSCAN_ModelParams['eps'],min_samples = DBSCAN_ModelParams['min_samples']).fit(xtrain) #targetData=db.fit_predict(xtrain) self.log.info('Status:- |... ML Algorithm applied: DBSCAN') labels = db.labels_ n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0) n_noise_ = list(labels).count(-1) self.log.info('------->Labels: '+str(labels)) self.log.info('------->No Of Cluster: '+str(n_clusters_)) self.log.info('------->No Of Noise Point: '+str(n_noise_)) score='NA' scoreParam=None params='' estimator=db model='DBSCAN' model_tried = '{"Model":"DBSCAN","Score":"NA"}' elif modelType == 'topicmodelling': model_type = 'TopicModelling' model='LDA' model_tried = '{"Model":"LDA","Score":"NA"}' LDAmodelparams=modelParams['LDA'] n_topics = LDAmodelparams['n_topics'] n_words_per_topic = LDAmodelparams['n_words_per_topic'] if n_topics == None or n_topics == 0 or n_topics == '': n_topics = 10 LDAmodelparams['n_topics'] = n_topics if n_words_per_topic == None or n_words_per_topic == 0 or n_words_per_topic == '': n_words_per_topic = 10 LDAmodelparams['n_words_per_topic'] = n_words_per_topic lda = LatentDirichletAllocation(n_components=n_topics,random_state=0) self.log.info('Status:- |... ML Algorithm applied: LDA') targetData=lda.fit_transform(xtrain) topics = self.get_topics(lda, topFeatures, n_words_per_topic) self.log.info(topics) score='NA' scoreParam=None params=LDAmodelparams estimator=lda return model_type,model,params, score, estimator,model_tried,xtrain,ytrain,xtest,ytest,threshold,pscore,rscore,method, topics # -*- coding: utf-8 -*- #Py Libraries import import numpy as np import pandas as pd import tensorflow as tf import matplotlib import matplotlib.pyplot as plt from sklearn.metrics import accuracy_score from tensorflow.keras.optimizers import Adam,SGD # from sklearn.preprocessing import MinMaxScaler from tensorflow.keras import Model, Sequential from tensorflow.keras.layers import Dense, Dropout from sklearn.model_selection import train_test_split from tensorflow.keras.losses import MeanSquaredLogarithmicError import os import json import keras from keras.layers import Input, Dense from keras.callbacks import ModelCheckpoint, EarlyStopping from sklearn.cluster import DBSCAN from sklearn.model_selection import RandomizedSearchCV from sklearn import metrics import traceback import seaborn as sns import warnings warnings.filterwarnings("ignore") import logging import joblib from sklearn import preprocessing ''' Py Engine: >= Python 3.8> anomalyDetectionAE class purpose: To findout anomalies in the user data using autoencoder mechanism. The file contains base class anomalyDetectionAE which act as entry point . mainAnomalyDetectionfn() method decide which algorithm (autoencoder or dbscan to be used based on user input). Two different approaches to find out anomalies, 1.LSTM approach (anomalyDetectionAE.aetsmodel_lstm() fn) 2. Autoencoder approach (AeDetector class) By default, autoencoder approach used (User can select in basic config file.) One more limitation is, time series data will be handled by autoencoder,lstm algs. DBScan algorithm is not supporting time series. Suggestion here is if time series data feature received, drop it. AION dataprofiler automatically remove time feature for DBScan. But for autoencoder we are passing time series. Parameter information: df: Input dataframe from aion base class paramSpace: Default parameter from basic config (user config settings) deployLocation: Deployment location. Detailed anomalies data information are stored in ../target/output loc. Target: only for supervised problems. anomalyMethod: Algorithm to be used (autoEncoder or DBScan) received from paramSpace. testSize: For supervised problems, unsupervised problems we are passing whole input data. datetimeFeature: data time feature for autoencoder mv_featurebased_ad_status: If <True> univariate feature for autoencoder enabled. We findout anomalies for each features selected by user. ''' ##For autoencoder (encoder-decoder) based base class, keras Model class act as meta class. Config params received from AION config file (GUI). The below class instance have keras subclassing call to run encoder and decoder. class AeDetector(Model): def __init__(self,train,test,units,latent_units,activation): super(AeDetector, self).__init__() #Because anomaly detection ,we r using 'sigmoid' activation for all problems last_layer_activation='sigmoid' self.encoder = tf.keras.Sequential([ Dense(units, activation=activation), Dense((units/2), activation=activation), Dense(latent_units, activation=activation) ]) self.decoder = tf.keras.Sequential([ Dense((units/2), activation=activation), Dense(units, activation=activation), Dense(train.shape[1], activation=last_layer_activation) ]) ## Using keras subclassing api def call(self, x): encoded = self.encoder(x) decoded = self.decoder(encoded) return decoded ##This below function create get_datetime class python file in target->scripts folder '''This aion_gettimegranularity class is used to retrive the time pattern (Granularity) of given datetime feature.''' class aion_gettimegranularity: cls_name="datetimeinformation" def __init__(self,dataframe, datetimefeature): self.df=dataframe self.datetimefeature=datetimefeature # self.log=logging.getLogger('AION') self.log = logging.getLogger('eion') self.log.info("To retrive the granularity of given datetime feature by aion.") def get_dfinfo(self,df): from io import StringIO buf = StringIO() df.info(buf=buf) #self.log.info(buf.getvalue()) return buf.getvalue() ##Main time granularity find function def get_granularity(self): try: ##get local df df_t=self.df buf_info=self.get_dfinfo(df_t) self.log.info(buf_info) df_t.drop(df_t.filter(regex='Unname'),axis=1,inplace=True) try: df_t[self.datetimefeature] = pd.to_datetime(df_t[self.datetimefeature]) except Exception as e: self.log.info("Datetime feature to python datetime format convertion error.\n"+str(e)) df_t['time_diff']=df_t[self.datetimefeature].diff().shift(-1) datetime_mean=df_t['time_diff'].mean() totsec = datetime_mean.total_seconds() ## Dict variable to store datetime details.Initialized all date param as False. status_time={"h":False,"m":False,"s":False, "us":False,"ns":False,"Y":False,"M":False,"D":False} if (datetime_mean.days == 0): if (totsec/3600 > 1): ## hour status_time['h']=True else: if (totsec/60 >1): ## minute status_time['m']=True else: if (totsec <= 1e-06 and totsec > 1e-09): ## microsecond status_time['us']=True elif (totsec<= 1e-09 and totsec >=1e-012): ## nanosecond status_time['ns']=True else: ## second status_time['s']=True else: days=datetime_mean.days if (days/365>1): ## year status_time['Y']=True else: if (days>30): ## month status_time['M']=True else: ## day status_time['D']=True time_pattern=None for k,v in status_time.items(): if (v == True): time_pattern=k self.log.info("<----- DateTime feature pattern (year/month/day/hour/minute/second/millisecond/microsecond/nanosecond) is: \t"+str(time_pattern)) try: try: df_t[self.datetimefeature] = pd.to_datetime(df_t[self.datetimefeature]) except Exception as e: pass df_t['Time_diff'] = ((df_t[self.datetimefeature])).diff(-1).dt.floor('T').dt.total_seconds().div(60).abs() time_threshold=1 df_t['anomalyType'] = (np.where((df_t['Time_diff'] != 1),"Point","Sequence")) df_t.drop("Time_diff",axis=1,inplace=True) except Exception as e: self.log.info("time_diff err message: "+str(e)) except Exception as e: print("time_diff err message: ",str(e)) return df_t ## AION Anomaly detection autoencoder main class. It receives input params from anomalyDetector class class anomalyDetectionAE: def __init__(self,df,paramSpace,deployLocation,target,anomalyMethod,testSize,datetimeFeature,mv_featurebased_ad_status): self.mc=None self.es=None #aion gui inputs self.df=df self.paramSpace=paramSpace self.deployLocation=deployLocation self.target=target self.anomalyMethod=anomalyMethod self.testSize=round(testSize,1) self.datetimeFeature=datetimeFeature self.log = logging.getLogger('eion') self.mv_featurebased_ad_status=mv_featurebased_ad_status """ Uncomment below for debug purpose. """ # self.log.info("anomalyDetectionAE constructor: df head: \n"+str(df.head())) # self.log.info("anomalyDetectionAE constructor: df type: \n"+str(type(df))) # self.log.info("anomalyDetectionAE constructor: df len: \n"+str(len(df))) # self.log.info("anomalyDetectionAE constructor: self.datetimeFeature: \n"+str((self.datetimeFeature))) def targetnumClass(self,data): count_classes = pd.value_counts(data, sort = True) num_of_classes= len(count_classes) return num_of_classes def configload(self): cwd = os.path.abspath(os.path.dirname(__file__)) file_name='config.json' try: config_file=os.path.normpath(os.path.join(cwd,'config',file_name)) except Exception as e: self.log.info("<---- config path error. Error Msg: ---->"+str(e)) with open(config_file, 'r') as file: data = json.load(file) datapath=str(data["data"]) target=str(data["target"]) anomaly_algorithm=str(data["anomalydetection_algorithm"]) ae_hyperparameter=data["autoEncoder"] dbscan_hyperparameter=data["DBScan"] return datapath,target,ae_hyperparameter,anomaly_algorithm,dbscan_hyperparameter ## model summary def summary(self,model): return model.summary() ##To load ae model def model_load(self, path): cwd = os.path.abspath(os.path.dirname(__file__)) file_name=path try: model_location=os.path.normpath(os.path.join(cwd,'model',file_name)) except Exception as e: self.log.info("<---- Model path error. Error Msg: ---->"+str(e)) loaded_model = joblib.load(model_location) return loaded_model ## Load dataset def dataload(self,datapath): cwd = os.path.abspath(os.path.dirname(__file__)) file_name=datapath try: data_file=os.path.normpath(os.path.join(cwd,'data',file_name)) except Exception as e: self.log.info("<---- data path error. Error Msg:: ---->"+str(e)) df = pd.read_csv(data_file) return df ## Create dataframe with time sequence data, if not time series, sequence length always 1. def create_dataset(self,X, y, time_steps=1): Xs, ys = [], [] for i in range(len(X) - time_steps): v = X.iloc[i:(i + time_steps)].values Xs.append(v) ys.append(y.iloc[i + time_steps]) return np.array(Xs), np.array(ys) ## model for time series based AE encoder, decoder fn def aetsmodel_lstm(self,n_dims, n_timesteps, n_bottleneck,units,activation,df): # inputs = Input(shape = (n_timesteps, n_dims)) inputs = Input(shape = (df.shape[1], df.shape[2])) e = keras.layers.LSTM(units, activation = activation, return_sequences = True)(inputs) ## code layer or compressed form of data produced by the autoencoder, bottleneck layer latent_space = keras.layers.LSTM(n_bottleneck, activation = activation, return_sequences = False, name = 'bottleneck_layer')(e) e = keras.layers.RepeatVector(n_timesteps)(latent_space) decoder = keras.layers.LSTM(n_bottleneck, activation = activation, return_sequences = True)(e) decoder = keras.layers.LSTM(units, activation = activation, return_sequences = True)(decoder) outputs = keras.layers.TimeDistributed(Dense(n_dims))(decoder) model = Model(inputs = inputs, outputs = outputs) return model ## adding some model checkpoints to ensure the best values will be saved and early stopping to prevent the model from running unnecessary. def callbacks(self, **kwargs): self.mc = ModelCheckpoint(filepath = kwargs.get("filename"), save_best_only = True, verbose = 0) self.es = EarlyStopping(monitor = kwargs.get("monitor"), patience = kwargs.get("patience")) return self.es,self.mc ##This below function create get_datetime class python file in target->scripts folder '''This aion_gettimegranularity class is used to retrive the time pattern (for getting time granularity) of given datetime feature.''' def create_datetime_pyfile(self): try: datetimepattern_code=r"""## import pandas as pd import numpy as np class aion_gettimegranularity: cls_name="datetimeinformation" def __init__(self,dataframe, datetimefeature): self.df=dataframe self.datetimefeature=datetimefeature def get_dfinfo(self,df): from io import StringIO buf = StringIO() df.info(buf=buf) #print(buf.getvalue()) return buf.getvalue() def get_granularity(self): try: ##get local df df_t=self.df buf_info=self.get_dfinfo(df_t) df_t.drop(df_t.filter(regex='Unname'),axis=1,inplace=True) try: df_t[self.datetimefeature] = pd.to_datetime(df_t[self.datetimefeature]) except Exception as e: pass # print("Datetime feature to python datetime format convertion error.\n",e) df_t['time_diff']=df_t[self.datetimefeature].diff().shift(-1) datetime_mean=df_t['time_diff'].mean() totsec = datetime_mean.total_seconds() ## Dict variable to store datetime details.Initialized all date param as False. status_time={"h":False,"m":False,"s":False,"us":False,"ns":False,"Y":False,"M":False,"D":False} if (datetime_mean.days == 0): if (totsec/3600 > 1): ## hour status_time['h']=True else: if (totsec/60 >1): ## minute status_time['m']=True else: if (totsec <= 1e-06 and totsec > 1e-09): ## microsecond status_time['us']=True elif (totsec<= 1e-09 and totsec >=1e-012): ## nanosecond status_time['ns']=True else: ## second status_time['s']=True else: days=datetime_mean.days if (days/365>1): ## year status_time['Y']=True else: if (days>30): ## month status_time['M']=True else: ## day status_time['D']=True time_pattern=None for k,v in status_time.items(): if (v == True): time_pattern=k #print("<----- DateTime feature pattern (year/month/day/hour/minute/second/millisecond/microsecond/nanosecond) is: \t",(time_pattern)) try: try: df_t[self.datetimefeature] = pd.to_datetime(df_t[self.datetimefeature]) except Exception as e: pass df_t['Time_diff'] = ((df_t[self.datetimefeature])).diff(-1).dt.floor('T').dt.total_seconds().div(60).abs() time_threshold=1 df_t['anomalyType'] = np.where((df_t['Time_diff'] != 1),"Point","Sequence") df_t.drop("Time_diff",axis=1,inplace=True) except Exception as e: print("time_diff err message: ",str(e)) except Exception as e: pass # print("get_granularity err msg: ",(e)) return df_t """ cwd=self.deployLocation file_name='aion_granularity'+'.py' try: data_file=os.path.normpath(os.path.join(cwd,'script',file_name)) with open(data_file,'w') as file: file.write(datetimepattern_code) except Exception as error: self.log.info("<---- datetimepattern_code write Error.: ---->"+str(error)) self.log.info("datetimepattern source code created at target folder...\n") except Exception as error: self.log.info("<---- datetimepattern_code function Error.: ---->"+str(error)) ## Simple mlp based autoencoder model, not used now. # def aetsmodel_lstm(self,X_train): # model = keras.Sequential() # # autoencoder encoder # model.add(keras.layers.LSTM( # units=64, # input_shape=(X_train.shape[1], X_train.shape[2]) # )) # model.add(keras.layers.Dropout(rate=0.2)) # model.add(keras.layers.RepeatVector(n=X_train.shape[1])) # # autoencoder decoder # model.add(keras.layers.LSTM(units=64, return_sequences=True)) # model.add(keras.layers.Dropout(rate=0.2)) # model.add( # keras.layers.TimeDistributed( # keras.layers.Dense(units=X_train.shape[2]) # ) # ) # return model ## To find optimal anomaly threshold value def find_threshold(self,model, x_train_scaled): reconstructions = model.predict(x_train_scaled) # provides losses of individual instances msle reconstruction_errors = tf.keras.losses.mae(reconstructions, x_train_scaled) # threshold for anomaly scores threshold = np.mean(reconstruction_errors.numpy())+ 2*np.std(reconstruction_errors.numpy()) return threshold ## compiling the model with adam optimizer and mean squared error loss def model_compile(self, model,lr, loss, opt): if opt == "adam": opt = Adam(learning_rate = lr) else: opt = SGD(learning_rate = lr) model.compile(loss = loss, optimizer = opt) ## save anomaly points in aion target folder def save_anomalyvalues(self,df,file_name): # cwd = os.path.abspath(os.path.dirname(__file__)) cwd=self.deployLocation file_name=file_name+'.csv' try: out_path=os.path.normpath(os.path.join(cwd,'output')) if not os.path.isdir(out_path): os.makedirs(out_path) data_file=os.path.normpath(os.path.join(cwd,'output',file_name)) except Exception as error: self.log.info("<---- autoencoder artifact_dir path. Error Msg: ---->"+str(error)) try: df.to_csv(data_file,index=False) except Exception as e: self.log.info("<---- Saving log data frame error. Error Msg: ---->"+str(e)) ## model summary def summary(self,model): return model.summary() ##Method to find subsequence and point anomalies aion_gettimegranularity def find_point_subsequence_anomalies(self,datetime_column,dataframe=None): try: dataframe.reset_index(level=0, inplace=True) try: dataframe[datetime_column] = pd.to_datetime(dataframe[datetime_column]) except Exception as e: self.log.info("Dataframe contains no datetime feature.Err.Msg: \n"+str(e)) pass try: ##Below commented part using normalize with time delta, find point anomalies.But not used,just for reference. ##get day to check difference #date_f = dataframe[datetime_column].dt.normalize() ##compare successive rows and identify group size #dataframe['anomaly_value'] = np.where(dataframe[datetime_column].groupby(date_f.ne(date_f.shift()).cumsum()).transform('size').gt(1),'subsequence_anomaly', 'Point_anomaly') ##Using get_timepattern method aion_gettimegranularity_obj=aion_gettimegranularity(dataframe,datetime_column) anomaly_info_df=aion_gettimegranularity_obj.get_granularity() except Exception as e: self.log.info("find_point_subsequence_anomalies,: aion_gettimegranularity err msg:: \n"+str(e)) self.log.info("find_point_subsequence_anomalies,: anomaly_info_df: \n"+str(anomaly_info_df)) except Exception as e: self.log.info("find_point_subsequence_anomalies,: err msg:: \n"+str(e)) return anomaly_info_df ## Auto encoder time series function call ## dataframe info() not working for py logging, so workaround we can get information in buffer and log it. def get_df_info(self,df): from io import StringIO buf = StringIO() df.info(buf=buf) #self.log.info(buf.getvalue()) return buf.getvalue() ## Method to detect time series based anomalies in user data. Using both lstm and dense based autoencoder approaches. def aionAEAnomalyTS(self,df,test_size_perc,target,time_steps,dropout,mv_unique_feature_ad): ae_hyperparameter=self.paramSpace anomaly_algorithm=self.anomalyMethod # test_size=float(self.testSize) test_size=0.0 # train_size=1-test_size train_size=1-test_size # train_size_perc=train_size*100 train_size=int(len(df) * train_size) try: timeseries_layers=ae_hyperparameter['timeseries_layers'] ## Here we are checking whether to use only LSTM layers for dnn or dense layers. Dense layers better for predicting point as well sequence anomalies in time series. if (timeseries_layers.lower() == 'lstm'): try: ## Need to get normalized data for threshold calculation. data_mean=df.mean(axis=0) data_std=df.std(axis=0) data=(df-data_mean)/data_std # train, test = df[:train_size], df[train_size:] train, test = data[:train_size], data[train_size:] test=train test1=test ## Need to copy test data train_index=train.index test_index=test.index cols = df.columns # train, test = train_test_split(df, test_size=test_size,random_state=42) X_train, y_train = self.create_dataset( train, train, time_steps ) X_test, y_test = self.create_dataset( test, test, time_steps ) n_dims=X_train.shape[2] n_timesteps=X_train.shape[1] opt=ae_hyperparameter['optimizer'] loss_fn=ae_hyperparameter["loss"] epochs=int(ae_hyperparameter['epochs']) batch_size=int(ae_hyperparameter['batch_size']) learning_rate=float(ae_hyperparameter['learning_rate']) n_bottleneck=int(ae_hyperparameter['latentspace_size']) units=int(ae_hyperparameter['hidden_units']) activation=ae_hyperparameter['activation'] ##For task 20731 minimum_threshold_user = str(ae_hyperparameter['min_threshold']) maximum_threshold_user = str(ae_hyperparameter['max_threshold']) autoencoder=self.aetsmodel_lstm(n_dims, n_timesteps, n_bottleneck,units,activation,X_train) ##To save file # cwd = os.path.abspath(os.path.dirname(__file__)) cwd=self.deployLocation try: artifact_dir=os.path.normpath(os.path.join(cwd,'output','autoenc_artifact_dir')) if not os.path.isdir(artifact_dir): os.makedirs(artifact_dir) except Exception as e: self.log.info("<---- Autoencoder artifact_dir path error. Error Msg: ---->"+str(e)) #dl callback fn to get best loss fn, early stopping & model checkpoint call backs es,mc=self.callbacks(filename = artifact_dir, patience = 5, monitor = "val_loss") self.model_compile(autoencoder,learning_rate, loss_fn, opt) X_train = np.reshape(X_train,(X_train.shape[0],X_train.shape[1],X_train.shape[2])) X_test = X_test.reshape((X_test.shape[0], X_test.shape[1],n_dims)) # y_test = y_test.reshape((y_test.shape[0], y_test.shape[1], n_dims)) model_hist = autoencoder.fit( X_train, X_train, epochs=epochs, batch_size=batch_size, validation_split=0.1, shuffle=False,callbacks = [mc, es] ) model_info=self.summary(autoencoder) X_train_pred = autoencoder.predict(X_train) train_mae_loss = np.mean(np.abs(X_train_pred - X_train), axis=1) ## Task 20731 if ((minimum_threshold_user and minimum_threshold_user.strip()) and (maximum_threshold_user and maximum_threshold_user.strip())): threshold = float(maximum_threshold_user) min_threshold = float(minimum_threshold_user) elif ((minimum_threshold_user.strip()) and (not maximum_threshold_user.strip())): threshold = np.mean(train_mae_loss) + 2*np.std(train_mae_loss) min_threshold = float(minimum_threshold_user) elif ((not minimum_threshold_user.strip()) and (maximum_threshold_user.strip())): threshold = float(maximum_threshold_user) min_threshold = np.mean(train_mae_loss) - 2*np.std(train_mae_loss) else: threshold = np.mean(train_mae_loss) + 2*np.std(train_mae_loss) min_threshold = np.mean(train_mae_loss) - 2*np.std(train_mae_loss) # threshold = np.mean(train_mae_loss) + np.std(train_mae_loss) self.log.info("Anomaly threshold max value based on loss fn (MAE): "+str(threshold)) self.log.info("Anomaly threshold min value based on loss fn (MAE): "+str(min_threshold)) X_test_pred = autoencoder.predict(X_test) test_mae_loss = np.mean(np.abs(X_test_pred - X_test), axis=1) test_score_df = pd.DataFrame(index=test_index[time_steps:]) if (n_dims >1): columns = [f'loss_{num}' for num in range(n_dims)] # test_score_df = pd.DataFrame(test_mae_loss, columns=columns, index=test_index[time_steps:]) test_score_df['loss'] = test_mae_loss.mean(axis=1) else: test_score_df['loss'] = test_mae_loss test_score_df['max_threshold'] = threshold test_score_df['min_threshold'] = min_threshold test_score_df['anomaly_value'] = (test_score_df.loss > test_score_df.max_threshold) test_score_df['anomaly_value'] = (test_score_df.loss < test_score_df.min_threshold) ## Newly added for lstm issue ## if coming dataframe have datetime index , copy it before concat (different indexed dfs) import pandas.api.types as ptypes # if (isinstance(test_score_df, pd.DatetimeIndex) and isinstance(df, pd.DatetimeIndex)): test_cp_index=None if (ptypes.is_datetime64_dtype(test_score_df.index) and ptypes.is_datetime64_dtype(df.index)): # self.log.info("test_score_df and df have datetime index cols") test_cp_index=test_score_df.index df_cp_index=df.index test_score_df=test_score_df.reset_index() df=df.reset_index() ##self.datetimeFeature test_score_df.dropna() try: test_score_df[self.datetimeFeature]=pd.to_datetime(test_score_df[self.datetimeFeature]) df[self.datetimeFeature]=pd.to_datetime(df[self.datetimeFeature]) except: pass try: final_df=pd.DataFrame() cols_to_use = df.columns.difference(test_score_df.columns) final_df = pd.merge(test_score_df, df[cols_to_use], left_index=True, right_index=True, how='inner') except Exception as e: self.log.info("final_df creation err msg: \n: "+str(e)) else: test_index=test_score_df.reset_index(drop=True) test_cp_index=test_index.index df_index=df.reset_index(drop=True) final_df=pd.DataFrame() final_df = test_score_df.join(df) final_df.dropna() ##Again set datetime index to dataframes,drop datetime feature column and set it as index. try: final_df.set_index(self.datetimeFeature,inplace=True) df.set_index(self.datetimeFeature,inplace=True) df.drop(self.datetimeFeature,axis=1,inplace=True) final_df.drop(self.datetimeFeature,axis=1,inplace=True) except: pass ## Below commented code used to print df.info() in log file (using get_df_info() methos). # self.log.info("anomaly final_df info: \n") # buf_info=self.get_df_info(final_df) # self.log.info(buf_info) # final_df=pd.DataFrame() ##Getback the datetime index back final_df.index=test_cp_index normal_prediction_df=test_score_df.loc[test_score_df['anomaly_value']==False] anomaly_prediction_df=test_score_df.loc[test_score_df['anomaly_value']==True] ## Newly added for lstm issue anomaly_prediction_df=pd.merge(anomaly_prediction_df, final_df, on=['loss', 'max_threshold','min_threshold', 'anomaly_value'], how="left") # anomaly_prediction_df.fillna(anomaly_prediction_df.mean(), inplace=True) anomaly_prediction_df['anomaly_value'] = anomaly_prediction_df['anomaly_value'].replace([np.inf, -np.inf], np.nan) # anomaly_prediction_df['anomaly_value'] = anomaly_prediction_df['anomaly_value'].replace([np.inf, -np.inf], np.nan) final_df['anomaly_value'] = final_df['anomaly_value'].replace([np.inf, -np.inf], np.nan) anomaly_prediction_df['anomaly_value'] = anomaly_prediction_df['anomaly_value'].replace({True: 1, False: 0}) final_df['anomaly_value'] = final_df['anomaly_value'].replace({True:1, False: 0}) #make sure no nan values after dataframe operations anomaly_prediction_df.dropna() final_df.dropna() # anomal_loss_threshold=anomaly_prediction_df #use if we want to save loss and threshold as dataframe info. self.log.info("Anomaly data with loss and threshold informations: \n"+str(anomaly_prediction_df)) """ Saving anomaly plots in target->output->anomaly_plot folder """ ## Goto if cond for multivariate whole dataset anomaly prediction, else goto else part for feature based ad prediction. if (mv_unique_feature_ad.lower()=='false'): for col in df.columns: df_subset = anomaly_prediction_df[col] fig, ax = plt.subplots() df[col].plot(legend=False, ax=ax) df_subset.plot(legend=False, ax=ax, color="r") plot_name=col ax.set_title(plot_name+"_Anomaly Data Plot") ax.set_xlabel("DateTime") ax.set_ylabel("Values") plot_name=plot_name+'_'+'anomalyplot.png' try: plot_dir=os.path.normpath(os.path.join(cwd,'output','anomaly_plot')) if not os.path.isdir(plot_dir): os.makedirs(plot_dir) plotpath=str(plot_dir)+'/'+plot_name except Exception as e: self.log.info("<---- plot_dir path error. Error Msg: ---->"+str(e)) if os.path.exists(plotpath): os.remove(plotpath) plt.savefig(plotpath) # plt.savefig(str(plot_dir)+'/'+plot_name) plt.clf() plt.cla() plt.close() else: df_subset = anomaly_prediction_df fig, ax = plt.subplots() df.plot(legend=False, ax=ax) ax.set_title("Anomaly Data Plot") ax.set_xlabel("X values") ax.set_ylabel("Y Values") df_subset.plot(legend=False, ax=ax, color="r") plot_name=df.columns[0] ax.set_title(plot_name+"_Anomaly Data Plot") # ax.set_xlabel("DateTime") # ax.set_ylabel("Values") # plot_name=df.columns[0] plot_name=plot_name+'_'+'anomalyplot.png' try: plot_dir=os.path.normpath(os.path.join(cwd,'output','anomaly_plot')) if not os.path.isdir(plot_dir): os.makedirs(plot_dir) plotpath=str(plot_dir)+'/'+plot_name except Exception as e: self.log.info("<---- plot_dir path error. Error Msg: ---->"+str(e)) if os.path.exists(plotpath): os.remove(plotpath) plt.savefig(plotpath) # plt.savefig(str(plot_dir)+'/'+plot_name) plt.clf() plt.cla() plt.close() #process dt feature and save anomalies. datetime_column=str(self.datetimeFeature) try: anomaly_prediction_df=self.find_point_subsequence_anomalies(datetime_column,anomaly_prediction_df) # normal_prediction_df=self.find_point_subsequence_anomalies(datetime_column,normal_prediction_df) except: ##If any issue in time series point anomaly detection, skip it. self.log.info("Detecting point anomalies have some issue,check datetime feature.") pass combined_df=pd.concat([anomaly_prediction_df,normal_prediction_df],ignore_index=True) combined_df['anomaly_value']=combined_df['anomaly_value'].fillna('Normal_Data') ## If categorical features in original df, then inverse transform the values. anomaly_prediction_df['anomaly_value'] = anomaly_prediction_df['anomaly_value'].replace({1: "Anomaly", 0: "Normal"}) final_df['anomaly_value'] = final_df['anomaly_value'].replace({1: "Anomaly", 0: "Normal"}) ##Now we are storing anomaly log (as dataframe) based on two options: 1. Anomalies based on all features, 2. Anomalies based on each individual feature. if (mv_unique_feature_ad.lower()=='true'): ## Multivariate and saving individual feature based anomalies self.save_anomalyvalues(anomaly_prediction_df,(str(feature_name)+'_ts_anomaly_dataframe')) # self.save_anomalyvalues(combined_df,(str(feature_name)+'_ts_overall_dataframe')) try: final_df=self.merge_pre_post_dfs(final_df) except Exception as e: self.log.info("Anomaly Detection Merge df exception:\n"+str(e)) #If merge fails, just out! pass self.save_anomalyvalues(final_df,(str(feature_name)+'_ts_overall_dataframe')) ## If we want anomaly dataframe with loss and threshold for each values (rows),please uncomment below line # self.save_anomalyvalues(anomal_loss_threshold,'ts_anomaly_dataframe_lt')) ## Save actual test data test_score_df #self.save_anomalyvalues(test_score_df,(str(feature_name)+'_testdata')) else: self.save_anomalyvalues(anomaly_prediction_df,'ts_anomaly_dataframe') # self.save_anomalyvalues(combined_df,'ts_normal_anomaly_dataframe') try: final_df=self.merge_pre_post_dfs(final_df) except Exception as e: self.log.info("Anomaly Detection Merge df exception:\n"+str(e)) #If merge fails, just out! pass self.save_anomalyvalues(final_df,'ts_overall_dataframe') ## If we want anomaly dataframe with loss and threshold for each values (rows),please uncomment below line # self.save_anomalyvalues(anomal_loss_threshold,'ts_anomaly_dataframe_lt')) ## Save actual test data test_score_df #self.save_anomalyvalues(test_score_df,'testdata') anomaly_info_df=final_df self.log.info("<---- Autoencoder time series data anomalies: ---->"+str(anomaly_prediction_df)) self.log.info("<---- Autoencoder time series:Number of anomalies in data:: ---->"+str(len(anomaly_prediction_df))) # return model except Exception as e: self.log.info("AD lstm traceback error: \n"+str(traceback.format_exc())) ## Dense layer based time series AD, most real world usecases, it is working best compared to lstm based.. elif (timeseries_layers.lower() == 'dense'): try: feature_name=df.columns feature_name = ' '.join(map(str, feature_name)) try: #Passing whole data,so test size set as zero. test_size=0.0 # train_size=1-test_size train_size=1-test_size # train_size_perc=train_size*100 train_size=int(len(df) * train_size) train_data,test_data = df[:train_size], df[train_size:] test_data=train_data except: #If any error comes,us sklearn train test split train_data,test_data = train_test_split(df,test_size=test_size,random_state=42) pass test_index=test_data.index ## to get datetime index units=int(ae_hyperparameter['hidden_units']) latent_units=int(ae_hyperparameter['latentspace_size']) activation=ae_hyperparameter['activation'] ##For task 20731 minimum_threshold_user = str(ae_hyperparameter['min_threshold']) maximum_threshold_user = str(ae_hyperparameter['max_threshold']) train_data=train_data.values test_data=test_data.values ## tss is time series flag, true or false autoencoder = AeDetector(train_data,test_data,units,latent_units,activation) opt=ae_hyperparameter['optimizer'] loss_fn=ae_hyperparameter["loss"] epochs=int(ae_hyperparameter['epochs']) batch_size=int(ae_hyperparameter['batch_size']) learning_rate=float(ae_hyperparameter['learning_rate']) cwd=self.deployLocation try: artifact_dir=os.path.normpath(os.path.join(cwd,'output','autoenc_artifact_dir')) if not os.path.isdir(artifact_dir): os.makedirs(artifact_dir) except Exception as e: self.log.info("<---- artifact_dir path error. Error Msg: ---->"+str(e)) es,mc=self.callbacks(filename = artifact_dir, patience = 5, monitor = "val_loss") self.model_compile(autoencoder,learning_rate, loss_fn, opt) # autoencoder.compile(optimizer='adam', loss='mae') autoencoder.fit(train_data, train_data, epochs = epochs, batch_size=batch_size, validation_data=(test_data, test_data),callbacks = [mc, es]) # reconstructed = autoencoder(train_data) reconstructed = autoencoder.predict(train_data) train_mae_loss = tf.keras.losses.mae(reconstructed, train_data) ## Task 20731 if ((minimum_threshold_user and minimum_threshold_user.strip()) and (maximum_threshold_user and maximum_threshold_user.strip())): threshold = float(maximum_threshold_user) min_threshold = float(minimum_threshold_user) elif ((minimum_threshold_user.strip()) and (not maximum_threshold_user.strip())): threshold = np.mean(train_mae_loss) + 2*np.std(train_mae_loss) min_threshold = float(minimum_threshold_user) elif ((not minimum_threshold_user.strip()) and (maximum_threshold_user.strip())): threshold = float(maximum_threshold_user) min_threshold = np.mean(train_mae_loss) - 2*np.std(train_mae_loss) else: threshold = np.mean(train_mae_loss) + 2*np.std(train_mae_loss) min_threshold = np.mean(train_mae_loss) - 2*np.std(train_mae_loss) # threshold = np.mean(train_mae_loss) + np.std(train_mae_loss) self.log.info("Anomaly threshold max value based on loss fn (MAE): "+str(threshold)) self.log.info("Anomaly threshold min value based on loss fn (MAE): "+str(min_threshold)) test_labels=None if (len(self.datetimeFeature) >= 1): time_series_data="True" else: time_series_data="False" pred,test_score_df,actual_data,anomaly_info_df = self.prediction(autoencoder, test_data,min_threshold, threshold,test_labels,time_series_data,time_steps,test_index) # normal_prediction_df=(anomaly_info_df[~anomaly_info_df['anomaly_value']]) normal_prediction_df=anomaly_info_df.loc[anomaly_info_df['anomaly_value']==False] anomaly_prediction_df=anomaly_info_df.loc[anomaly_info_df['anomaly_value']==True] #Below ts_dataframe_anomaly not for production, just for testing purpose. If uncommented, comment it. #self.save_anomalyvalues(anomaly_info_df,'ts_dataframe_normal') # anomal_loss_threshold=anomaly_prediction_df #use if we want to save loss and threshold as dataframe info. self.log.info("Anomaly data with loss and threshold informations: \n"+str(anomaly_prediction_df)) # anomaly_prediction_df_plot=anomaly_prediction_df """ Saving anomaly plots in target->output->anomaly_plot folder """ ## Only for multivariate (all features) based anomaly data plot ## Use of the below part if anomaly df columns came as numerical columns. # if not (df.columns.equals(anomaly_prediction_df.columns)): # num_cols = [] # try: # num_cols=[num_cols.append(float(col)) for col in anomaly_prediction_df.columns.values] # except ValueError: # pass # #Dense layer scaler conversion makes column names as int values, so here find the int cols and rename to original names. # if (num_cols): # anomaly_prediction_df=anomaly_prediction_df[num_cols] # anomaly_prediction_df.columns=df.columns # normal_prediction_df=normal_prediction_df[num_cols] # normal_prediction_df.columns=df.columns ## Goto if cond for multivariate whole dataset anomaly prediction, else goto else part for feature based ad prediction. if (mv_unique_feature_ad.lower()=='false'): # for col in df.columns: for col in actual_data.columns: df_subset = anomaly_prediction_df[col] fig, ax = plt.subplots() df[col].plot(legend=False, ax=ax) df_subset.plot(legend=False, ax=ax, color="r") plot_name=col ax.set_title(plot_name+"_Anomaly Data Plot") ax.set_xlabel("DateTime") ax.set_ylabel("Values") plot_name=plot_name+'_'+'anomalyplot.png' try: plot_dir=os.path.normpath(os.path.join(cwd,'output','anomaly_plot')) if not os.path.isdir(plot_dir): os.makedirs(plot_dir) plotpath=str(plot_dir)+'/'+plot_name except Exception as e: self.log.info("<---- plot_dir path error. Error Msg: ---->"+str(e)) if os.path.exists(plotpath): os.remove(plotpath) plt.savefig(plotpath) # plt.savefig(str(plot_dir)+'/'+plot_name) plt.clf() plt.cla() plt.close() else: df_subset = anomaly_prediction_df fig, ax = plt.subplots() df.plot(legend=False, ax=ax) ax.set_title("Anomaly Data Plot") ax.set_xlabel("DateTime") ax.set_ylabel("Values") df_subset.plot(legend=False, ax=ax, color="r") plot_name=df.columns[0] ax.set_title(plot_name+"_Anomaly Data Plot") # ax.set_xlabel("DateTime") # ax.set_ylabel("Values") # plot_name=df.columns[0] plot_name=plot_name+'_'+'anomalyplot.png' try: plot_dir=os.path.normpath(os.path.join(cwd,'output','anomaly_plot')) if not os.path.isdir(plot_dir): os.makedirs(plot_dir) plotpath=str(plot_dir)+'/'+plot_name except Exception as e: self.log.info("<---- plot_dir path error. Error Msg: ---->"+str(e)) if os.path.exists(plotpath): os.remove(plotpath) plt.savefig(plotpath) # plt.savefig(str(plot_dir)+'/'+plot_name) plt.clf() plt.cla() plt.close() datetime_column=str(self.datetimeFeature) # anomaly_prediction_df=self.find_point_subsequence_anomalies(datetime_column,anomaly_prediction_df) # normal_prediction_df=self.find_point_subsequence_anomalies(datetime_column,normal_prediction_df) try: anomaly_prediction_df=self.find_point_subsequence_anomalies(datetime_column,anomaly_prediction_df) # normal_prediction_df=self.find_point_subsequence_anomalies(datetime_column,normal_prediction_df) except: self.log.info("Detecting point anomalies have some issue,check datetime feature.") ##Just pass if datetime column provides issue, use without datetime column info pass combined_df=pd.concat([anomaly_prediction_df,normal_prediction_df],ignore_index=True) combined_df['anomaly_value']=combined_df['anomaly_value'].fillna('Normal_Data') ## If categorical features in original df, then inverse transform the values. try: # anomaly_info_df['anomaly_value']=anomaly_info_df['anomaly_value'].astype(str).replace(replace_values_F,'NormalDataPoint', regex=True) self.naming_anomalyvalues(anomaly_info_df) except Exception as e: self.log.info("anomaly_info_df exception err msg: \n"+str(e)) ##Now we are storing anomaly log (as dataframe) based on two options: 1. Anomalies based on all features, 2. Anomalies based on each individual feature. if (mv_unique_feature_ad.lower()=='true'): ## Multivariate and saving individual feature based anomalies self.save_anomalyvalues(anomaly_prediction_df,(str(feature_name)+'_ts_anomaly_dataframe')) try: anomaly_info_df=self.merge_pre_post_dfs(anomaly_info_df) except Exception as e: #If merge fails, just out!. self.log.info("Anomaly Detection Merge df exception :\n"+str(e)) finally: #check merging done or not, to be imp. pass self.save_anomalyvalues(anomaly_info_df,(str(feature_name)+'_ts_overall_dataframe')) '''For overall ordered output,uncomment the below.''' # self.save_anomalyvalues(combined_df,(str(feature_name)+'_ts_overall_dataframe_ordered')) ## If we want anomaly dataframe with loss and threshold for each values (rows),please uncomment below line # self.save_anomalyvalues(anomal_loss_threshold,'ts_anomaly_dataframe_lt') ## Save actual test data actual_data #self.save_anomalyvalues(actual_data,(str(feature_name)+'_testdata')) else: self.save_anomalyvalues(anomaly_prediction_df,'ts_anomaly_dataframe') try: anomaly_info_df=self.merge_pre_post_dfs(anomaly_info_df) except Exception as e: #If merge fails, just out!. self.log.info("Anomaly Detection Merge df exception :\n"+str(e)) finally: #check merging done or not, to be imp. pass self.save_anomalyvalues(anomaly_info_df,'ts_overall_dataframe') #Ordered data # self.save_anomalyvalues(combined_df,'ts_overall_dataframe_ordered') ## If we want anomaly dataframe with loss and threshold for each values (rows),please uncomment below line # self.save_anomalyvalues(anomal_loss_threshold,'ts_anomaly_dataframe_lt'), ## Save actual test data test_score_df #self.save_anomalyvalues(actual_data,'testdata') self.log.info("<---- Autoencoder time series anomalies : ---->"+str(anomaly_prediction_df)) self.log.info("<---- Autoencoder time series, Number of anomalies in data: ---->"+str(len(anomaly_prediction_df))) # self.save_anomalyvalues(anomaly_prediction_df,'ts_anomaly_dataframe') except Exception as e: self.log.info("dense layer anomaly error: \n"+str(traceback.format_exc())) else: self.log.info("Only LSTM and Dense layers supported for time series.") except Exception as e: self.log.info("<---- time series error msg: ---->"+str(e)) self.log.info("<---- time series error msg (detailed): ---->"+str(traceback.format_exc())) return autoencoder,anomaly_prediction_df,anomaly_info_df ## To normalize data,use when necessary def normalize_data(train_data,test_data): min_val=tf.reduce_min(train_data) max_val=tf.reduce_max(train_data) train_data = (train_data - min_val)/(max_val - min_val) test_data = (test_data - min_val)/(max_val - min_val) #converte the data into float train_data = tf.cast(train_data, dtype=tf.float32) test_data = tf.cast(test_data, dtype=tf.float32) return train_data,test_data ## Scaling data ,Not used because of our aion preprocessing data profiler option. use when necessary. def getScaledData(method='standard', train_df=None, test_df=None, feature_col='feature'): from sklearn.preprocessing import StandardScaler if method == 'standard': scaler = StandardScaler() else: scaler = MinMaxScaler() scaler = scaler.fit(train_df[[feature_col]]) train_df['scaled_'+feature_col] = scaler.transform(train_df[[feature_col]]) test_df['scaled_'+feature_col] = scaler.transform(test_df[[feature_col]]) return train_df, test_df, scaler ## prediction fn def prediction(self,model, data,min_threshold, threshold,test_labels,time_series_status,time_steps,test_index): # data1=scaler.inverse_transform(data) try: df_new=self.df.drop(self.datetimeFeature,axis=1,inplace=False) except: df_new=self.df try: actual_data=pd.DataFrame(self.df,columns=df_new.columns) except Exception as e: actual_data=pd.DataFrame(self.df) pass n_features=data.shape[1] self.log.info("prediction: number of features: \n"+str(n_features)) predicted_data = model.predict(data) loss = tf.keras.losses.mae(predicted_data, data) if (time_series_status.lower() == 'true'): test_score_df = pd.DataFrame(index=test_index) actual_data = actual_data.set_index(test_index) anomaly_info_df=pd.DataFrame() test_score_df['loss'] = loss test_score_df['max_threshold'] = threshold test_score_df['min_threshold'] = min_threshold ## Task 20731 #test_score_df['anomaly_value'] = test_score_df.apply(lambda x: x.loss > x.max_threshold or x.loss <= x.min_threshold, axis=1) test_score_df['anomaly_value'] = np.where((test_score_df["loss"] > test_score_df["max_threshold"]) | (test_score_df["loss"] <= test_score_df["min_threshold"]), True, False) anomaly_info_df = pd.concat([actual_data, test_score_df], axis=1) else: test_score_df = pd.DataFrame() anomaly_info_df=pd.DataFrame() test_score_df['loss'] = loss #test_score_df['threshold'] = threshold test_score_df['max_threshold'] = threshold test_score_df['min_threshold'] = min_threshold ## Task 20731 #test_score_df['anomaly_value'] = (test_score_df.loss >= test_score_df.max_threshold) #test_score_df['anomaly_value'] = (test_score_df.loss < test_score_df.min_threshold) test_score_df['anomaly_value'] = np.where((test_score_df["loss"] > test_score_df["max_threshold"]) | (test_score_df["loss"] <= test_score_df["min_threshold"]), True, False) anomaly_info_df = pd.concat([actual_data, test_score_df], axis=1) return tf.math.less(loss, threshold),test_score_df,actual_data,anomaly_info_df ##Not used now, for data ploting purpose # def plot(self,autoencoder,data, n): # enc_img = autoencoder.encoder(data) # dec_img = autoencoder.decoder(enc_img) # plt.plot(data[n], 'b') # plt.plot(dec_img[n], 'r') # plt.fill_between(np.arange(data.shape[1]), data[n], dec_img[n], color = 'lightcoral') # plt.legend(labels=['Input', 'Reconstruction', 'Error']) # plt.show() ## autoencoder fn for non timeseries data def ae_nontimeseriesmodelfn(self,df,target): autoencoder=None mv_unique_feature_ad=self.mv_featurebased_ad_status #For supervised non time series problems, we need to remove datetime feature. This will help scaler algs process the numeric data only. try: if (target == ''): try: test_size=0.0 # train_size=1-test_size train_size=1-test_size # train_size_perc=train_size*100 train_size=int(len(df) * train_size) train_data,test_data = df[:train_size], df[train_size:] test_data=train_data except: test_size=float(self.testSize) train_data,test_data = train_test_split(df,test_size=test_size,random_state=42) pass ae_hyperparameter=self.paramSpace units=int(ae_hyperparameter['hidden_units']) latent_units=int(ae_hyperparameter['latentspace_size']) activation=ae_hyperparameter['activation'] ##For task 20731 minimum_threshold_user = str(ae_hyperparameter['min_threshold']) maximum_threshold_user = str(ae_hyperparameter['max_threshold']) train_data=train_data.values test_data=test_data.values autoencoder = AeDetector(train_data,test_data,units,latent_units,activation) opt=ae_hyperparameter['optimizer'] loss_fn=ae_hyperparameter["loss"] # loss_fn='binary_crossentropy' epochs=int(ae_hyperparameter['epochs']) batch_size=int(ae_hyperparameter['batch_size']) learning_rate=float(ae_hyperparameter['learning_rate']) # autoencoder.save('../output/autoenc',save_format='tf') # cwd = os.path.abspath(os.path.dirname(__file__)) cwd=self.deployLocation try: artifact_dir=os.path.normpath(os.path.join(cwd,'output','autoenc_artifact_dir')) if not os.path.isdir(artifact_dir): os.makedirs(artifact_dir) except Exception as e: self.log.info("<---- artifact_dir path error. Error Msg: ---->"+str(e)) es,mc=self.callbacks(filename = artifact_dir, patience = 5, monitor = "val_loss") # es,mc=self.callbacks(filename = "../output/autoenc.sav", patience = 3, monitor = "val_loss") self.model_compile(autoencoder,learning_rate, loss_fn, opt) # autoencoder.compile(optimizer='adam', loss='mae') autoencoder.fit(train_data, train_data, epochs = epochs, batch_size=batch_size, validation_data=(test_data, test_data),callbacks = [mc, es]) reconstructed = autoencoder(train_data) train_mae_loss = tf.keras.losses.mae(reconstructed, train_data) #threshold = np.mean(train_mae_loss) + 2*np.std(train_mae_loss) #min_threshold = np.mean(train_mae_loss)- 2*np.std(train_mae_loss) ## Task 20731 if ((minimum_threshold_user and minimum_threshold_user.strip()) and (maximum_threshold_user and maximum_threshold_user.strip())): threshold = float(maximum_threshold_user) min_threshold = float(minimum_threshold_user) elif ((minimum_threshold_user.strip()) and (not maximum_threshold_user.strip())): threshold = np.mean(train_mae_loss) + 2*np.std(train_mae_loss) min_threshold = float(minimum_threshold_user) elif ((not minimum_threshold_user.strip()) and (maximum_threshold_user.strip())): threshold = float(maximum_threshold_user) min_threshold = np.mean(train_mae_loss) - 2*np.std(train_mae_loss) else: threshold = np.mean(train_mae_loss) + 2*np.std(train_mae_loss) min_threshold = np.mean(train_mae_loss) - 2*np.std(train_mae_loss) # threshold = np.mean(train_mae_loss) + np.std(train_mae_loss) self.log.info("Anomaly Upper Threshold value based on loss fn (MAE): "+str(threshold)) self.log.info("Anomaly lower_threshold value based on loss fn (MAE): "+str(min_threshold)) test_labels=None ## No test labels passed pred,test_score_df,actual_data,anomaly_info_df = self.prediction(autoencoder, test_data, min_threshold,threshold,test_labels,'False',None,None) # normal_prediction_df=(anomaly_info_df[~anomaly_info_df['anomaly_value']]) normal_prediction_df=anomaly_info_df.loc[anomaly_info_df['anomaly_value']==False] anomaly_prediction_df=anomaly_info_df.loc[anomaly_info_df['anomaly_value']==True] self.log.info("<---- Autoencoder (non timeseries) based anomaly detection, anomalies in data: ---->"+str(anomaly_prediction_df)) self.log.info("<---- Number of anomalies in data: ---->"+str(len(anomaly_prediction_df))) self.save_anomalyvalues(anomaly_prediction_df,'anomaly_dataframe') # combined_df=pd.concat([anomaly_prediction_df,normal_prediction_df],ignore_index=True) self.log.info("<---- Autoencoder (non timeseries) based anomaly detection, overall data (both anomaly and non-anomaly ) in data: ---->"+str(anomaly_info_df)) # self.save_anomalyvalues(combined_df,'overall_dataframe') ## If categorical features in original df, then inverse transform the values. try: ##anomaly_info_df,total dataframe. self.naming_anomalyvalues(anomaly_info_df) except Exception as e: self.log.info("anomaly_info_df exception err msg: \n"+str(e)) ##Now we are storing anomaly log (as dataframe) based on two options: 1. Anomalies based on all features, 2. Anomalies based on each individual feature. if (mv_unique_feature_ad.lower()=='true'): ## Multivariate and saving individual feature based anomalies self.save_anomalyvalues(anomaly_prediction_df,(str(feature_name)+'_anomaly_dataframe')) try: anomaly_info_df=self.merge_pre_post_dfs(anomaly_info_df) except Exception as e: #If merge fails, just out!. self.log.info("Anomaly Detection Merge df exception :\n"+str(e)) finally: #check merging done or not, to be imp. pass self.save_anomalyvalues(anomaly_info_df,(str(feature_name)+'_overall_dataframe')) ## Save actual test data actual_data #self.save_anomalyvalues(actual_data,(str(feature_name)+'_testdata')) else: self.save_anomalyvalues(anomaly_prediction_df,'anomaly_dataframe') try: anomaly_info_df=self.merge_pre_post_dfs(anomaly_info_df) except Exception as e: #If merge fails, just out!. self.log.info("Anomaly Detection Merge df exception :\n"+str(e)) finally: #check merging done or not, to be imp. pass self.save_anomalyvalues(anomaly_info_df,'overall_dataframe') #Ordered data # self.save_anomalyvalues(combined_df,'ts_overall_dataframe_ordered') ## If we want anomaly dataframe with loss and threshold for each values (rows),please uncomment below line # self.save_anomalyvalues(anomal_loss_threshold,'ts_anomaly_dataframe_lt'), ## Save actual test data test_score_df #self.save_anomalyvalues(actual_data,'testdata') self.log.info("<---- Autoencoder non time series / supervised problem anomalies : ---->"+str(anomaly_prediction_df)) #ploting df_subset = anomaly_prediction_df fig, ax = plt.subplots() df.plot(legend=False, ax=ax) df_subset.plot(legend=False, ax=ax, color="r") ax.set_title("Anomaly Data Plot") ax.set_xlabel("DateTime") ax.set_ylabel("Values") plot_name='anomalyplot.png' try: plot_dir=os.path.normpath(os.path.join(cwd,'output','anomaly_plot')) if not os.path.isdir(plot_dir): os.makedirs(plot_dir) plotpath=str(plot_dir)+'/'+plot_name except Exception as e: self.log.info("<---- plot_dir path error. Error Msg: ---->"+str(e)) if os.path.exists(plotpath): os.remove(plotpath) plt.savefig(plotpath) # plt.savefig(str(plot_dir)+'/'+plot_name) plt.clf() plt.cla() plt.close() else: y=df[target] X=df.drop(target, axis=1) train_data,test_data,train_labels,test_labels=train_test_split(X,y,test_size=0.2,random_state=42) count_classes = pd.value_counts(df[target], sort = True) num_of_classes= len(count_classes) self.log.info("train_data info: \n"+str(train_data.info())) if (num_of_classes >= 2): # scaler = StandardScaler() # train_data = scaler.fit_transform(train_data) # test_data = scaler.fit_transform(test_data) # self.saveScaler(scaler) train_labels = train_labels.astype(bool) test_labels = test_labels.astype(bool) n_train_data = train_data[train_labels] n_test_data = test_data[test_labels] # data1=scaler.inverse_transform(n_test_data) n_test_data_actual=pd.DataFrame(n_test_data) ##anomaly data an_train_data = train_data[~train_labels] an_test_data = test_data[~test_labels] n_train_data = train_data[train_labels] n_test_data = test_data[test_labels] ae_hyperparameter=self.paramSpace # autoencoder = AeDetector(n_train_data,n_test_data) activation=ae_hyperparameter['activation'] units=int(ae_hyperparameter['hidden_units']) latent_units=int(ae_hyperparameter['latentspace_size']) ##For task 20731 minimum_threshold_user = str(ae_hyperparameter['min_threshold']) maximum_threshold_user = str(ae_hyperparameter['max_threshold']) autoencoder = AeDetector(n_train_data,n_test_data,units,latent_units,activation) opt=ae_hyperparameter['optimizer'] loss_fn=ae_hyperparameter["loss"] batch_size=int(ae_hyperparameter['batch_size']) # loss_fn='binary_crossentropy' epochs=int(ae_hyperparameter['epochs']) learning_rate=float(ae_hyperparameter['learning_rate']) cwd=self.deployLocation try: artifact_dir=os.path.normpath(os.path.join(cwd,'output','autoenc_artifact_dir')) if not os.path.isdir(artifact_dir): os.makedirs(artifact_dir) except Exception as e: self.log.info("<---- artifact_dir path error. Error Msg: ---->"+str(e)) es,mc=self.callbacks(filename = artifact_dir, patience = 5, monitor = "val_loss") self.model_compile(autoencoder,learning_rate, loss_fn, opt) # autoencoder.compile(optimizer='adam', loss='mae') autoencoder.fit(n_train_data, n_train_data, epochs = epochs, batch_size=batch_size, validation_data=(n_test_data, n_test_data),callbacks = [mc, es]) model_info=self.summary(autoencoder) self.log.info("<---- Auto encoder anomaly detection model information: ---->"+str(model_info)) # reconstructed = autoencoder(n_train_data) reconstructed = autoencoder.predict(n_train_data) #threshold = self.find_threshold(autoencoder, n_train_data) train_mae_loss = tf.keras.losses.mae(reconstructed, n_train_data) pred=tf.math.less(train_mae_loss, threshold) ## Task 20731 if ((minimum_threshold_user and minimum_threshold_user.strip()) and (maximum_threshold_user and maximum_threshold_user.strip())): threshold = float(maximum_threshold_user) min_threshold = float(minimum_threshold_user) elif ((minimum_threshold_user.strip()) and (not maximum_threshold_user.strip())): threshold = np.mean(train_mae_loss) + 2*np.std(train_mae_loss) min_threshold = float(minimum_threshold_user) elif ((not minimum_threshold_user.strip()) and (maximum_threshold_user.strip())): threshold = float(maximum_threshold_user) min_threshold = np.mean(train_mae_loss) - 2*np.std(train_mae_loss) else: threshold = np.mean(train_mae_loss) + 2*np.std(train_mae_loss) min_threshold = np.mean(train_mae_loss) - 2*np.std(train_mae_loss) self.log.info("Anomaly threshold max value based on loss fn (MAE): "+str(threshold)) self.log.info("Anomaly threshold min value based on loss fn (MAE): "+str(min_threshold)) pred,test_score_df,actual_data,anomaly_info_df = self.prediction(autoencoder, n_test_data, min_threshold,threshold,test_labels,'False',None,None) # normal_prediction_df=(anomaly_info_df[~anomaly_info_df['anomaly_value']]) normal_prediction_df=anomaly_info_df.loc[anomaly_info_df['anomaly_value']==False] # normal_prediction_df.to_csv('normal_prediction_df.csv') # anomaly_prediction_df=(anomaly_info_df[anomaly_info_df['anomaly_value']]) anomaly_prediction_df=anomaly_info_df.loc[anomaly_info_df['anomaly_value']==True] self.log.info("<---- Autoencoder (non timeseries) based anomaly detection, overall data (both anomaly and non-anomaly ) in data: ---->"+str(anomaly_info_df)) # self.save_anomalyvalues(combined_df,'overall_dataframe') ## If categorical features in original df, then inverse transform the values. try: ##anomaly_info_df,total dataframe. self.naming_anomalyvalues(anomaly_info_df) except Exception as e: self.log.info("anomaly_info_df exception err msg: \n"+str(e)) ##Now we are storing anomaly log (as dataframe) based on two options: 1. Anomalies based on all features, 2. Anomalies based on each individual feature. if (mv_unique_feature_ad.lower()=='true'): ## Multivariate and saving individual feature based anomalies self.save_anomalyvalues(anomaly_prediction_df,(str(feature_name)+'_anomaly_dataframe')) try: anomaly_info_df=self.merge_pre_post_dfs(anomaly_info_df) except Exception as e: #If merge fails, just out!. self.log.info("Anomaly Detection Merge df exception :\n"+str(e)) finally: #check merging done or not, to be imp. pass self.save_anomalyvalues(anomaly_info_df,(str(feature_name)+'_overall_dataframe')) ## Save actual test data actual_data #self.save_anomalyvalues(actual_data,(str(feature_name)+'_testdata')) else: self.save_anomalyvalues(anomaly_prediction_df,'anomaly_dataframe') try: anomaly_info_df=self.merge_pre_post_dfs(anomaly_info_df) except Exception as e: #If merge fails, just out!. self.log.info("Anomaly Detection Merge df exception :\n"+str(e)) finally: #check merging done or not, to be imp. pass self.save_anomalyvalues(anomaly_info_df,'overall_dataframe') ## Save actual test data test_score_df #self.save_anomalyvalues(actual_data,'testdata') self.log.info("<----Number of anomalies in data: ---->"+str(len(anomaly_prediction_df))) """ Plot to show case anomalies, now commented, for testing purpose uncomment and check visually anomalies. """ #ploting df_subset = anomaly_prediction_df fig, ax = plt.subplots() df.plot(legend=False, ax=ax) df_subset.plot(legend=False, ax=ax, color="r") # plt.show() ax.set_title("Anomaly Data Plot") ax.set_xlabel("DateTime") ax.set_ylabel("Values") plot_name='anomalyplot.png' try: plot_dir=os.path.normpath(os.path.join(cwd,'output','anomaly_plot')) if not os.path.isdir(plot_dir): os.makedirs(plot_dir) plotpath=str(plot_dir)+'/'+plot_name except Exception as e: self.log.info("<---- plot_dir path error. Error Msg: ---->"+str(e)) if os.path.exists(plotpath): os.remove(plotpath) plt.savefig(plotpath) # plt.savefig(str(plot_dir)+'/'+plot_name) plt.clf() plt.cla() plt.close() else: self.log.info("<---- Check dataset and basic configurations. ---->") except Exception as e: self.log.info("<---- Non time series anomaly detection error msg: ---->"+str(e)) self.log.info("<---- Non time series anomaly detection error msg (detailed): ---->"+str(traceback.format_exc())) return autoencoder,anomaly_prediction_df,anomaly_info_df ## Hyperparameter tuning autoencoders, not implemented def hyperparamtuning_AE(self): try: self.log.info ("autoencoder hyperparam tuning.not implemented.") except Exception as e: self.log.info("autoencoder hyperparam tuning error: "+str(e)) pass ## randomsearch for dbscan def hyperparamtuning_dbscan(self,model,tuner,Parameter_Trials,data): params=model.get_params().keys() try: labels = model.labels_ #df = pd.DataFrame(labels) try: scorer = metrics.silhouette_score(data, labels) except: pass if (tuner.lower() == 'randomsearch'): # Parameters to try cluster_labels = model.labels_ Random_Search = RandomizedSearchCV(model, Parameter_Trials, n_iter=50,cv=5, scoring='adjusted_rand_score', refit=True, n_jobs=1, verbose=5) RandomSearchResults=Random_Search.fit(data) # Fetching the best hyperparameters best_params=RandomSearchResults.best_params_ # All the parameter combinations tried by RandomizedSearchCV RandomSearchResults.cv_results_['params'] except Exception as e: self.log.info("<---- dbscan hpt error msg: ---->"+str(e)) self.log.info("<---- dbscan hpt error msg (detailed): ---->"+str(traceback.format_exc())) return best_params ## Reading aion postprocess data from target->AION_usecaseNo->data->postprocess data def read_inputdata(self): cwd=self.deployLocation try: in_path=os.path.normpath(os.path.join(cwd,'data')) if not os.path.isdir(in_path): self.log.info("<---- Anomaly detection target data folder not available.--->\n") postprocesseddata=os.path.normpath(os.path.join(cwd,'data','postprocesseddata.csv')) postprocessed_df=pd.read_csv(postprocesseddata) except Exception as e: self.log.info("<---- Anomaly detection target data folder not available, Reading postprocess csv file issue. Error Msg: ---->"+str(e)) return postprocessed_df ## Get original dataframe values using preprocess pipe after output data created. ##get_label_dict fn not used now. Use if preprocess_pipe based transform needed. def get_label_dict(self, pipe): label_dict = {} dict_pipe={} for (comp_name, component) in pipe.transformer_list: if 'labelencoding' in comp_name: i=1 for step in component.steps: key='val'+'_'+str(i) ordinalencoder=step[1] dict_pipe[f'val_{i}']=ordinalencoder # dict_pipe[key].append(ordinalencoder) label_dict.update(dict_pipe) i=i+1 return label_dict else: continue return label_dict ## Decode label features using aion preprocessed_pipe model,not used now. If we need to use preprocess pipe for inverse transform,use below block. def decoder_labeled_features(self,df): import joblib try: cwd=self.deployLocation # in_path=os.path.normpath(os.path.join(cwd,'data')) if not os.path.isdir(in_path): self.log.info("<---- Anomaly detection target model folder not available.--->\n") preprocessed_pipe=os.path.normpath(os.path.join(cwd,'model','preprocess_pipe.pkl')) model = joblib.load(preprocessed_pipe) label_dict = get_label_dict(model) encoder=label_dict.get('val_4') num_cols = orig_data.select_dtypes(include=np.number).columns.tolist() cat_cols = orig_data.select_dtypes(exclude=np.number).columns.tolist() cat_col_actual=[] for col in cat_cols: try: df1=encoder.inverse_transform(df[col]) cat_col_actual.append(col) except: pass df1=pd.DataFrame(data=df1) df1.columns=cat_cols df2=df[num_cols] df_anomalyinfo_col=df['anomaly_value'] df_list = [df2, df1, df_anomalyinfo_col] # List of your dataframes combined_df = pd.concat(df_list, join='outer', axis=1).fillna(0) except: combined_df=None pass return combined_df ## save predicted data and actual data columns. For get back user original data features # def merge_pre_post_dfs(self,out_df=None): cwd=self.deployLocation anomaly_algorithm=str(self.anomalyMethod) try: in_path=os.path.normpath(os.path.join(cwd,'data')) if not os.path.isdir(in_path): self.log.info("<---- Anomaly detection target data folder not available.--->\n") preprocessed_file=os.path.normpath(os.path.join(cwd,'data','preprocesseddata.csv')) preprocessed_df=pd.read_csv(preprocessed_file) ## cat_cols will get categorical col from preprocessed, cat_diff_cols will get common cat col between output df and preprocessed. cat_cols=preprocessed_df.select_dtypes(exclude=np.number).columns.tolist() num_cols = preprocessed_df.select_dtypes(include=np.number).columns.tolist() cat_diff_cols=list(set(cat_cols).intersection(out_df.columns.tolist())) diff_cols=list(set(preprocessed_df.columns).difference(out_df.columns)) if (cat_diff_cols): if (len(preprocessed_df) == len(out_df)): #Drop each categorical col of original data from output df (which have numerical converted values). So, in merging can be done on perfect columns try: ## get common categorical col name between actual and output df for col in cat_diff_cols : out_df.drop(col,axis=1,inplace=True) except: self.log.info("drop col not possible, pass the step.") #Just continue pass diff_cols=list(set(preprocessed_df.columns).difference(out_df.columns)) try: ## Check any datetime column in output df and preprocesseddata import pandas.api.types as ptypes outdf_dt_index_check=ptypes.is_datetime64_dtype(out_df.index) #Is output df have datetime col if (outdf_dt_index_check): if ((self.datetimeFeature.lower() !='na' and self.datetimeFeature)): try: preprocessed_df[self.datetimeFeature] = pd.to_datetime(preprocessed_df[self.datetimeFeature]) preprocessed_df.set_index(self.datetimeFeature, inplace=True) except Exception as e: self.log.info("Given data not contain datetime specified."+str(traceback.format_exc())) ## Below step ,making datetime index to date time column. for merging and droping purpose. preprocessed_df.reset_index(inplace=True) preprocessed_df.rename(columns={"index":self.datetimeFeature},inplace=True) out_df.reset_index(inplace=True) out_df.rename(columns={"index":self.datetimeFeature},inplace=True) else: ## If no datetime column, we need to keep both dataframe index columns as unique. so making them as int index. preprocessed_df.reset_index(inplace=True, drop=True) out_df.reset_index(inplace=True, drop=True) pass ## below part is to get status of index columns type (datetime,int or str), commented now. If needed for debug,pls use. # dt_index_check=ptypes.is_datetime64_dtype(out_df.index) # int_index_check=ptypes.is_numeric_dtype(out_df.index) # str_index_check=ptypes.is_string_dtype(out_df.index) ## Get common column between preprocess and output df try: if (anomaly_algorithm.lower() == 'autoencoder'): common_cols=out_df.drop(['loss','max_threshold','min_threshold','anomaly_value'],axis=1) common_cols.drop(common_cols.filter(regex="Unname"),axis=1, inplace=True) merge_on_cols=common_cols.columns.tolist() combined_df = preprocessed_df.merge(out_df, on=merge_on_cols,how='inner') ## Drop duplicate based on columns except time # drop_duplicate_on=merge_on_cols.extend(cat_diff_cols) drop_duplicate_on=merge_on_cols+cat_diff_cols combined_df = combined_df.drop_duplicates(drop_duplicate_on, keep=False) else: ## otherwise, it is dbscan algorithm common_cols=out_df.drop(['cluster','anomaly_value'],axis=1) common_cols.drop(common_cols.filter(regex="Unname"),axis=1, inplace=True) merge_on_cols=common_cols.columns.tolist() combined_df = preprocessed_df.merge(out_df, on=merge_on_cols,how='inner') ## Drop duplicate based on columns except time # drop_duplicate_on=merge_on_cols+cat_diff_cols combined_df = combined_df.drop_duplicates(merge_on_cols, keep='last') except: combined_df=out_df pass ## Just for reference, in future if you want different merge/join option # combined_df = pd.merge(preprocessed_df[diff_cols],out_df, left_index=True, right_index=True, how='inner') except Exception as e: self.log.info("<---- merge error msg : ---->"+str(e)) self.log.info("<---- merge error msg (detailed): ---->"+str(traceback.format_exc())) pass ## if both data frame have different columns (preprocessed and outdf) else: self.log.info("User data is preprocessed and data cleaning happened.So, actual data and processed data length mismatch. So,data records range may vary.") try: # combined_df=self.decoder_labeled_features(out_df) combined_df = preprocessed_df.merge(out_df,on=num_cols,how='inner') # combined_df = combined_df.drop_duplicates(cat_cols, keep='last') combined_df = combined_df.drop_duplicates(num_cols, keep='last') except: ## If nothing in merge works,then make outdf as final dataframe. try: ## If above merge fails, change drop_duplicate hyperparam keep='last' last appearance of key occurance. combined_df = preprocessed_df.merge(out_df,on=num_cols,how='inner') combined_df = combined_df.drop_duplicates(cat_cols, keep=False) except: #If nothing is works, just keep out_df as combined df combined_df=out_df ## if no common categorical col found between preprocessed and outdf. else: ## If merge not works,then make outdf as final dataframe. if (len(cat_cols) > 0): try: combined_df = preprocessed_df.merge(out_df,on=num_cols,how='inner') combined_df = combined_df.drop_duplicates(cat_cols, keep='last') except: #make safe for return combined_df=out_df else: ##If no categorical features available combined_df = preprocessed_df.merge(out_df,on=num_cols,how='inner') combined_df = combined_df.drop_duplicates(num_cols, keep=False) except Exception as e: self.log.info("<---- Anomaly detection target data folder not available, dataframe merging issue. Error Msg: ---->"+str(e)) self.log.info("making output df as final merged data, no categorical column found in output anomaly data. It is user responsibility to check the anomaly data.") #make safe for return combined_df=out_df return combined_df ## for module reusability, this below naming fn created. def naming_anomalyvalues(self,df): replace_values_T='|'.join(['TRUE','True','true']) replace_values_F='|'.join(['FALSE','False','false']) try: df['anomaly_value']=df['anomaly_value'].astype(str).replace(replace_values_T,'AnomalyDataPoint', regex=True) except: df['anomaly_value']=df['anomaly_value'].replace(replace_values_T,'AnomalyDataPoint', regex=True) df['anomaly_value']=df['anomaly_value'].astype(str).replace(replace_values_F,'NormalDataPoint', regex=True) return df ## DBScan based anomaly detection def dbscan_ad(self,data,eps,min_samples,cols): try: tuner='randomsearch' Parameter_Trials={'eps':eps, 'min_samples':min_samples} model = DBSCAN(algorithm='auto') hist = model.fit(data) pred = model.fit_predict(data) best_params = self.hyperparamtuning_dbscan(model,tuner,Parameter_Trials,data) self.log.info("<---- Best hyper parameters for dbscan: ---->"+str(best_params)) best_eps=best_params['eps'] best_min_samples=best_params['min_samples'] if (best_min_samples < len(cols)): min_samples=len(cols)+1 if (best_eps < 0.2): best_eps=0.2 self.log.info("best_eps: \n"+str(best_eps)) self.log.info("best_min_samples: \n"+str(best_min_samples)) best_model=DBSCAN(algorithm='auto',eps = best_eps, min_samples = best_min_samples) hist = best_model.fit(data) pred = best_model.fit_predict(data) best_labels=best_model.labels_ cluster_name = ["Cluster"+str(i) for i in set(best_labels)] # outliers = data[best_model.labels_ == -1] outlier_df = data.copy() outlier_df.loc[:,'cluster'] = best_model.labels_ outliers_final=outlier_df[outlier_df['cluster']==-1] outliers_final['anomaly_value']=outliers_final['cluster']==-1 normaldata= outlier_df[outlier_df['cluster']!=-1] self.log.info("<---- DBScan: Anomalies in data: ---->"+str(outliers_final)) self.log.info("<---- DBScan: Number of anomalies in data: ---->"+str(len(outliers_final))) # num_cat_features=len(self.cat_cols) try: self.save_anomalyvalues(outliers_final,'dbscan_anomaly_dataframe') self.save_anomalyvalues(normaldata,'dbscan_normaldata_dataframe') outlier_df['anomaly_value']=outlier_df['cluster']==-1 outlier_df=self.naming_anomalyvalues(outlier_df) ##Convert results to original input data form for end user ease of understanding try: outlier_df=self.merge_pre_post_dfs(outlier_df) except Exception as e: self.log.info("Anomaly Detection Merge df exception:\n"+str(e)) #If merge fails, just out! pass self.save_anomalyvalues(outlier_df,'dbscan_overall_dataframe') except Exception as e: self.log.info("DBScan inversetransform err. msg: \n"+str(e)) no_clusters = len(set(best_labels)) - (1 if -1 in best_labels else 0) self.log.info("<---- DBScan: No of clusters: ---->"+str(no_clusters)) n_noise_ = list(best_labels).count(-1) ## Ploting the dbscan clusters plot_name='dbscan_anomalyplot.png' fig, ax = plt.subplots() ax.set_title("DBScan Clusters") ax.scatter(data.iloc[:, 0], data.iloc[:, 1], c=best_labels) outliers_plot = data[best_model.labels_ == -1] ax.scatter(outliers_plot.iloc[:, 0], outliers_plot.iloc[:, 1], c='red') cwd=self.deployLocation try: plot_dir=os.path.normpath(os.path.join(cwd,'output','anomaly_plot')) if not os.path.isdir(plot_dir): os.makedirs(plot_dir) plotpath=str(plot_dir)+'/'+plot_name except Exception as e: self.log.info("<---- plot_dir path error. Error Msg: ---->"+str(e)) if os.path.exists(plotpath): os.remove(plotpath) plt.savefig(plotpath) plt.clf() plt.cla() plt.close() except Exception as e: self.log.info("<---- dbscan error msg: ---->"+str(e)) self.log.info("<---- dbscan error msg (detailed): ---->"+str(traceback.format_exc())) return best_model,outliers_final ## Inverse transform fn for categorical data def inverse_transform(self,df,cat_cols,le_model): df_new=pd.DataFrame() df_new.index=df.index df_reset_index=df.reset_index(drop=True) for col in cat_cols: df_reset_index[col] = le_model.inverse_transform(df_reset_index[col].astype(int)) df_reset_index.index=df_new.index df=df_reset_index return df ##If data comes without going via aion data profiler, we can use this below preprcessing fn () ##Preprocess fn for categorical data , not used now. def preprocessfn_categorical(self,df): try: cat_cols=self.cat_cols preprocessed_df=None le=preprocessing.LabelEncoder() self.le_model=le label_encoded_df = df.copy() for col in cat_cols: label_encoded_df[col]=le.fit_transform(label_encoded_df[col]) except Exception as e: self.log.info("preprocessfn_categorical error traceback."+str(traceback.format_exc())) return label_encoded_df,cat_cols ## Design pattern: Factory,Adapter. Detect antoencoder object or dbscan object based on input params. The interface can be used for anyother extention. Not created any abstract class. ##Main autoencoder based anomaly detection function, from here, sub modules will be called. def mainAnomalyDetectionfn(self): df=self.df ## reading post processed data from target->usecase->data directory # df=self.read_inputdata() ## Below line overwrite incoming df with postprocesseddata self.log.info("<----------- In autoencoder based anomaly detection algorithm main process module, the incoming dataframe information as below: \n") buf_info=self.get_df_info(df) self.log.info(buf_info) model_location=None time_series_data=None # mv_unique_feature_ad='' ae_hyperparameter=self.paramSpace # self.log.info("mainAnomalyDetectionfn df: \n"+str(df)) self.log.info("paramSpace in mainano: \n"+str(self.paramSpace)) target=self.target anomaly_algorithm=str(self.anomalyMethod) mv_unique_feature_ad=self.mv_featurebased_ad_status # df=self.dataload(datapath) df.drop(df.filter(regex="Unname"),axis=1, inplace=True) df.drop(df.filter(regex="truncated"),axis=1, inplace=True) # cols2remove=df.columns[df.columns.str.startswith('Unname')] # df.drop(cols2remove,axis=1,inplace=True) # df.to_csv("C:\Users\jayaramakrishnans\AppData\Local\Programs\HCLTech\AION\data\target\actual_df.csv") datetime_column=self.datetimeFeature len_dtf=len(self.datetimeFeature) ##create datetime pattern source py file in target dir. self.create_datetime_pyfile() # cat_cols=None if (self.datetimeFeature.lower() == 'na' or self.datetimeFeature==''): len_dtf=0 if (len_dtf >= 1 ): time_series_data="True" else: time_series_data="False" self.datetimeFeature='' try: if (target != ''): if (anomaly_algorithm.lower() == 'autoencoder'): self.log.info("Supervised anomaly detection started.\n") """ Below part for supervised time series anomaly detection.If timeseries anomaly (supervised) used in future, please uncomemnt and use below code snippet. """ # if (ae_hyperparameter['time_series_data'].lower() == 'true'): # print("Given data is time series data and supervised learning category, because it target is labeled one.") # datetime_column=str(self.datetimeFeature) # time_steps=int(ae_hyperparameter['time_steps']) # test_size_perc=int(ae_hyperparameter['test_size_perc']) # df[datetime_column] = pd.to_datetime(df[datetime_column]) # df.set_index(datetime_column, inplace=True) # self.aionAEAnomalyTS(df,test_size_perc,target,time_steps) ## Removing datetime feature for supervised autoencoder (currently timeseries anomaly not supported in supervised anomaly detection autoencoder) test_size_perc=self.testSize*100 df=df.dropna() model,anomaly_prediction_df,combined_df = self.ae_nontimeseriesmodelfn(df,target) # print("*** End of Autoencoder based non time series Anomaly detection. *** \n") self.log.info("*** End of Autoencoder based non time series Anomaly detection. ***") features=df.columns if (len(features)== 1): # print("Problem type is Univariate time series anomaly detection.\n") self.log.info("Problem type is Univariate time series anomaly detection.\n") test_size_perc=self.testSize*100 df=df.dropna() model,anomaly_prediction_df,combined_df = self.ae_nontimeseriesmodelfn(df,target) elif (len(features) > 1): df.drop(df.filter(regex="Unname"),axis=1, inplace=True) test_size_perc=self.testSize*100 df=df.dropna() model,anomaly_prediction_df,combined_df = self.ae_nontimeseriesmodelfn(df,target) if (mv_unique_feature_ad.lower()=='true'): self.log.info("\n\n *** Below is the anomaly values based on each feature of multivariate time series data. ***") df.drop(df.filter(regex="Unname"),axis=1, inplace=True) multivariate_cols= df.columns.values.tolist() for col in multivariate_cols: col=str(col) ## creating dataframe for one of feature in multivariate dataset. multivariate_col_df = df[col].to_frame() model,anomaly_prediction_df,combined_df = self.ae_nontimeseriesmodelfn(multivariate_col_df,target) else: if (anomaly_algorithm.lower() == 'dbscan'): self.log.info("DBScan algorithm not supported for supervised training. \n") else: try: self.log.info("Unsupervised anomaly detection started....\n") if (anomaly_algorithm.lower() == 'autoencoder'): test_size_perc=self.testSize*100 if (time_series_data.lower() == 'true'): mv_unique_feature_ad=self.mv_featurebased_ad_status dropout=float(ae_hyperparameter['dropout']) datetime_column=str(self.datetimeFeature) time_steps=int(ae_hyperparameter['time_steps']) target=None features=df.columns if (len(features)== 1): self.log.info("Problem type is Univariate time series anomaly detection.\n") model,anomaly_prediction_df,combined_df = self.aionAEAnomalyTS(df,test_size_perc,target,time_steps,dropout,'False') elif (len(features) > 1): df.drop(df.filter(regex="Unname"),axis=1, inplace=True) self.log.info("Problem type is Multivariate time series anomaly detection.\n") self.log.info("*** Detecting anomaly in the given multi feature (Multivariate) data. Anomaly values based on all the features passed to the aion anomaly AE algorithm. ***") model,anomaly_prediction_df,combined_df = self.aionAEAnomalyTS(df,test_size_perc,target,time_steps,dropout,'False') if (mv_unique_feature_ad.lower()=='true'): self.log.info("\n\n *** Below is the anomaly values based on each feature of multivariate time series data. ***") df.drop(df.filter(regex="Unname"),axis=1, inplace=True) multivariate_cols= df.columns.values.tolist() # for i in range(1,len(features)): for col in multivariate_cols: col=str(col) ## creating dataframe for one of feature in multivariate dataset. multivariate_col = df[col].to_frame() feature_based_model,anomaly_prediction_df_mv,combined_df = self.aionAEAnomalyTS(multivariate_col,test_size_perc,target,time_steps,dropout,mv_unique_feature_ad) """ Below code snippet is commented, the purpose is AION giving 3 option to user for time series, < Three types: univariate_tsd(single_timeseriesdetection), multivariate_tsd, multivariate_seperate_tsd>, based on that specific sub function called. """ # if (ae_hyperparameter['timeseries_ad_type'].lower() =='univariate_tsad'): ## univariate_tsd # self.aionAEAnomalyTS(df,test_size_perc,target,time_steps,dropout) # elif (ae_hyperparameter['timeseries_ad_type'].lower() =='multivariate_tsad'): ##multivariate_tsd # if (len(features) <=1): # # self.aionAEAnomalyTS(df,test_size_perc,target,time_steps,dropout) # print("Given data looks like univariate data. Cannot apply multivariate. Check data and select appropriate timeseries anomaly detection option.") # else: # self.aionAEAnomalyTS(df,test_size_perc,target,time_steps,dropout) # elif (ae_hyperparameter['timeseries_ad_type'].lower() =='multivariate_featurebased_tsad'): ## multivariate_seperate_tsd # # features=df.columns # df.drop(df.filter(regex="Unname"),axis=1, inplace=True) # multivariate_cols= df.columns.values.tolist() # print("multivariate_cols: \n",multivariate_cols) # print("multivariate_cols type: \n",type(multivariate_cols)) # if (len(features) <=1): # # self.aionAEAnomalyTS(df,test_size_perc,target,time_steps,dropout) # print("Given data looks like univariate data. Cannot use multivariate.") # else: # # for i in range(1,len(features)): # for col in multivariate_cols: # print("processing multivariate feature name: ",col) # col=str(col) # multivariate_col = df[col].to_frame() # print("multivariate_col type: \n",type(multivariate_col)) # self.aionAEAnomalyTS(multivariate_col,test_size_perc,target,time_steps,dropout) # print("*** End of Autoencoder based time series Anomaly detection.*** \n") self.log.info("*** End of Autoencoder based non time series Anomaly detection. ***") else: target='' df=df.dropna() model,anomaly_prediction_df,combined_df = self.ae_nontimeseriesmodelfn(df,target) elif (anomaly_algorithm.lower() == 'dbscan'): # df=df.dropna() self.log.info("*** DBScan algorithm enabled. ***") cols=df.columns dbscan_hyperparameter=self.paramSpace eps = list(dbscan_hyperparameter['eps']) # eps=list(dbscan_hyperparameter['eps']) # min_samples=list(dbscan_hyperparameter['min_samples']) min_samples = list(dbscan_hyperparameter['min_samples']) model,outliers = self.dbscan_ad(df,eps,min_samples,cols) except Exception as e: self.log.info("Unsupervised anomaly detection error msg: "+str(traceback.format_exc())) ##main ae AeDetector except Exception as e: self.log.info("<---- Main fn error msg of anomaly detection for debug purpose: ---->"+str(e)) self.log.info("<---- Main fn error msg of anomaly detection for debug purpose: (detailed): ---->"+str(traceback.format_exc())) return model ## For testing standalone code if __name__ == '__main__': # print ("main function....") target=None df=None hparams=None AEAD=anomalyDetectionAE() datapath,target,ae_hyperparameter = AEAD.mainAnomalyDetectionfn() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import time import os import sys import logging from sklearn.metrics import accuracy_score, make_scorer from sklearn.model_selection import train_test_split from sklearn.svm import OneClassSVM from sklearn.ensemble import IsolationForest import pickle from sklearn import metrics import numpy as np import pandas as pd from learner.aion_matrix import aion_matrix from learner.parameters import parametersDefine from sklearn.metrics import f1_score from sklearn import model_selection from learner.anomalyDetectionAE import anomalyDetectionAE class anomalyDetector(object): def __init__(self): self.log = logging.getLogger('eion') def startanomalydetector(self,df,target,labelMaps,inlierLabels,learnerJson,model_type,saved_model,anomalyMethod,deployLocation,predicted_data_file,testPercentage,anomalyconfig,datetimeFeature,mv_featurebased_ad_status): try: self.log.info("startanomalydetector.... \n") from io import StringIO buf = StringIO() df.info(buf=buf) #self.log.info(buf.getvalue()) self.log.info("User data info : \n"+str(buf.getvalue())) try: df[datetimeFeature] = pd.to_datetime(df[datetimeFeature]) df.set_index(datetimeFeature, inplace=True) #If still the datetime column exist in feature list, drop it. Because we already made datetime feature as index (datetimeindex) df.drop(datetimeFeature,axis=1,inplace=True) except Exception as e: pass ae_df=df paramObj=parametersDefine() anomalyMethod=anomalyMethod inlierLabels=inlierLabels anomalyDetectionType="" inlierLabelList=inlierLabels.split(",") self.log.info("<---- inlierLabels ---->"+inlierLabels) self.log.info("<---- anomalyMethod ---->"+str(anomalyMethod)) if target != "": self.log.info('Status:- |... AnomalyDetection: Supervised') self.log.info("One class based anomaly Detection by relabeling data to fit one class models") combinedString="" dfStr="" anomalyDetectionType="supervised" if not anomalyMethod.lower() == "autoencoder": ##Added for auto encoder self.log.info("startanomalydetector: df: \n"+str(df)) #task 12627 if labelMaps == {}: for inlierVal in inlierLabelList: inlier=inlierVal dfStr = "x ==" + inlier + " or " combinedString+= dfStr func= combinedString.strip(" or ") else: for inlierVal in inlierLabelList: try: if inlierVal.isnumeric(): inlierVal = int(inlierVal) # inlier=str(labelMaps[inlierVal]) ##Wrongly assigned inlier values to labelMaps dict key. inlier = str(inlierVal) dfStr = "x ==" + str(inlier) + " or " combinedString+= dfStr except Exception as e: raise Exception(e) func= combinedString.strip(" or ") labelMaps={'InlierLabel':1,'NonInlierLabel':-1} targetData=df[target] df['anomaly'] = df[target].apply(lambda x: 1 if eval(func) else -1 ) anomtargetData=df['anomaly'] self.log.info("dataframe after relabeling the data") self.log.info(df.head()) self.log.info("target column value counts with inliers and outliers") self.log.info(df['anomaly'].value_counts()) df.drop([target, "anomaly"], axis=1, inplace=True) outliers = anomtargetData[anomtargetData == -1] self.log.info("outliers in data") self.log.info(outliers.shape[0]) self.log.info("outlier fraction") self.log.info(outliers.shape[0]/targetData.shape[0]) if int(testPercentage) != 0: testSize= testPercentage/100 xtrain, xtest, ytrain, ytest = train_test_split(df, anomtargetData, test_size = testSize) else: xtrain =df xtest =df ytrain=anomtargetData ytest =anomtargetData if anomalyMethod.lower() == "isolationforest": modelName="isolationforest" paramSpace=anomalyconfig['modelParams']['IsolationForest'] paramDict =paramObj.paramDefine(paramSpace,'random') ftwo_scorer = make_scorer(accuracy_score) isolation_forest = model_selection.RandomizedSearchCV(IsolationForest(), paramDict, scoring=ftwo_scorer, n_iter=10) mod = isolation_forest.fit(xtrain,ytrain) model = mod.best_estimator_ elif anomalyMethod.lower() == "oneclasssvm": modelName="oneClassSVM" fthree_scorer = make_scorer(accuracy_score) paramSpace=anomalyconfig['modelParams']['oneclassSVM'] paramDict =paramObj.paramDefine(paramSpace,'random') one_class = model_selection.RandomizedSearchCV(OneClassSVM(), paramDict, scoring=fthree_scorer, n_iter=10) mod = one_class.fit(xtrain,ytrain) model = mod.best_estimator_ elif anomalyMethod.lower() == "autoencoder": modelName='autoencoder' testSize=testPercentage/100 self.log.info("Aion Autoencoder anomaly detection started..") paramSpace=anomalyconfig['modelParams']['AutoEncoder'] adae_obj=anomalyDetectionAE(ae_df,paramSpace,deployLocation,target,anomalyMethod,testSize,datetimeFeature,mv_featurebased_ad_status) model=adae_obj.mainAnomalyDetectionfn() self.log.info("Aion Autoencoder anomaly detection completed..") else: self.log.info("IsolationForest, OneClassSVM and autoencoder are supported models") modelName = "" model = "" else: self.log.info('Status:- |... AnomalyDetection: Unsupervised') self.log.info("unsupervised anomaly detection") anomalyDetectionType="unsupervised" model=None xtrain =df xtest = df ytrain = pd.DataFrame() if anomalyMethod.lower() == "isolationforest": paramSpace=anomalyconfig['modelParams']['IsolationForest'] paramDict =paramObj.paramDefine(paramSpace,'random') modelName="isolationforest" def scorer_f(estimator, X): return np.mean(estimator.score_samples(X)) isolation_forest = model_selection.RandomizedSearchCV(IsolationForest(), paramDict, scoring=scorer_f, n_iter=10) mod = isolation_forest.fit(xtrain) self.log.info('---------> Best Param: '+str(mod.best_params_)) model = mod.best_estimator_ elif anomalyMethod.lower() == "oneclasssvm": paramSpace=anomalyconfig['modelParams']['oneclassSVM'] paramDict =paramObj.paramDefine(paramSpace,'random') modelName="oneClassSVM" def scorer_f1(estimator, X): return np.mean(estimator.score_samples(X)) one_class = model_selection.RandomizedSearchCV(OneClassSVM(), paramDict, scoring=scorer_f1, n_iter=10) model = one_class.fit(xtrain) self.log.info('---------> Best Param: '+str(model.best_params_)) model = model.best_estimator_ elif anomalyMethod.lower() == "autoencoder": ae_df.drop(ae_df.filter(regex="Unname"),axis=1, inplace=True) modelName='autoencoder' testSize= testPercentage/100 self.log.info("Aion Autoencoder anomaly detection started..") paramSpace=anomalyconfig['modelParams']['AutoEncoder'] adae_obj=anomalyDetectionAE(ae_df,paramSpace,deployLocation,target,anomalyMethod,testSize,datetimeFeature,mv_featurebased_ad_status) model=adae_obj.mainAnomalyDetectionfn() self.log.info("Aion Autoencoder anomaly detection completed..") elif anomalyMethod.lower() == "dbscan": testSize=testPercentage/100 ae_df.drop(ae_df.filter(regex="Unname"),axis=1, inplace=True) modelName='dbscan' self.log.info("Aion DBScan anomaly detection started..") paramSpace=anomalyconfig['modelParams']['DBScan'] adae_obj=anomalyDetectionAE(ae_df,paramSpace,deployLocation,target,anomalyMethod,testSize,datetimeFeature,mv_featurebased_ad_status) model=adae_obj.mainAnomalyDetectionfn() self.log.info("Aion DBScan anomaly detection completed..") else: self.log.info("IsolationForest,OneClassSVM,autoencoder and DBScan are supported models") modelName = "" model = "" self.log.info('Status:- |... AnomalyDetection Algorithm applied: '+modelName) if (anomalyMethod.lower() == "autoencoder" or anomalyMethod.lower() == "dbscan"): if (anomalyMethod.lower() == "autoencoder"): ## Since autoencoder is implemented using tf.keras, saving the model in h5 format. If we save it in .sav format will give 'TensorSliceReader constructor' error. saved_model=saved_model.replace('.sav','') filename = os.path.join(deployLocation,'model',saved_model) model.save(filename,save_format="tf") elif (anomalyMethod.lower() == "dbscan"): filename = os.path.join(deployLocation,'model',saved_model) pickle.dump(model, open(filename, 'wb')) matrix='' trainmatrix='' accuracy = 0 else: filename = os.path.join(deployLocation,'model',saved_model) pickle.dump(model, open(filename, 'wb')) loaded_model=pickle.load(open(filename, 'rb')) pred_testData=loaded_model.predict(xtest) pred_trainData = loaded_model.predict(xtrain) pred_trainScore = loaded_model.decision_function(xtrain) self.log.info("<--- predicted values of testdata --->") self.log.info(pred_testData) if anomalyDetectionType == "supervised" : df_predicted = pd.DataFrame() df_predicted['actual'] = ytest df_predicted['predict'] = pred_testData df_predicted.to_csv(predicted_data_file) preds = pred_testData targs = ytest unique_elements_ytest, counts_elements_ytest = np.unique(targs, return_counts=True) unique_elements_pred, counts_elements_pred = np.unique(preds, return_counts=True) ''' for i in range(0,len(unique_elements_ytest)): self.log.info("unique value :" +str(unique_elements_ytest[i]) + " count in input testdata: " + str(counts_elements_ytest[i]) +" count in predicted testdata: " + str(counts_elements_pred[i])) self.log.info("\n") ''' self.log.info("\n======= Performance matrix on Test Data ======") aion_matrixobj = aion_matrix() self.log.info("-------> Test Matrix: ") matrix = aion_matrixobj.getClassificationPerformaceMatrix(targs,preds,labelMaps) self.log.info("-------> Train Matrix: ") trainmatrix = aion_matrixobj.getClassificationPerformaceMatrix(ytrain,pred_trainData,labelMaps) #self.log.info("-------> Confusion Matrix: ") self.log.info(metrics.confusion_matrix(targs,preds)) self.log.info("-------> accuracy for inliers: ") accuracy = metrics.accuracy_score(targs, preds) self.log.info(metrics.accuracy_score(targs, preds)) self.log.info("-------> precision for inliers --->") self.log.info(metrics.precision_score(targs, preds)) self.log.info("-------> recall for inliers ---> ") self.log.info(metrics.recall_score(targs, preds)) self.log.info("-------> f1 for inliers--->") self.log.info(metrics.f1_score(targs, preds)) self.log.info("-------> area under curve (auc) for inliers --->") self.log.info(metrics.roc_auc_score(targs, preds)) self.log.info("-------> precision for outliers --->") self.log.info(1-metrics.precision_score(targs, preds)) self.log.info("-------> recall for outliers ---> ") self.log.info(1-metrics.recall_score(targs, preds)) self.log.info("-------> f1 for outliers--->") self.log.info(1-metrics.f1_score(targs, preds)) self.log.info("======= Performance matrix on Test Data End ======\n") else: df_predicted = xtrain df_predicted['predict'] = pred_trainData df_predicted['score'] = pred_trainScore df_predicted.to_csv(predicted_data_file, index=False) matrix = '' trainmatrix = '' accuracy = 'NA' return modelName,model,matrix,trainmatrix,accuracy,labelMaps except Exception as inst: self.log.info("Error: anomalyDetector failed "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from learner.optimizetechnique import OptimizationTq import warnings from learner.parameters import parametersDefine from learner.defaultAlgos import defaultParams from hyperopt import fmin, tpe, hp, STATUS_OK, Trials import time import logging import os import sys import json from sklearn.svm import SVR from sklearn.linear_model import LinearRegression from sklearn.linear_model import Lasso from sklearn.linear_model import Ridge from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor from xgboost import XGBRegressor from lightgbm import LGBMRegressor from catboost import CatBoostRegressor from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error,make_scorer from sklearn.metrics import mean_squared_error from learner.aion_matrix import aion_matrix from uncertainties.aionUQ import aionUQ import mlflow class RegressionModel(): def __init__(self,modelList,params,scoreParam,cvSplit,numIter,geneticParam,trainX,trainY,testX,testY,method,deployLocation): self.modelList =modelList self.params =params self.trainX =trainX self.trainY =trainY self.testX = testX self.testY = testY self.method =method self.scoreParam=scoreParam self.cvSplit=cvSplit self.numIter=numIter self.geneticParam=geneticParam self.log = logging.getLogger('eion') self.deployLocation = deployLocation self.uq_x_train = trainX self.uq_x_test = testX self.uq_y_train = trainY self.uq_y_test = testY self.AlgorithmNames={'Linear Regression':'LinearRegression','Lasso':'Lasso','Ridge':'Ridge','Decision Tree':'DecisionTreeRegressor','Random Forest':'RandomForestRegressor','Extreme Gradient Boosting (XGBoost)':'XGBRegressor','Light Gradient Boosting (LightGBM)': 'LGBMRegressor', 'Categorical Boosting (CatBoost)': 'CatBoostRegressor','Bagging (Ensemble)':'BaggingRegressor','Stacking (Ensemble)':'StackingRegressor','Voting (Ensemble)':'VotingRegressor','Neural Architecture Search':'NAS'} self.modelToAlgoNames = {value: key for key, value in self.AlgorithmNames.items()} def logMlflow(self, runName, params, metrices, estimator, algoName=None): with mlflow.start_run(run_name = runName): for k,v in params.items(): mlflow.log_param(k, v) for k,v in metrices.items(): mlflow.log_metric(k, v) if algoName == 'CatBoostRegressor': mlflow.catboost.log_model(estimator, "model") else: mlflow.sklearn.log_model(estimator, "model") model_uri = mlflow.get_artifact_uri("model") """ for some dataset evaluate takes more than 90 min, so commenting till some solution is not found evaluate_data = self.testX.copy() evaluate_data['target'] = self.testY.copy() mlflow.evaluate(model_uri, data=evaluate_data, targets='target', model_type="regressor") del evaluate_data """ def regressionModelling(self,modelOrFeatureBased, code_configure): paramObj=parametersDefine() bestModel='' bestParams={} import sys bestScore=-sys.float_info.max #bugfix 11656 scoredetails = '' self.log.info('\n---------- Regression Model has started ----------') try: self.log.info('Status:- |... Search Optimization Method applied: '+self.method) for modelName in self.modelList: objClf = aion_matrix() if modelName in ['Bagging (Ensemble)','Voting (Ensemble)','Stacking (Ensemble)','Neural Architecture Search']: if modelName == 'Bagging (Ensemble)': from ensemble.ensemble_bagging import ensemble_bagging ensemble_bagging_obj = ensemble_bagging(self.params[modelName],self.scoreParam,0,0) estimator,modelParams,score,model = ensemble_bagging_obj.ensemble_bagging__regressor(self.trainX,self.trainY,self.testX,self.testY) if modelName == 'Stacking (Ensemble)': from ensemble.ensemble_stacking import ensemble_stacking ensemble_stacking_obj = ensemble_stacking(self.params[modelName],self.scoreParam) estimator,modelParams,score,model = ensemble_stacking_obj.ensemble_stacking__regressor(self.trainX,self.trainY,self.testX,self.testY,self.modelList) if modelName == 'Voting (Ensemble)': from ensemble.ensemble_voting import ensemble_voting ensemble_voting_obj = ensemble_voting(self.params[modelName],self.scoreParam) estimator,modelParams,score,model = ensemble_voting_obj.ensemble_voting__regressor(self.trainX,self.trainY,self.testX,self.testY,self.modelList) ''' if modelName == 'Neural Architecture Search': from nas.aionNAS import aionNAS objNAS = aionNAS('Regression',self.params[modelName],self.trainX,self.testX,self.trainY,self.testY,self.deployLocation) estimator,modelParams,score,model=objNAS.nasMain(self.scoreParam) ''' if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"'+self.modelToAlgoNames[model]+'","FeatureEngineering":"'+str(modelOrFeatureBased)+'","Score":'+str(score)+',"ModelUncertainty":"NA"}' if self.scoreParam == "r2": if score > bestScore: bestScore =score bestModel =model bestParams=modelParams bestEstimator=estimator else: if abs(score) < bestScore or bestScore == -sys.float_info.max: bestScore =abs(score) bestModel =model bestParams=modelParams bestEstimator=estimator self.log.info('Status:- |... ML Algorithm applied: '+modelName) self.log.info('Status:- |... Score: '+objClf.get_print_score(self.scoreParam)+'='+str(round(score,2))+'\n') continue if modelName not in self.params: continue paramSpace=self.params[modelName].copy() algoName = self.AlgorithmNames[modelName] paramDict =paramObj.paramDefine(paramSpace,self.method) if self.method == 'bayesopt': code_configure.add_model(algoName,paramSpace) else: paramDictCopy = paramDict # numpy array is not json serializable #numpy is already imported but still np.ndarray raise error import numpy as np for key,value in paramDictCopy.items(): if isinstance(value, np.ndarray): paramDictCopy[key] = paramDictCopy[key].tolist() code_configure.add_model(algoName,paramDictCopy) if not self.method == 'bayesopt': paramSize = paramObj.getParamSpaceSize(paramDict) else: paramSize = 0 if (self.method == 'bayesopt' and not paramDict) or (not self.method == 'bayesopt' and paramSize<=1): try: start = time.time() #function call defObj = defaultParams(algoName,paramDict,self.scoreParam,0,0,paramSize) estimator, modelParams, model,score =defObj.startTrainingRegression(self.trainX,self.trainY,self.testX,self.testY) executionTime = time.time() - start self.log.info('---------> Total Execution: ' + str(executionTime) + '\n') if (scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"' + self.modelToAlgoNames[model] + '","FeatureEngineering":"' + str( modelOrFeatureBased) + '","Score":' + str(score) + ',"ModelUncertainty":"NA"}' if self.scoreParam == "r2": if score > bestScore: bestScore = score bestModel = model bestParams = modelParams bestEstimator = estimator else: if abs(score) < bestScore or bestScore == -sys.float_info.max: bestScore = abs(score) bestModel = model bestParams = modelParams bestEstimator = estimator self.log.info('Status:- |... ML Algorithm applied: ' + modelName) self.log.info('Status:- |... Score: ' + objClf.get_print_score(self.scoreParam) + '=' + str( round(score, 2)) + '\n') except Exception as inst: self.log.info('\n < ---------- Model Execution Failed Start--------->') self.log.info('\n<-------' + modelName + ' Model Execution failed!!!.' + str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) self.log.info('\n < ---------- Model Execution Failed End --------->') continue trainingStatus = 'Success' if self.method =='grid': try: self.log.info("-------> Optimization Method :Grid Search") self.log.info("-------> Model Name: "+str(modelName)) opTq =OptimizationTq(algoName,paramDict,self.cvSplit,self.scoreParam,self.numIter,self.trainX,self.trainY) start = time.time() model,modelParams,score,estimator=opTq.gridSearchOpt() executionTime=time.time() - start if not self.testX.empty: predictedData = estimator.predict(self.testX) if 'neg_mean_squared_error' in self.scoreParam: meanssquatederror = mean_squared_error(self.testY,predictedData) score = meanssquatederror elif 'neg_root_mean_squared_error' in self.scoreParam: rootmeanssquatederror=mean_squared_error(self.testY,predictedData,squared=False) score = rootmeanssquatederror elif 'mae' in self.scoreParam: meanabsoluteerror=mean_absolute_error(self.testY,predictedData) score = meanabsoluteerror elif 'r2' in self.scoreParam: r2score=r2_score(self.testY,predictedData) score = r2score problemName = estimator.__class__.__name__ runName = algoName + '_' + modelOrFeatureBased metrices = {} metrices["score"] = score try: self.logMlflow(runName, modelParams, metrices, estimator,algoName) except Exception as e: self.log.info('\n-----> ML flow error!!!.' + str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) # raise pass uq_jsonobject = '' try: if model in ['XGBRegressor','LGBMRegressor','CatBoostRegressor']: self.log.info('-----> Model Uncertainty Not Supported') else: problemName = estimator.__class__.__name__ uqObj=aionUQ(None,None,None,problemName,modelParams,estimator,None,None,self.deployLocation) total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject=uqObj.uqMain_BBMRegression(self.uq_x_train,self.uq_x_test,self.uq_y_train,self.uq_y_test,"aionuq") self.log.info("-------> model_confidence: "+str(total_picp_percentage)+str('%')) self.log.info("-------> model_uncertainty: "+str(total_Uncertainty_percentage)+str('%')) except: pass if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"'+self.modelToAlgoNames[model]+'","FeatureEngineering":"'+str(modelOrFeatureBased)+'","Score":'+str(abs(score))+',"ModelUncertainty":'+str(json.dumps(uq_jsonobject))+'}' self.log.info('---------> Total Execution: '+str(executionTime)+'\n') if self.scoreParam == "r2": if score > bestScore: bestScore =score bestModel =model bestParams=modelParams bestEstimator=estimator else: if abs(score) < bestScore or bestScore == -sys.float_info.max: bestScore =abs(score) bestModel =model bestParams=modelParams bestEstimator=estimator except Exception as inst: self.log.info('\n < ---------- Model Execution Failed Start--------->') self.log.info('\n<-------'+ modelName+' Model Execution failed!!!.'+str(inst)) self.log.info('\n < ---------- Model Execution Failed End --------->') trainingStatus = 'Error (Exception)' elif self.method == 'random': try: self.log.info("-------> Optimization Method :Random Search") self.log.info("-------> Model Name: "+str(modelName)) opTq =OptimizationTq(algoName,paramDict,self.cvSplit,self.scoreParam,self.numIter,self.trainX,self.trainY) start = time.time() model,modelParams,score,estimator=opTq.randomSearchOpt() executionTime=time.time() - start if not self.testX.empty: predictedData = estimator.predict(self.testX) if 'neg_mean_squared_error' in self.scoreParam: meanssquatederror = mean_squared_error(self.testY,predictedData) score = meanssquatederror elif 'neg_root_mean_squared_error' in self.scoreParam: rootmeanssquatederror=mean_squared_error(self.testY,predictedData,squared=False) score = rootmeanssquatederror elif 'mae' in self.scoreParam: meanabsoluteerror=mean_absolute_error(self.testY,predictedData) score = meanabsoluteerror elif 'r2' in self.scoreParam: r2score=r2_score(self.testY,predictedData) score = r2score if self.scoreParam == "r2": if score>bestScore: bestScore =score bestModel =model bestParams=modelParams bestEstimator=estimator else: if abs(score) < bestScore or bestScore == -sys.float_info.max: bestScore =abs(score) bestModel =model bestParams=modelParams bestEstimator=estimator problemName = estimator.__class__.__name__ runName = algoName + '_' + modelOrFeatureBased metrices = {} metrices["score"] = score try: self.logMlflow(runName, modelParams, metrices, estimator,algoName) except Exception as e: self.log.info('\n-----> ML flow error!!!.' + str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) # raise pass uq_jsonobject = '' try: if model in ['XGBRegressor','LGBMRegressor','CatBoostRegressor']: self.log.info('-----> Model Uncertainty Not Supported') else: problemName = estimator.__class__.__name__ uqObj=aionUQ(None,None,None,problemName,modelParams,estimator,None,None,self.deployLocation) total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject=uqObj.uqMain_BBMRegression(self.uq_x_train,self.uq_x_test,self.uq_y_train,self.uq_y_test,"aionuq") self.log.info("-------> model_confidence: "+str(total_picp_percentage)+str('%')) self.log.info("-------> model_uncertainty: "+str(total_Uncertainty_percentage)+str('%')) except Exception as e: print(e) pass if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"'+self.modelToAlgoNames[model]+'","FeatureEngineering":"'+str(modelOrFeatureBased)+'","Score":'+str(abs(score))+',"ModelUncertainty":'+str(json.dumps(uq_jsonobject))+'}' except Exception as inst: self.log.info('\n < ---------- Model Execution Failed Start--------->') self.log.info('\n<-------'+ modelName+' Model Execution failed!!!.'+str(inst)) self.log.info('\n < ---------- Model Execution Failed End --------->') trainingStatus = 'Error (Exception)' elif self.method == 'bayesopt': try: self.log.info("-------> Optimization Method :BayesOpt Search") self.log.info("-------> Model Name: "+str(modelName)) opTq =OptimizationTq(algoName,paramDict,self.cvSplit,self.scoreParam,self.numIter,self.trainX,self.trainY) fun=opTq.f trials = Trials() start = time.time() best = fmin(fun,paramDict,algo=tpe.suggest, max_evals=100, trials=trials) executionTime=time.time() - start results = sorted(trials.results, key = lambda x: x['loss']) bestresult=results[0] model=bestresult['model'] score=bestresult['score'] modelParams=bestresult['params'] res = ', '.join("{!s}={!r}".format(key,val) for (key,val) in modelParams.items()) modelObj=eval(model+'('+res+')') estimator = modelObj.fit(self.trainX,self.trainY) if not self.testX.empty: predictedData = estimator.predict(self.testX) if 'neg_mean_squared_error' in self.scoreParam: meanssquatederror = mean_squared_error(self.testY,predictedData) score = meanssquatederror elif 'neg_root_mean_squared_error' in self.scoreParam: rootmeanssquatederror=mean_squared_error(self.testY,predictedData,squared=False) score = rootmeanssquatederror elif 'mae' in self.scoreParam: meanabsoluteerror=mean_absolute_error(self.testY,predictedData) score = meanabsoluteerror elif 'r2' in self.scoreParam: r2score=r2_score(self.testY,predictedData) score = r2score problemName = estimator.__class__.__name__ runName = algoName + '_' + modelOrFeatureBased metrices = {} metrices["score"] = score try: self.logMlflow(runName, modelParams, metrices, estimator,algoName) except Exception as e: self.log.info('\n-----> ML flow error!!!.' + str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) # raise pass if self.scoreParam == "r2": if score>bestScore: bestScore =score bestModel =model bestParams=modelParams bestEstimator=estimator else: if abs(score) < bestScore or bestScore == -sys.float_info.max: bestScore =abs(score) bestModel =model bestParams=modelParams bestEstimator=estimator uq_jsonobject = '' try: if model in ['XGBRegressor','LGBMRegressor','CatBoostRegressor']: self.log.info('-----> Model Uncertainty Not Supported') else: problemName = estimator.__class__.__name__ uqObj=aionUQ(None,None,None,problemName,modelParams,estimator,None,None,self.deployLocation) total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject=uqObj.uqMain_BBMRegression(self.uq_x_train,self.uq_x_test,self.uq_y_train,self.uq_y_test,"aionuq") self.log.info("-------> model_confidence: "+str(total_picp_percentage)+str('%')) self.log.info("-------> model_uncertainty: "+str(total_Uncertainty_percentage)+str('%')) except: pass if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"'+self.modelToAlgoNames[model]+'","FeatureEngineering":"'+str(modelOrFeatureBased)+'","Score":'+str(abs(score))+',"ModelUncertainty":'+str(json.dumps(uq_jsonobject))+'}' self.log.info('---------> Total Execution: '+str(executionTime)+'\n') except Exception as inst: self.log.info('\n < ---------- Model Execution Failed Start--------->') self.log.info('\n<-------'+ modelName+' Model Execution failed!!!.'+str(inst)) self.log.info('\n < ---------- Model Execution Failed End --------->') trainingStatus = 'Error (Exception)' else: trainingStatus = 'Error (HyperTunning Algo Not Supported)' pass self.log.info('Status:- |... ML Algorithm applied: '+modelName) if trainingStatus.lower() == 'success': self.log.info('Status:- |... Score after hyperparameter tuning: '+objClf.get_print_score(self.scoreParam)+'='+str(round(score,2))+'\n') else: self.log.info('Status:- |... Training Error : '+trainingStatus+'\n') if bestModel != 'None': self.log.info('---------- Regression Model End ---------- \n') self.log.info('\n------- Best Model and its parameters -------------') self.log.info('Status:- |... Best Algorithm selected: '+str(self.modelToAlgoNames[bestModel])+' Score='+str(round(bestScore,2))) self.log.info("-------> Best Name: "+str(bestModel)) self.log.info("-------> Best Score: "+str(bestScore)) else: raise Exception("Sorry, no model is trained") return self.modelToAlgoNames[bestModel],bestParams,bestScore,bestEstimator,scoredetails except Exception as inst: self.log.info( '\n-----> regressionModel failed!!!.'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np # from learner.classificationModel import ClassifierModel from learner.aion_matrix import aion_matrix from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error,make_scorer from sklearn.metrics import mean_squared_error class defaultParams(): def __init__(self, modelName, paramDict, scoreParam, MakeFP0, MakeFN0,paramSize): self.modelName = modelName self.paramDict = paramDict self.scoreParam = scoreParam self.MakeFP0 = MakeFP0 self.MakeFN0 = MakeFN0 self.dictsize = paramSize def paramDictConvertion(self): if self.dictsize != 0: for keys in self.paramDict.keys(): self.paramDict[keys] = self.paramDict[keys][0] def startTrainingClassification(self, trainX, trainY, testX, testY): threshold = -1 precisionscore = -1 recallscore = -1 objClf = aion_matrix() self.paramDictConvertion() if self.modelName == 'LogisticRegression': from sklearn import linear_model estimator = linear_model.LogisticRegression() if self.modelName == 'GaussianNB': from sklearn.naive_bayes import GaussianNB estimator = GaussianNB() if self.modelName == 'SVC': from sklearn import svm estimator = svm.SVC() if self.modelName == 'KNeighborsClassifier': from sklearn.neighbors import KNeighborsClassifier estimator = KNeighborsClassifier() if self.modelName == 'DecisionTreeClassifier': from sklearn.tree import DecisionTreeClassifier estimator = DecisionTreeClassifier() if self.modelName == 'RandomForestClassifier': from sklearn.ensemble import RandomForestClassifier estimator = RandomForestClassifier() if self.modelName == 'GradientBoostingClassifier': from sklearn.ensemble import GradientBoostingClassifier estimator = GradientBoostingClassifier() if self.modelName == 'XGBClassifier': import xgboost as xgb estimator = xgb.XGBClassifier() if self.modelName == 'CatBoostClassifier': from catboost import CatBoostClassifier estimator = CatBoostClassifier() if self.modelName == 'LGBMClassifier': from lightgbm import LGBMClassifier estimator = LGBMClassifier() if self.dictsize != 0: estimator.set_params(**self.paramDict) estimator.fit(trainX, trainY) if not testX.empty: predictedData = estimator.predict(testX) score = objClf.get_score(self.scoreParam, testY, predictedData) if self.MakeFP0: self.log.info('-------- Calculate Threshold for FP Start-------') startRange = 0.0 endRange = 1.0 stepsize = 0.01 threshold_range = np.arange(startRange, endRange, stepsize) threshold, precisionscore, recallscore = objClf.check_threshold(estimator, trainX, trainY, threshold_range, 'FP', self.modelName) self.log.info('-------- Calculate Threshold for FP End-------') if self.MakeFN0: self.log.info('-------- Calculate Threshold for FN Start-------') startRange = 1.0 endRange = 0.0 stepsize = -0.01 threshold_range = np.arange(startRange, endRange, stepsize) threshold, precisionscore, recallscore = objClf.check_threshold(estimator, trainX, trainY, threshold_range, 'FN', self.modelName) self.log.info('-------- Calculate Threshold for FN End-------') else: predictedData = estimator.predict(trainX) score = objClf.get_score(self.scoreParam, trainY, predictedData) if self.MakeFP0: self.log.info('-------- Calculate Threshold for FP Start-------') startRange = 0.0 endRange = 1.0 stepsize = 0.01 threshold_range = np.arange(startRange, endRange, stepsize) threshold, precisionscore, recallscore = objClf.check_threshold(estimator, trainX, trainY, threshold_range, 'FP', self.modelName) self.log.info('-------- Calculate Threshold for FP End-------') if self.MakeFN0: self.log.info('-------- Calculate Threshold for FN Start-------') startRange = 1.0 endRange = 0.0 stepsize = -0.01 threshold_range = np.arange(startRange, endRange, stepsize) threshold, precisionscore, recallscore = objClf.check_threshold(estimator, trainX, trainY, threshold_range, 'FN', self.modelName) self.log.info('-------- Calculate Threshold for FN End-------') # status, bscore, bthres, brscore, bpscore = objClf.getBestModel(self.MakeFP0,self.MakeFN0, threshold, # bestthreshold, recallscore, bestrecallscore, # precisionscore, bestprecisionscore, score, # bestScore) return estimator, estimator.get_params(), self.modelName, score, threshold, precisionscore, recallscore def startTrainingRegression(self, trainX, trainY, testX, testY): #objClf = aion_matrix() try: score = 0 self.paramDictConvertion() if self.modelName=="LinearRegression": from sklearn import linear_model estimator = linear_model.LinearRegression() if self.modelName=="Lasso": from sklearn import linear_model estimator = linear_model.Lasso() if self.modelName=="Ridge": from sklearn import linear_model estimator = linear_model.Ridge() if self.modelName=="DecisionTreeRegressor": from sklearn.tree import DecisionTreeRegressor estimator = DecisionTreeRegressor() if self.modelName=="RandomForestRegressor": from sklearn.ensemble import RandomForestRegressor estimator = RandomForestRegressor() if self.modelName== "XGBRegressor": import xgboost as xgb estimator = xgb.XGBRegressor() if self.modelName == 'CatBoostRegressor': from catboost import CatBoostRegressor estimator = CatBoostRegressor() if self.modelName == 'LGBMRegressor': from lightgbm import LGBMRegressor estimator = LGBMRegressor() if self.dictsize != 0: estimator.set_params(**self.paramDict) estimator.fit(trainX, trainY) except Exception as e: print(e) if not testX.empty: predictedData = estimator.predict(testX) if 'neg_mean_squared_error' in self.scoreParam: meanssquatederror = mean_squared_error(testY, predictedData) score = meanssquatederror elif 'neg_root_mean_squared_error' in self.scoreParam: rootmeanssquatederror = mean_squared_error(testY, predictedData, squared=False) score = rootmeanssquatederror elif 'mae' in self.scoreParam: meanabsoluteerror = mean_absolute_error(testY, predictedData) score = meanabsoluteerror elif 'r2' in self.scoreParam: r2score = r2_score(testY, predictedData) score = r2score else: predictedData = estimator.predict(trainX) if 'neg_mean_squared_error' in self.scoreParam: meanssquatederror = mean_squared_error(trainY, predictedData) score = meanssquatederror elif 'neg_root_mean_squared_error' in self.scoreParam: rootmeanssquatederror = mean_squared_error(trainY, predictedData, squared=False) score = rootmeanssquatederror elif 'mae' in self.scoreParam: meanabsoluteerror = mean_absolute_error(trainY, predictedData) score = meanabsoluteerror elif 'r2' in self.scoreParam: r2score = r2_score(trainY, predictedData) score = r2score return estimator, estimator.get_params(), self.modelName, score ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import sys import json import shutil import tarfile import logging import subprocess from os.path import expanduser import platform from pathlib import Path, PurePosixPath import tensorflow.compat.v2 as tf os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) from google.protobuf import text_format ''' from object_detection import model_lib_v2 from object_detection import model_main_tf2 from object_detection import exporter_lib_v2 from object_detection.utils import config_util from object_detection.protos import pipeline_pb2 ''' from learner.cloudServer import awsGPUTraining class objectDetector(object): def __init__(self, dataLocation, pretrainedModels, modelDirName,remoteTrainingConfig): self.log = logging.getLogger('eion') self.dataLocation = dataLocation self.pretrainedModels = Path(pretrainedModels) self.modelDirName = Path(modelDirName['file']) self.modelURLDict = modelDirName self.gpu = remoteTrainingConfig['Enable'] self.serverConfig = remoteTrainingConfig self.modelOutput = Path(dataLocation).parent/"export" if remoteTrainingConfig['Enable']: ''' if not Path(serverConfigFile).is_file(): raise ValueError("Gpu training is enabled but server config file is not present.") with open(serverConfigFile) as fObj: self.serverConfig = json.load(fObj) ''' self.tfRecordLoc = PurePosixPath('aion/data/od') self.pipelineLoc = PurePosixPath('aion/data/od') self.labelMapLoc = PurePosixPath('aion/data/od') self.gpuPretrainedModelPath = PurePosixPath('aion/pretrainedModels')/self.modelDirName else: self.tfRecordLoc = Path(dataLocation) self.pipelineLoc = Path(dataLocation) self.labelMapLoc = Path(dataLocation) self.gpuPretrainedModelPath = None def prepareConfig(self, detectionModel, num_classes, n_epoch, batch_size): home = expanduser("~") if platform.system() == 'Windows': modelPath = os.path.join(home,'AppData','Local','HCLT','AION','PreTrainedModels','ObjectDetection') else: modelPath = os.path.join(home,'HCLT','AION','PreTrainedModels','ObjectDetection') pipeline_config = str(modelPath/self.modelDirName/"pipeline.config") checkPoint = "ckpt-0" with open(str(modelPath/self.modelDirName/"checkpoint/checkpoint")) as f: line = f.readline() checkPoint = line.split(':')[1].strip()[1:-1] #(model_checkpoint_path: "ckpt-301") to ckpt-301 checkPoint = "checkpoint/"+checkPoint from object_detection.utils import config_util configs = config_util.get_configs_from_pipeline_file(pipeline_config) model_config = configs['model'] if detectionModel.lower() == 'ssd': model_config.ssd.num_classes = num_classes configs['train_config'].fine_tune_checkpoint_type = "detection" elif detectionModel.lower() == 'centernet': model_config.center_net.num_classes = num_classes configs['train_config'].fine_tune_checkpoint_type = "fine_tune" elif detectionModel.lower() == 'fasterrcnn': model_config.faster_rcnn.num_classes = num_classes configs['train_config'].fine_tune_checkpoint_type = "detection" else: raise ValueError("{} Model is not supported for object detection.\n".format(detectionModel)) if self.gpu: checkpointPath = str(self.gpuPretrainedModelPath / checkPoint) else: checkpointPath = str(modelPath/self.modelDirName/checkPoint) configs['train_config'].fine_tune_checkpoint = checkpointPath configs['train_config'].num_steps = n_epoch configs['train_config'].batch_size = batch_size configs['train_input_config'].tf_record_input_reader.input_path[:] = [str(self.tfRecordLoc/"train.tfrecord")] configs['train_input_config'].label_map_path = str(self.labelMapLoc/"label_map.pbtxt") configs['eval_input_config'].tf_record_input_reader.input_path[:] = [self.dataLocation + "/test.tfrecord"] configs['eval_input_config'].label_map_path = self.dataLocation + "/label_map.pbtxt" # Save new pipeline config new_pipeline_proto = config_util.create_pipeline_proto_from_configs(configs) config_util.save_pipeline_config(new_pipeline_proto, self.dataLocation) def __exportModel(self): self.log.info('-------> exporting trained Model') from object_detection.protos import pipeline_pb2 from object_detection import exporter_lib_v2 pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.io.gfile.GFile(str(self.pipelineLoc/"pipeline.config"), 'r') as f: text_format.Merge(f.read(), pipeline_config) text_format.Merge('', pipeline_config) exporter_lib_v2.export_inference_graph( 'image_tensor', pipeline_config, self.dataLocation, str(self.modelOutput)) def startObjectDetector(self): if self.gpu: self.log.info('-------> Training on the cloud machine') self.log.info('Status:- |...Remote Machine Training') with open(self.dataLocation+'\model.config', 'w')as f: json.dump( self.modelURLDict, f) awsGpu = awsGPUTraining(self.serverConfig) try: awsGpu.start_instance() awsGpu.copy_files_to_server(self.dataLocation) awsGpu.start_executing_notebook() self.log.info('-------> Downloading trained model file') tarFile = awsGpu.copy_file_from_server(self.dataLocation) with tarfile.open(tarFile) as tar: tar.extractall(self.dataLocation) awsGpu.stop_server_instance() except: awsGpu.stop_server_instance() raise extractedPath = Path(self.dataLocation)/Path(tarFile).name.split('.')[0] filesList = extractedPath.glob('**/*') for file in filesList: if file.parent == extractedPath: if file.name == "export": shutil.copytree(file, self.modelOutput) elif file.is_dir(): shutil.copytree(file, Path(self.dataLocation)/file.name) else: shutil.copy2(file, self.dataLocation) shutil.rmtree(extractedPath) Path(tarFile).unlink() shutil.copy2(self.dataLocation + "/label_map.pbtxt", str(self.modelOutput)) else: self.log.info('-------> Training on the local machine') self.log.info('Status:- |...Local Machine Training') tf.config.set_soft_device_placement(True) strategy = tf.compat.v2.distribute.MirroredStrategy() with strategy.scope(): try: from object_detection import model_lib_v2 model_lib_v2.train_loop( pipeline_config_path=str(self.pipelineLoc/"pipeline.config"), model_dir=str(self.dataLocation)) except Exception: raise self.__exportModel() shutil.copy2(str(self.labelMapLoc/"label_map.pbtxt"), str(self.modelOutput)) def evaluateObjectDetector(self, model_dir, pipeline_config_dir=None, checkpoint_dir=None): if checkpoint_dir == None: checkpoint_dir = model_dir if pipeline_config_dir == None: pipeline_config_dir = model_dir self.log.info('-------> Evaluation started') from object_detection import model_main_tf2 cmd = '"{}" "{}" --model_dir="{}" --pipeline_config_path="{}/pipeline.config" --checkpoint_dir="{}" --eval_timeout=6'.format(sys.executable, model_main_tf2.__file__, model_dir, model_dir, checkpoint_dir) result = subprocess.run(cmd , capture_output=True, text=True,shell=True) precisionParam = ['Average Precision', 'Average Recall'] text = result.stdout.split('\n') stats = {} keys = [] try: for x in text: for y in precisionParam: indx = x.find(y) if indx != -1: keyValue = x[indx:].split(' = ') stats[keyValue[0]] = keyValue[1] keys.append(keyValue[0]) except Exception as e: raise ValueError("Error in evaluation: " + str(e)) self.log.info('-------> Evaluation statistics:') self.log.info(stats) return stats, keys ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import SGDClassifier, PassiveAggressiveClassifier from sklearn.linear_model import SGDRegressor, PassiveAggressiveRegressor from sklearn.linear_model import SGDClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import GradientBoostingClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import cross_val_score from sklearn.svm import SVC from hyperopt import fmin, tpe, hp, STATUS_OK, Trials from sklearn.svm import SVR import xgboost as xgb from xgboost import XGBClassifier from lightgbm import LGBMClassifier from catboost import CatBoostClassifier from xgboost import XGBRegressor from lightgbm import LGBMRegressor from catboost import CatBoostRegressor from sklearn.linear_model import LinearRegression from sklearn.linear_model import Lasso from sklearn.linear_model import Ridge from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor import warnings warnings.filterwarnings('ignore') import time import logging import sys,os class StreamToLogger(object): def __init__(self, logger, log_level=logging.INFO): self.logger = logger self.log_level = log_level self.linebuf = '' def write(self, buf): for line in buf.rstrip().splitlines(): self.logger.log(self.log_level, 'Model:- Iteration:: '+line.rstrip()) class OptimizationTq(): def __init__(self,modelName,tuneParams,cvSplit,scoreParam,nIter,trainX,trainY,geneticParam=None): self.data = None self.model=modelName self.params =tuneParams self.cvSplit=cvSplit self.scoreParam=scoreParam self.trainX =trainX self.trainY = trainY self.geneticParam=geneticParam if geneticParam else {} self.nIter =nIter self.count =0 self.best =0 self.log = logging.getLogger('eion') def gridSearchOpt(self): try: sl = StreamToLogger(self.log, logging.INFO) oldStdout = sys.stdout sys.stdout = sl self.log.info('Model:-Model Name:: '+str(self.model)) modelObj=eval(self.model+'()') gridOp = GridSearchCV(modelObj, param_grid=self.params,scoring=self.scoreParam, cv=self.cvSplit,verbose=10) gridFit=gridOp.fit(self.trainX,self.trainY) self.log.info('Model:-Model Name:: '+str(self.model)) self.log.info('Model:-ScoringType:: '+str(gridFit.scorer_)) self.log.info('Model:-Best Param:: '+str(gridFit.best_params_)) self.log.info('Model:-Validation Score:: '+str(gridFit.best_score_)) self.log.info('Model:-CV Result:: '+str(gridFit.cv_results_)) self.log.info('Model:-Best Estimator:: '+str(gridFit.best_estimator_)) sys.stdout = oldStdout return self.model,gridFit.best_params_,gridFit.best_score_,gridFit.best_estimator_ except Exception as inst: self.log.info("gridSearchOpt failed ==>"+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def randomSearchOpt(self): try: sl = StreamToLogger(self.log, logging.INFO) oldStdout = sys.stdout sys.stdout = sl self.log.info('Model:-Model Name:: '+str(self.model)) modelObj=eval(self.model+'()') randomOp = RandomizedSearchCV(modelObj, param_distributions=self.params,scoring=self.scoreParam,n_iter=self.nIter,cv=self.cvSplit,verbose=10) randomFit=randomOp.fit(self.trainX,self.trainY) self.log.info('Model:-Model Name:: '+str(self.model)) self.log.info('Model:-ScoringType:: '+str(randomFit.scorer_)) self.log.info('Model:-Best Param:: '+str(randomFit.best_params_)) self.log.info('Model:-Validation Score:: '+str(randomFit.best_score_)) self.log.info('Model:-CV Result:: '+str(randomFit.cv_results_)) self.log.info('Model:-Best Estimator:: '+str(randomFit.best_estimator_)) sys.stdout = oldStdout return self.model,randomFit.best_params_,randomFit.best_score_,randomFit.best_estimator_ except Exception as inst: self.log.info("RandomsearchOptimization failed ==>"+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def bayesianOpt(self,params): modelObj=eval(self.model+'(**'+str(params)+')') score=cross_val_score(modelObj, self.trainX, self.trainY,scoring=self.scoreParam,cv=self.cvSplit) return score.mean() def f(self,params): best=self.best count=self.count parameters=params count += 1 classObj=OptimizationTq(self.model,self.params,self.cvSplit,self.scoreParam,self.nIter,self.trainX,self.trainY) acc = classObj.bayesianOpt(parameters.copy()) return {'loss':-acc,'score': acc, 'status': STATUS_OK,'model' :self.model,'params': params} ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import boto3 import json import time import requests import datetime import uuid import shutil from websocket import create_connection from botocore.exceptions import ClientError import tarfile from pathlib import Path, PurePosixPath from stat import S_ISDIR from fabric import Connection import time import logging class awsGPUTraining(): def __init__(self, config): local_config = {"location":{"data":"aion/data/od", "code":"", "pretrainedModel":"aion/pretrainedModels"}, "jupyter":{"header":{"Authorization":"Token f3af05d5348301997fb014f245569e872d27bb9018fd70d2"}, "portNo":"8888", "notebook_path":"aion/code/AWS_GPU_OD_Training.ipynb"}} self.serverConfig = config["server"] self.sshConfig = config["ssh"] self.log = logging.getLogger('eion') self.codeLocation = local_config["location"]["code"] self.dataLocation = local_config["location"]["data"] self.pretrainedModelLocation = local_config["location"]["pretrainedModel"] self.jupyterConfig = local_config["jupyter"] self.serverIP = "" if self.serverConfig["awsAccessKeyId"] == "" or self.serverConfig["awsSecretAccessKey"] == "": raise ValueError("Cloud server configuration is not available.") if len(self.serverConfig["InstanceIds"]) == 0 and self.serverConfig["amiId"] == "": raise ValueError("Please provide either InstanceIds or amiId in server config") self.instanceId = [] self.separate_instance = False if self.serverConfig["amiId"] != "": self.separate_instance = True else: if len(self.serverConfig["InstanceIds"]): if isinstance(self.serverConfig["InstanceIds"], list): self.instanceId = self.serverConfig["InstanceIds"] elif isinstance(self.serverConfig["InstanceIds"], str): self.instanceId = [self.serverConfig["InstanceIds"]] self.ec2_client = boto3.client(self.serverConfig["serverName"], region_name=self.serverConfig["regionName"], aws_access_key_id=self.serverConfig["awsAccessKeyId"], aws_secret_access_key=self.serverConfig["awsSecretAccessKey"]) def __sftp_exists(self, sftp, path): try: sftp.stat(path) return True except:# IOError, e: #if e.errno == errno.ENOENT: return False def __rmtree(self, sftp, remotepath, level=0): for f in sftp.listdir_attr(remotepath): rpath = str(PurePosixPath(remotepath)/f.filename) if S_ISDIR(f.st_mode): self.__rmtree(sftp, rpath, level=(level + 1)) sftp.rmdir(rpath) else: rpath = str(PurePosixPath(remotepath)/f.filename) sftp.remove(rpath) def copy_files_to_server(self, location): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) client.sudo('rm -rf {}/*'.format(self.dataLocation)) tarFile = str((PurePosixPath(self.dataLocation).parent/PurePosixPath(self.dataLocation).name).with_suffix(".tar.gz")) client.put(location+'/test.tfrecord', self.dataLocation+'/test.tfrecord') client.put(location+'/train.tfrecord', self.dataLocation+'/train.tfrecord') client.put(location+'/pipeline.config', self.dataLocation+'/pipeline.config') client.put(location+'/label_map.pbtxt', self.dataLocation+'/label_map.pbtxt') client.put(location+'/model.config', self.dataLocation+'/model.config') if self.jupyterConfig != "": client.run("touch {}".format(self.dataLocation+'/log.txt')) except Exception as e: raise ValueError("Error in copying data to cloud server. " + str(e)) def __myexec(self, ssh, cmd, timeout, want_exitcode=False): # one channel per command stdin, stdout, stderr = ssh.exec_command(cmd) # get the shared channel for stdout/stderr/stdin channel = stdout.channel # we do not need stdin. stdin.close() # indicate that we're not going to write to that channel anymore channel.shutdown_write() # read stdout/stderr in order to prevent read block hangs stdout_chunks = [] stdout_chunks.append(stdout.channel.recv(len(stdout.channel.in_buffer))) # chunked read to prevent stalls while not channel.closed or channel.recv_ready() or channel.recv_stderr_ready(): # stop if channel was closed prematurely, and there is no data in the buffers. got_chunk = False readq, _, _ = select.select([stdout.channel], [], [], timeout) for c in readq: if c.recv_ready(): stdout_chunks.append(stdout.channel.recv(len(c.in_buffer))) got_chunk = True if c.recv_stderr_ready(): # make sure to read stderr to prevent stall stderr.channel.recv_stderr(len(c.in_stderr_buffer)) got_chunk = True ''' 1) make sure that there are at least 2 cycles with no data in the input buffers in order to not exit too early (i.e. cat on a >200k file). 2) if no data arrived in the last loop, check if we already received the exit code 3) check if input buffers are empty 4) exit the loop ''' if not got_chunk \ and stdout.channel.exit_status_ready() \ and not stderr.channel.recv_stderr_ready() \ and not stdout.channel.recv_ready(): # indicate that we're not going to read from this channel anymore stdout.channel.shutdown_read() # close the channel stdout.channel.close() break # exit as remote side is finished and our bufferes are empty # close all the pseudofiles stdout.close() stderr.close() if want_exitcode: # exit code is always ready at this point return (''.join(stdout_chunks), stdout.channel.recv_exit_status()) return ''.join(stdout_chunks) def __myexec1(self, ssh, cmd, timeout, want_exitcode=False): # one channel per command stdin, stdout, stderr = ssh.exec_command(cmd, get_pty=True) for line in iter(stderr.readline, ""): print(line, end="") stdin.close() stdout.close() stderr.close() def executeCode(self): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) cmd = 'python3.8 {} {} {}'.format(self.codeLocation, self.dataLocation, self.pretrainedModelLocation) client.run( cmd) except Exception as e: raise ValueError("Error in running code on cloud server. " + str(e)) def start_executing_notebook(self): try: publicIp_Port = self.serverIP + ":" + self.jupyterConfig["portNo"] conURL = "ws://" + publicIp_Port base = 'http://' + publicIp_Port + '' headers = self.jupyterConfig["header"] url = base + '/api/kernels' flag = True while flag: # deadlock need to add timeout response = requests.post(url, headers=headers) flag = False kernel = json.loads(response.text) # Load the notebook and get the code of each cell url = base + '/api/contents/' + self.jupyterConfig["notebook_path"] response = requests.get(url, headers=headers) file = json.loads(response.text) code = [c['source'] for c in file['content']['cells'] if len(c['source']) > 0 and c['cell_type']=='code' ] ws = create_connection(conURL + "/api/kernels/" + kernel["id"] + "/channels", header=headers) def send_execute_request(code): msg_type = 'execute_request'; content = {'code': code, 'silent': False} hdr = {'msg_id': uuid.uuid1().hex, 'username': 'test', 'session': uuid.uuid1().hex, 'data': datetime.datetime.now().isoformat(), 'msg_type': msg_type, 'version': '5.0'} msg = {'header': hdr, 'parent_header': hdr, 'metadata': {}, 'content': content} return msg for c in code: ws.send(json.dumps(send_execute_request(c))) # We ignore all the other messages, we just get the code execution output # (this needs to be improved for production to take into account errors, large cell output, images, etc.) error_msg = '' traceback_msg = '' for i in range(0, len(code)): msg_type = ''; while msg_type != "stream": rsp = json.loads(ws.recv()) msg_type = rsp["msg_type"] if msg_type == 'error': raise ValueError("Error on Cloud machine: "+rsp['content']['evalue']) ws.close() self.log.info('Status:- |...Execution Started`') except ClientError as e: raise ValueError(e) def __wait_for_completion(self, sftp, remoteLogFile, localLogFile): waiting = True error_msg = "" while waiting: time.sleep(5 * 60) try: sftp.get(str(remoteLogFile), str(localLogFile)) with open(localLogFile, "r") as f: content = f.readlines() for x in content: if "Error" in x: waiting = False error_msg = x if "success" in x: waiting = False except: raise (str(e)) return error_msg def copy_file_from_server(self, localPath): try: client = Connection( host=self.serverIP, user=self.sshConfig["userName"], connect_kwargs={ "key_filename": self.sshConfig["keyFilePath"], }, ) remoteLogFile = PurePosixPath(self.dataLocation)/'log.txt' localLogFile = Path(localPath)/'remote_log.txt' client.get(str(remoteLogFile), str(localLogFile)) tarFile = (PurePosixPath(self.dataLocation).parent/PurePosixPath(self.dataLocation).name).with_suffix(".tar.gz") client.get(str(tarFile), str(Path(localPath)/tarFile.name)) except: raise return str(Path(localPath)/tarFile.name) def create_instance(self): instances = self.ec2_client.run_instances( ImageId=self.serverConfig["amiId"], MinCount=1, MaxCount=1, InstanceType="t2.xlarge", KeyName="AION_GPU", SecurityGroupIds = ["sg-02c3a6c8dd67edb74"] ) self.instanceId = [instances['Instances'][0]['InstanceId']] def start_instance(self): if self.separate_instance: self.create_instance() try: response = self.ec2_client.start_instances(InstanceIds=self.instanceId, DryRun=True) except Exception as e: if 'DryRunOperation' not in str(e): raise ValueError("Error in starting the EC2 instance, check server configuration. " + str(e)) try: running_state_code = 16 response = self.ec2_client.start_instances(InstanceIds=self.instanceId, DryRun=False) instance_status_code = 0 while instance_status_code != running_state_code: response = self.ec2_client.describe_instances(InstanceIds=self.instanceId) instance_status_code = response['Reservations'][0]['Instances'][0]['State']['Code'] if instance_status_code == running_state_code: self.serverIP = response['Reservations'][0]['Instances'][0]['PublicIpAddress'] break except ClientError as e: raise ValueError("Error in starting the EC2 instance. " + str(e)) def terminate_instance(self): ec2 = boto3.resource(self.serverConfig["serverName"], region_name=self.serverConfig["regionName"], aws_access_key_id=self.serverConfig["awsAccessKeyId"], aws_secret_access_key=self.serverConfig["awsSecretAccessKey"]) ec2.instances.filter(InstanceIds=self.instanceId).terminate() # for terminating an ec2 instance def stop_server_instance(self): try: self.ec2_client.stop_instances(InstanceIds=self.instanceId, DryRun=True) except Exception as e: if 'DryRunOperation' not in str(e): raise stopped_state_code = 80 # Dry run succeeded, call stop_instances without dryrun try: response = self.ec2_client.stop_instances(InstanceIds=self.instanceId, DryRun=False) response = self.ec2_client.describe_instances(InstanceIds=self.instanceId) instance_status_code = 0 while instance_status_code != stopped_state_code: response = self.ec2_client.describe_instances(InstanceIds=self.instanceId) instance_status_code = response['Reservations'][0]['Instances'][0]['State']['Code'] if instance_status_code == stopped_state_code: break except: raise ValueError("Error in stopping the EC2 instance {}.Please stop it manually ".format(self.instanceId[0])) if self.separate_instance: try: self.terminate_instance() except: raise ValueError("Error in terminating the EC2 instance {}.Please terminate it manually ".format(self.instanceId[0])) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import time import os import sys import numpy as np from numpy import arange from numpy import argmax import json from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix from sklearn.metrics import recall_score from sklearn.metrics import precision_score from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import GradientBoostingClassifier from sklearn.linear_model import SGDClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import roc_curve from sklearn.metrics import roc_auc_score from sklearn.metrics import f1_score from sklearn.svm import SVC from xgboost import XGBClassifier from lightgbm import LGBMClassifier from catboost import CatBoostClassifier from sklearn.preprocessing import binarize from learner.optimizetechnique import OptimizationTq from learner.defaultAlgos import defaultParams from learner.parameters import parametersDefine from hyperopt import fmin, tpe, hp, STATUS_OK, Trials import logging from learner.aion_matrix import aion_matrix import mlflow from pathlib import Path from uncertainties.aionUQ import aionUQ # apply threshold to positive probabilities to create labels def to_labels(pos_probs, threshold): return (pos_probs >= threshold).astype('int') class ClassifierModel(): def __init__(self,modelList,params,scoreParam,cvSplit,numIter,geneticParam,trainX,trainY,testX,testY,method,modelType,MakeFP0,MakeFN0,deployLocation): self.modelList =modelList self.params =params self.trainX =trainX self.X =trainX self.trainY =trainY self.testX = testX self.testY = testY self.method =method self.scoreParam=scoreParam self.cvSplit=cvSplit self.numIter=numIter self.geneticParam=geneticParam self.MakeFP0= MakeFP0 self.MakeFN0=MakeFN0 self.log = logging.getLogger('eion') self.modelType = modelType self.uq_x_train = trainX self.uq_x_test = testX self.uq_y_train = trainY self.uq_y_test = testY self.deployLocation = deployLocation self.AlgorithmNames={'Logistic Regression':'LogisticRegression','Stochastic Gradient Descent':'SGDClassifier','Naive Bayes':'GaussianNB','Support Vector Machine':'SVC','K Nearest Neighbors':'KNeighborsClassifier','Decision Tree':'DecisionTreeClassifier','Random Forest':'RandomForestClassifier','Gradient Boosting':'GradientBoostingClassifier','Extreme Gradient Boosting (XGBoost)':'XGBClassifier','Categorical Boosting (CatBoost)': 'CatBoostClassifier','Light Gradient Boosting (LightGBM)': 'LGBMClassifier','Bagging (Ensemble)':'BaggingClassifier','Stacking (Ensemble)':'StackingClassifier','Voting (Ensemble)':'VotingClassifier','Deep Q Network':'DQN','Dueling Deep Q Network':'DDQN','Neural Architecture Search':'NAS'} self.modelToAlgoNames = {value: key for key, value in self.AlgorithmNames.items()} def check_threshold(self,estimator,testX,testY,threshold_range,checkParameter,modelName): thresholdx = -1 for threshold in threshold_range: predictedData = estimator.predict_proba(testX) predictedData = binarize(predictedData[:,1].reshape(-1, 1),threshold=threshold) p_score = precision_score(testY, predictedData) #self.log.info('-------------> Precision:'+str(p_score)) r_score = recall_score(testY, predictedData) #self.log.info('-------------> Rscore:'+str(r_score)) #self.log.info(confusion_matrix(testY, predictedData)) tn, fp, fn, tp = confusion_matrix(testY, predictedData).ravel() if(checkParameter.lower() == 'fp'): if fp == 0: if(p_score == 1): thresholdx = threshold self.log.info('---------------> Best Threshold:'+str(threshold)) self.log.info('---------------> Best Precision:'+str(p_score)) self.log.info('---------------> Best Recall:'+str(r_score)) self.log.info('---------------> TN:'+str(tn)) self.log.info('---------------> FP:'+str(fp)) self.log.info('---------------> FN:'+str(fn)) self.log.info('---------------> TP:'+str(tp)) break if(checkParameter.lower() == 'fn'): if fn == 0: if(r_score == 1): thresholdx = threshold self.log.info('---------------> Best Threshold:'+str(threshold)) self.log.info('---------------> Best Precision:'+str(p_score)) self.log.info('---------------> Best Recall:'+str(r_score)) self.log.info('---------------> TN:'+str(tn)) self.log.info('---------------> FP:'+str(fp)) self.log.info('---------------> FN:'+str(fn)) self.log.info('---------------> TP:'+str(tp)) break return(thresholdx,p_score,r_score) def getBestModel(self,fp0,fn0,threshold,bestthreshold,rscore,brscore,pscore,bpscore,tscore,btscore): cmodel = False if(threshold != -1): if(bestthreshold == -1): cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif fp0: if rscore > brscore: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif rscore == brscore: if tscore > btscore or btscore == -0xFFFF: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif fn0: if pscore > bpscore: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif pscore == bpscore: if tscore > btscore or btscore == -0xFFFF: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore else: if tscore > btscore or btscore == -0xFFFF: cmodel = True btscore = tscore else: if(bestthreshold == -1): if tscore > btscore or btscore == -0xFFFF: cmodel = True btscore = tscore return cmodel,btscore,bestthreshold,brscore,bpscore def logMlflow(self, runName, params, metrices, estimator, algoName=None): with mlflow.start_run(run_name = runName): for k,v in params.items(): mlflow.log_param(k, v) for k,v in metrices.items(): mlflow.log_metric(k, v) if algoName == 'CatBoostClassifier': mlflow.catboost.log_model(estimator, "model") else: mlflow.sklearn.log_model(estimator, "model") model_uri = mlflow.get_artifact_uri("model") """ for some dataset evaluate takes more than 90 min, so commenting till some solution is not found evaluate_data = self.testX.copy() evaluate_data['label'] = self.testY.copy() mlflow.evaluate(model_uri, data=evaluate_data, targets='label', model_type="classifier") del evaluate_data """ def classModelling(self, modelOrFeatureBased,code_configure): paramObj=parametersDefine() bestModel='None' bestParams={} bestScore=-0xFFFF bestEstimator = 'None' bestpipelineModel='None' scoredetails = '' threshold = -1 bestthreshold = -1 precisionscore =-1 bestprecisionscore=-1 recallscore = -1 bestrecallscore=-1 self.log.info('\n---------- ClassifierModel has started ----------') objClf = aion_matrix() try: self.log.info('Status:- |... Search Optimization Method applied: '+self.method) for modelName in self.modelList: if modelName in ['Bagging (Ensemble)','Voting (Ensemble)','Stacking (Ensemble)','Dueling Deep Q Network','Deep Q Network','Neural Architecture Search']: if modelName == 'Bagging (Ensemble)': from ensemble.ensemble_bagging import ensemble_bagging ensemble_bagging_obj = ensemble_bagging(self.params[modelName],self.scoreParam,self.MakeFP0,self.MakeFN0) estimator,modelParams,score,model,threshold,precisionscore,recallscore = ensemble_bagging_obj.ensemble_bagging_classifier(self.trainX,self.trainY,self.testX,self.testY) if modelName == 'Stacking (Ensemble)': from ensemble.ensemble_stacking import ensemble_stacking ensemble_stacking_obj = ensemble_stacking(self.params[modelName],self.scoreParam) estimator,modelParams,score,model,threshold,precisionscore,recallscore = ensemble_stacking_obj.ensemble_stacking_classifier(self.trainX,self.trainY,self.testX,self.testY,self.MakeFP0,self.MakeFN0,self.modelList) if modelName == 'Voting (Ensemble)': from ensemble.ensemble_voting import ensemble_voting ensemble_voting_obj = ensemble_voting("",self.scoreParam) #bug 12437 status,estimator,modelParams,score,model,threshold,precisionscore,recallscore = ensemble_voting_obj.ensemble_voting_classifier(self.trainX,self.trainY,self.testX,self.testY,self.MakeFP0,self.MakeFN0,self.modelList) if status != "SUCCESS": #bug 12437 continue if modelName == 'Deep Q Network': from reinforcement.DRL_train import ReinformentLearning rlObj = ReinformentLearning(self.params[modelName],self.scoreParam,'Classification') estimator,modelParams,score,model,threshold,precisionscore,recallscore = rlObj.TrainRL(self.trainX,self.trainY,self.testX,self.testY,'DQN',self.deployLocation) if modelName == 'Dueling Deep Q Network': from reinforcement.DRL_train import ReinformentLearning rlObj = ReinformentLearning(self.params[modelName],self.scoreParam,'Classification') estimator,modelParams,score,model,threshold,precisionscore,recallscore = rlObj.TrainRL(self.trainX,self.trainY,self.testX,self.testY,'DDQN',self.deployLocation) ''' if modelName == 'Neural Architecture Search': from nas.aionNAS import aionNAS objNAS = aionNAS('Classification',self.params[modelName],self.trainX,self.testX,self.trainY,self.testY,self.deployLocation) estimator,modelParams,score,model,threshold,precisionscore,recallscore=objNAS.nasMain(self.scoreParam) ''' if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"'+self.modelToAlgoNames[model]+'","FeatureEngineering":"'+str(modelOrFeatureBased)+'","Score":'+str(score)+',"ModelUncertainty":"NA"}' status,bscore,bthres,brscore,bpscore = self.getBestModel(self.MakeFP0,self.MakeFN0,threshold,bestthreshold,recallscore,bestrecallscore,precisionscore,bestprecisionscore,score,bestScore) if status: bestScore =bscore bestModel =model bestParams=modelParams bestEstimator=estimator bestthreshold = bthres bestrecallscore = brscore bestprecisionscore = bpscore self.log.info('Status:- |... ML Algorithm applied: '+modelName) self.log.info('Status:- |... Score: '+objClf.get_print_score(self.scoreParam)+'='+str(round(score,2))+'\n') continue paramSpace=self.params[modelName].copy() algoName = self.AlgorithmNames[modelName] paramDict =paramObj.paramDefine(paramSpace,self.method) if not self.method == 'bayesopt': paramSize = paramObj.getParamSpaceSize(paramDict) else: paramSize = 0 if (self.method == 'bayesopt' and not paramDict) or (not self.method == 'bayesopt' and paramSize<=0): try: start = time.time() #function call defObj = defaultParams(algoName,paramDict,self.scoreParam,self.MakeFP0, self.MakeFN0,paramSize) estimator, modelParams, model,score, threshold, precisionscore, recallscore =defObj.startTrainingClassification(self.trainX,self.trainY,self.testX,self.testY) executionTime = time.time() - start if (scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"' + self.modelToAlgoNames[model] + '","FeatureEngineering":"' + str( modelOrFeatureBased) + '","Score":' + str(score) + ',"ModelUncertainty":"NA"}' status, bscore, bthres, brscore, bpscore = self.getBestModel(self.MakeFP0, self.MakeFN0,threshold, bestthreshold,recallscore, bestrecallscore,precisionscore, bestprecisionscore,score, bestScore) self.log.info('---------> Total Execution: ' + str(executionTime) + '\n') if status: bestScore = bscore bestModel = model bestParams = modelParams bestEstimator = estimator bestthreshold = bthres bestrecallscore = brscore bestprecisionscore = bpscore self.log.info('Status:- |... ML Algorithm applied: ' + modelName) self.log.info('Status:- |... Score: ' + objClf.get_print_score(self.scoreParam) + '=' + str( round(score, 2)) + '\n') except Exception as inst: self.log.info('\n < ---------- Model Execution Failed Start--------->') self.log.info('\n<-------' + modelName + ' Model Execution failed!!!.' + str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) self.log.info('\n < ---------- Model Execution Failed End --------->') continue # call algorithms with default valuepass if self.method == 'bayesopt': code_configure.add_model(algoName,paramSpace) else: paramDictCopy = paramDict # numpy array is not json serializable #numpy is already imported but still np.ndarray raise error import numpy as np for key,value in paramDictCopy.items(): if isinstance(value, np.ndarray): paramDictCopy[key] = paramDictCopy[key].tolist() code_configure.add_model(algoName,paramDictCopy) trainingStatus = 'Success' if self.method =='grid': try: self.log.info("-------> Optimization Method :Grid Search") self.log.info("-------> Model Name: "+str(modelName)) opTq =OptimizationTq(algoName,paramDict,self.cvSplit,self.scoreParam,self.numIter,self.trainX,self.trainY) start = time.time() model,modelParams,score,estimator=opTq.gridSearchOpt() executionTime=time.time() - start if not self.testX.empty: predictedData = estimator.predict(self.testX) score = objClf.get_score(self.scoreParam,self.testY,predictedData) else: score = score*100 problemName = estimator.__class__.__name__ runName = algoName + '_' + modelOrFeatureBased metrices = {} metrices["score"] = score try: self.logMlflow(runName, modelParams, metrices, estimator, algoName) except Exception as e: self.log.info('----------> ML Flow error!!!. ' + str(e)) # usnish pass output_jsonobject = "" problemName = estimator.__class__.__name__ self.log.info('----------> Testing Score: '+str(score)) try: if ((estimator.__class__.__name__ == "ABCMeta") or (model in ['SGDClassifier','XGBClassifier','CatBoostClassifier','LGBMClassifier']) ): self.log.info('-----> Model Uncertainty Not Supported') else: uqObj=aionUQ(None,None,None,problemName,modelParams,estimator,None,None,self.deployLocation) accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertainty_per=uqObj.uqMain_BBMClassification(self.uq_x_train,self.uq_x_test,self.uq_y_train,self.uq_y_test,"aionuq") self.log.info("-------> model_confidence: "+str(model_confidence_per)+str('%')) self.log.info("-------> model_uncertainty: "+str(model_uncertainty_per)+str('%')) except: pass if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"'+self.modelToAlgoNames[model]+'","FeatureEngineering":"'+str(modelOrFeatureBased)+'","Score":'+str(score)+',"ModelUncertainty":'+str(json.dumps(output_jsonobject))+'}' self.log.info('----------> Testing Score: '+str(score)) import numpy as np if self.MakeFP0: self.log.info('-------- Calculate Threshold for FP Start-------') startRange = 0.0 endRange = 1.0 stepsize = 0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = self.check_threshold(estimator,self.trainX,self.trainY,threshold_range,'FP',algoName) self.log.info('-------- Calculate Threshold for FP End-------') if self.MakeFN0: self.log.info('-------- Calculate Threshold for FN Start-------') startRange = 1.0 endRange = 0.0 stepsize = -0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = self.check_threshold(estimator,self.trainX,self.trainY,threshold_range,'FN',algoName) self.log.info('-------- Calculate Threshold for FN End-------') self.log.info('----------> Total Execution: '+str(executionTime)+'\n') status,bscore,bthres,brscore,bpscore = self.getBestModel(self.MakeFP0,self.MakeFN0,threshold,bestthreshold,recallscore,bestrecallscore,precisionscore,bestprecisionscore,score,bestScore) if status: bestScore =bscore bestModel =model bestParams=modelParams bestEstimator=estimator bestthreshold = bthres bestrecallscore = brscore bestprecisionscore = bpscore except Exception as inst: self.log.info('\n < ---------- Model Execution Failed Start--------->') self.log.info('\n<-------'+ modelName+' Model Execution failed!!!.'+str(inst)) self.log.info('\n < ---------- Model Execution Failed End --------->') trainingStatus = 'Error (Exception)' elif self.method == 'random': try: self.log.info("-------> Optimization Method :Random Search") self.log.info("-------> Model Name: "+str(modelName)) start = time.time() opTq =OptimizationTq(algoName,paramDict,self.cvSplit,self.scoreParam,self.numIter,self.trainX,self.trainY) model,modelParams,score,estimator=opTq.randomSearchOpt() executionTime=time.time() - start if not self.testX.empty: predictedData = estimator.predict(self.testX) score = objClf.get_score(self.scoreParam,self.testY,predictedData) else: score = score*100 problemName = estimator.__class__.__name__ runName = algoName + '_' + modelOrFeatureBased metrices = {} metrices["score"] = score try: self.logMlflow(runName, modelParams, metrices, estimator, algoName) except Exception as e: self.log.info('----------> ML Flow error!!!. ' + str(e)) # usnish pass import numpy as np if self.MakeFP0: self.log.info('-------- Calculate Threshold for FP Start-------') startRange = 0.0 endRange = 1.0 stepsize = 0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = self.check_threshold(estimator,self.trainX,self.trainY,threshold_range,'FP',algoName) self.log.info('-------- Calculate Threshold for FP End-------') if self.MakeFN0: self.log.info('-------- Calculate Threshold for FN Start-------') startRange = 1.0 endRange = 0.0 stepsize = -0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = self.check_threshold(estimator,self.trainX,self.trainY,threshold_range,'FN',algoName) self.log.info('-------- Calculate Threshold for FN End-------') if threshold != -1: if not self.testX.empty: predictedData = estimator.predict_proba(self.testX) predictedData = binarize(predictedData[:,1].reshape(-1, 1),threshold=threshold) score = objClf.get_score(self.scoreParam,self.testY,predictedData) else: predictedData = estimator.predict_proba(self.trainX) predictedData = binarize(predictedData[:,1].reshape(-1, 1),threshold=threshold) score = objClf.get_score(self.scoreParam,self.trainY,predictedData) self.log.info('---------> Total Execution: '+str(executionTime)+'\n') output_jsonobject = "" problemName = estimator.__class__.__name__ self.log.info('----------> Testing Score: '+str(score)) try: if ((estimator.__class__.__name__ == "ABCMeta") or (model in ['SGDClassifier','XGBClassifier','CatBoostClassifier','LGBMClassifier']) ): self.log.info('-----> Model Uncertainty Not Supported') else: uqObj=aionUQ(None,None,None,problemName,modelParams,estimator,None,None,self.deployLocation) accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertainty_per=uqObj.uqMain_BBMClassification(self.uq_x_train,self.uq_x_test,self.uq_y_train,self.uq_y_test,"aionuq") self.log.info("-------> model_confidence: "+str(model_confidence_per)+str('%')) self.log.info("-------> model_uncertainty: "+str(model_uncertainty_per)+str('%')) except Exception as e: pass if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"'+self.modelToAlgoNames[model]+'","FeatureEngineering":"'+str(modelOrFeatureBased)+'","Score":'+str(score)+',"ModelUncertainty":'+str(json.dumps(output_jsonobject))+'}' status,bscore,bthres,brscore,bpscore = self.getBestModel(self.MakeFP0,self.MakeFN0,threshold,bestthreshold,recallscore,bestrecallscore,precisionscore,bestprecisionscore,score,bestScore) if status: bestScore =bscore bestModel =model bestParams=modelParams bestEstimator=estimator bestthreshold = threshold bestrecallscore = recallscore bestprecisionscore = precisionscore except Exception as inst: self.log.info('\n < ---------- Model Execution Failed Start--------->') self.log.info('\n<-------'+ modelName+' Model Execution failed!!!.'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) self.log.info('\n < ---------- Model Execution Failed End --------->') trainingStatus = 'Error (Exception)' elif self.method == 'bayesopt': try: self.log.info("-------> Optimization Method :BayesOpt") self.log.info("-------> Model Name: "+str(modelName)) opTq =OptimizationTq(algoName,paramDict,self.cvSplit,self.scoreParam,self.numIter,self.trainX,self.trainY) fun=opTq.f trials = Trials() start = time.time() best = fmin(fun,paramDict,algo=tpe.suggest, max_evals=self.numIter, trials=trials) executionTime=time.time() - start results = sorted(trials.results, key = lambda x: x['loss']) bestresult=results[0] model=bestresult['model'] score=bestresult['score'] modelParams=bestresult['params'] executionTime=time.time() - start res = ', '.join("{!s}={!r}".format(key,val) for (key,val) in modelParams.items()) modelObj=eval(model+'('+res+')') estimator = modelObj.fit(self.trainX,self.trainY) if not self.testX.empty: predictedData = estimator.predict(self.testX) score = objClf.get_score(self.scoreParam,self.testY,predictedData) problemName = estimator.__class__.__name__ runName = algoName + '_' + modelOrFeatureBased metrices = {} metrices["score"] = score try: self.logMlflow(runName, modelParams, metrices, estimator, algoName) except Exception as e: self.log.info('----------> ML Flow error!!!. ' + str(e)) # usnish pass output_jsonobject = "" problemName = estimator.__class__.__name__ self.log.info('----------> Testing Score: '+str(score)) try: if ((estimator.__class__.__name__ == "ABCMeta") or (model in ['SGDClassifier','XGBClassifier','CatBoostClassifier','LGBMClassifier']) ): self.log.info('-----> Model Uncertainty Not Supported') else: uqObj=aionUQ(None,None,None,problemName,modelParams,estimator,None,None,self.deployLocation) accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertainty_per=uqObj.uqMain_BBMClassification(self.uq_x_train,self.uq_x_test,self.uq_y_train,self.uq_y_test,"aionuq") self.log.info("-------> model_confidence: "+str(model_confidence_per)+str('%')) self.log.info("-------> model_uncertainty: "+str(model_uncertainty_per)+str('%')) except: pass if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"'+self.modelToAlgoNames[model]+'","FeatureEngineering":"'+str(modelOrFeatureBased)+'","Score":'+str(score)+',"ModelUncertainty":'+str(json.dumps(output_jsonobject))+'}' ''' test_accuracy = accuracy_score(self.testY,predictedData) test_precision = precision_score(self.testY,predictedData,average='macro') self.log.info('---------> Test Accuracy: '+str(test_accuracy)) self.log.info('---------> Test Precision: '+str(test_precision)) ''' import numpy as np if self.MakeFP0: self.log.info('-------- Calculate Threshold for FP Start-------') startRange = 0.0 endRange = 1.0 stepsize = 0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = self.check_threshold(estimator,self.testX,self.testY,threshold_range,'FP',algoName) self.log.info('-------- Calculate Threshold for FP End-------') if self.MakeFN0: self.log.info('-------- Calculate Threshold for FN Start-------') startRange = 1.0 endRange = 0.0 stepsize = -0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = self.check_threshold(estimator,self.testX,self.testY,threshold_range,'FN',algoName) self.log.info('-------- Calculate Threshold for FN End-------') self.log.info('---------> Total Execution: '+str(executionTime)+'\n') status,bscore,bthres,brscore,bpscore = self.getBestModel(self.MakeFP0,self.MakeFN0,threshold,bestthreshold,recallscore,bestrecallscore,precisionscore,bestprecisionscore,score,bestScore) if status: bestScore =score bestModel =model bestParams=modelParams res = ', '.join("{!s}={!r}".format(key,val) for (key,val) in bestParams.items()) modelObj=eval(bestModel+'('+res+')') bestEstimator=estimator bestthreshold = threshold bestrecallscore = recallscore bestprecisionscore = precisionscore except Exception as inst: self.log.info('\n < ---------- Model Execution Failed Start--------->') self.log.info('\n<-------'+ modelName+' Model Execution failed!!!.'+str(inst)) self.log.info('\n < ---------- Model Execution Failed End --------->') trainingStatus = 'Error (Exception)' else: trainingStatus = 'Error (HyperTunning Algo Not Supported)' pass self.log.info('Status:- |... ML Algorithm applied: '+modelName) if trainingStatus.lower() == 'success': self.log.info('Status:- |... Score after hyperparameter tuning: '+objClf.get_print_score(self.scoreParam)+'='+str(round(score,2))+'\n') else: self.log.info('Status:- |... Training Error : '+trainingStatus+'\n') self.log.info('---------- ClassifierModel End ---------- \n') if bestModel != 'None': self.log.info('\n------- Best Model and its parameters -------------') self.log.info('Status:- |... Best Algorithm selected: '+str(self.modelToAlgoNames[bestModel])+' Score='+str(round(bestScore,2))) self.log.info("-------> Best Name: "+str(bestModel)) self.log.info("-------> Best Score: "+str(bestScore)) return self.modelToAlgoNames[bestModel],bestParams,bestScore,bestEstimator,scoredetails,bestthreshold,bestprecisionscore,bestrecallscore else: raise Exception("Sorry, no model is trained") except Exception as inst: self.log.info( '\n-----> ClassifierModel failed!!!.'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import warnings warnings.filterwarnings('ignore') import pandas as pd from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report from sklearn.metrics import roc_curve, auc from sklearn.metrics import roc_auc_score from sklearn.metrics import accuracy_score from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error,make_scorer from sklearn.metrics import mean_squared_error from sklearn.metrics import recall_score from sklearn.metrics import precision_score from sklearn.metrics import f1_score import logging import numpy as np from sklearn.preprocessing import binarize from sklearn.preprocessing import LabelBinarizer from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error,make_scorer from sklearn.metrics import mean_squared_error class aion_matrix: def __init__(self): self.log = logging.getLogger('eion') def get_print_score(self,matrix): if 'accuracy' in str(matrix).lower(): return 'Accuracy' elif 'recall' in str(matrix).lower(): return 'Recall' elif 'precision' in str(matrix).lower(): return 'Precision' elif 'f1_score' in str(matrix).lower(): return 'F1_Score' elif 'roc_auc' in str(matrix).lower(): return 'ROC_AUC' elif 'mse' in str(matrix).lower() or 'neg_mean_squared_error' in str(matrix).lower(): return 'Mean Squared Error(MSE)' elif 'rmse' in str(matrix).lower() or 'neg_root_mean_squared_error' in str(matrix).lower(): return 'Root Mean Suared Error(RMSE)' elif 'mae' in str(matrix).lower() or 'neg_mean_absolute_error' in str(matrix).lower(): return 'Mean Absolute Error (MAE)' elif 'r2' in str(matrix).lower(): return 'R-Squared(R2)' else: return 'Unknown' def get_score(self,matrix,actual,predict): if 'accuracy' in str(matrix).lower(): ensemble_score = accuracy_score(actual,predict) ensemble_score = ensemble_score*100 elif 'recall' in str(matrix).lower(): ensemble_score = recall_score(actual,predict,average='macro') ensemble_score = ensemble_score*100 elif 'precision' in str(matrix).lower(): ensemble_score = precision_score(actual,predict,average='macro') ensemble_score = ensemble_score*100 elif 'f1_score' in str(matrix).lower(): ensemble_score = f1_score(actual,predict, average='macro') ensemble_score = ensemble_score*100 elif 'roc_auc' in str(matrix).lower(): try: ensemble_score = roc_auc_score(actual,predict,average="macro") except: try: actual = pd.get_dummies(actual) predict = pd.get_dummies(predict) ensemble_score = roc_auc_score(actual,predict, average='weighted', multi_class='ovr') except: ensemble_score = 0 ensemble_score = ensemble_score*100 elif ('mse' in str(matrix).lower()) or ('neg_mean_squared_error' in str(matrix).lower()): ensemble_score = mean_squared_error(actual,predict) elif ('rmse' in str(matrix).lower()) or ('neg_root_mean_squared_error' in str(matrix).lower()): ensemble_score=mean_squared_error(actual,predict,squared=False) elif ('mae' in str(matrix).lower()) or ('neg_mean_absolute_error' in str(matrix).lower()): ensemble_score=mean_absolute_error(actual,predict) elif 'r2' in str(matrix).lower(): ensemble_score=r2_score(actual,predict) return round(ensemble_score,2) def getClassificationPerformaceMatrix(self,le_trainY,predictedData,labelMaps): setOfyTrue = set(le_trainY) unqClassLst = list(setOfyTrue) if(str(labelMaps) != '{}'): inv_mapping_dict = {v: k for k, v in labelMaps.items()} unqClassLst2 = (pd.Series(unqClassLst)).map(inv_mapping_dict) unqClassLst2 = list(unqClassLst2) else: unqClassLst2 = unqClassLst indexName = [] columnName = [] targetnames=[] for item in unqClassLst2: indexName.append("act:"+str(item)) columnName.append("pre:"+str(item)) targetnames.append(str(item)) matrixconfusion = pd.DataFrame(confusion_matrix(le_trainY,predictedData, labels = unqClassLst),index = indexName, columns = columnName) #pd.set_option('expand_frame_repr', False) pd.set_option('display.max_columns',len(targetnames)+2) self.log.info('-------> Confusion Matrix: ') self.log.info(matrixconfusion) pd.reset_option('display.max_columns') #pd.reset_option('expand_frame_repr') #self.log.info('-------> Confusion Matrix With Labels: ') #self.log.info(confusion_matrix(le_trainY,predictedData, labels = unqClassLst)) #print(unqClassLst2) classificationreport = pd.DataFrame(classification_report(le_trainY, predictedData, labels = unqClassLst,target_names=targetnames,output_dict=True)).transpose() self.log.info('-------> Classification Report: ') self.log.info(classificationreport) lb = LabelBinarizer() lb.fit(le_trainY) transformTarget= lb.transform(le_trainY) transformPredict = lb.transform(predictedData) rocaucscore = roc_auc_score(transformTarget,transformPredict,average="macro") self.log.info('-------> ROC AUC SCORE :'+str(rocaucscore)) matrixconfusion = matrixconfusion.to_json(orient='index') classificationreport = classificationreport.to_json(orient='index') matrix = '"ConfusionMatrix":'+matrixconfusion+',"ClassificationReport":'+classificationreport+',"ROC_AUC_SCORE":'+str(rocaucscore) return(matrix) def get_regression_matrix(self,targetData,predictedData): r2score=r2_score(targetData, predictedData) self.log.info('-------> R2_score :'+str(r2score)) meanabsoluteerror=(mean_absolute_error(targetData, predictedData)) self.log.info('-------> MAE :'+str(meanabsoluteerror)) meanssquatederror=mean_squared_error(targetData, predictedData) self.log.info('-------> MSE :'+str(meanssquatederror)) rootmeanssquatederror=mean_squared_error(targetData, predictedData,squared=False) self.log.info('-------> RMSE :'+str(rootmeanssquatederror)) targetArray, predictedArray = np.array(targetData), np.array(predictedData) try: EPSILON = 1e-10 meanpercentageerror=np.mean(np.abs((targetArray - predictedArray) / (targetArray+EPSILON)))*100 except ZeroDivisionError: meanpercentageerror = 0 self.log.info('-------> MAPE :'+str(meanpercentageerror)) try: normalised_rmse_percentage = round(((rootmeanssquatederror/ ( np.max(targetData) - np.min(targetData) )) * 100), 4) except Exception as e: normalised_rmse_percentage = -1 self.log.info('-------> Normalised RMSE percentage :'+str(normalised_rmse_percentage)) matrix = '"MAE":'+str(meanabsoluteerror)+',"R2Score":'+str(r2score)+',"MSE":'+str(meanssquatederror)+',"MAPE":'+str(meanpercentageerror)+',"RMSE":'+str(rootmeanssquatederror)+',"Normalised RMSE(%)":'+str(normalised_rmse_percentage) return matrix def getbestfeatureModel(self,modelType,scoreParam,score1,score2,model1,model2,threshold1,pscore1,rscore1,threshold2,pscore2,rscore2): best_feature_model = 'Model1' self.log.info('\n ---------- Summary Start ------------') if modelType.lower() == "classification": if(threshold1 == -1 and threshold2 == -1): if score1> score2: self.log.info('-------> Best Features: Model1') self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model1' else: self.log.info('-------> Best Features: Model2') self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model2' elif(threshold1 == -1): self.log.info('-------> Best Features: Model2') self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model2' elif(threshold1 == -2): self.log.info('-------> Best Features: Model1') self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model1' else: if pscore1 == pscore2: if rscore1 > rscore2: self.log.info('-------> Best Features: Model1') self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model1' else: self.log.info('-------> Best Features: Model2') self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model2' elif rscore1 == rscore2: if pscore1 > pscore2: self.log.info('-------> Best Features: Model1') self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model1' else: self.log.info('-------> Best Features: Model2') self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model2' elif modelType.lower() == "regression": if scoreParam == "r2" or scoreParam == "explained_variance": if score1> score2 : self.log.info('-------> Best Features: Model1') self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model1' else: self.log.info('-------> Best Features: Model2') self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model2' else: if score1< score2 : self.log.info('-------> Best Features: Model1') self.log.info('-------> Best Model: '+str(model1)) self.log.info('-------> Best Score: '+str(score1)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model1' else: self.log.info('-------> Best Features: Model2') self.log.info('-------> Best Model: '+str(model2)) self.log.info('-------> Best Score: '+str(score2)) self.log.info('-------> Scoring Param: '+str(scoreParam)) best_feature_model = 'Model2' self.log.info('---------- Summary End ------------\n') return(best_feature_model) def check_threshold(self,estimator,testX,testY,threshold_range,checkParameter,modelName): thresholdx = -1 for threshold in threshold_range: predictedData = estimator.predict_proba(testX) predictedData = binarize(predictedData[:,1].reshape(-1, 1),threshold=threshold)#bug 12437 p_score = precision_score(testY, predictedData) #self.log.info('-------------> Precision:'+str(p_score)) r_score = recall_score(testY, predictedData) #self.log.info('-------------> Rscore:'+str(r_score)) #self.log.info(confusion_matrix(testY, predictedData)) tn, fp, fn, tp = confusion_matrix(testY, predictedData).ravel() if(checkParameter.lower() == 'fp'): if fp == 0: if(p_score == 1): thresholdx = threshold self.log.info('---------------> Best Threshold:'+str(threshold)) self.log.info('---------------> Best Precision:'+str(p_score)) self.log.info('---------------> Best Recall:'+str(r_score)) self.log.info('---------------> TN:'+str(tn)) self.log.info('---------------> FP:'+str(fp)) self.log.info('---------------> FN:'+str(fn)) self.log.info('---------------> TP:'+str(tp)) break if(checkParameter.lower() == 'fn'): if fn == 0: if(r_score == 1): thresholdx = threshold self.log.info('---------------> Best Threshold:'+str(threshold)) self.log.info('---------------> Best Precision:'+str(p_score)) self.log.info('---------------> Best Recall:'+str(r_score)) self.log.info('---------------> TN:'+str(tn)) self.log.info('---------------> FP:'+str(fp)) self.log.info('---------------> FN:'+str(fn)) self.log.info('---------------> TP:'+str(tp)) break return(thresholdx,p_score,r_score) def getBestModel(self,fp0,fn0,threshold,bestthreshold,rscore,brscore,pscore,bpscore,tscore,btscore): cmodel = False if(threshold != -1): if(bestthreshold == -1): cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif fp0: if rscore > brscore: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif rscore == brscore: if tscore > btscore or btscore == -0xFFFF: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif fn0: if pscore > bpscore: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif pscore == bpscore: if tscore > btscore or btscore == -0xFFFF: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore else: if tscore > btscore or btscore == -0xFFFF: cmodel = True btscore = tscore else: if(bestthreshold == -1): if tscore > btscore or btscore == -0xFFFF: cmodel = True btscore = tscore return cmodel,btscore,bestthreshold,brscore,bpscore ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import tensorflow from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, Flatten from tensorflow.keras.layers import Conv2D, MaxPooling2D from tensorflow.keras.utils import to_categorical from tensorflow.keras.preprocessing import image import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from tensorflow.keras.layers import Input from tensorflow.keras.models import Model from tensorflow.keras.optimizers import Adam from tensorflow.keras.applications import VGG16 from tensorflow.keras.callbacks import EarlyStopping import logging from sklearn.preprocessing import LabelEncoder from statistics import mean import sys from learner.machinelearning import machinelearning from learner.aion_matrix import aion_matrix from profiler.imageAug import ImageAugmentation from pathlib import Path class ImageLearning: def __init__(self,dataFrame,input_directory,outputdir,modelname,hyperParam, AugEnabled,keepAugImages,operations,augConf): self.image_list = dataFrame self.input_directory = input_directory self.outputdir = outputdir self.modelname = modelname self.hyperParam = hyperParam self.labelMapping={} self.log = logging.getLogger('eion') self.AIONNumericDtypes=['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] self.AugEnabled = AugEnabled self.keepAugImages = keepAugImages self.operations = operations self.augConf = augConf def TrainCAST(self,predicted_data_file): datatype = self.image_list['Label'].dtypes if datatype not in self.AIONNumericDtypes: labelEncode= LabelEncoder() self.image_list['Label'] = self.image_list['Label'].apply(str) self.image_list['Label'] = labelEncode.fit_transform(self.image_list['Label']) self.labelMapping = dict(zip(labelEncode.classes_, labelEncode.transform(labelEncode.classes_))) self.log.info('\n-------> First Ten Rows of Input Data After Encoding: ') self.log.info(self.image_list.head(10)) self.log.info('Status:- |... Target Feature Encoding Done') if not os.path.exists(self.outputdir): os.makedirs(self.outputdir) train_df, test_df = train_test_split(self.image_list, random_state=42, test_size=self.hyperParam['test_split_ratio']) if self.AugEnabled: csv_file = "tempTrainDf.csv" train_df.to_csv(csv_file, index=False) ia = ImageAugmentation(self.input_directory, csv_file) csv_file = ia.augment("imageclassification", self.operations,None,self.augConf) train_df = pd.read_csv(csv_file) Path(csv_file).unlink() train_image = [] train_df.reset_index(drop=True, inplace=True) for i in range(train_df.shape[0]): #print(os.path.join(self.input_directory,str(self.image_list['File'][i]))) img = image.load_img(os.path.join(self.input_directory,str(train_df['File'][i])), target_size=(self.hyperParam['img_width'],self.hyperParam['img_height'],self.hyperParam['img_channel']), grayscale=False) img = image.img_to_array(img) img = img/255 train_image.append(img) test_image = [] test_df.reset_index(drop=True, inplace=True) for i in range(test_df.shape[0]): #print(os.path.join(self.input_directory,str(self.image_list['File'][i]))) img = image.load_img(os.path.join(self.input_directory,str(test_df['File'][i])), target_size=(self.hyperParam['img_width'],self.hyperParam['img_height'],self.hyperParam['img_channel']), grayscale=False) img = image.img_to_array(img) img = img/255 test_image.append(img) self.log.info('Status:- |... Image Loading Done') X_train = np.array(train_image) y_train = train_df['Label'] X_test = np.array(test_image) y_test = test_df['Label'] ytrain = y_train.values ytrain = to_categorical(ytrain) ytest = y_test.values ytest = to_categorical(ytest) #print(y) self.log.info("Loading Imagenet Weights...") if self.modelname == "densenet": self.log.info('Loading Densenet model') baseModel = tensorflow.keras.applications.DenseNet121(weights="imagenet", include_top=False, input_tensor=Input(shape=(self.hyperParam['img_width'], self.hyperParam['img_height'], self.hyperParam['img_channel']))) #98 elif self.modelname == "inception": self.log.info('Loading Inception model') baseModel = tensorflow.keras.applications.InceptionV3(weights="imagenet", include_top=False, input_tensor=Input(shape=(self.hyperParam['img_width'], self.hyperParam['img_height'], self.hyperParam['img_channel']))) #97 headModel = baseModel.output headModel = Flatten(name="flatten")(headModel) headModel = Dense(1024, activation='relu')(headModel) headModel = Dropout(0.5)(headModel) headModel = Dense(2, activation='sigmoid')(headModel) model = Model(inputs=baseModel.input, outputs=headModel) self.log.info("[INFO] compiling model...") opt = Adam(lr=self.hyperParam['lr']) model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"]) #early_stop = EarlyStopping(monitor='val_loss',patience=2) #history = model.fit(X_train, y_train, epochs=hyperparam_config['epochs'], validation_data=(X_test, y_test), callbacks=[early_stop]) history = model.fit(X_train, ytrain, epochs=self.hyperParam['epochs'], validation_data=(X_test, ytest)) self.log.info('Status:- |... Image Classification Algorithm applied:'+str(self.modelname)) #Saving trained model weights model.save_weights(os.path.join(self.outputdir, self.modelname)) saved_model = self.modelname modelname = self.modelname prediction = model.predict(X_train) predictedData = np.argmax(prediction,axis=1) mlobj = machinelearning() self.log.info('\n--------- Performance Matrix with Train Data ---------') trainingperformancematrix = mlobj.getClassificationPerformaceMatrix(y_train, predictedData,self.labelMapping) prediction = model.predict(X_test) predictedData = np.argmax(prediction,axis=1) self.log.info('\n--------- Performance Matrix with Test Data ---------') performancematrix = mlobj.getClassificationPerformaceMatrix(y_test, predictedData,self.labelMapping) df_test = pd.DataFrame() df_test['actual'] = y_test df_test['predict'] = predictedData df_test.to_csv(predicted_data_file) objClf = aion_matrix() scoring_param = 'Accuracy' score = objClf.get_score(scoring_param,y_test,predictedData) #score = mean(history.history['accuracy']) if self.AugEnabled and not self.keepAugImages: ia.removeAugmentedImages(train_df) scoredetails = '{"Model":"'+modelname+'","Score":'+str(round(score,2))+'}' self.log.info('Status:- |... Score Accuracy: '+str(round(score,2))) return saved_model,modelname,'ImageClassification',scoring_param,score,scoredetails,self.labelMapping,trainingperformancematrix,performancematrix ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import warnings warnings.simplefilter("ignore") import os import numpy as np from numpy import asarray import cv2 import sys import random import glob as glob import math as m # for gamma function, called from scipy.special import gamma as tgamma import matplotlib.image as mpimg import skimage from libsvm import svmutil,svm #import svmutil from svmutil import * from svm import * from PIL import Image from collections import Counter from imutils import paths import matplotlib.pyplot as plt import json ################################################################################### #Input - # AGGD fit model, takes input as the MSCN Image / Pair-wise Product #Output - best values of image parameters #Defination - used as internal method to measure_ImageQualityScore ################################################################################### def AGGDfit(structdis): # variables to count positive pixels / negative pixels and their squared sum poscount = 0 negcount = 0 possqsum = 0 negsqsum = 0 abssum = 0 poscount = len(structdis[structdis > 0]) # number of positive pixels negcount = len(structdis[structdis < 0]) # number of negative pixels # calculate squared sum of positive pixels and negative pixels possqsum = np.sum(np.power(structdis[structdis > 0], 2)) negsqsum = np.sum(np.power(structdis[structdis < 0], 2)) # absolute squared sum abssum = np.sum(structdis[structdis > 0]) + np.sum(-1 * structdis[structdis < 0]) # calculate left sigma variance and right sigma variance lsigma_best = np.sqrt((negsqsum/negcount)) rsigma_best = np.sqrt((possqsum/poscount)) gammahat = lsigma_best/rsigma_best # total number of pixels - totalcount totalcount = structdis.shape[1] * structdis.shape[0] rhat = m.pow(abssum/totalcount, 2)/((negsqsum + possqsum)/totalcount) rhatnorm = rhat * (m.pow(gammahat, 3) + 1) * (gammahat + 1)/(m.pow(m.pow(gammahat, 2) + 1, 2)) prevgamma = 0 prevdiff = 1e10 sampling = 0.001 gam = 0.2 # vectorized function call for best fitting parameters vectfunc = np.vectorize(func, otypes = [np.float], cache = False) # calculate best fit params gamma_best = vectfunc(gam, prevgamma, prevdiff, sampling, rhatnorm) return [lsigma_best, rsigma_best, gamma_best] def func(gam, prevgamma, prevdiff, sampling, rhatnorm): while(gam < 10): r_gam = tgamma(2/gam) * tgamma(2/gam) / (tgamma(1/gam) * tgamma(3/gam)) diff = abs(r_gam - rhatnorm) if(diff > prevdiff): break prevdiff = diff prevgamma = gam gam += sampling gamma_best = prevgamma return gamma_best def compute_features(img): scalenum = 2 feat = [] # make a copy of the image im_original = img.copy() # scale the images twice for itr_scale in range(scalenum): im = im_original.copy() # normalize the image im = im / 255.0 # calculating MSCN coefficients mu = cv2.GaussianBlur(im, (7, 7), 1.166) mu_sq = mu * mu sigma = cv2.GaussianBlur(im*im, (7, 7), 1.166) sigma = (sigma - mu_sq)**0.5 # structdis is the MSCN image structdis = im - mu structdis /= (sigma + 1.0/255) # calculate best fitted parameters from MSCN image best_fit_params = AGGDfit(structdis) # unwrap the best fit parameters lsigma_best = best_fit_params[0] rsigma_best = best_fit_params[1] gamma_best = best_fit_params[2] # append the best fit parameters for MSCN image feat.append(gamma_best) feat.append((lsigma_best*lsigma_best + rsigma_best*rsigma_best)/2) # shifting indices for creating pair-wise products shifts = [[0,1], [1,0], [1,1], [-1,1]] # H V D1 D2 for itr_shift in range(1, len(shifts) + 1): OrigArr = structdis reqshift = shifts[itr_shift-1] # shifting index # create transformation matrix for warpAffine function M = np.float32([[1, 0, reqshift[1]], [0, 1, reqshift[0]]]) ShiftArr = cv2.warpAffine(OrigArr, M, (structdis.shape[1], structdis.shape[0])) Shifted_new_structdis = ShiftArr Shifted_new_structdis = Shifted_new_structdis * structdis # shifted_new_structdis is the pairwise product # best fit the pairwise product best_fit_params = AGGDfit(Shifted_new_structdis) lsigma_best = best_fit_params[0] rsigma_best = best_fit_params[1] gamma_best = best_fit_params[2] constant = m.pow(tgamma(1/gamma_best), 0.5)/m.pow(tgamma(3/gamma_best), 0.5) meanparam = (rsigma_best - lsigma_best) * (tgamma(2/gamma_best)/tgamma(1/gamma_best)) * constant # append the best fit calculated parameters feat.append(gamma_best) # gamma best feat.append(meanparam) # mean shape feat.append(m.pow(lsigma_best, 2)) # left variance square feat.append(m.pow(rsigma_best, 2)) # right variance square # resize the image on next iteration im_original = cv2.resize(im_original, (0,0), fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC) return feat def img_MeasureImageQuality(dataset_directory): """ #################################################################################### #Input - img_path #Output - Quality index of input image #Defination - function to calculate BRISQUE quality score in range of 0 and 100 [0:good;100:bad] #################################################################################### """ imgfile_dict = {} for file in os.listdir(dataset_directory): if (file.endswith(".jfif") or file.endswith(".png") or file.endswith(".jpg") or file.endswith(".jpeg")): filename = os.path.join(dataset_directory , file) if os.path.isfile(filename)==False: sys.exit() file_extension = os.path.splitext(filename)[1] if file_extension==".jfif": extension=".jfif" if file_extension==".png": extension=".png" if file_extension==".jpg": extension=".jpg" if file_extension==".jpeg": extension=".jpeg" if (extension not in [".jpg",".jpeg",".jfif",".png"]): sys.exit() try: # read image from given path dis = cv2.imread(filename, 1) if(dis is None): sys.exit(0) # convert to gray scale dis = cv2.cvtColor(dis, cv2.COLOR_BGR2GRAY) # compute feature vectors of the image features = compute_features(dis) # rescale the brisqueFeatures vector from -1 to 1 x = [0] # pre loaded lists from C++ Module to rescale brisquefeatures vector to [-1, 1] min_= [0.336999 ,0.019667 ,0.230000 ,-0.125959 ,0.000167 ,0.000616 ,0.231000 ,-0.125873 ,0.000165 ,0.000600 ,0.241000 ,-0.128814 ,0.000179 ,0.000386 ,0.243000 ,- 0.133080 ,0.000182 ,0.000421 ,0.436998 ,0.016929 ,0.247000 ,-0.200231 ,0.000104 ,0.000834 ,0.257000 ,-0.200017 ,0.000112 ,0.000876 ,0.257000 ,-0.155072 , 0.000112 ,0.000356 ,0.258000 ,-0.154374 ,0.000117 ,0.000351] max_= [9.999411, 0.807472, 1.644021, 0.202917, 0.712384, 0.468672, 1.644021, 0.169548, 0.713132, 0.467896, 1.553016, 0.101368, 0.687324, 0.533087, 1.554016, 0.101000 , 0.689177, 0.533133, 3.639918, 0.800955, 1.096995, 0.175286, 0.755547, 0.399270, 1.095995, 0.155928, 0.751488, 0.402398, 1.041992, 0.093209, 0.623516, 0.532925, 1.042992, 0.093714, 0.621958, 0.534484] # append the rescaled vector to x for i in range(0, 36): min = min_[i] max = max_[i] x.append(-1 + (2.0/(max - min) * (features[i] - min))) modelPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'allmodel.txt') # load model model = svmutil.svm_load_model(modelPath) # create svm node array from python list x, idx = gen_svm_nodearray(x[1:], isKernel=(model.param.kernel_type == PRECOMPUTED)) x[36].index = -1 # set last index to -1 to indicate the end. # get important parameters from model svm_type = model.get_svm_type() is_prob_model = model.is_probability_model() nr_class = model.get_nr_class() if svm_type in (ONE_CLASS, EPSILON_SVR, NU_SVC): # here svm_type is EPSILON_SVR as it's regression problem nr_classifier = 1 dec_values = (c_double * nr_classifier)() # calculate the quality score of the image using the model and svm_node_array qualityscore = svmutil.libsvm.svm_predict_probability(model, x, dec_values) imgfile_dict[file] = round(qualityscore,2) #print ("Quality Score of the given image is: ", qualityscore, "[0:Good;100:Bad]") except: pass finally: warnings.simplefilter("ignore") #print(imgfile_dict) return imgfile_dict # calculate moode def mode(arr): if arr==[]: return None else: return max(set(arr), key=arr.count) def img_EDA(dataset_directory): """ #################################################################################### #Input - dataset_directory with all type of Images #Output - mean,median and mode image size, channels type, extensions, recommendation of images etc #Defination - img_EDA takes the all images and print the EDA results #################################################################################### """ imgeda_dict = {} # check input directory if os.path.isdir(dataset_directory)==False: print("folder does not exist") sys.exit() width_list=[] height_list=[] k=[] c=[] cnum=[] v=[] ext=[] cnt=0 for item in os.listdir(dataset_directory): if (item.endswith(".jfif") or item.endswith(".png") or item.endswith(".jpg") or item.endswith(".jpeg")): if os.path.isfile(os.path.join(dataset_directory , item)): im = Image.open(os.path.join(dataset_directory , item)) c.append(im.mode) cnum.append(len(im.mode)) width_list.append(im.width) height_list.append(im.height) k.append(im.size) v.append(im.width*im.height) f, e = os.path.splitext(os.path.join(dataset_directory , item)) ext.append(e) cnt=cnt+1 # calculate biggest and smallest image img_dict={} for key, val in zip(k, v): img_dict[key] = val max_key = max(img_dict, key=img_dict.get) #max_key min_key = min(img_dict, key=img_dict.get) #min_key imgeda_dict['Channels'] = set(c) imgeda_dict['Extensions'] = set(ext) imgeda_dict['Total_Images'] = cnt imgeda_dict['Smallest_Image'] = min_key imgeda_dict['Largest_Image'] = max_key imgeda_dict['Mean_Width'] = int(np.mean(width_list)) imgeda_dict['Mean_Height'] = int(np.mean(height_list)) imgeda_dict['Median_Width'] = int(np.median(width_list)) imgeda_dict['Median_Height'] = int(np.median(height_list)) imgeda_dict['Mode_Width'] = int(mode(width_list)) imgeda_dict['Mode_Height'] = int(mode(height_list)) imgeda_dict['Recomended_Mean_Width_Height'] = (int(np.mean(width_list)),int(np.mean(height_list))) imgeda_dict['Recomended_Median_Width_Height'] = (int(np.median(width_list)),int(np.median(height_list))) imgeda_dict['Recomended_Mode_Width_Height'] = (int(mode(width_list)),int(mode(height_list))) imgeda_dict['Size_Distribution'] = dict(Counter(k).items()) imgeda_dict['Channel_Mean'] = np.mean(cnum) imgeda_dict['Channel_Standard_Deviation'] = np.std(cnum) ''' print('*-----------------------<<< RESULTS >>>-------------------------*') print() print('%-30s | ' % 'Channels', set(c)) print('%-30s | ' % 'Extensions', set(ext)) print('*---------------------------------------------------------------*') print('%-30s | ' % 'Total Images', cnt) print('%-30s | ' % 'Smallest Image', min_key) print('%-30s | ' % 'Largest Image', max_key) print('*---------------------------------------------------------------*') print('%-30s | ' % 'Mean Width', int(np.mean(width_list))) print('%-30s | ' % 'Mean Height', int(np.mean(height_list))) print('*---------------------------------------------------------------*') print('%-30s | ' % 'Median Width', int(np.median(width_list))) print('%-30s | ' % 'Median Height', int(np.median(height_list))) print('*---------------------------------------------------------------*') print('%-30s | ' % 'Mode Width', int(mode(width_list))) print('%-30s | ' % 'Mode Height', int(mode(height_list))) print('*---------------------------------------------------------------*') print('%-30s | ' % 'recommended size by mean(w,h)',(int(np.mean(width_list)),int(np.mean(height_list)))) print('*---------------------------------------------------------------*') print('%-30s | ' % 'recommended size by median(w,h)',(int(np.median(width_list)),int(np.median(height_list)))) print('*---------------------------------------------------------------*') print('%-30s | ' % 'recommended size by mode(w,h)',(int(mode(width_list)),int(mode(height_list)))) print('*---------------------------------------------------------------*') print('%-30s | ' % 'distribution of sizes',dict(Counter(k).items()) ) print('*---------------------------------------------------------------*') print('%-30s | ' % 'channel mean',np.mean(cnum)) print('%-30s | ' % 'channel standard deviation',np.std(cnum)) ''' #print(imgeda_dict) return imgeda_dict def dhash(image, hashSize=8): # convert the image to grayscale and resize the grayscale image, # adding a single column (width) so we can compute the horizontal # gradient gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) resized = cv2.resize(gray, (hashSize + 1, hashSize)) # compute the (relative) horizontal gradient between adjacent # column pixels diff = resized[:, 1:] > resized[:, :-1] # convert the difference image to a hash and return it return sum([2 ** i for (i, v) in enumerate(diff.flatten()) if v]) def img_duplicatefinder(dataset_directory): # grab the paths to all images in our input dataset directory and # then initialize our hashes dictionary print("[INFO] computing image hashes...") imagePaths = list(paths.list_images(dataset_directory)) hashes = {} duplimg_list = [] remove_file = 0 # loop over our image paths for imagePath in imagePaths: # load the input image and compute the hash image = cv2.imread(imagePath) h = dhash(image) # grab all image paths with that hash, add the current image # path to it, and store the list back in the hashes dictionary p = hashes.get(h, []) p.append(imagePath) hashes[h] = p # loop over the image hashes for (h, hashedPaths) in hashes.items(): # check to see if there is more than one image with the same hash if len(hashedPaths) > 1: #print(hashedPaths) duplimg_list.append(hashedPaths) return duplimg_list def img_plot_colour_hist(dataset_directory): import io, base64, urllib red_values = []; green_values = []; blue_values = []; all_channels = [] imagePaths = list(paths.list_images(dataset_directory)) for imagePath in imagePaths: img = np.array(Image.open(imagePath)) red_values.append(np.mean(img[:, :, 0])) green_values.append(np.mean(img[:, :, 1])) blue_values.append(np.mean(img[:, :, 2])) all_channels.append(np.mean(img)) _, axes = plt.subplots(ncols=4, nrows=1, constrained_layout=True, figsize=(16, 3), sharey=True) for ax, column, vals, c in zip( axes, ['red', 'green', 'blue', 'all colours'], [red_values, green_values, blue_values, all_channels], 'rgbk' ): ax.hist(vals, bins=100, color=c) ax.set_title(f'{column} hist') plt.suptitle("Image Dataset Colour Distribution") buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) string = base64.b64encode(buf.read()) uri = 'data:image/png;base64,' + urllib.parse.quote(string) return uri ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.utils import to_categorical from keras.preprocessing import image import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from keras.utils import to_categorical from Fkeras.layers import Input from keras.models import Model from keras.optimizers import Adam from keras.applications import VGG16 from tensorflow.keras.callbacks import EarlyStopping from sklearn.metrics import classification_report,confusion_matrix,precision_recall_curve import seaborn as sns def PredictCAST(test_csv, test_dataset_directory, load_model_dir, model_name, hparams_config_file): hyperparam_config = hparams_config_file['img_classifier'] print("[Info] loading imagenet weights...") #baseModel = keras.applications.ResNet101(weights="imagenet", include_top=False, input_tensor=Input(shape=(128, 128, 3))) if model_name == "densenet": print('Loading Densenet model') baseModel = keras.applications.DenseNet121(weights="imagenet", include_top=False, input_tensor=Input(shape=(hyperparam_config['img_width'],hyperparam_config['img_height'],hyperparam_config['img_channel']))) #98 elif model_name == "inception": print('Loading Inception model') baseModel = keras.applications.InceptionV3(weights="imagenet", include_top=False, input_tensor=Input(shape=(hyperparam_config['img_width'],hyperparam_config['img_height'],hyperparam_config['img_channel']))) #97 headModel = baseModel.output headModel = Flatten(name="flatten")(headModel) headModel = Dense(1024, activation='relu')(headModel) headModel = Dropout(0.5)(headModel) headModel = Dense(2, activation='sigmoid')(headModel) model = Model(inputs=baseModel.input, outputs=headModel) print("[INFO] compiling model...") opt = Adam(lr=hyperparam_config['lr']) model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"]) model.load_weights(os.path.join(load_model_dir, model_name)) #model.load_weights(load_model_dir) test_image = [] for i in range(test_csv.shape[0]): img = image.load_img(test_dataset_directory + '/' + str(test_csv['file_name'][i]), target_size=(hyperparam_config['img_width'],hyperparam_config['img_height'],hyperparam_config['img_channel']), grayscale=False) img = image.img_to_array(img) img = img/255 test_image.append(img) test_images = np.array(test_image) test_labels = test_csv['class'].values test_labels = to_categorical(test_labels) # making predictions prediction = model.predict(test_images) prediction = np.argmax(prediction,axis=1) print('Classification Report : ') print(classification_report(test_csv['class'],prediction)) sns.heatmap(confusion_matrix(test_csv['class'],prediction),annot=True) plt.show() print('Confusion matrix : ') print(confusion_matrix(test_csv['class'],prediction)) print("[INFO] Evaluating model accuracy and loss...Take some moment...") test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2) print('\nTest accuracy:', test_acc) print('\nTest loss:', test_loss) print("Prediction Completed...") ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.utils import to_categorical from keras.preprocessing import image import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from keras.utils import to_categorical from keras.layers import Input from keras.models import Model from keras.optimizers import Adam from keras.applications import VGG16 from tensorflow.keras.callbacks import EarlyStopping from sklearn.metrics import classification_report,confusion_matrix,precision_recall_curve import seaborn as sns import cv2 def PredictCAST(test_image, load_model_dir, model_name, hparams_config_file): hyperparam_config = hparams_config_file['img_classifier'] print("[Info] loading imagenet weights...") #baseModel = keras.applications.ResNet101(weights="imagenet", include_top=False, input_tensor=Input(shape=(128, 128, 3))) if model_name == "densenet": print('Loading Densenet model') baseModel = keras.applications.DenseNet121(weights="imagenet", include_top=False, input_tensor=Input(shape=(hyperparam_config['img_width'],hyperparam_config['img_height'],hyperparam_config['img_channel']))) #98 elif model_name == "inception": print('Loading Inception model') baseModel = keras.applications.InceptionV3(weights="imagenet", include_top=False, input_tensor=Input(shape=(hyperparam_config['img_width'],hyperparam_config['img_height'],hyperparam_config['img_channel']))) #97 headModel = baseModel.output headModel = Flatten(name="flatten")(headModel) headModel = Dense(1024, activation='relu')(headModel) headModel = Dropout(0.5)(headModel) headModel = Dense(2, activation='sigmoid')(headModel) model = Model(inputs=baseModel.input, outputs=headModel) print("[INFO] compiling model...") opt = Adam(lr=hyperparam_config['lr']) model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"]) model.load_weights(os.path.join(load_model_dir, model_name)) img = cv2.imread(test_image) img = cv2.resize(img, (hyperparam_config['img_width'],hyperparam_config['img_height'])) orig = img.copy() img = image.img_to_array(img) img = np.expand_dims(img, axis=0) img = img/255 print("[Info] predicting output") #prediction = model.predict_classes(img) prediction = model.predict(img) prediction = np.argmax(prediction,axis=1) print(prediction) if (prediction<0.5): print("def_front") cv2.putText(orig, "def_front", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) else: print("ok_front") cv2.putText(orig, "ok_front", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) plt.imshow(orig) plt.axis('off') plt.show() print("Prediction Completed...") ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import warnings warnings.filterwarnings('ignore') import logging import sklearn from random import sample from numpy.random import uniform import numpy as np import math import pickle import os import json from math import isnan from sklearn.preprocessing import binarize from sklearn.preprocessing import LabelEncoder import pandas as pd from sklearn.preprocessing import LabelBinarizer from sklearn.model_selection import train_test_split from incremental.incClassificationModel import incClassifierModel from incremental.incRegressionModel import incRegressionModel class incMachineLearning(object): def __init__(self,mlobj): self.features=[] self.mlobj=mlobj self.log = logging.getLogger('eion') def startLearning(self,mlconfig,modelType,modelParams,modelList,scoreParam,features,targetColumn,dataFrame,xtrain,ytrain,xtest,ytest,categoryCountList,targetType,deployLocation,iterName,iterVersion,trained_data_file,predicted_data_file,labelMaps): model = 'None' params = 'None' score = 0xFFFF estimator = None model_tried = '' threshold = -1 pscore = -1 rscore = -1 topics = {} if(targetColumn != ''): targetData = dataFrame[targetColumn] datacolumns=list(dataFrame.columns) if targetColumn in datacolumns: datacolumns.remove(targetColumn) scoreParam = self.mlobj.setScoreParams(scoreParam,modelType,categoryCountList) self.log.info('\n-------------- Training ML: Start --------------') model_type,model,params, score, estimator,model_tried,xtrain,ytrain,xtest,ytest,threshold,pscore,rscore,method,incObj=self.startLearnerModule(mlconfig,modelType,modelParams,modelList,scoreParam,targetColumn,dataFrame,xtrain,ytrain,xtest,ytest,categoryCountList,targetType,deployLocation,iterName,iterVersion,trained_data_file,labelMaps) self.log.info('-------------- Training ML: End --------------\n') filename = os.path.join(deployLocation,'production','model',model+'.pkl') saved_model = model+'.pkl' pickle.dump(estimator, open(filename, 'wb')) df_test = xtest.copy() df_test.reset_index(inplace = True,drop=True) trainPredictedData = incObj.bestTrainPredictedData predictedData = incObj.bestPredictedData try: if(model_type == 'Classification'): self.log.info('\n--------- Performance Matrix with Train Data ---------') train_matrix = self.mlobj.getClassificationPerformaceMatrix(ytrain,trainPredictedData,labelMaps) self.log.info('--------- Performance Matrix with Train Data End ---------\n') self.log.info('\n--------- Performance Matrix with Test Data ---------') performancematrix = self.mlobj.getClassificationPerformaceMatrix(ytest,predictedData,labelMaps) ytest.reset_index(inplace=True,drop=True) df_test['actual'] = ytest df_test['predict'] = predictedData self.log.info('--------- Performance Matrix with Test Data End ---------\n') matrix = performancematrix elif(model_type == 'Regression'): self.log.info('\n--------- Performance Matrix with Train Data ---------') train_matrix = self.mlobj.get_regression_matrix(ytrain, trainPredictedData) self.log.info('--------- Performance Matrix with Train Data End ---------\n') self.log.info('\n--------- Performance Matrix with Test Data ---------') matrix = self.mlobj.get_regression_matrix(ytest, predictedData) ytest.reset_index(inplace=True, drop=True) df_test['actual'] = ytest df_test['predict'] = predictedData self.log.info('--------- Performance Matrix with Test Data End ---------\n') except Exception as Inst: self.log.info('--------- Error Performance Matrix ---------\n') self.log.info(str(Inst)) df_test['predict'] = predictedData matrix = "" train_matrix = "" self.log.info('--------- Performance Matrix with Test Data End ---------\n') df_test.to_csv(predicted_data_file) return 'Success',model_type,model,saved_model,matrix,train_matrix,xtrain.shape,model_tried,score,filename,self.features,threshold,pscore,rscore,method,estimator,xtrain,ytrain,xtest,ytest,topics,params def startLearnerModule(self,mlconfig,modelType,modelParams,modelList,scoreParam,targetColumn,dataFrame,xtrain,ytrain,xtest,ytest,categoryCountList,targetType,deployLocation,iterName,iterVersion,trained_data_file,labelMaps): matrix = '' threshold = -1 pscore = -1 rscore = -1 datacolumns=list(xtrain.columns) if targetColumn in datacolumns: datacolumns.remove(targetColumn) self.features =datacolumns self.log.info('-------> Features Used For Training the Model: '+(str(self.features))[:500]) xtrain = xtrain[self.features] xtest = xtest[self.features] method = mlconfig['optimizationMethod'] method = method.lower() geneticParam = '' optimizationHyperParameter = mlconfig['optimizationHyperParameter'] cvSplit = optimizationHyperParameter['trainTestCVSplit'] nIter = int(optimizationHyperParameter['iterations']) if(method.lower() == 'genetic'): geneticParam = optimizationHyperParameter['geneticparams'] scoreParam = scoreParam if 'thresholdTunning' in mlconfig: thresholdTunning = mlconfig['thresholdTunning'] else: thresholdTunning = 'NA' if cvSplit == "": cvSplit =None else: cvSplit =int(cvSplit) if modelType == 'classification': model_type = "Classification" MakeFP0 = False MakeFN0 = False if(len(categoryCountList) == 2): if(thresholdTunning.lower() == 'fp0'): MakeFP0 = True elif(thresholdTunning.lower() == 'fn0'): MakeFN0 = True noOfClasses= len(labelMaps) incObjClf = incClassifierModel(noOfClasses,modelList, modelParams, scoreParam, cvSplit, nIter,geneticParam, xtrain,ytrain,xtest,ytest,method,modelType,MakeFP0,MakeFN0,deployLocation) model, params, score, estimator,model_tried,threshold,pscore,rscore = incObjClf.firstFit() incObj = incObjClf elif modelType == 'regression': model_type = "Regression" incObjReg = incRegressionModel(modelList, modelParams, scoreParam, cvSplit, nIter,geneticParam, xtrain,ytrain,xtest,ytest,method,deployLocation) model,params,score,estimator,model_tried = incObjReg.firstFit() incObj = incObjReg return model_type,model,params, score, estimator,model_tried,xtrain,ytrain,xtest,ytest,threshold,pscore,rscore,method, incObj ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from learner.optimizetechnique import OptimizationTq from learner.parameters import parametersDefine import time import logging import os import sys from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error,make_scorer from sklearn.metrics import mean_squared_error from learner.aion_matrix import aion_matrix class incRegressionModel(): def __init__(self,modelList,params,scoreParam,cvSplit,numIter,geneticParam,trainX,trainY,testX,testY,method,deployLocation): self.modelList =modelList self.params =params self.trainX =trainX self.trainY =trainY self.testX = testX self.testY = testY self.method =method self.scoreParam=scoreParam self.cvSplit=cvSplit self.numIter=numIter self.geneticParam=geneticParam self.log = logging.getLogger('eion') self.deployLocation = deployLocation self.bestTrainPredictedData = None self.bestPredictedData = None self.AlgorithmNames={'Online Linear Regression':'Online Linear Regression', 'Online Decision Tree Regressor':'Online Decision Tree Regressor', 'Online KNN Regressor':'Online KNN Regressor'} self.modelToAlgoNames = {value: key for key, value in self.AlgorithmNames.items()} def firstFit(self): bestModel='' bestParams={} import sys bestScore=-sys.float_info.max #bugfix 11656 scoredetails = '' self.log.info('\n---------- Regression Model has started ----------') try: for modelName in self.modelList: if modelName not in self.params: continue paramSpace=self.params[modelName] algoName = self.AlgorithmNames[modelName] from incremental.riverML import riverML riverMLObj = riverML() self.log.info("-------> Model Name: "+str(modelName)) start = time.time() model, modelParams, estimator, trainPredictedData = riverMLObj.startLearn('regression',algoName,paramSpace,self.trainX, self.trainY) modelParams = str(modelParams) executionTime=time.time() - start self.log.info('---------> Total Execution: '+str(executionTime)) predictedData = riverMLObj.getPrediction(estimator,self.testX) if 'neg_mean_squared_error' in self.scoreParam: meanssquatederror = mean_squared_error(self.testY,predictedData) score = meanssquatederror elif 'neg_root_mean_squared_error' in self.scoreParam: rootmeanssquatederror=mean_squared_error(self.testY,predictedData,squared=False) score = rootmeanssquatederror elif 'neg_mean_absolute_error' in self.scoreParam: meanabsoluteerror=mean_absolute_error(self.testY,predictedData) score = meanabsoluteerror elif 'r2' in self.scoreParam: r2score=r2_score(self.testY,predictedData) score = round(r2score*100, 2) if self.scoreParam == "r2": if score>bestScore: bestScore =score bestModel =model bestParams=modelParams bestEstimator=estimator self.bestTrainPredictedData = trainPredictedData self.bestPredictedData = predictedData else: if abs(score) < bestScore or bestScore == -sys.float_info.max: bestScore =abs(score) bestModel =model bestParams=modelParams bestEstimator=estimator self.bestTrainPredictedData = trainPredictedData self.bestPredictedData = predictedData metrices = {} metrices["score"] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"'+self.modelToAlgoNames[model]+'","Score":'+str(abs(score))+'}' self.log.info('Status:- |... ML Algorithm applied: '+modelName) self.log.info("Status:- |... Testing Score: "+str(score)) self.log.info('---------- Regression Model End ---------- \n') self.log.info('\n------- Best Model and its parameters -------------') self.log.info('Status:- |... Best Algorithm selected: '+str(self.modelToAlgoNames[bestModel])+' Score='+str(round(bestScore,2))) self.log.info("-------> Best Name: "+str(bestModel)) self.log.info("-------> Best Score: "+str(bestScore)) return self.modelToAlgoNames[bestModel],bestParams,bestScore,bestEstimator,scoredetails except Exception as inst: self.log.info( '\n-----> regressionModel failed!!!.'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import time import os import sys import numpy as np from sklearn.metrics import confusion_matrix from sklearn.metrics import recall_score from sklearn.metrics import precision_score from sklearn.preprocessing import binarize from learner.optimizetechnique import OptimizationTq from learner.parameters import parametersDefine import logging from learner.aion_matrix import aion_matrix # apply threshold to positive probabilities to create labels def to_labels(pos_probs, threshold): return (pos_probs >= threshold).astype('int') class incClassifierModel(): def __init__(self,noOfClasses,modelList,params,scoreParam,cvSplit,numIter,geneticParam,trainX,trainY,testX,testY,method,modelType,MakeFP0,MakeFN0,deployLocation): self.noOfClasses = noOfClasses self.modelList =modelList self.params =params self.trainX =trainX self.X =trainX self.trainY =trainY self.testX = testX self.testY = testY self.method =method self.scoreParam=scoreParam self.cvSplit=cvSplit self.numIter=numIter self.geneticParam=geneticParam self.MakeFP0= MakeFP0 self.MakeFN0=MakeFN0 self.log = logging.getLogger('eion') self.modelType = modelType self.deployLocation = deployLocation self.isRiverModel = False self.AlgorithmNames={'Online Logistic Regression':'Online Logistic Regression', 'Online Softmax Regression':'Online Softmax Regression', 'Online Decision Tree Classifier':'Online Decision Tree Classifier', 'Online KNN Classifier':'Online KNN Classifier'} self.modelToAlgoNames = {value: key for key, value in self.AlgorithmNames.items()} def check_threshold(self,estimator,testX,testY,threshold_range,checkParameter,modelName): thresholdx = -1 for threshold in threshold_range: predictedData = estimator.predict_proba(testX) predictedData = binarize(predictedData[:,1].reshape(-1, 1),threshold=threshold)#bug 12437 p_score = precision_score(testY, predictedData) r_score = recall_score(testY, predictedData) tn, fp, fn, tp = confusion_matrix(testY, predictedData).ravel() if(checkParameter.lower() == 'fp'): if fp == 0: if(p_score == 1): thresholdx = threshold self.log.info('---------------> Best Threshold:'+str(threshold)) self.log.info('---------------> Best Precision:'+str(p_score)) self.log.info('---------------> Best Recall:'+str(r_score)) self.log.info('---------------> TN:'+str(tn)) self.log.info('---------------> FP:'+str(fp)) self.log.info('---------------> FN:'+str(fn)) self.log.info('---------------> TP:'+str(tp)) break if(checkParameter.lower() == 'fn'): if fn == 0: if(r_score == 1): thresholdx = threshold self.log.info('---------------> Best Threshold:'+str(threshold)) self.log.info('---------------> Best Precision:'+str(p_score)) self.log.info('---------------> Best Recall:'+str(r_score)) self.log.info('---------------> TN:'+str(tn)) self.log.info('---------------> FP:'+str(fp)) self.log.info('---------------> FN:'+str(fn)) self.log.info('---------------> TP:'+str(tp)) break return(thresholdx,p_score,r_score) def getBestModel(self,fp0,fn0,threshold,bestthreshold,rscore,brscore,pscore,bpscore,tscore,btscore): cmodel = False if(threshold != -1): if(bestthreshold == -1): cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif fp0: if rscore > brscore: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif rscore == brscore: if tscore > btscore or btscore == -0xFFFF: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif fn0: if pscore > bpscore: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore elif pscore == bpscore: if tscore > btscore or btscore == -0xFFFF: cmodel = True bestthreshold = threshold brscore = rscore bpscore = pscore btscore = tscore else: if tscore > btscore or btscore == -0xFFFF: cmodel = True btscore = tscore else: if(bestthreshold == -1): if tscore > btscore or btscore == -0xFFFF: cmodel = True btscore = tscore return cmodel,btscore,bestthreshold,brscore,bpscore def firstFit(self): bestModel='None' bestParams={} bestScore=-0xFFFF bestEstimator = 'None' scoredetails = '' threshold = -1 bestthreshold = -1 precisionscore =-1 bestprecisionscore=-1 recallscore = -1 bestrecallscore=-1 self.bestTrainPredictedData = None self.bestPredictedData = None self.log.info('\n---------- ClassifierModel has started ----------') objClf = aion_matrix() try: for modelName in self.modelList: paramSpace=self.params[modelName] algoName = self.AlgorithmNames[modelName] from incremental.riverML import riverML riverMLObj = riverML() self.log.info("-------> Model Name: "+str(modelName)) start = time.time() model, modelParams, estimator, trainPredictedData = riverMLObj.startLearn('classification',algoName,paramSpace,self.trainX, self.trainY, self.noOfClasses) modelParams = str(modelParams) predictedData = riverMLObj.getPrediction(estimator,self.testX) executionTime=time.time() - start self.testY.reset_index(inplace=True, drop=True) score = objClf.get_score(self.scoreParam,self.testY.values.flatten(),predictedData.values.flatten()) self.log.info(str(score)) metrices = {} metrices["score"] = score threshold = -1 precisionscore = precision_score(self.testY, predictedData, average='macro') recallscore = recall_score(self.testY, predictedData, average='macro') self.log.info('---------> Total Execution: '+str(executionTime)) if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"'+self.modelToAlgoNames[model]+'","Score":'+str(score)+'}' status,bscore,bthres,brscore,bpscore = self.getBestModel(self.MakeFP0,self.MakeFN0,threshold,bestthreshold,recallscore,bestrecallscore,precisionscore,bestprecisionscore,score,bestScore) if status: bestScore =bscore bestModel =model bestParams=modelParams bestEstimator=estimator bestthreshold = threshold bestrecallscore = recallscore bestprecisionscore = precisionscore self.bestTrainPredictedData = trainPredictedData self.bestPredictedData = predictedData self.log.info('Status:- |... ML Algorithm applied: '+modelName) self.log.info("Status:- |... Testing Score: "+str(score)) self.log.info('---------- ClassifierModel End ---------- \n') self.log.info('\n------- Best Model and its parameters -------------') self.log.info('Status:- |... Best Algorithm selected: '+str(self.modelToAlgoNames[bestModel])+' Score='+str(round(bestScore,2))) self.log.info("-------> Best Name: "+str(bestModel)) self.log.info("-------> Best Score: "+str(bestScore)) return self.modelToAlgoNames[bestModel],bestParams,bestScore,bestEstimator,scoredetails,bestthreshold,bestprecisionscore,bestrecallscore except Exception as inst: self.log.info( '\n-----> ClassifierModel failed!!!.'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' #System imports import logging import os import sys import pickle #Sci-Tools imports import numpy as np import pandas as pd from sklearn.preprocessing import LabelEncoder from scipy import stats from word2number import w2n #river imports from river.preprocessing import StatImputer from river import stats, compose, anomaly class incProfiler(): def __init__(self): self.DtypesDic={} self.pandasNumericDtypes=['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] self.allNumberTypeCols = [] #all number type columns self.allNumCols = [] #only numerical columns which includes num features and target if it is numerical self.allCatCols = [] self.numFtrs = [] self.catFtrs = [] self.textFtrs = [] self.textVectorFtrs = [] self.numDiscreteCols = [] self.numContinuousCols = [] self.wordToNumericFeatures=[] self.emptyCols=[] self.missingCols = [] self.targetColumn = "" self.le_dict = {} self.configDict = {} self.incFill = None self.incLabelMapping = None self.incCatEncoder = None self.incScaler = None self.incOutlierRem = None self.log = logging.getLogger('eion') def pickleDump(self, model, path): if model is not None: with open(path, 'wb') as f: pickle.dump(model, f) def saveProfilerModels(self, deployLocation): if isinstance(self.incFill['num_fill'], StatImputer) or isinstance(self.incFill['cat_fill'], StatImputer): self.pickleDump(self.incFill, os.path.join(deployLocation,'production','profiler','incFill.pkl')) self.pickleDump(self.incLabelMapping, os.path.join(deployLocation,'production','profiler','incLabelMapping.pkl')) self.pickleDump(self.incCatEncoder, os.path.join(deployLocation,'production','profiler','incCatEncoder.pkl')) self.pickleDump(self.incScaler, os.path.join(deployLocation,'production','profiler','incScaler.pkl')) self.pickleDump(self.incOutlierRem, os.path.join(deployLocation,'production','profiler','incOutlierRem.pkl')) def featureAnalysis(self, df, conf_json, targetFeature): try: self.log.info('-------> Remove Duplicate Rows') noofdplicaterows = df.duplicated(keep='first').sum() df = df.drop_duplicates(keep="first") df = df.reset_index(drop=True) self.log.info('Status:- |... Duplicate row treatment done: '+str(noofdplicaterows)) self.log.info(df.head(5)) self.log.info( '\n----------- Inspecting Features -----------') ctn_count = 0 df = df.replace('-', np.nan) df = df.replace('?', np.nan) dataFDtypes=self.dataFramecolType(df) numerical_ratio = float(conf_json['numericFeatureRatio']) categoricalMaxLabel = int(conf_json['categoryMaxLabel']) indexFeatures = [] numOfRows = df.shape[0] dataCols = df.columns for item in dataFDtypes: if(item[1] == 'object'): filteredDf,checkFlag = self.smartFilter(item[0],df,numerical_ratio) if(checkFlag): self.wordToNumericFeatures.append(item[0]) self.log.info('----------> Data Type Converting to numeric :Yes') try: df[item[0]]=filteredDf[item[0]].astype(float) except: pass ctn_count = ctn_count+1 else: count = (df[item[0]] - df[item[0]].shift() == 1).sum() if((numOfRows - count) == 1): self.log.info( '-------> Feature :'+str(item[0])) self.log.info('----------> Sequence Feature') indexFeatures.append(item[0]) self.configDict['wordToNumCols'] = self.wordToNumericFeatures self.configDict['emptyFtrs'] = indexFeatures self.log.info('Status:- |... Feature inspection done for numeric data: '+str(ctn_count)+' feature(s) converted to numeric') self.log.info('Status:- |... Feature word to numeric treatment done: '+str(self.wordToNumericFeatures)) self.log.info( '----------- Inspecting Features End -----------\n') except Exception as inst: self.log.info("Error in Feature inspection: "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) try: self.log.info('\n---------- Dropping Index features ----------') self.log.info('Index Features to remove '+str(indexFeatures)) if len(indexFeatures) > 0: dataCols = list(set(dataCols) - set(indexFeatures)) for empCol in indexFeatures: self.log.info('-------> Drop Feature: '+empCol) df = df.drop(columns=[empCol]) self.log.info('---------- Dropping Index features End----------\n') dataFDtypes=self.dataFramecolType(df) categoricalMaxLabel = int(conf_json['categoryMaxLabel']) for item in dataFDtypes: self.DtypesDic[item[0]] = item[1] nUnique=len(df[item[0]].unique().tolist()) if item[1] in self.pandasNumericDtypes: self.allNumberTypeCols.append(item[0]) if nUnique >= categoricalMaxLabel: self.allNumCols.append(item[0]) #pure numerical if item[1] in ['int16', 'int32', 'int64']: self.numDiscreteCols.append(item[0]) elif item[1] in ['float16', 'float32', 'float64']: self.numContinuousCols.append(item[0]) else: self.allCatCols.append(item[0]) elif item[1] != 'bool': if (nUnique >= categoricalMaxLabel) and targetFeature != item[0]: self.textFtrs.append(item[0]) else: col = item[0] if (max(df[col].astype(str).str.split().str.len()) > 10) and targetFeature != item[0]: self.textFtrs.append(item[0]) else: self.allCatCols.append(item[0]) else: self.allCatCols.append(item[0]) misval_ratio = float(conf_json['misValueRatio']) self.configDict['misval_ratio'] = misval_ratio missingCols, emptyCols = self.getMissingVals(df, dataCols, misval_ratio) if targetFeature in emptyCols: raise Exception('Target column '+str(targetFeature)+' cannot be empty') dataCols = list(set(dataCols) - set(emptyCols)) self.log.info('\n---------- Dropping empty features ----------') for empCol in emptyCols: self.log.info('-------> Drop Feature: '+empCol) df = df.drop(columns=[empCol]) self.log.info('---------- Dropping empty features End----------\n') self.log.info('Status:- |... Empty feature treatment done: '+str(len(emptyCols))+' empty feature(s) found') self.log.info('-------> Data Frame Shape After Dropping (Rows,Columns): '+str(df.shape)) self.allNumCols = list(set(self.allNumCols) - set(emptyCols)) self.allCatCols = list(set(self.allCatCols) - set(emptyCols)) self.textFtrs = list(set(self.textFtrs) - set(emptyCols)) missingValFtrs = list(set(missingCols) - set(emptyCols)) self.log.info(str(len(missingValFtrs))+' feature(s) found with missing value(s)') self.log.info('\n-------> Numerical continuous columns :'+(str(self.numContinuousCols))[:500]) self.log.info('-------> Numerical discrete columns :'+(str(self.numDiscreteCols))[:500]) self.log.info('-------> Non numerical columns :'+(str(self.allCatCols))[:500]) self.log.info('-------> Text columns :'+(str(self.textFtrs))[:500]) except Exception as inst: self.log.info("Error in segregating numerical and categorical columns: "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return df, missingValFtrs, emptyCols, dataCols, self.allNumCols, self.allCatCols, self.textFtrs def createIncProfiler(self, df, conf_json, allNumCols, numFtrs, allCatCols, textFtrs, missingValFtrs): self.incLabelMapping = None catFtrs = allCatCols.copy() #LabelEncoding if self.targetColumn in allCatCols: catFtrs.remove(self.targetColumn) self.incLabelMapping = LabelEncoder() df[self.targetColumn] = df[self.targetColumn].apply(str) self.incLabelMapping.fit(df[self.targetColumn]) self.le_dict = dict(zip(self.incLabelMapping.classes_, self.incLabelMapping.transform(self.incLabelMapping.classes_))) self.log.info('----------> Encoded Values of Target Labels: '+(str(self.le_dict))[:500]) #self.incFill --> {num_fill:SI/0.0/'drop', cat_fill:SI/0.0/'drop'} #fill self.incFill = {} self.incCatEncoder = None self.incScaler = None self.incOutlierRem = None num_fill_method = 'Mean' for x in list(conf_json['numericalFillMethod'].keys()): if conf_json['numericalFillMethod'][x] == 'True': num_fill_method = x break if num_fill_method.lower() =='mean': num_fill = [(col, stats.Mean()) for col in allNumCols] self.incFill['num_fill'] = StatImputer(*num_fill) elif num_fill_method.lower() =='min': num_fill = [(col, stats.Min()) for col in allNumCols] self.incFill['num_fill'] = StatImputer(*num_fill) elif num_fill_method.lower() == 'max': num_fill = [(col, stats.Max()) for col in allNumCols] self.incFill['num_fill'] = StatImputer(*num_fill) elif num_fill_method.lower() =='zero': self.incFill['num_fill'] = 'zero' elif num_fill_method.lower() =='drop': self.incFill['num_fill'] = 'drop' else: num_fill = [(col, stats.Mean()) for col in allNumCols] self.incFill['num_fill'] = StatImputer(*num_fill) cat_fill_method = 'Mode' for x in list(conf_json['categoricalFillMethod'].keys()): if conf_json['categoricalFillMethod'][x] == 'True': cat_fill_method = x break if cat_fill_method.lower() =='zero': self.incFill['cat_fill'] = 'zero' elif cat_fill_method.lower() == 'mode': cat_fill = [(col, stats.Mode()) for col in allCatCols] self.incFill['cat_fill'] = StatImputer(*cat_fill) elif cat_fill_method.lower() =='drop': self.incFill['cat_fill'] = 'drop' #CatEncoding for x in list(conf_json['categoryEncoding'].keys()): if conf_json['categoryEncoding'][x] == 'True': catEncoder = x break catEncHow = 'Mean' for x in list(conf_json['targetEncodingParams']['how'].keys()): if conf_json['targetEncodingParams']['how'][x] == 'True': catEncHow = x break if self.targetColumn in catFtrs: catFtrs.remove(self.targetColumn) if len(catFtrs) > 0: from river.feature_extraction import TargetAgg if catEncHow.lower() == 'mean': agg_stat = stats.Mean() if catEncHow.lower() == 'bayesianmean' or catEncHow.lower() == 'bayesian mean': agg_stat = stats.BayesianMean(prior=0.5, prior_weight=50) self.incCatEncoder = TargetAgg( by=catFtrs[0], how=agg_stat) for col in catFtrs[1:]: self.incCatEncoder += TargetAgg( by=col, how=agg_stat) self.incCatEncoder|= compose.Discard(*catFtrs) #Scaling normalization_status = 'False' normalization_method = "" if 'normalization' in conf_json: nor_supported_methods = conf_json['normalization'] for k in nor_supported_methods.keys(): if conf_json['normalization'][k].lower() == 'true': normalization_status='True' normalization_method =k break if normalization_status.lower() == "true" and len(numFtrs) > 0: from sklearn.preprocessing import MinMaxScaler, StandardScaler, MaxAbsScaler if self.targetColumn in numFtrs: numFtrs.remove(self.targetColumn) if normalization_method.lower() =='standardscaler': self.incScaler = StandardScaler() elif normalization_method.lower() =='minmaxscaler' or normalization_method.lower() =='minmax': self.incScaler = MinMaxScaler() elif normalization_method.lower() =='maxabsscaler' or normalization_method.lower() =='maxabs': self.incScaler = MaxAbsScaler() else: self.incScaler = None #OutlierRemoval outlier_status = 'False' outlier_method = 'None' for x in list(conf_json['outlierDetection'].keys()): if conf_json['outlierDetection'][x] == 'True': outlier_method = x outlier_status = 'True' break if outlier_status and numFtrs: outlierMethodNames = list(conf_json['outlierDetectionParams'].keys()) if outlier_method.lower() == 'oneclasssvm' or outlier_method.lower() == 'one class svm': for x in outlierMethodNames: if x[0].lower() == 'o': key = x break params = conf_json['outlierDetectionParams'][key] self.log.info('<--- one class SVM with quantile filter --->') self.incOutlierRem = anomaly.QuantileFilter(anomaly.OneClassSVM(nu=float(params['nu'])),q=float(params['q'])) elif outlier_method.lower() =='halfspacetrees' or outlier_method.lower() =='half space trees': for x in outlierMethodNames: if x[0].lower() == 'h': key = x break params = conf_json['outlierDetectionParams'][key] self.log.info('<--- Half space trees with quantile filter --->') self.incOutlierRem = anomaly.QuantileFilter(anomaly.HalfSpaceTrees(n_trees=int(params['n_trees']),height=int(params['height']), window_size=int(params['window_size'])) ,q=float(params['q'])) else: self.log.info("No method is provided for outlier analysis") def getMissingVals(self,dataframe,columns,misval_ratio): try: self.log.info( '\n----------- Detecting for Missing Values -----------') nonNAArray=[] numOfRows = dataframe.shape[0] for i in columns: numNa=dataframe.loc[(pd.isna(dataframe[i])),i ].shape[0] nonNAArray.append(tuple([i,numNa])) for item in nonNAArray: numofMissingVals = item[1] if(numofMissingVals !=0): self.log.info('-------> Feature '+str(item[0])) self.log.info('----------> Number of Empty Rows '+str(numofMissingVals)) self.missingCols.append(item[0]) if(numofMissingVals >= numOfRows * misval_ratio): self.log.info('----------> Empty: Yes') self.log.info('----------> Permitted Rows: '+str(int(numOfRows * misval_ratio))) self.emptyCols.append(item[0]) if(len(self.missingCols) !=0): self.log.info( '----------- Detecting for Missing Values End -----------\n') return self.missingCols, self.emptyCols else: self.log.info( '-------> Missing Value Features :Not Any') self.log.info( '----------- Detecting for Missing Values End -----------\n') return self.missingCols, self.emptyCols except Exception as e: self.log.info("getMissingVals failed ==>" +str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return None, None def startIncProfiler(self,df,conf_json,targetFeature,deployLocation,problemType): try: self.targetColumn = targetFeature df, missingValFtrs, emptyFtrs, dataFtrs, allNumCols, allCatCols, textFtrs = self.featureAnalysis(df, conf_json, self.targetColumn) if len(textFtrs)>0: self.log.info('Text Features are not supported. Dropping '+str(textFtrs)[:500]) df = df.drop(columns=textFtrs) catFtrs = allCatCols.copy() numFtrs = allNumCols.copy() if self.targetColumn in catFtrs: catFtrs.remove(self.targetColumn) if targetFeature in allNumCols: numFtrs.remove(targetFeature) self.configDict['targetCol'] = self.targetColumn self.configDict['numFtrs'] = numFtrs self.configDict['catFtrs'] = catFtrs self.configDict['allNumCols'] = allNumCols self.configDict['allCatCols'] = allCatCols self.configDict['allFtrs'] = numFtrs+catFtrs try: self.log.info('\n---------- Creating Incremental profiler models ----------') self.createIncProfiler(df, conf_json, allNumCols, numFtrs, allCatCols, textFtrs, missingValFtrs) self.log.info('\n--------- Incremental profiler models have been created ---------') except Exception as inst: self.log.info("Error in creating Incremental profiler models"+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) raise try: #mvt # if missingValFtrs: if self.incFill['num_fill'] == 'drop': df = df.dropna(axis = 0, subset=allNumCols) self.configDict['num_fill'] = 'drop' elif self.incFill['num_fill'] == 'zero': df[allNumCols] = df[allNumCols].fillna(value = 0.0) self.configDict['num_fill'] = 'zero' else: df = df.astype(object).where(df.notna(), None) df[allNumCols]= df[allNumCols].apply(lambda row: self.apply_river_model(row.to_dict(), self.incFill ['num_fill']), axis='columns') self.configDict['num_fill'] = {col:self.incFill['num_fill'].stats[col].get() for col in allNumCols} if self.incFill['cat_fill'] == 'drop': df = df.dropna(axis = 0, subset=allCatCols) self.configDict['cat_fill'] = 'drop' elif self.incFill['cat_fill'] == 'zero': df[allCatCols] = df[allCatCols].fillna(value = 0.0) self.configDict['cat_fill'] = 'zero' else: df = df.astype(object).where(df.notna(), None) df[allCatCols]= df[allCatCols].apply(lambda row: self.apply_river_model(row.to_dict(), self.incFill['cat_fill']), axis='columns') self.configDict['cat_fill'] = {col:self.incFill['cat_fill'].stats[col].get() for col in allCatCols} self.log.info('\nStatus:- |... Missing value treatment done') except Exception as inst: self.log.info("Error in Missing value treatment "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) raise try: #labelenc if self.incLabelMapping: df[targetFeature] = self.incLabelMapping.transform(df[targetFeature]) # self.configDict['labelMapping'] = self.le_dict except Exception as inst: self.log.info("Error in Label mapping "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) raise try: #catenc if self.incCatEncoder: self.log.info('\n--------- Converting Non Numerical Categorical Features to Numerical Features ---------') self.encTarget = targetFeature if problemType.lower() == 'regression': from sklearn.preprocessing import StandardScaler sc = StandardScaler() self.encTarget = 'scaledTarget' df['scaledTarget'] = sc.fit_transform(df[targetFeature].to_numpy().reshape(-1,1)) encCols = catFtrs.copy() encCols.append(self.encTarget) self.configDict['encCols'] = encCols self.configDict['encTarget'] = self.encTarget transformed_data = df[encCols].apply(lambda row: self.apply_enc(row.to_dict()), axis='columns') if targetFeature in transformed_data.columns: transformed_data.drop(targetFeature, inplace=True, axis = 1) if problemType.lower() == 'regression': df.drop('scaledTarget', inplace=True, axis = 1) df[catFtrs] = transformed_data # self.log.info('Status:- |... Target Encoding state is as follows: ') self.configDict['catEnc'] = [] if len(catFtrs) == 1: col = catFtrs[0] self.configDict['catEnc'].append({col:self.incCatEncoder['TargetAgg'].state.to_dict()}) else: for i, col in enumerate(catFtrs): if i==0: no = '' else: no = str(i) self.configDict['catEnc'].append({col:self.incCatEncoder['TransformerUnion']['TargetAgg'+no].state.to_dict()}) # print(self.incCatEncoder['TransformerUnion']['TargetAgg'].state) # self.log.info(self.incCatEncoder) self.log.info('Status:- |... Categorical to numeric feature conversion done: '+str(len(catFtrs))+' features converted') except Exception as inst: self.log.info("Error in categorical encoding "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) raise try: #scaler if self.incScaler: self.log.info("\n---------- Data Normalization has started ----------") self.incScaler = self.incScaler.partial_fit(df[numFtrs]) df[numFtrs] = self.incScaler.transform(df[numFtrs]) self.log.info( "---------- Normalization Done on Following features ----------") self.log.info(numFtrs) self.log.info('Status:- |... Normalization treatment done') except Exception as inst: self.log.info("Error in normalization "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) raise try: #outlierrem if self.incOutlierRem: self.log.info('\n---------- Performing outlier analysis ----------') df = df[df[numFtrs].apply(lambda x: False if self.apply_od_pipe(x.to_dict()) else True, axis=1)] self.log.info('\n <--- dataframe after outlier analysis --->') df.reset_index(drop=True, inplace=True) self.log.info(df.head(5)) self.log.info('Status:- |... Outlier treatment done') self.log.info('\n <--- shape of dataframe after outlier analysis --->') self.log.info(df.shape) except Exception as inst: self.log.info("Error in outlier treatment "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) raise #saveprofiler self.log.info('\n---------- Saving profiler models ----------') self.saveProfilerModels(deployLocation) self.log.info('<--- Profiler models saved at '+deployLocation+' --->') return df,targetFeature,missingValFtrs,numFtrs,catFtrs,self.le_dict,self.configDict,textFtrs,emptyFtrs,self.wordToNumericFeatures except Exception as inst: self.log.info("Error: dataProfiler failed "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def transformData(self, df, targetFeature, missingValFtrs,numFtrs, catFtrs, textFtrs): try: df = df.drop_duplicates(keep="first") df = df.reset_index(drop=True) df = df.replace('-', np.nan) df = df.replace('?', np.nan) text_mv_cols = list(set(missingValFtrs).intersection(set(textFtrs))) if len(text_mv_cols)>0: df[text_mv_cols] = df[text_mv_cols].fillna(value = 'NA') if 'num_fill' in self.configDict: if self.configDict['num_fill'] == 'drop': df = df.dropna(axis = 0, subset=self.allNumCols) elif self.configDict['num_fill'] == 'zero': df[self.allNumCols] = df[self.allNumCols].fillna(value = 0.0) else: for x in self.allNumCols: df[x] = df[x].fillna(value = self.configDict['num_fill'][x]) if 'cat_fill' in self.configDict: if self.configDict['cat_fill'] == 'drop': df = df.dropna(axis = 0, subset=self.allCatCols) elif self.configDict['cat_fill'] == 'zero': df[self.allCatCols] = df[self.allCatCols].fillna(value = 0.0) else: for x in self.allCatCols: df[x] = df[x].fillna(value = self.configDict['cat_fill'][x]) if self.incLabelMapping: df[targetFeature] = self.incLabelMapping.transform(df[targetFeature]) if self.incCatEncoder: transformed_data = df[catFtrs].apply(lambda row: self.apply_enc(row.to_dict(), isTrain=False), axis='columns') df[catFtrs] = transformed_data if self.incScaler: df[numFtrs] = self.incScaler.transform(df[numFtrs]) return df except Exception as inst: self.log.info("Error: DataProfiling transformation failed "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def checknumStr(self,dataframe,feature): try: dataframe[feature]=dataframe[feature].apply(lambda x: self.testStr(x)) return dataframe except: self.log.info("checknumStr failed") return dataframe #test whether the value is numeric /string def testStr(self,value): try: x=eval(value) return np.nan except: return value """ Missing values analysis Detects number of missing values in each column of dataframe """ def checksRows(self,dataframe,target_column,dataColumns): self.log.info( '\n----------- Checking Target Feature Empty Rows -----------') if self.targetColumn != '': numNa=dataframe.loc[(pd.isna(dataframe[self.targetColumn])),self.targetColumn].shape[0] self.log.info('------->No of Empty Rows in Target Fields: '+str(numNa)) if numNa >0: self.log.info('-------> Remove Empty Target Field Rows') dataframe = dataframe.dropna(axis=0, subset=[self.targetColumn]) self.log.info('-------> Remove Duplicate Rows') dataframe = dataframe.dropna(axis=0,how='all',subset=dataColumns) noofdplicaterows = dataframe.duplicated(keep='first').sum() dataframe = dataframe.drop_duplicates(keep="first") dataframe = dataframe.reset_index(drop=True) return dataframe,noofdplicaterows def apply_river_model(self, x, profModel): profModel.learn_one(x) return pd.Series(profModel.transform_one(x)) def apply_enc(self, x, isTrain=True): if isTrain: y = x[self.encTarget] self.incCatEncoder.learn_one(x, y) return pd.Series(self.incCatEncoder.transform_one(x)) def apply_od_pipe(self, x): score = self.incOutlierRem.score_one(x) is_anomaly = self.incOutlierRem.classify(score) self.incOutlierRem.learn_one(x) return is_anomaly #Convert Words To Number def s2n(self,value): try: x=eval(value) return x except: try: return w2n.word_to_num(value) except: return np.nan def convertWordToNumeric(self,dataframe,feature): try: dataframe[feature]=dataframe[feature].apply(lambda x: self.s2n(x)) return dataframe except Exception as inst: self.log.info("convertWordToNumeric Failed ===>"+str(inst)) return dataframe #test whether the value is numeric /string def testNum(self,value): try: x=eval(value) return x except: return np.nan ##check for numeric values in string column def checkNumeric(self,dataframe,feature): try: dataframe[feature]=dataframe[feature].apply(lambda x: self.testNum(x)) return dataframe except Exception as inst: self.log.info("checkNumeric Failed ===>"+str(inst)) return dataframe def smartFilter(self,feature,df,numericRatio): try: distinctCount = len(df[feature].unique()) numOfRows = df.shape[0] tempDataFrame=df.copy(deep=True) if(distinctCount != 1): self.log.info('-------> Feature :'+str(feature)) testDf = self.checkNumeric(tempDataFrame,feature) tempDf = testDf[feature] tempDf = tempDf.dropna() numberOfNonNullVals = tempDf.count() if(numberOfNonNullVals > int(numOfRows * numericRatio)): tempDataFrame=df.copy(deep=True) testDf = self.convertWordToNumeric(tempDataFrame,feature) tempDf = testDf[feature] tempDf = tempDf.dropna() self.log.info('----------> Numeric Status :Yes') return testDf,True else: #Wasnt't a numerical feature self.log.info('----------> Numeric Status :No') #numDf = self.checknumStr(df,feature) return df,False else: self.log.info( '\n---> No Numerics found in :' +str(feature)) return df,False except: self.log.info( '\n---> No Numerics found in :'+str(feature)) return df,False def dataFramecolType(self,dataFrame): dataFDtypes=[] try: dataColumns=list(dataFrame.columns) for i in dataColumns: dataType=dataFrame[i].dtypes dataFDtypes.append(tuple([i,str(dataType)])) return dataFDtypes except: self.log.info("error in dataFramecolyType") return dataFDtypes import logging import pickle import os import sys import pandas as pd from river import stream from river.linear_model import LogisticRegression, SoftmaxRegression, LinearRegression from river.tree import ExtremelyFastDecisionTreeClassifier, HoeffdingAdaptiveTreeRegressor # from river.ensemble import AdaptiveRandomForestRegressor, AdaptiveRandomForestClassifier from river.neighbors import KNNClassifier, KNNRegressor from river.multiclass import OneVsRestClassifier from river.optim import SGD, Adam, AdaDelta, NesterovMomentum, RMSProp # from river.optim.losses import CrossEntropy, Log, MultiClassLoss, Poisson, RegressionLoss, BinaryLoss, Huber # from river.optim.initializers import Normal class riverML(object): def __init__(self): self.algoDict={'Online Logistic Regression':LogisticRegression, 'Online Softmax Regression':SoftmaxRegression, 'Online Decision Tree Classifier':ExtremelyFastDecisionTreeClassifier, 'Online KNN Classifier':KNNClassifier,'Online Linear Regression':LinearRegression, 'Online Decision Tree Regressor':HoeffdingAdaptiveTreeRegressor, 'Online KNN Regressor':KNNRegressor} self.optDict={'sgd': SGD, 'adam':Adam, 'adadelta':AdaDelta, 'nesterovmomentum':NesterovMomentum, 'rmsprop':RMSProp} self.log = logging.getLogger('eion') def getPrediction(self, model,X): testStream = stream.iter_pandas(X) preds = [] for (xi,yi) in testStream: pred = model.predict_one(xi) preds.append(pred) return pd.DataFrame(preds) def startLearn(self,problemType,algoName,params,xtrain,ytrain,noOfClasses=None): try: model = self.algoDict[algoName] params = self.parseParams(params, algoName) if problemType == 'classification': if noOfClasses>2: model = OneVsRestClassifier(classifier=model(**params)) else: model = model(**params) else: model = model(**params) trainStream = stream.iter_pandas(xtrain, ytrain) #head start for i, (xi, yi) in enumerate(trainStream): if i>100: break if yi!=None: model.learn_one(xi, yi) trainPredictedData = [] trainStream = stream.iter_pandas(xtrain, ytrain) for i, (xi, yi) in enumerate(trainStream): if yi!=None: trainPredictedData.append(model.predict_one(xi)) model.learn_one(xi, yi) trainPredictedData = pd.DataFrame(trainPredictedData) return algoName, params, model, trainPredictedData except Exception as inst: self.log.info( '\n-----> '+algoName+' failed!!!.'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def parseParams(self, params, algoName): try: from learner.parameters import parametersDefine paramsObj = parametersDefine() paramDict =paramsObj.paramDefine(params,method=None) paramDict = {k:v[0] for k,v in paramDict.items()} if algoName=='Online Logistic Regression' or algoName=='Online Softmax Regression' or algoName=='Online Linear Regression': opt = self.optDict[paramDict.pop('optimizer').lower()] lr = float(paramDict.pop('optimizer_lr')) paramDict['optimizer'] = opt(lr) return paramDict except Exception as inst: self.log.info( '\n-----> Parameter parsing failed!!!.'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import time import os import sys import numpy as np import pandas as pd from sklearn import model_selection from sklearn.model_selection import train_test_split, KFold, cross_val_score from sklearn.model_selection import KFold #Classification metrics lib import logging import warnings warnings.filterwarnings('always') # "error", "ignore", "always", "default", "module" or "once" from learner.aion_matrix import aion_matrix from sklearn.preprocessing import binarize class ensemble_bagging(): def __init__(self,ensemble_params,scoreParam,MakeFP0,MakeFN0): self.ensemble_params = ensemble_params self.scoreParam=scoreParam self.MakeFP0 = MakeFP0 self.MakeFN0 = MakeFN0 self.log = logging.getLogger('eion') def add_alg2dict(self,k,v): b_dict={} b_dict[k]=v return b_dict def getSelected_algs_params(self,problemType,ensembleType,ensembleConfig): from learner.parameters import parametersDefine paramObj=parametersDefine() ensClass_algs_params={} algs_status={} for key,val in ensembleConfig.items(): for s,p in val.items(): if (s == "enable" and p == "True"): params = val['param'] params_eval = paramObj.paramDefine(params,None) params_eval = {param_key: param_value[0] for param_key, param_value in params_eval.items()} ensClass_algs_params[key]=params_eval else: pass return ensClass_algs_params def listEnsembleClassBaggingAlgs(self,ensClass_algs_params): from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier ensembleBaggingClassList=list() for key,val in ensClass_algs_params.items(): if (key == 'Logistic Regression'): lr=LogisticRegression() lr=lr.set_params(**val) ensembleBaggingClassList.append(lr) elif (key == 'Support Vector Machine'): svm=SVC() svm=svm.set_params(**val) ensembleBaggingClassList.append(svm) elif (key == 'Naive Bayes'): nb=GaussianNB() nb=nb.set_params(**val) ensembleBaggingClassList.append(nb) elif (key == 'K Nearest Neighbors'): knn=KNeighborsClassifier() knn=knn.set_params(**val) ensembleBaggingClassList.append(knn) elif (key == 'Decision Tree'): dt=DecisionTreeClassifier() dt=dt.set_params(**val) ensembleBaggingClassList.append(dt) elif (key == 'Random Forest'): rf=RandomForestClassifier() rf=rf.set_params(**val) ensembleBaggingClassList.append(rf) else: pass return ensembleBaggingClassList def listEnsembleRegBaggingAlgs(self,ensReg_algs_params): from sklearn.linear_model import Ridge from sklearn.linear_model import LinearRegression from sklearn.ensemble import RandomForestRegressor from sklearn.tree import DecisionTreeRegressor ensembleBaggingRegList=list() for key,val in ensReg_algs_params.items(): if (key == 'Linear Regression'): lir=LinearRegression() lir=lir.set_params(**val) ensembleBaggingRegList.append(lir) elif (key == 'Decision Tree'): dtr=DecisionTreeRegressor() dtr=dtr.set_params(**val) ensembleBaggingRegList.append(dtr) elif (key == 'Ridge'): ridge=Ridge() ridge=ridge.set_params(**val) ensembleBaggingRegList.append(ridge) else: ensembleBaggingRegList=[] return ensembleBaggingRegList def ensemble_bagging_classifier(self,X_train,y_train, X_test, y_test): ## New changes from sklearn.ensemble import BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier ensemble_method = "Bagging_classifier" problemType='classification' ensembleType='bagging' model_dict=self.ensemble_params ensClass_algs_params = self.getSelected_algs_params(problemType,ensembleType,model_dict) ensembleBaggingList = self.listEnsembleClassBaggingAlgs(ensClass_algs_params) # clf_array = model_list clf_array=ensembleBaggingList # no. of base classifier num_trees = len(clf_array) # max_samples=float(max_samples) n_estimators = num_trees # random_state=seed bagging_mean={} bagging_std={} accuracy_basealgs_train={} accuracy_basealgs_test={} blable="" accuracy_score_test=0 kfold = model_selection.KFold(n_splits=10, random_state=None) bestScore=-0xFFFF scoredetails = '' threshold = -1 bestthreshold = -1 precisionscore =-1 bestprecisionscore=-1 recallscore = -1 bestrecallscore=-1 objClf = aion_matrix() if (ensemble_method == "Bagging_classifier"): #bagging ensemble of base classifier .e.g. KNeighborsClassifier base estimators, each built on random subsets of 40% of the samples and 50% of the features. for clf in clf_array: self.log.info('-----------> Ensemble Algorithm '+str(clf.__class__.__name__)) clf.fit(X_train, y_train) bagging_clf = BaggingClassifier(clf,n_estimators = num_trees, random_state=10) bagging_clf.fit(X_train, y_train) bagging_scores = cross_val_score(bagging_clf, X_train, y_train, cv=kfold,n_jobs=-1) #bagging_ensemble_t=bagging_clf.fit(X_train, y_train) if not X_test.empty: bag_predict=bagging_clf.predict(X_test) accuracy_score_test = objClf.get_score(self.scoreParam,y_test,bag_predict) else: accuracy_score_test = bagging_scores MakeFP0 = False MakeFN0 = False if self.MakeFP0: self.log.info('-------- Ensemble: Calculate Threshold for FP Start-------') startRange = 0.0 endRange = 1.0 stepsize = 0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = objClf.check_threshold(bagging_clf,X_train,y_train,threshold_range,'FP','') MakeFP0 = True self.log.info('-------- Calculate Threshold for FP End-------') if self.MakeFN0: self.log.info('-------- Ensemble: Calculate Threshold for FN Start-------') startRange = 1.0 endRange = 0.0 stepsize = -0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = objClf.check_threshold(bagging_clf,X_train,y_train,threshold_range,'FN','') MakeFN0 = True self.log.info('-------- Calculate Threshold for FN End-------') if threshold != -1: if not X_test.empty: predictedData = bagging_clf.predict_proba(X_test) predictedData = binarize(predictedData[:,1].reshape(-1, 1),threshold=threshold) #bug 12437 accuracy_score_test = objClf.get_score(self.scoreParam,y_test,predictedData) status,bscore,bthres,brscore,bpscore = objClf.getBestModel(MakeFP0,MakeFN0,threshold,bestthreshold,recallscore,bestrecallscore,precisionscore,bestprecisionscore,accuracy_score_test,bestScore) if status: bestScore =bscore bestModel =bagging_clf.__class__.__name__ bestEstimator=bagging_clf bestthreshold = bthres bestBaseModel = clf.__class__.__name__ bestrecallscore = brscore bestprecisionscore = bpscore else: pass best_alg_name=bestEstimator.__class__.__name__ self.log.info('-----------> Best Bagging Classifier Model '+str(bestBaseModel)) self.log.info('-----------> Best Score '+str(bestScore)) # self.log.info('-----------> Threshold '+str(bestthreshold)) #bug 12438 if bestthreshold != -1: if not X_test.empty: predictedData_test = bestEstimator.predict_proba(X_test) predictedData_test = binarize(predictedData_test[:,1].reshape(-1, 1),threshold=bestthreshold) #bug 12437 predictedData_train = bestEstimator.predict_proba(X_train) predictedData_train = binarize(predictedData_train[:,1].reshape(-1, 1),threshold=bestthreshold) #bug 12437 else: if not X_test.empty: predictedData_test = bestEstimator.predict(X_test) predictedData_train = bestEstimator.predict(X_train) return bestEstimator,bestEstimator.get_params(),bestScore,best_alg_name,bestthreshold,bestprecisionscore,bestrecallscore def ensemble_bagging__regressor(self,X_train,y_train, X_test, y_test): from sklearn.ensemble import BaggingRegressor ensemble_method='Bagging_regressor' problemType='regression' ensembleType='bagging' model_dict=self.ensemble_params ensReg_algs_params = self.getSelected_algs_params(problemType,ensembleType,model_dict) ensembleBaggingList = self.listEnsembleRegBaggingAlgs(ensReg_algs_params) scoredetails = '' aion_matrixobj = aion_matrix() reg_array = ensembleBaggingList num_trees = len(reg_array) #self.log.info(num_trees) # max_samples=float(max_samples) n_estimators = num_trees r_state=10 bestModel='' bestParams={} bestScore=-sys.float_info.max #extension of bugfix 11656 objClf = aion_matrix() for reg in reg_array: self.log.info('-----------> Ensemble Algorithm '+str(reg.__class__.__name__)) nmodel=reg.fit(X_train, y_train) model = reg.__class__.__name__ estimator = BaggingRegressor(base_estimator=reg, random_state=r_state) bagging_ensemble_t=estimator.fit(X_train, y_train) predictedData = estimator.predict(X_test) score = objClf.get_score(self.scoreParam,y_test,predictedData) if self.scoreParam == "r2": if score > bestScore: bestScore =score bestModel =model bestEstimator=estimator else: if abs(score) < bestScore or bestScore == -sys.float_info.max: #extension of bugfix 11656 bestScore =abs(score) bestModel =model bestEstimator=estimator best_alg_name=bestEstimator.__class__.__name__ self.log.info('-----------> Best Ensemble Algorithm '+str(bestModel)) return bestEstimator,bestEstimator.get_params(),bestScore,best_alg_name ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import logging import warnings from sklearn.ensemble import VotingClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import Ridge from sklearn.preprocessing import binarize from sklearn.ensemble import VotingRegressor from sklearn.svm import SVC from sklearn.linear_model import LinearRegression from sklearn.ensemble import RandomForestRegressor from sklearn.tree import DecisionTreeRegressor from learner.aion_matrix import aion_matrix warnings.filterwarnings('always') class ensemble_voting(): def __init__(self,ensemble_params,scoreParam): self.ensemble_params = ensemble_params self.scoreParam=scoreParam self.final_estimator_r='' self.final_estimator_c='' self.log = logging.getLogger('eion') ''' Read the aion config "Ensemble-Voting", parse the algorithm and associated params based on enable or True status.Not used now ''' def getSelected_algs_params(self,problemType,ensembleType,ensembleConfig): from learner.parameters import parametersDefine paramObj=parametersDefine() ensClass_algs_params={} # algs_status={} for key,val in ensembleConfig.items(): for s,p in val.items(): if (s == "enable" and p == "True"): params = val['param'] params_eval = paramObj.paramDefine(params,None) params_eval = {param_key: param_value[0] for param_key, param_value in params_eval.items()} ensClass_algs_params[key]=params_eval else: pass return ensClass_algs_params ''' To make array of voting algorithm based on user config list. Not used now, in future if needed similar line with bagging ensemble, please use this. ''' def listEnsembleClassVotingAlgs(self,ensClass_algs_params): ensembleVotingClassList=list() for key,val in ensClass_algs_params.items(): if (key == 'Logistic Regression'): lr=LogisticRegression() lr=lr.set_params(**val) ensembleVotingClassList.append(lr) elif (key == 'Support Vector Machine'): svm=SVC() svm=svm.set_params(**val) ensembleVotingClassList.append(svm) elif (key == 'Naive Bayes'): nb=GaussianNB() nb=nb.set_params(**val) ensembleVotingClassList.append(nb) elif (key == 'K Nearest Neighbors'): knn=KNeighborsClassifier() knn=knn.set_params(**val) ensembleVotingClassList.append(knn) elif (key == 'Decision Tree'): dt=DecisionTreeClassifier() dt=dt.set_params(**val) ensembleVotingClassList.append(dt) elif (key == 'Random Forest'): rf=RandomForestClassifier() rf=rf.set_params(**val) ensembleVotingClassList.append(rf) else: ## Algorithm not found in config, so forming empty alg list. If needs, make list with default alg. ensembleVotingClassList=[] pass return ensembleVotingClassList ''' To make array of voting regression algorithm based on user config list. Not used now, in future if needed similar line with bagging ensemble, please use this. ''' def listEnsembleRegVotingAlgs(self,ensReg_algs_params): ensembleVotingRegList=list() for key,val in ensReg_algs_params.items(): if (key == 'Linear Regression'): lir=LinearRegression() lir=lir.set_params(**val) ensembleVotingRegList.append(lir) elif (key == 'Decision Tree'): dtr=DecisionTreeRegressor() dtr=dtr.set_params(**val) ensembleVotingRegList.append(dtr) elif (key == 'Ridge'): ridge=Ridge() ridge=ridge.set_params(**val) ensembleVotingRegList.append(ridge) else: ## Algorithm not found in config, so forming empty alg list. If needs, make list with default alg. ensembleVotingRegList=[] return ensembleVotingRegList def ensemble_voting_classifier(self,X_train,y_train, X_test, y_test,MakeFP0,MakeFN0,modelList): #bug 12437 status='ERROR' model=None estimator=None score=None params=None threshold = -1 precisionscore =-1 recallscore = -1 objClf = aion_matrix() try: lr = LogisticRegression(solver='lbfgs',random_state=1,max_iter=200) rf = RandomForestClassifier(random_state=1) gnb = GaussianNB() svc = SVC(probability=True) #Need to keep probability=True, because cross_val_score,predict_proba fn calls knn=KNeighborsClassifier(n_neighbors=5) base_estimators = [] if 'Logistic Regression' in modelList: base_estimators.append(('LogisticRegression', lr)) self.log.info('-------- Ensemble: Logistic Regression-------') if 'Random Forest' in modelList: base_estimators.append(('RandomForestClassifier', rf)) self.log.info('-------- Ensemble: Random Forest-------') if 'Naive Bayes' in modelList: base_estimators.append(('GaussianNB', gnb)) self.log.info('-------- Ensemble: Naive Bayes-------') if 'Support Vector Machine' in modelList: self.log.info('-------- Ensemble: Support Vector Machine-------') base_estimators.append(('SVC', svc)) if 'K Nearest Neighbors' in modelList: base_estimators.append(('KNeighborsClassifier', knn)) self.log.info('-------- Ensemble: K Nearest Neighbors-------') if len(base_estimators) == 0: self.log.info('-------- Ensemble Voting is only supported for Logistic Regression, Random Forest Classifier, Naive Bayes, SVM and KNN -------') status = "UNSUPPORTED" return status, estimator,params,score,model,threshold,precisionscore,recallscore eclf1 = VotingClassifier(base_estimators, voting='soft') eclf1.fit(X_train, y_train) y_predict = eclf1.predict(X_test) score = objClf.get_score(self.scoreParam,y_test,y_predict) self.log.info('-------- Ensemble (VoteClassifier) Soft Score:'+str(score)) if MakeFP0: self.log.info('-------- Ensemble: Calculate Threshold for FP Start-------') startRange = 0.0 endRange = 1.0 stepsize = 0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = objClf.check_threshold(eclf1,X_train,y_train,threshold_range,'FP','') self.log.info('-------- Calculate Threshold for FP End-------') elif MakeFN0: self.log.info('-------- Ensemble: Calculate Threshold for FN Start-------') startRange = 1.0 endRange = 0.0 stepsize = -0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = objClf.check_threshold(eclf1,X_train,y_train,threshold_range,'FN','') self.log.info('-------- Calculate Threshold for FN End-------') if threshold != -1: predictedData = eclf1.predict_proba(X_test) predictedData = binarize(predictedData[:,1].reshape(-1, 1),threshold=threshold) #bug 12437 score = objClf.get_score(self.scoreParam,y_test,predictedData) status = 'SUCCESS' model =eclf1.__class__.__name__ estimator=eclf1 params = estimator.get_params() #bug 12437 - Removed ensemble hard voting as predict_proba in the later stages will break except Exception as Inst: #bug 12437 self.log.info('--------- Error in Ensemble Voting ---------\n') self.log.info(str(Inst)) return status,estimator,params,score,model,threshold,precisionscore,recallscore def ensemble_voting__regressor(self,X_train,y_train, X_test, y_test,modelList): scoredetails = '' vr_predict=None vr_model=None try: lr = LinearRegression() rfr = RandomForestRegressor(n_estimators=10, random_state=1) dtr=DecisionTreeRegressor() base_estimators = [] if 'Linear Regression' in modelList: base_estimators.append(('LinearRegression', lr)) if 'Decision Tree' in modelList: base_estimators.append(('DecisionTreeRegressor', dtr)) if 'Random Forest' in modelList: base_estimators.append(('RandomForestRegressor', rfr)) if len(base_estimators) == 0: base_estimators = [('LinearRegression', lr), ('RandomForestRegressor', rfr),('DecisionTreeRegressor', dtr)] voting_reg = VotingRegressor(base_estimators) vr_model=voting_reg.fit(X_train,y_train) vr_predict=voting_reg.predict(X_test) best_vr_alg=voting_reg.__class__.__name__ self.log.info('-----------> Voting regression Model '+str(best_vr_alg)) except Exception as e: self.log.info("voting regression Exception info: \n") self.log.info(e) aion_matrixobj = aion_matrix() score = aion_matrixobj.get_score(self.scoreParam,y_test,vr_predict) return voting_reg,voting_reg.get_params(),score,best_vr_alg ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np #Classification metrics lib import logging import warnings from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.preprocessing import binarize from sklearn.svm import SVC from sklearn.ensemble import StackingClassifier from sklearn.linear_model import LinearRegression from sklearn.ensemble import RandomForestRegressor from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import StackingRegressor from sklearn.svm import LinearSVR from sklearn.linear_model import RidgeCV from sklearn.linear_model import LassoCV from learner.aion_matrix import aion_matrix warnings.filterwarnings('always') # "error", "ignore", "always", "default", "module" or "once" class ensemble_stacking(): def __init__(self,ensemble_params,scoreParam): self.ensemble_params = ensemble_params self.scoreParam=scoreParam self.final_estimator_r='' self.final_estimator_c='' self.log = logging.getLogger('eion') ## Read the aion config "Ensemble-Stacking", parse the algorithm and associated params based on enable or True status. def getSelected_algs_params(self,problemType,ensembleType,ensembleConfig): from learner.parameters import parametersDefine paramObj=parametersDefine() ensClass_algs_params={} # algs_status={} for key,val in ensembleConfig.items(): for s,p in val.items(): if (s == "enable" and p == "True"): params = val['param'] params_eval = paramObj.paramDefine(params,None) params_eval = {param_key: param_value[0] for param_key, param_value in params_eval.items()} ensClass_algs_params[key]=params_eval else: pass return ensClass_algs_params ## To make array of stacking algorithm based on user config list. Not used now, in future if needed similar line with bagging ensemble, please use this. def listEnsembleClassStackingAlgs(self,ensClass_algs_params): ensembleBaggingClassList=list() for key,val in ensClass_algs_params.items(): # print(key) if (key == 'Logistic Regression'): lr=LogisticRegression() lr=lr.set_params(**val) ensembleBaggingClassList.append(lr) elif (key == 'Support Vector Machine'): svm=SVC() svm=svm.set_params(**val) ensembleBaggingClassList.append(svm) elif (key == 'Naive Bayes'): nb=GaussianNB() nb=nb.set_params(**val) ensembleBaggingClassList.append(nb) elif (key == 'K Nearest Neighbors'): knn=KNeighborsClassifier() knn=knn.set_params(**val) ensembleBaggingClassList.append(knn) elif (key == 'Decision Tree'): dt=DecisionTreeClassifier() dt=dt.set_params(**val) ensembleBaggingClassList.append(dt) elif (key == 'Random Forest'): rf=RandomForestClassifier() rf=rf.set_params(**val) ensembleBaggingClassList.append(rf) else: ensembleBaggingClassList=[] pass return ensembleBaggingClassList ## To make array of stacking regression algorithm based on user config list. Not used now, in future if needed similar line with bagging ensemble, please use this. def listEnsembleRegStackingAlgs(self,ensReg_algs_params): ensembleBaggingRegList=list() for key,val in ensReg_algs_params.items(): if (key == 'LinearSVR'): lir=LinearSVR() lir=lir.set_params(**val) ensembleBaggingRegList.append(lir) elif (key == 'LinearRegression'): lr=LinearRegression() lr=lr.set_params(**val) ensembleBaggingRegList.append(lr) elif (key == 'LassoCV'): lcv=LassoCV() lcv=lcv.set_params(**val) ensembleBaggingRegList.append(lcv) elif (key == 'RandomForestRegressor'): rfr=RandomForestRegressor() rfr=rfr.set_params(**val) ensembleBaggingRegList.append(rfr) elif (key == 'RidgeCV'): ridge=RidgeCV() ridge=ridge.set_params(**val) ensembleBaggingRegList.append(ridge) else: ## NO algorithms found in configuration settings, instead of sending empty array,we can add any one of algorithms. ensembleBaggingRegList=[] return ensembleBaggingRegList def extract_params(self,dict): self.dict=dict for k,v in self.dict.items(): return k,v def stacking_params(self): for k,v in self.ensemble_params.items(): try: if (k == "max_features_percentage"): max_features_percentage=float(v) elif (k == "max_samples"): max_samples=float(v) elif (k == "seed"): seed=int(v) elif (k == "final_estimator_stack_c"): final_estimator_c=str(v) elif (k == "final_estimator_stack_r"): final_estimator_r=str(v) else: self.log.info("Invalid Param in ensemble advanced configuration.\n") except Exception as e: self.log.info("\n Ensemble config param parsing error"+str(e)) continue return final_estimator_c,final_estimator_r,seed,max_samples,max_features_percentage def ensemble_stacking_classifier(self,X_train,y_train, X_test, y_test,MakeFP0,MakeFN0,modelList): final_estimator_c,final_estimator_r,seed,max_samples,max_features_percentage= self.stacking_params() final_estimator_c="" final_estimator=final_estimator_c scoredetails='' lr = LogisticRegression(solver='lbfgs',random_state=1,max_iter=200) rf = RandomForestClassifier(random_state=2) gnb = GaussianNB() svc = SVC(probability=True) #Need to keep probability=True, because of cross_val_score,predict_proba fn calls knn=KNeighborsClassifier(n_neighbors=5) try: if (final_estimator == 'LogisticRegression'): final_estimator_a=lr elif (final_estimator == 'RandomForestClassifier'): final_estimator_a=rf elif (final_estimator == 'GaussianNB'): final_estimator_a=gnb elif (final_estimator == 'SVC'): final_estimator_a=svc elif (final_estimator == 'KNeighborsClassifier'): final_estimator_a=knn else: final_estimator_a=lr except Exception as e: final_estimator_a=lr self.log.info("Given stacking regression final estimator algorithm issue, using default one (LogisticRegression) as final_estimator now.\n") self.log.info(e) #stacking estimators base_estimators = [] if 'Logistic Regression' in modelList: base_estimators.append(('LogisticRegression', lr)) if 'Random Forest' in modelList: base_estimators.append(('RandomForestClassifier', rf)) if 'Naive Bayes' in modelList: base_estimators.append(('GaussianNB', gnb)) if 'Support Vector Machine' in modelList: base_estimators.append(('SVC', svc)) if 'K Nearest Neighbors' in modelList: base_estimators.append(('KNeighborsClassifier', knn)) if len(base_estimators) == 0: base_estimators = [('LogisticRegression', lr),('RandomForestClassifier', rf),('GaussianNB', gnb),('SVC', svc),('KNeighborsClassifier', knn)] stacking_c = StackingClassifier(estimators=base_estimators, final_estimator=final_estimator_a) stacking_c.fit(X_train, y_train) y_predict=stacking_c.predict(X_test) objClf = aion_matrix() accuracy_score_test = objClf.get_score(self.scoreParam,y_test,y_predict) MakeFP0 = False MakeFN0 = False threshold = -1 recallscore = -1 precisionscore =-1 if MakeFP0: self.log.info('-------- Ensemble: Calculate Threshold for FP Start-------') startRange = 0.0 endRange = 1.0 stepsize = 0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = objClf.check_threshold(stacking_c,X_train,y_train,threshold_range,'FP','') MakeFP0 = True self.log.info('-------- Calculate Threshold for FP End-------') elif MakeFN0: self.log.info('-------- Ensemble: Calculate Threshold for FN Start-------') startRange = 1.0 endRange = 0.0 stepsize = -0.01 threshold_range = np.arange(startRange,endRange,stepsize) threshold,precisionscore,recallscore = objClf.check_threshold(stacking_c,X_train,y_train,threshold_range,'FN','') MakeFN0 = True self.log.info('-------- Calculate Threshold for FN End-------') if threshold != -1: predictedData = stacking_c.predict_proba(X_test) predictedData = binarize(predictedData[:,1].reshape(-1, 1),threshold=threshold) #bug 12437 accuracy_score_test = objClf.get_score(self.scoreParam,y_test,predictedData) best_alg_stacking=stacking_c.__class__.__name__ self.log.info('-----------> Best Stacking Classifier Model '+str(best_alg_stacking)) self.log.info('-----------> Best Score '+str(accuracy_score_test)) return stacking_c,stacking_c.get_params(),accuracy_score_test,best_alg_stacking,threshold,precisionscore,recallscore def ensemble_stacking__regressor(self,X_train,y_train, X_test, y_test,modelList): final_estimator_c,final_estimator_r,seed,max_samples,max_features_percentage= self.stacking_params() final_estimator=final_estimator_r final_estimator_a=None scoredetails='' lr=LinearRegression() rcv=RidgeCV() svr=LinearSVR() lcv=LassoCV() rf=RandomForestRegressor(random_state=42) try: if (final_estimator == 'LinearRegression'): final_estimator_a=lr if (final_estimator == 'RidgeCV'): final_estimator_a=rcv elif (final_estimator == 'LinearSVR'): final_estimator_a=svr elif (final_estimator == 'LassoCV'): final_estimator_a=lcv elif (final_estimator == 'RandomForestRegressor'): final_estimator_a=rf else: #default is RidgeCV final_estimator_a=rcv except Exception as e: self.log.info("stacking regression Exception info: \n") self.log.info(e) final_estimator_a=rcv base_estimators = [] if 'Linear Regression' in modelList: base_estimators.append(('LinearRegression', lr)) if 'Ridge' in modelList: base_estimators.append(('RidgeCV', rcv)) if 'LinearSVR' in modelList: base_estimators.append(('LinearSVR', svr)) if 'Lasso' in modelList: base_estimators.append(('LassoCV', lcv)) if 'Random Forest' in modelList: base_estimators.append(('RandomForestRegressor', rf)) if len(base_estimators) == 0: base_estimators = [('LinearRegression', lr),('RidgeCV', rcv),('LinearSVR', svr),('LassoCV', lcv),('RandomForestRegressor', rf)] self.log.info("Stacking Base Alogs :"+str(base_estimators)) self.log.info("Final Estimator :"+final_estimator) stacking_regressor = StackingRegressor(estimators=base_estimators,final_estimator=final_estimator_a) stacking_r_model=stacking_regressor.fit(X_train, y_train) stacking_rpredict=stacking_regressor.predict(X_test) best_stacking_alg=stacking_regressor.__class__.__name__ #Accuracy accuracy_score_best=stacking_regressor.score(X_test, y_test) aion_matrixobj = aion_matrix() score = aion_matrixobj.get_score(self.scoreParam,y_test,stacking_rpredict) return stacking_regressor,stacking_regressor.get_params(),score,best_stacking_alg from .genetic_optimization import GeneticOptimizationCV from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import SGDClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import GradientBoostingClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LinearRegression from sklearn.linear_model import Lasso from sklearn.linear_model import Ridge from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor import warnings warnings.filterwarnings('ignore') from genetic_selection import GeneticSelectionCV def GeneticOptimizationCV(model,genetic_params,n_iter,scoring,cv): n_generations = n_iter estimator = model selector = GeneticSelectionCV(estimator,cv=cv,**genetic_params,n_generations=n_generations,scoring=scoring) return selector import itertools import logging from typing import Optional, Dict, Union from nltk import sent_tokenize import torch from transformers import( AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel, PreTrainedTokenizer, ) logger = logging.getLogger(__name__) class QGPipeline: """Poor man's QG pipeline""" def __init__( self, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, ans_model: PreTrainedModel, ans_tokenizer: PreTrainedTokenizer, qg_format: str, use_cuda: bool ): self.model = model self.tokenizer = tokenizer self.ans_model = ans_model self.ans_tokenizer = ans_tokenizer self.qg_format = qg_format self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu" self.model.to(self.device) if self.ans_model is not self.model: self.ans_model.to(self.device) assert self.model.__class__.__name__ in ["T5ForConditionalGeneration", "BartForConditionalGeneration"] if "T5ForConditionalGeneration" in self.model.__class__.__name__: self.model_type = "t5" else: self.model_type = "bart" def __call__(self, inputs: str): inputs = " ".join(inputs.split()) sents, answers = self._extract_answers(inputs) flat_answers = list(itertools.chain(*answers)) if len(flat_answers) == 0: return [] if self.qg_format == "prepend": qg_examples = self._prepare_inputs_for_qg_from_answers_prepend(inputs, answers) else: qg_examples = self._prepare_inputs_for_qg_from_answers_hl(sents, answers) qg_inputs = [example['source_text'] for example in qg_examples] questions = self._generate_questions(qg_inputs) output = [{'answer': example['answer'], 'question': que} for example, que in zip(qg_examples, questions)] return output def _generate_questions(self, inputs): inputs = self._tokenize(inputs, padding=True, truncation=True) outs = self.model.generate( input_ids=inputs['input_ids'].to(self.device), attention_mask=inputs['attention_mask'].to(self.device), max_length=32, num_beams=4, ) questions = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in outs] return questions def _extract_answers(self, context): sents, inputs = self._prepare_inputs_for_ans_extraction(context) inputs = self._tokenize(inputs, padding=True, truncation=True) outs = self.ans_model.generate( input_ids=inputs['input_ids'].to(self.device), attention_mask=inputs['attention_mask'].to(self.device), max_length=32, ) dec = [self.ans_tokenizer.decode(ids, skip_special_tokens=False) for ids in outs] answers = [item.split('<sep>') for item in dec] answers = [i[:-1] for i in answers] return sents, answers def _tokenize(self, inputs, padding=True, truncation=True, add_special_tokens=True, max_length=512 ): inputs = self.tokenizer.batch_encode_plus( inputs, max_length=max_length, add_special_tokens=add_special_tokens, truncation=truncation, padding="max_length" if padding else False, pad_to_max_length=padding, return_tensors="pt" ) return inputs def _prepare_inputs_for_ans_extraction(self, text): sents = sent_tokenize(text) inputs = [] for i in range(len(sents)): source_text = "extract answers:" for j, sent in enumerate(sents): if i == j: sent = "<hl> %s <hl>" % sent source_text = "%s %s" % (source_text, sent) source_text = source_text.strip() if self.model_type == "t5": source_text = source_text + " </s>" inputs.append(source_text) return sents, inputs def _prepare_inputs_for_qg_from_answers_hl(self, sents, answers): inputs = [] for i, answer in enumerate(answers): if len(answer) == 0: continue for answer_text in answer: sent = sents[i] sents_copy = sents[:] answer_text = answer_text.strip() ans_start_idx = 0 # ans_start_idx = sent.index(answer_text) # if answer_text in sent: # ans_start_idx = sent.index(answer_text) # else: # continue sent = f"{sent[:ans_start_idx]} <hl> {answer_text} <hl> {sent[ans_start_idx + len(answer_text): ]}" sents_copy[i] = sent source_text = " ".join(sents_copy) source_text = f"generate question: {source_text}" if self.model_type == "t5": source_text = source_text + " </s>" inputs.append({"answer": answer_text, "source_text": source_text}) return inputs def _prepare_inputs_for_qg_from_answers_prepend(self, context, answers): flat_answers = list(itertools.chain(*answers)) examples = [] for answer in flat_answers: source_text = f"answer: {answer} context: {context}" if self.model_type == "t5": source_text = source_text + " </s>" examples.append({"answer": answer, "source_text": source_text}) return examples class MultiTaskQAQGPipeline(QGPipeline): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, inputs: Union[Dict, str]): if type(inputs) is str: # do qg return super().__call__(inputs) else: # do qa return self._extract_answer(inputs["question"], inputs["context"]) def _prepare_inputs_for_qa(self, question, context): source_text = f"question: {question} context: {context}" if self.model_type == "t5": source_text = source_text + " </s>" return source_text def _extract_answer(self, question, context): source_text = self._prepare_inputs_for_qa(question, context) inputs = self._tokenize([source_text], padding=False) outs = self.model.generate( input_ids=inputs['input_ids'].to(self.device), attention_mask=inputs['attention_mask'].to(self.device), max_length=16, ) answer = self.tokenizer.decode(outs[0], skip_special_tokens=True) return answer class E2EQGPipeline: def __init__( self, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, use_cuda: bool ) : self.model = model self.tokenizer = tokenizer self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu" self.model.to(self.device) assert self.model.__class__.__name__ in ["T5ForConditionalGeneration", "BartForConditionalGeneration"] if "T5ForConditionalGeneration" in self.model.__class__.__name__: self.model_type = "t5" else: self.model_type = "bart" self.default_generate_kwargs = { "max_length": 256, "num_beams": 4, "length_penalty": 1.5, "no_repeat_ngram_size": 3, "early_stopping": True, } def __call__(self, context: str, **generate_kwargs): inputs = self._prepare_inputs_for_e2e_qg(context) # TODO: when overrding default_generate_kwargs all other arguments need to be passsed # find a better way to do this if not generate_kwargs: generate_kwargs = self.default_generate_kwargs input_length = inputs["input_ids"].shape[-1] # max_length = generate_kwargs.get("max_length", 256) # if input_length < max_length: # logger.warning( # "Your max_length is set to {}, but you input_length is only {}. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)".format( # max_length, input_length # ) # ) outs = self.model.generate( input_ids=inputs['input_ids'].to(self.device), attention_mask=inputs['attention_mask'].to(self.device), **generate_kwargs ) prediction = self.tokenizer.decode(outs[0], skip_special_tokens=True) questions = prediction.split("<sep>") questions = [question.strip() for question in questions[:-1]] return questions def _prepare_inputs_for_e2e_qg(self, context): source_text = f"generate questions: {context}" if self.model_type == "t5": source_text = source_text + " </s>" inputs = self._tokenize([source_text], padding=False) return inputs def _tokenize( self, inputs, padding=True, truncation=True, add_special_tokens=True, max_length=512 ): inputs = self.tokenizer.batch_encode_plus( inputs, max_length=max_length, add_special_tokens=add_special_tokens, truncation=truncation, padding="max_length" if padding else False, pad_to_max_length=padding, return_tensors="pt" ) return inputs SUPPORTED_TASKS = { "question-generation": { "impl": QGPipeline, "default": { "model": "valhalla/t5-small-qg-hl", "ans_model": "valhalla/t5-small-qa-qg-hl", } }, "multitask-qa-qg": { "impl": MultiTaskQAQGPipeline, "default": { "model": "valhalla/t5-small-qa-qg-hl", } }, "e2e-qg": { "impl": E2EQGPipeline, "default": { "model": "valhalla/t5-small-e2e-qg", } } } def pipeline( task: str, model: Optional = None, tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None, qg_format: Optional[str] = "highlight", ans_model: Optional = None, ans_tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None, use_cuda: Optional[bool] = True, **kwargs, ): # Retrieve the task if task not in SUPPORTED_TASKS: raise KeyError("Unknown task {}, available tasks are {}".format(task, list(SUPPORTED_TASKS.keys()))) targeted_task = SUPPORTED_TASKS[task] task_class = targeted_task["impl"] # Use default model/config/tokenizer for the task if no model is provided if model is None: model = targeted_task["default"]["model"] # Try to infer tokenizer from model or config name (if provided as str) if tokenizer is None: if isinstance(model, str): tokenizer = model else: # Impossible to guest what is the right tokenizer here raise Exception( "Impossible to guess which tokenizer to use. " "Please provided a PretrainedTokenizer class or a path/identifier to a pretrained tokenizer." ) # Instantiate tokenizer if needed if isinstance(tokenizer, (str, tuple)): if isinstance(tokenizer, tuple): # For tuple we have (tokenizer name, {kwargs}) tokenizer = AutoTokenizer.from_pretrained(tokenizer[0], **tokenizer[1]) else: tokenizer = AutoTokenizer.from_pretrained(tokenizer) # Instantiate model if needed if isinstance(model, str): model = AutoModelForSeq2SeqLM.from_pretrained(model) if task == "question-generation": if ans_model is None: # load default ans model ans_model = targeted_task["default"]["ans_model"] ans_tokenizer = AutoTokenizer.from_pretrained(ans_model) ans_model = AutoModelForSeq2SeqLM.from_pretrained(ans_model) else: # Try to infer tokenizer from model or config name (if provided as str) if ans_tokenizer is None: if isinstance(ans_model, str): ans_tokenizer = ans_model else: # Impossible to guest what is the right tokenizer here raise Exception( "Impossible to guess which tokenizer to use. " "Please provided a PretrainedTokenizer class or a path/identifier to a pretrained tokenizer." ) # Instantiate tokenizer if needed if isinstance(ans_tokenizer, (str, tuple)): if isinstance(ans_tokenizer, tuple): # For tuple we have (tokenizer name, {kwargs}) ans_tokenizer = AutoTokenizer.from_pretrained(ans_tokenizer[0], **ans_tokenizer[1]) else: ans_tokenizer = AutoTokenizer.from_pretrained(ans_tokenizer) if isinstance(ans_model, str): ans_model = AutoModelForSeq2SeqLM.from_pretrained(ans_model) if task == "e2e-qg": return task_class(model=model, tokenizer=tokenizer, use_cuda=use_cuda) elif task == "question-generation": return task_class(model=model, tokenizer=tokenizer, ans_model=ans_model, ans_tokenizer=ans_tokenizer, qg_format=qg_format, use_cuda=use_cuda) else: return task_class(model=model, tokenizer=tokenizer, ans_model=model, ans_tokenizer=tokenizer, qg_format=qg_format, use_cuda=use_cuda) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import numpy as np import pandas as pd import time import sys import logging from reinforcement.agents.ddqn import TrainDDQN from reinforcement.agents.dqn import TrainDQN from reinforcement.utils import rounded_dict from tensorflow.keras.layers import Dense, Dropout from sklearn.model_selection import train_test_split from learner.machinelearning import machinelearning from learner.aion_matrix import aion_matrix from reinforcement.metrics import network_predictions from learner.machinelearning import machinelearning os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # CPU is faster than GPU on structured data #def TrainRL(input_csv_file, model_save_path, rl_config, RL_Algo_Name): class ReinformentLearning(): def __init__(self,rlConfig,scoreParam,modelType): self.rl_config= rlConfig self.scoreParam = scoreParam self.log = logging.getLogger('eion') self.modelType = modelType def TrainRL(self,xtrain,ytrain,xtest,ytest,algorithm,deployLocation): try: scoredetails = '' X_train, xval, y_train, yval = train_test_split(xtrain, ytrain, test_size=0.2, stratify=ytrain) X_train = np.array(X_train) y_train = np.array(y_train) xval = np.array(xval) yval = np.array(yval) valueCount=ytrain.value_counts() categoryCountList=valueCount.tolist() xtest = np.array(xtest) ytest = np.array(ytest) objClf = aion_matrix() episodes = self.rl_config['episodes'] # Total number of episodes warmup_steps = self.rl_config['warmup_steps'] # Amount of warmup steps to collect data with random policy memory_length = warmup_steps # Max length of the Replay Memory batch_size = self.rl_config['batch_size'] collect_steps_per_episode = self.rl_config['collect_steps_per_episode'] collect_every = self.rl_config['collect_every'] target_update_period = self.rl_config['target_update_period'] # Period to overwrite the target Q-network with the default Q-network target_update_tau = self.rl_config['target_update_tau'] # Soften the target model update n_step_update = self.rl_config['n_step_update'] learning_rate = self.rl_config['learning_rate'] # Learning rate gamma = self.rl_config['gamma'] # Discount factor min_epsilon = self.rl_config['min_epsilon'] # Minimal and final chance of choosing random action decay_episodes = episodes // 10 # Number of episodes to decay from 1.0 to `min_epsilon`` layers = [Dense(128, activation="relu"), #need modification Dense(64, activation="relu"), Dense(32, activation="relu"), Dense(len(np.unique(y_train)), activation=None)] logFilePath=os.path.join(deployLocation,'log') if algorithm == "DQN": start = time.time() modelName = "DQN" model_save_path = os.path.dirname(__file__) model = TrainDQN(episodes, warmup_steps, learning_rate, gamma, min_epsilon, decay_episodes, target_update_period=target_update_period,target_update_tau=target_update_tau, batch_size=batch_size, collect_steps_per_episode=collect_steps_per_episode,memory_length=memory_length, collect_every=collect_every, n_step_update=n_step_update, model_path=model_save_path,log_dir=logFilePath) model.compile_model(X_train,y_train,layers) model.q_net.summary() model.train(xval,yval) network = model.get_network() predictedytrain=network_predictions(network,np.array(xtrain)) predictedytest = network_predictions(network,np.array(xtest)) if "DDQN" == algorithm: start = time.time() modelName = "DDQN" model = TrainDDQN(episodes, warmup_steps, learning_rate, gamma, min_epsilon, decay_episodes, target_update_period=target_update_period,target_update_tau=target_update_tau, batch_size=batch_size, collect_steps_per_episode=collect_steps_per_episode,memory_length=memory_length, collect_every=collect_every, n_step_update=n_step_update,log_dir=logFilePath) model.compile_model(X_train,y_train,layers) model.q_net.summary() model.train(xval,yval) network = model.get_network() predictedytrain=network_predictions(network,np.array(xtrain)) predictedytest = network_predictions(network,np.array(xtest)) score = objClf.get_score(self.scoreParam,ytest,predictedytest) score = round(score,2) return (network,self.rl_config,score,algorithm,-1,-1,-1) except Exception as inst: self.log.info( '\n-----> RL Failed!!!.'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) import os import numpy as np import pandas as pd import time from DeepRL.agents.ddqn import TrainDDQN from DeepRL.agents.dqn import TrainDQN from DeepRL.dataprocess import get_train_test_val from DeepRL.utils import rounded_dict from tensorflow.keras.layers import Dense, Dropout from sklearn.model_selection import train_test_split os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # CPU is faster than GPU on structured data def PredictRL(input_csv_file, model_load_path, RL_hparams_config_file, RL_Algo_Name): if not (os.path.exists(model_load_path)): os.makedirs(model_load_path) episodes = RL_hparams_config_file['DeepRL']['episodes'] # Total number of episodes warmup_steps = RL_hparams_config_file['DeepRL']['warmup_steps'] # Amount of warmup steps to collect data with random policy memory_length = warmup_steps # Max length of the Replay Memory batch_size = RL_hparams_config_file['DeepRL']['batch_size'] collect_steps_per_episode = RL_hparams_config_file['DeepRL']['collect_steps_per_episode'] collect_every = RL_hparams_config_file['DeepRL']['collect_every'] target_update_period = RL_hparams_config_file['DeepRL']['target_update_period'] # Period to overwrite the target Q-network with the default Q-network target_update_tau = RL_hparams_config_file['DeepRL']['target_update_tau'] # Soften the target model update n_step_update = RL_hparams_config_file['DeepRL']['n_step_update'] learning_rate = RL_hparams_config_file['DeepRL']['learning_rate'] # Learning rate gamma = RL_hparams_config_file['DeepRL']['gamma'] # Discount factor min_epsilon = RL_hparams_config_file['DeepRL']['min_epsilon'] # Minimal and final chance of choosing random action decay_episodes = episodes // 10 # Number of episodes to decay from 1.0 to `min_epsilon`` #path = '/home/renith/Renith/Project/AION/Reinforcement/RL_Classification/Code/rl_text_classification/telemetry_data.csv' data = pd.read_csv(input_csv_file) device5 = data[data['device_id'] == "Device_1"] device5 = device5.drop(['device_id'], axis = 1) device5.reset_index(drop=True, inplace=True) target_value = [] for i in range(device5['device_status'].shape[0]): if(device5['device_status'][i] == "NORMAL"): target_value.append(0.0) else: target_value.append(1.0) device5['target'] = target_value device5 = device5.drop(['device_status'], axis = 1) X_test = device5.iloc[:,1:-1] y_test = device5.iloc[:,-1] X_test = X_test.astype(np.float32) y_test = y_test.astype(np.int32) #Normalization mini, maxi = X_test.min(axis=0), X_test.max(axis=0) X_test -= mini X_test /= maxi - mini min_class = [1] #Minority class maj_class = [0] #Majority class #X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.8, stratify=y_train) #X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, stratify=y_train) #X_train = np.array(X_train) #y_train = np.array(y_train) #X_val = np.array(X_val) #y_val = np.array(y_val) X_test = np.array(X_test) y_test = np.array(y_test) #X_train, y_train, X_test, y_test, X_val, y_val = get_train_test_val(X_train.values, y_train.values, X_test.values, y_test.values, # min_class, maj_class, val_frac=0.2) layers = [Dense(128, activation="relu"), Dense(64, activation="relu"), Dense(32, activation="relu"), Dense(2, activation=None)] if(RL_Algo_Name == "DDQN"): model = TrainDDQN(episodes, warmup_steps, learning_rate, gamma, min_epsilon, decay_episodes, target_update_period=target_update_period, target_update_tau=target_update_tau, batch_size=batch_size, collect_steps_per_episode=collect_steps_per_episode, memory_length=memory_length, collect_every=collect_every, n_step_update=n_step_update, model_path=model_load_path) elif(RL_Algo_Name == "DQN"): model = TrainDQN(episodes, warmup_steps, learning_rate, gamma, min_epsilon, decay_episodes, target_update_period=target_update_period, target_update_tau=target_update_tau, batch_size=batch_size, collect_steps_per_episode=collect_steps_per_episode, memory_length=memory_length, collect_every=collect_every, n_step_update=n_step_update, model_path=model_load_path) model.compile_model(X_test, y_test, layers) model.q_net.summary() #model.train(X_val, y_val, "F1") #print("Training Ended !!!!") stats = model.evaluate(X_test, y_test) print(rounded_dict(stats)) #stats = model.evaluate(X_train, y_train) #print(rounded_dict(stats)) import matplotlib.pyplot as plt import numpy as np import seaborn as sns from sklearn.metrics import (auc, average_precision_score, confusion_matrix, f1_score, precision_recall_curve, roc_curve,precision_score,recall_score) from tensorflow import constant from tf_agents.trajectories import time_step def network_predictions(network, X: np.ndarray) -> dict: """Computes y_pred using a given network. Input is array of data entries. :param network: The network to use to calculate metrics :type network: (Q)Network :param X: X data, input to network :type X: np.ndarray :return: Numpy array of predicted targets for given X :rtype: np.ndarray """ if not isinstance(X, np.ndarray): raise ValueError(f"`X` must be of type `np.ndarray` not {type(X)}") q, _ = network(X, step_type=constant([time_step.StepType.FIRST] * X.shape[0]), training=False) return np.argmax(q.numpy(), axis=1) # Max action for each x in X def decision_function(network, X: np.ndarray) -> dict: """Computes the score for the predicted class of each x in X using a given network. Input is array of data entries. :param network: The network to use to calculate the score per x in X :type network: (Q)Network :param X: X data, input to network :type X: np.ndarray :return: Numpy array of scores for given X :rtype: np.ndarray """ if not isinstance(X, np.ndarray): raise ValueError(f"`X` must be of type `np.ndarray` not {type(X)}") q, _ = network(X, step_type=constant([time_step.StepType.FIRST] * X.shape[0]), training=False) return np.max(q.numpy(), axis=1) # Value of max action for each x in X def classification_metrics(y_true: list, y_pred: list) -> dict: """Computes metrics using y_true and y_pred. :param y_true: True labels :type y_true: np.ndarray :param y_pred: Predicted labels, corresponding to y_true :type y_pred: np.ndarray :return: Dictionairy containing Geometric Mean, F1, Precision, Recall, TP, TN, FP, FN :rtype: dict """ if not isinstance(y_true, (list, tuple, np.ndarray)): raise ValueError(f"`y_true` must be of type `list` not {type(y_true)}") if not isinstance(y_pred, (list, tuple, np.ndarray)): raise ValueError(f"`y_pred` must be of type `list` not {type(y_pred)}") if len(y_true) != len(y_pred): raise ValueError("`X` and `y` must be of same length.") #G_mean = np.sqrt(recall * specificity) # Geometric mean of recall and specificity F1 = f1_score(y_true, y_pred, average='macro') # Default F-measure recall = recall_score(y_true,y_pred,average='macro') precision = precision_score(y_true,y_pred,average='macro') return {"F1": F1, "Precision": precision, "Recall": recall} def plot_pr_curve(network, X_test: np.ndarray, y_test: np.ndarray, X_train: np.ndarray = None, y_train: np.ndarray = None) -> None: # pragma: no cover """Plots PR curve of X_test and y_test of given network. Optionally plots PR curve of X_train and y_train. Average precision is shown in the legend. :param network: The network to use to calculate the PR curve :type network: (Q)Network :param X_test: X data, input to network :type X_test: np.ndarray :param y_test: True labels for `X_test` :type y_test: np.ndarray :param X_train: Optional X data to plot validation PR curve :type X_train: np.ndarray :param y_train: True labels for `X_val` :type y_train: np.ndarray :return: None :rtype: NoneType """ plt.plot((0, 1), (1, 0), color="black", linestyle="--", label="Baseline") # TODO: Consider changing baseline if X_train is not None and y_train is not None: y_val_score = decision_function(network, X_train) val_precision, val_recall, _ = precision_recall_curve(y_train, y_val_score) val_AP = average_precision_score(y_train, y_val_score) plt.plot(val_recall, val_precision, label=f"Train AP: {val_AP:.3f}") y_test_score = decision_function(network, X_test) test_precision, test_recall, _ = precision_recall_curve(y_test, y_test_score) test_AP = average_precision_score(y_test, y_test_score) plt.plot(test_recall, test_precision, label=f"Test AP: {test_AP:.3f}") plt.xlim((-0.05, 1.05)) plt.ylim((-0.05, 1.05)) plt.xlabel("Recall") plt.ylabel("Precision") plt.title("PR Curve") plt.gca().set_aspect("equal", adjustable="box") plt.legend(loc="lower left") plt.grid(True) plt.show() def plot_roc_curve(network, X_test: np.ndarray, y_test: np.ndarray, X_train: np.ndarray = None, y_train: np.ndarray = None) -> None: # pragma: no cover """Plots ROC curve of X_test and y_test of given network. Optionally plots ROC curve of X_train and y_train. Average precision is shown in the legend. :param network: The network to use to calculate the PR curve :type network: (Q)Network :param X_test: X data, input to network :type X_test: np.ndarray :param y_test: True labels for `X_test` :type y_test: np.ndarray :param X_train: Optional X data to plot validation PR curve :type X_train: np.ndarray :param y_train: True labels for `X_val` :type y_train: np.ndarray :return: None :rtype: NoneType """ plt.plot((0, 1), (0, 1), color="black", linestyle="--", label="Baseline") # TODO: Consider changing baseline if X_train is not None and y_train is not None: y_train_score = decision_function(network, X_train) fpr_train, tpr_train, _ = roc_curve(y_train, y_train_score) plt.plot(fpr_train, tpr_train, label=f"Train AUROC: {auc(fpr_train, tpr_train):.2f}") y_test_score = decision_function(network, X_test) fpr_test, tpr_test, _ = roc_curve(y_test, y_test_score) plt.plot(fpr_test, tpr_test, label=f"Test AUROC: {auc(fpr_test, tpr_test):.2f}") plt.xlim((-0.05, 1.05)) plt.ylim((-0.05, 1.05)) plt.xlabel("False Positive Rate") plt.ylabel("True Positive Rate") plt.title("ROC Curve") plt.gca().set_aspect("equal", adjustable="box") plt.legend(loc="lower right") plt.grid(True) plt.show() def plot_confusion_matrix(TP: int, FN: int, FP: int, TN: int) -> None: # pragma: no cover """Plots confusion matric of given TP, FN, FP, TN. :param TP: True Positive :type TP: int :param FN: False Negative :type FN: int :param FP: False Positive :type FP: int :param TN: True Negative :type TN: int :return: None :rtype: NoneType """ if not all(isinstance(i, (int, np.integer)) for i in (TP, FN, FP, TN)): raise ValueError("Not all arguments are integers.") ticklabels = ("Minority", "Majority") sns.heatmap(((TP, FN), (FP, TN)), annot=True, fmt="_d", cmap="viridis", xticklabels=ticklabels, yticklabels=ticklabels) plt.title("Confusion matrix") plt.xlabel("Predicted labels") plt.ylabel("True labels") plt.show() import os from typing import List import numpy as np import pandas as pd from sklearn.model_selection import train_test_split def split_csv(fp: str = "./data/creditcard.csv", fp_dest: str = "./data", name: str = "credit", test_size: int = 0.5, strat_col: str = "Class") -> None: """Splits a csv file in two, in a stratified fashion. Format for filenames will be `{name}0.csv and `{name}1.csv`. :param fp: The path at which the csv file is located. :type fp: str :param fp_dest: The path to save the train and test files. :type fp_dest: str :param name: The prefix for the files. :type name: str :param test_size: The fraction of total size for the test file. :type test_size: float :param strat_col: The column in the original csv file to stratify. :return: None, two files located at `fp_dest`. :rtype: NoneType """ if not os.path.isfile(fp): raise FileNotFoundError(f"File at {fp} does not exist.") if not os.path.isdir(fp_dest): raise ValueError(f"Directory at {fp_dest} does not exist.") if not 0 < test_size < 1: raise ValueError(f"{test_size} is not in interval 0 < x < 1.") df = pd.read_csv(fp) if not (strat_col in df.columns): raise ValueError(f"Stratify column {strat_col} not found in DataFrame.") train, test = train_test_split(df, test_size=test_size, stratify=df[strat_col]) train.to_csv(f"{fp_dest}/{name}0.csv", index=False) test.to_csv(f"{fp_dest}/{name}1.csv", index=False) def rounded_dict(d: dict, precision: int = 6) -> dict: """Rounds all values in a dictionairy to `precision` digits after the decimal point. :param d: Dictionairy containing only floats or ints as values :type d: dict :return: Rounded dictionairy :rtype: dict """ return {k: round(v, precision) for k, v in d.items()} def imbalance_ratio(y: np.ndarray, min_classes: List[int] = [1], maj_classes: List[int] = [0]) -> float: """Calculates imbalance ratio of minority class(es) and majority class(es). :param y: y-vector with labels. :type y: np.ndarray :param min_classes: The labels of the minority classes :type min_classes: list :param maj_classes: The labels of the minority classes :type maj_classes: list :return: The imbalance ratio :rtype: float """ return np.isin(y, min_classes).sum() / np.isin(y, maj_classes).sum() import os from typing import List, Tuple import numpy as np from pandas import read_csv from sklearn.model_selection import train_test_split from sklearn.utils import shuffle from tensorflow.keras.datasets import cifar10, fashion_mnist, imdb, mnist from tensorflow.keras.preprocessing.sequence import pad_sequences TrainTestData = Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray] TrainTestValData = Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] def load_image(data_source: str) -> TrainTestData: """ Loads one of the following image datasets: {mnist, famnist, cifar10}. Normalizes the data. Returns X and y for both train and test datasets. Dtypes of X's and y's will be `float32` and `int32` to be compatible with `tf_agents`. :param data_source: Either mnist, famnist or cifar10 :type data_source: str :return: Tuple of (X_train, y_train, X_test, y_test) containing original split of train/test :rtype: tuple """ reshape_shape = -1, 28, 28, 1 if data_source == "mnist": (X_train, y_train), (X_test, y_test) = mnist.load_data() elif data_source == "famnist": (X_train, y_train), (X_test, y_test) = fashion_mnist.load_data() elif data_source == "cifar10": (X_train, y_train), (X_test, y_test) = cifar10.load_data() reshape_shape = -1, 32, 32, 3 else: raise ValueError("No valid `data_source`.") X_train = X_train.reshape(reshape_shape).astype(np.float32) # Float32 is the expected dtype for the observation spec in the env X_test = X_test.reshape(reshape_shape).astype(np.float32) X_train /= 255 # /= is not available when casting int to float: https://stackoverflow.com/a/48948461/10603874 X_test /= 255 y_train = y_train.reshape(y_train.shape[0], ).astype(np.int32) y_test = y_test.reshape(y_test.shape[0], ).astype(np.int32) return X_train, y_train, X_test, y_test def load_csv(fp_train: str, fp_test: str, label_col: str, drop_cols: List[str], normalization: bool = False) -> TrainTestData: """ Loads any csv-file from local filepaths. Returns X and y for both train and test datasets. Option to normalize the data with min-max normalization. Only csv-files with float32 values for the features and int32 values for the labels supported. Source for dataset: https://mimic-iv.mit.edu/ :param fp_train: Location of the train csv-file :type fp_train: str :param fp_test: Location of the test csv-file :type fp_test: str :param label_col: The name of the column containing the labels of the data :rtype label_col: str :param drop_cols: List of the names of the columns to be dropped. `label_col` gets dropped automatically :rtype drop_cols: List of strings :param normalization: Normalize the data with min-max normalization? :type normalization: bool :return: Tuple of (X_train, y_train, X_test, y_test) containing original split of train/test :rtype: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray] """ if not os.path.isfile(fp_train): raise FileNotFoundError(f"`fp_train` {fp_train} does not exist.") if not os.path.isfile(fp_test): raise FileNotFoundError(f"`fp_test` {fp_test} does not exist.") if not isinstance(normalization, bool): raise TypeError(f"`normalization` must be of type `bool`, not {type(normalization)}") X_train = read_csv(fp_train).astype(np.float32) # DataFrames directly converted to float32 X_test = read_csv(fp_test).astype(np.float32) y_train = X_train[label_col].astype(np.int32) y_test = X_test[label_col].astype(np.int32) X_train.drop(columns=drop_cols + [label_col], inplace=True) # Dropping cols and label column X_test.drop(columns=drop_cols + [label_col], inplace=True) # Other data sources are already normalized. RGB values are always in range 0 to 255. if normalization: mini, maxi = X_train.min(axis=0), X_train.max(axis=0) X_train -= mini X_train /= maxi - mini X_test -= mini X_test /= maxi - mini return X_train.values, y_train.values, X_test.values, y_test.values # Numpy arrays def load_imdb(config: Tuple[int, int] = (5_000, 500)) -> TrainTestData: """Loads the IMDB dataset. Returns X and y for both train and test datasets. :param config: Tuple of number of most frequent words and max length of each sequence. :type config: str :return: Tuple of (X_train, y_train, X_test, y_test) containing original split of train/test :rtype: tuple """ if not isinstance(config, (tuple, list)): raise TypeError(f"{type(config)} is no valid datatype for `config`.") if len(config) != 2: raise ValueError("Tuple length of `config` must be 2.") if not all(i > 0 for i in config): raise ValueError("All integers of `config` must be > 0.") (X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=config[0]) X_train = pad_sequences(X_train, maxlen=config[1]) X_test = pad_sequences(X_test, maxlen=config[1]) y_train = y_train.astype(np.int32) y_test = y_test.astype(np.int32) return X_train, y_train, X_test, y_test def get_train_test_val(X_train: np.ndarray, y_train: np.ndarray, X_test: np.ndarray, y_test: np.ndarray, min_classes: List[int], maj_classes: List[int], imb_ratio: float = None, imb_test: bool = True, val_frac: float = 0.25, print_stats: bool = True) -> TrainTestValData: """ Imbalances data and divides the data into train, test and validation sets. The imbalance rate of each individual dataset is approx. the same as the given `imb_ratio`. :param X_train: The X_train data :type X_train: np.ndarray :param y_train: The y_train data :type y_train: np.ndarray :param X_test: The X_test data :type X_test: np.ndarray :param y_test: The y_test data :type y_test: np.ndarray :param min_classes: List of labels of all minority classes :type min_classes: list :param maj_classes: List of labels of all majority classes. :type maj_classes: list :param imb_ratio: Imbalance ratio for minority to majority class: len(minority datapoints) / len(majority datapoints) If the `imb_ratio` is None, data will not be imbalanced and will only be relabeled to 1's and 0's. :type imb_ratio: float :param imb_test: Imbalance the test dataset? :type imb_test: bool :param val_frac: Fraction to take from X_train and y_train for X_val and y_val :type val_frac: float :param print_stats: Print the imbalance ratio of the imbalanced data? :type print_stats: bool :return: Tuple of (X_train, y_train, X_test, y_test, X_val, y_val) :rtype: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] """ if not 0 < val_frac < 1: raise ValueError(f"{val_frac} is not in interval 0 < x < 1.") if not isinstance(print_stats, bool): raise TypeError(f"`print_stats` must be of type `bool`, not {type(print_stats)}.") X_train, y_train = imbalance_data(X_train, y_train, min_classes, maj_classes, imb_ratio=imb_ratio) # Only imbalance test-data if imb_test is True X_test, y_test = imbalance_data(X_test, y_test, min_classes, maj_classes, imb_ratio=imb_ratio if imb_test else None) # stratify=y_train to ensure class balance is kept between train and validation datasets X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=val_frac, stratify=y_train) if print_stats: p_train, p_test, p_val = [((y == 1).sum(), imbalance_ratio(y)) for y in (y_train, y_test, y_val)] print(f"Imbalance ratio `p`:\n" f"\ttrain: n={p_train[0]}, p={p_train[1]:.6f}\n" f"\ttest: n={p_test[0]}, p={p_test[1]:.6f}\n" f"\tvalidation: n={p_val[0]}, p={p_val[1]:.6f}") return X_train, y_train, X_test, y_test, X_val, y_val def imbalance_data(X: np.ndarray, y: np.ndarray, min_class: List[int], maj_class: List[int], imb_ratio: float = None) -> Tuple[np.ndarray, np.ndarray]: """ Split data in minority and majority, only values in {min_class, maj_class} will be kept. (Possibly) decrease minority rows to match the imbalance rate. If initial imb_ratio of dataset is lower than given `imb_ratio`, the imb_ratio of the returned data will not be changed. If the `imb_ratio` is None, data will not be imbalanced and will only be relabeled to 1's and 0's. """ if not isinstance(X, np.ndarray): raise TypeError(f"`X` must be of type `np.ndarray` not {type(X)}") if not isinstance(y, np.ndarray): raise TypeError(f"`y` must be of type `np.ndarray` not {type(y)}") if X.shape[0] != y.shape[0]: raise ValueError("`X` and `y` must contain the same amount of rows.") if not isinstance(min_class, (list, tuple)): raise TypeError("`min_class` must be of type list or tuple.") if not isinstance(maj_class, (list, tuple)): raise TypeError("`maj_class` must be of type list or tuple.") if (imb_ratio is not None) and not (0 < imb_ratio < 1): raise ValueError(f"{imb_ratio} is not in interval 0 < imb_ratio < 1.") if imb_ratio is None: # Do not imbalance data if no `imb_ratio` is given imb_ratio = 1 X_min = X[np.isin(y, min_class)] # Mask the correct indexes X_maj = X[np.isin(y, maj_class)] # Only keep data/labels for x in {min_class, maj_class} and forget all other min_len = int(X_maj.shape[0] * imb_ratio) # Amount of rows to select from minority classes to get to correct imbalance ratio # Keep all majority rows, decrease minority rows to match `imb_ratio` X_min = X_min[np.random.choice(X_min.shape[0], min(min_len, X_min.shape[0]), replace=False), :] X_imb = np.concatenate([X_maj, X_min]).astype(np.float32) y_imb = np.concatenate((np.zeros(X_maj.shape[0]), np.ones(X_min.shape[0]))).astype(np.int32) X_imb, y_imb = shuffle(X_imb, y_imb) return X_imb, y_imb import os import pickle from datetime import datetime import numpy as np import tensorflow as tf from reinforcement.environments.classifierenv import ClassifierEnv from reinforcement.metrics import (classification_metrics, decision_function, network_predictions, plot_pr_curve, plot_roc_curve) from reinforcement.utils import imbalance_ratio from tensorflow import data from tensorflow.keras.optimizers import Adam #from tf_agents.agents.dqn.dqn_agent import DdqnAgent from tf_agents.agents import DqnAgent from tf_agents.drivers.dynamic_step_driver import DynamicStepDriver from tf_agents.environments.tf_py_environment import TFPyEnvironment from tf_agents.networks.sequential import Sequential from tf_agents.policies.random_tf_policy import RandomTFPolicy from tf_agents.replay_buffers.tf_uniform_replay_buffer import \ TFUniformReplayBuffer from tf_agents.utils import common class TrainDQN(): """Wrapper for DDQN training, validation, saving etc.""" def __init__(self, episodes: int, warmup_steps: int, learning_rate: float, gamma: float, min_epsilon: float, decay_episodes: int, model_path: str = None, log_dir: str = None, batch_size: int = 64, memory_length: int = None, collect_steps_per_episode: int = 1, val_every: int = None, target_update_period: int = 1, target_update_tau: float = 1.0, progressbar: bool = True, n_step_update: int = 1, gradient_clipping: float = 1.0, collect_every: int = 1) -> None: """ Wrapper to make training easier. Code is partly based of https://www.tensorflow.org/agents/tutorials/1_dqn_tutorial :param episodes: Number of training episodes :type episodes: int :param warmup_steps: Number of episodes to fill Replay Buffer with random state-action pairs before training starts :type warmup_steps: int :param learning_rate: Learning Rate for the Adam Optimizer :type learning_rate: float :param gamma: Discount factor for the Q-values :type gamma: float :param min_epsilon: Lowest and final value for epsilon :type min_epsilon: float :param decay_episodes: Amount of episodes to decay from 1 to `min_epsilon` :type decay_episodes: int :param model_path: Location to save the trained model :type model_path: str :param log_dir: Location to save the logs, usefull for TensorBoard :type log_dir: str :param batch_size: Number of samples in minibatch to train on each step :type batch_size: int :param memory_length: Maximum size of the Replay Buffer :type memory_length: int :param collect_steps_per_episode: Amount of data to collect for Replay Buffer each episiode :type collect_steps_per_episode: int :param collect_every: Step interval to collect data during training :type collect_every: int :param val_every: Validate the model every X episodes using the `collect_metrics()` function :type val_every: int :param target_update_period: Update the target Q-network every X episodes :type target_update_period: int :param target_update_tau: Parameter for softening the `target_update_period` :type target_update_tau: float :param progressbar: Enable or disable the progressbar for collecting data and training :type progressbar: bool :return: None :rtype: NoneType """ self.episodes = episodes # Total episodes self.warmup_steps = warmup_steps # Amount of warmup steps before training self.batch_size = batch_size # Batch size of Replay Memory self.collect_steps_per_episode = collect_steps_per_episode # Amount of steps to collect data each episode self.collect_every = collect_every # Step interval to collect data during training self.learning_rate = learning_rate # Learning Rate self.gamma = gamma # Discount factor self.min_epsilon = min_epsilon # Minimal chance of choosing random action self.decay_episodes = decay_episodes # Number of episodes to decay from 1.0 to `EPSILON` self.target_update_period = target_update_period # Period for soft updates self.target_update_tau = target_update_tau self.progressbar = progressbar # Enable or disable the progressbar for collecting data and training self.n_step_update = n_step_update self.gradient_clipping = gradient_clipping # Clip the loss self.compiled = False NOW = "DQN" #datetime.now().strftime("%Y%m%d_%H%M%S") if memory_length is not None: self.memory_length = memory_length # Max Replay Memory length else: self.memory_length = warmup_steps if val_every is not None: self.val_every = val_every # Validate the policy every `val_every` episodes else: self.val_every = self.episodes // min(50, self.episodes) # Can't validate the model 50 times if self.episodes < 50 if model_path is not None: #if os.path.exists(model_path + "/" + NOW + ".pkl"): # os.remove(model_path + "/" + NOW + ".pkl") self.model_path = model_path + "/" + NOW + ".pkl" else: self.model_path = "./models/" + NOW + ".pkl" if log_dir is None: log_dir = "./logs/" + NOW self.writer = tf.summary.create_file_writer(log_dir) def compile_model(self, X_train, y_train, layers: list = [], imb_ratio: float = None, loss_fn=common.element_wise_squared_loss) -> None: """Initializes the neural networks, DDQN-agent, collect policies and replay buffer. :param X_train: Training data for the model. :type X_train: np.ndarray :param y_train: Labels corresponding to `X_train`. 1 for the positive class, 0 for the negative class. :param y_train: np.ndarray :param layers: List of layers to feed into the TF-agents custom Sequential(!) layer. :type layers: list :param imb_ratio: The imbalance ratio of the data. :type imb_ratio: float :param loss_fn: Callable loss function :type loss_fn: tf.compat.v1.losses :return: None :rtype: NoneType """ if imb_ratio is None: imb_ratio = imbalance_ratio(y_train) self.train_env = TFPyEnvironment(ClassifierEnv(X_train, y_train, imb_ratio)) self.global_episode = tf.Variable(0, name="global_episode", dtype=np.int64, trainable=False) # Global train episode counter # Custom epsilon decay: https://github.com/tensorflow/agents/issues/339 epsilon_decay = tf.compat.v1.train.polynomial_decay( 1.0, self.global_episode, self.decay_episodes, end_learning_rate=self.min_epsilon) self.q_net = Sequential(layers, self.train_env.observation_spec()) self.agent = DqnAgent(self.train_env.time_step_spec(), self.train_env.action_spec(), q_network=self.q_net, optimizer=Adam(learning_rate=self.learning_rate), td_errors_loss_fn=loss_fn, train_step_counter=self.global_episode, target_update_period=self.target_update_period, target_update_tau=self.target_update_tau, gamma=self.gamma, epsilon_greedy=epsilon_decay, n_step_update=self.n_step_update, gradient_clipping=self.gradient_clipping) self.agent.initialize() self.random_policy = RandomTFPolicy(self.train_env.time_step_spec(), self.train_env.action_spec()) self.replay_buffer = TFUniformReplayBuffer(data_spec=self.agent.collect_data_spec, batch_size=self.train_env.batch_size, max_length=self.memory_length) self.warmup_driver = DynamicStepDriver(self.train_env, self.random_policy, observers=[self.replay_buffer.add_batch], num_steps=self.warmup_steps) # Uses a random policy self.collect_driver = DynamicStepDriver(self.train_env, self.agent.collect_policy, observers=[self.replay_buffer.add_batch], num_steps=self.collect_steps_per_episode) # Uses the epsilon-greedy policy of the agent self.agent.train = common.function(self.agent.train) # Optimalization self.warmup_driver.run = common.function(self.warmup_driver.run) self.collect_driver.run = common.function(self.collect_driver.run) self.compiled = True def train(self, *args) -> None: """Starts the training of the model. Includes warmup period, metrics collection and model saving. :param *args: All arguments will be passed to `collect_metrics()`. This can be usefull to pass callables, testing environments or validation data. Overwrite the TrainDQN.collect_metrics() function to use your own *args. :type *args: Any :return: None :rtype: NoneType, last step is saving the model as a side-effect """ assert self.compiled, "Model must be compiled with model.compile_model(X_train, y_train, layers) before training." # Warmup period, fill memory with random actions if self.progressbar: print(f"\033[92mCollecting data for {self.warmup_steps:_} steps... This might take a few minutes...\033[0m") self.warmup_driver.run(time_step=None, policy_state=self.random_policy.get_initial_state(self.train_env.batch_size)) if self.progressbar: print(f"\033[92m{self.replay_buffer.num_frames():_} frames collected!\033[0m") dataset = self.replay_buffer.as_dataset(sample_batch_size=self.batch_size, num_steps=self.n_step_update + 1, num_parallel_calls=data.experimental.AUTOTUNE).prefetch(data.experimental.AUTOTUNE) iterator = iter(dataset) def _train(): experiences, _ = next(iterator) return self.agent.train(experiences).loss _train = common.function(_train) # Optimalization ts = None policy_state = self.agent.collect_policy.get_initial_state(self.train_env.batch_size) print('Before Collect Metrics') self.collect_metrics(*args) # Initial collection for step 0 print('After Collect Metrics') for _ in range(self.episodes): if not self.global_episode % self.collect_every: # Collect a few steps using collect_policy and save to `replay_buffer` if self.collect_steps_per_episode != 0: ts, policy_state = self.collect_driver.run(time_step=ts, policy_state=policy_state) # Sample a batch of data from `replay_buffer` and update the agent's network train_loss = _train() if not self.global_episode % self.val_every: with self.writer.as_default(): tf.summary.scalar("train_loss", train_loss, step=self.global_episode) self.collect_metrics(*args) def collect_metrics(self, X_val: np.ndarray, y_val: np.ndarray, save_best: str = None): """Collects metrics using the trained Q-network. :param X_val: Features of validation data, same shape as X_train :type X_val: np.ndarray :param y_val: Labels of validation data, same shape as y_train :type y_val: np.ndarra :param save_best: Saving the best model of all validation runs based on given metric: Choose one of: {Gmean, F1, Precision, Recall, TP, TN, FP, FN} This improves stability since the model at the last episode is not guaranteed to be the best model. :type save_best: str """ y_pred = network_predictions(self.agent._target_q_network, X_val) print('classification_metrics') stats = classification_metrics(y_val, y_pred) print('Before AVGQ') avgQ = np.mean(decision_function(self.agent._target_q_network, X_val)) # Max action for each x in X print('After AVGQ') if save_best is not None: if not hasattr(self, "best_score"): # If no best model yet self.best_score = 0.0 if stats.get(save_best) >= self.best_score: # Overwrite best model self.save_network() # Saving directly to avoid shallow copy without trained weights self.best_score = stats.get(save_best) with self.writer.as_default(): tf.summary.scalar("AverageQ", avgQ, step=self.global_episode) # Average Q-value for this epoch for k, v in stats.items(): tf.summary.scalar(k, v, step=self.global_episode) def evaluate(self, X_test, y_test): """ Final evaluation of trained Q-network with X_test and y_test. Optional PR and ROC curve comparison to X_train, y_train to ensure no overfitting is taking place. :param X_test: Features of test data, same shape as X_train :type X_test: np.ndarray :param y_test: Labels of test data, same shape as y_train :type y_test: np.ndarray :param X_train: Features of train data :type X_train: np.ndarray :param y_train: Labels of train data :type y_train: np.ndarray """ #if hasattr(self, "best_score"): # print(f"\033[92mBest score: {self.best_score:6f}!\033[0m") # network = self.load_network(self.model_path) # Load best saved model #else: # network = self.agent._target_q_network # Load latest target model #network = self.load_network(self.model_path) #if (X_train is not None) and (y_train is not None): # plot_pr_curve(network, X_test, y_test, X_train, y_train) # plot_roc_curve(network, X_test, y_test, X_train, y_train) y_pred = network_predictions(self.agent._target_q_network, X_test) return classification_metrics(y_test, y_pred) def save_network(self): print('save_network') """Saves Q-network as pickle to `model_path`.""" with open(self.model_path, "wb") as f: # Save Q-network as pickle pickle.dump(self.agent._target_q_network, f) def get_network(self): """Static method to load Q-network pickle from given filepath. :param fp: Filepath to the saved pickle of the network :type fp: str :returns: The network-object loaded from a pickle file. :rtype: tensorflow.keras.models.Model """ return self.agent._target_q_network import os import pickle from datetime import datetime import numpy as np import tensorflow as tf from reinforcement.environments.classifierenv import ClassifierEnv from reinforcement.metrics import (classification_metrics, decision_function, network_predictions, plot_pr_curve, plot_roc_curve) from reinforcement.utils import imbalance_ratio from tensorflow import data from tensorflow.keras.optimizers import Adam from tf_agents.agents.dqn.dqn_agent import DdqnAgent from tf_agents.drivers.dynamic_step_driver import DynamicStepDriver from tf_agents.environments.tf_py_environment import TFPyEnvironment from tf_agents.networks.sequential import Sequential from tf_agents.policies.random_tf_policy import RandomTFPolicy from tf_agents.replay_buffers.tf_uniform_replay_buffer import \ TFUniformReplayBuffer from tf_agents.utils import common class TrainDDQN(): """Wrapper for DDQN training, validation, saving etc.""" def __init__(self, episodes: int, warmup_steps: int, learning_rate: float, gamma: float, min_epsilon: float, decay_episodes: int, model_path: str = None, log_dir: str = None, batch_size: int = 64, memory_length: int = None, collect_steps_per_episode: int = 1, val_every: int = None, target_update_period: int = 1, target_update_tau: float = 1.0, progressbar: bool = True, n_step_update: int = 1, gradient_clipping: float = 1.0, collect_every: int = 1) -> None: """ Wrapper to make training easier. Code is partly based of https://www.tensorflow.org/agents/tutorials/1_dqn_tutorial :param episodes: Number of training episodes :type episodes: int :param warmup_steps: Number of episodes to fill Replay Buffer with random state-action pairs before training starts :type warmup_steps: int :param learning_rate: Learning Rate for the Adam Optimizer :type learning_rate: float :param gamma: Discount factor for the Q-values :type gamma: float :param min_epsilon: Lowest and final value for epsilon :type min_epsilon: float :param decay_episodes: Amount of episodes to decay from 1 to `min_epsilon` :type decay_episodes: int :param model_path: Location to save the trained model :type model_path: str :param log_dir: Location to save the logs, usefull for TensorBoard :type log_dir: str :param batch_size: Number of samples in minibatch to train on each step :type batch_size: int :param memory_length: Maximum size of the Replay Buffer :type memory_length: int :param collect_steps_per_episode: Amount of data to collect for Replay Buffer each episiode :type collect_steps_per_episode: int :param collect_every: Step interval to collect data during training :type collect_every: int :param val_every: Validate the model every X episodes using the `collect_metrics()` function :type val_every: int :param target_update_period: Update the target Q-network every X episodes :type target_update_period: int :param target_update_tau: Parameter for softening the `target_update_period` :type target_update_tau: float :param progressbar: Enable or disable the progressbar for collecting data and training :type progressbar: bool :return: None :rtype: NoneType """ self.episodes = episodes # Total episodes self.warmup_steps = warmup_steps # Amount of warmup steps before training self.batch_size = batch_size # Batch size of Replay Memory self.collect_steps_per_episode = collect_steps_per_episode # Amount of steps to collect data each episode self.collect_every = collect_every # Step interval to collect data during training self.learning_rate = learning_rate # Learning Rate self.gamma = gamma # Discount factor self.min_epsilon = min_epsilon # Minimal chance of choosing random action self.decay_episodes = decay_episodes # Number of episodes to decay from 1.0 to `EPSILON` self.target_update_period = target_update_period # Period for soft updates self.target_update_tau = target_update_tau self.progressbar = progressbar # Enable or disable the progressbar for collecting data and training self.n_step_update = n_step_update self.gradient_clipping = gradient_clipping # Clip the loss self.compiled = False NOW = "DDQN" #datetime.now().strftime("%Y%m%d_%H%M%S") if memory_length is not None: self.memory_length = memory_length # Max Replay Memory length else: self.memory_length = warmup_steps if val_every is not None: self.val_every = val_every # Validate the policy every `val_every` episodes else: self.val_every = self.episodes // min(50, self.episodes) # Can't validate the model 50 times if self.episodes < 50 if model_path is not None: #if os.path.exists(model_path + "/" + NOW + ".pkl"): # os.remove(model_path + "/" + NOW + ".pkl") self.model_path = model_path + "/" + NOW + ".pkl" else: self.model_path = "./models/" + NOW + ".pkl" if log_dir is None: log_dir = "./logs/" + NOW self.writer = tf.summary.create_file_writer(log_dir) def compile_model(self, X_train, y_train, layers: list = [], imb_ratio: float = None, loss_fn=common.element_wise_squared_loss) -> None: """Initializes the neural networks, DDQN-agent, collect policies and replay buffer. :param X_train: Training data for the model. :type X_train: np.ndarray :param y_train: Labels corresponding to `X_train`. 1 for the positive class, 0 for the negative class. :param y_train: np.ndarray :param layers: List of layers to feed into the TF-agents custom Sequential(!) layer. :type layers: list :param imb_ratio: The imbalance ratio of the data. :type imb_ratio: float :param loss_fn: Callable loss function :type loss_fn: tf.compat.v1.losses :return: None :rtype: NoneType """ if imb_ratio is None: imb_ratio = imbalance_ratio(y_train) self.train_env = TFPyEnvironment(ClassifierEnv(X_train, y_train, imb_ratio)) self.global_episode = tf.Variable(0, name="global_episode", dtype=np.int64, trainable=False) # Global train episode counter # Custom epsilon decay: https://github.com/tensorflow/agents/issues/339 epsilon_decay = tf.compat.v1.train.polynomial_decay( 1.0, self.global_episode, self.decay_episodes, end_learning_rate=self.min_epsilon) self.q_net = Sequential(layers, self.train_env.observation_spec()) self.agent = DdqnAgent(self.train_env.time_step_spec(), self.train_env.action_spec(), q_network=self.q_net, optimizer=Adam(learning_rate=self.learning_rate), td_errors_loss_fn=loss_fn, train_step_counter=self.global_episode, target_update_period=self.target_update_period, target_update_tau=self.target_update_tau, gamma=self.gamma, epsilon_greedy=epsilon_decay, n_step_update=self.n_step_update, gradient_clipping=self.gradient_clipping) self.agent.initialize() self.random_policy = RandomTFPolicy(self.train_env.time_step_spec(), self.train_env.action_spec()) self.replay_buffer = TFUniformReplayBuffer(data_spec=self.agent.collect_data_spec, batch_size=self.train_env.batch_size, max_length=self.memory_length) self.warmup_driver = DynamicStepDriver(self.train_env, self.random_policy, observers=[self.replay_buffer.add_batch], num_steps=self.warmup_steps) # Uses a random policy self.collect_driver = DynamicStepDriver(self.train_env, self.agent.collect_policy, observers=[self.replay_buffer.add_batch], num_steps=self.collect_steps_per_episode) # Uses the epsilon-greedy policy of the agent self.agent.train = common.function(self.agent.train) # Optimalization self.warmup_driver.run = common.function(self.warmup_driver.run) self.collect_driver.run = common.function(self.collect_driver.run) self.compiled = True def train(self, *args) -> None: """Starts the training of the model. Includes warmup period, metrics collection and model saving. :param *args: All arguments will be passed to `collect_metrics()`. This can be usefull to pass callables, testing environments or validation data. Overwrite the TrainDDQN.collect_metrics() function to use your own *args. :type *args: Any :return: None :rtype: NoneType, last step is saving the model as a side-effect """ assert self.compiled, "Model must be compiled with model.compile_model(X_train, y_train, layers) before training." # Warmup period, fill memory with random actions if self.progressbar: print(f"\033[92mCollecting data for {self.warmup_steps:_} steps... This might take a few minutes...\033[0m") self.warmup_driver.run(time_step=None, policy_state=self.random_policy.get_initial_state(self.train_env.batch_size)) if self.progressbar: print(f"\033[92m{self.replay_buffer.num_frames():_} frames collected!\033[0m") dataset = self.replay_buffer.as_dataset(sample_batch_size=self.batch_size, num_steps=self.n_step_update + 1, num_parallel_calls=data.experimental.AUTOTUNE).prefetch(data.experimental.AUTOTUNE) iterator = iter(dataset) def _train(): experiences, _ = next(iterator) return self.agent.train(experiences).loss _train = common.function(_train) # Optimalization ts = None policy_state = self.agent.collect_policy.get_initial_state(self.train_env.batch_size) self.collect_metrics(*args) # Initial collection for step 0 for _ in range(self.episodes): if not self.global_episode % self.collect_every: # Collect a few steps using collect_policy and save to `replay_buffer` if self.collect_steps_per_episode != 0: ts, policy_state = self.collect_driver.run(time_step=ts, policy_state=policy_state) # Sample a batch of data from `replay_buffer` and update the agent's network train_loss = _train() if not self.global_episode % self.val_every: with self.writer.as_default(): tf.summary.scalar("train_loss", train_loss, step=self.global_episode) self.collect_metrics(*args) def collect_metrics(self, X_val: np.ndarray, y_val: np.ndarray, save_best: str = None): """Collects metrics using the trained Q-network. :param X_val: Features of validation data, same shape as X_train :type X_val: np.ndarray :param y_val: Labels of validation data, same shape as y_train :type y_val: np.ndarray :param save_best: Saving the best model of all validation runs based on given metric: Choose one of: {Gmean, F1, Precision, Recall, TP, TN, FP, FN} This improves stability since the model at the last episode is not guaranteed to be the best model. :type save_best: str """ y_pred = network_predictions(self.agent._target_q_network, X_val) stats = classification_metrics(y_val, y_pred) avgQ = np.mean(decision_function(self.agent._target_q_network, X_val)) # Max action for each x in X if save_best is not None: if not hasattr(self, "best_score"): # If no best model yet self.best_score = 0.0 if stats.get(save_best) >= self.best_score: # Overwrite best model self.save_network() # Saving directly to avoid shallow copy without trained weights self.best_score = stats.get(save_best) with self.writer.as_default(): tf.summary.scalar("AverageQ", avgQ, step=self.global_episode) # Average Q-value for this epoch for k, v in stats.items(): tf.summary.scalar(k, v, step=self.global_episode) def evaluate(self,X_train,y_train, X_test, y_test): """ Final evaluation of trained Q-network with X_test and y_test. Optional PR and ROC curve comparison to X_train, y_train to ensure no overfitting is taking place. :param X_test: Features of test data, same shape as X_train :type X_test: np.ndarray :param y_test: Labels of test data, same shape as y_train :type y_test: np.ndarray :param X_train: Features of train data :type X_train: np.ndarray :param y_train: Labels of train data :type y_train: np.ndarray """ #if hasattr(self, "best_score"): # print(f"\033[92mBest score: {self.best_score:6f}!\033[0m") # network = self.load_network(self.model_path) # Load best saved model #else: # network = self.agent._target_q_network # Load latest target model #network = self.load_network(self.model_path) #if (X_train is not None) and (y_train is not None): # plot_pr_curve(network, X_test, y_test, X_train, y_train) # plot_roc_curve(network, X_test, y_test, X_train, y_train) y_pred = network_predictions(self.agent._target_q_network, X_test) return classification_metrics(y_test, y_pred) def get_network(self): #network = self.load_network(self.model_path) return self.agent._target_q_network def save_network(self, filename_rl): #usnish """Saves Q-network as pickle to `model_path`.""" with open(self.filename_rl, "wb") as f: # Save Q-network as pickle pickle.dump(self.agent._target_q_network, f) @staticmethod def load_network(fp: str): """Static method to load Q-network pickle from given filepath. :param fp: Filepath to the saved pickle of the network :type fp: str :returns: The network-object loaded from a pickle file. :rtype: tensorflow.keras.models.Model """ with open(fp, "rb") as f: # Load the Q-network network = pickle.load(f) return network import numpy as np from tf_agents.environments.py_environment import PyEnvironment from tf_agents.specs.array_spec import ArraySpec, BoundedArraySpec from tf_agents.trajectories import time_step as ts class ClassifierEnv(PyEnvironment): """ Custom `PyEnvironment` environment for imbalanced classification. Based on https://www.tensorflow.org/agents/tutorials/2_environments_tutorial """ def __init__(self, X_train: np.ndarray, y_train: np.ndarray, imb_ratio: float): """Initialization of environment with X_train and y_train. :param X_train: Features shaped: [samples, ..., ] :type X_train: np.ndarray :param y_train: Labels shaped: [samples] :type y_train: np.ndarray :param imb_ratio: Imbalance ratio of the data :type imb_ratio: float :returns: None :rtype: NoneType """ #print('1') self._action_spec = BoundedArraySpec(shape=(), dtype=np.int32, minimum=0, maximum=(len(np.unique(y_train)) - 1), name="action") #print(y_train) self._observation_spec = ArraySpec(shape=X_train.shape[1:], dtype=X_train.dtype, name="observation") #print('3') self._episode_ended = False self.X_train = X_train self.y_train = y_train self.imb_ratio = imb_ratio # Imbalance ratio: 0 < imb_ratio < 1 self.id = np.arange(self.X_train.shape[0]) # List of IDs to connect X and y data self.episode_step = 0 # Episode step, resets every episode self._state = self.X_train[self.id[self.episode_step]] def action_spec(self): """ Definition of the discrete actionspace. 1 for the positive/minority class, 0 for the negative/majority class. """ return self._action_spec def observation_spec(self): """Definition of the continous statespace e.g. the observations in typical RL environments.""" return self._observation_spec def _reset(self): """Shuffles data and returns the first state of the shuffled data to begin training on new episode.""" np.random.shuffle(self.id) # Shuffle the X and y data self.episode_step = 0 # Reset episode step counter at the end of every episode self._state = self.X_train[self.id[self.episode_step]] self._episode_ended = False # Reset terminal condition return ts.restart(self._state) def _step(self, action: int): """ Take one step in the environment. If the action is correct, the environment will either return 1 or `imb_ratio` depending on the current class. If the action is incorrect, the environment will either return -1 or -`imb_ratio` depending on the current class. """ if self._episode_ended: # The last action ended the episode. Ignore the current action and start a new episode return self.reset() env_action = self.y_train[self.id[self.episode_step]] # The label of the current state self.episode_step += 1 if action == env_action: # Correct action if env_action: # Minority reward = 1 # True Positive else: # Majority reward = self.imb_ratio # True Negative else: # Incorrect action if env_action: # Minority reward = -1 # False Negative self._episode_ended = True # Stop episode when minority class is misclassified else: # Majority reward = -self.imb_ratio # False Positive if self.episode_step == self.X_train.shape[0] - 1: # If last step in data self._episode_ended = True self._state = self.X_train[self.id[self.episode_step]] # Update state with new datapoint if self._episode_ended: return ts.termination(self._state, reward) else: return ts.transition(self._state, reward) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' # -*- coding: utf-8 -*- # -*- coding: utf-8 -*- import logging logging.getLogger('tensorflow').disabled = True import json import mlflow import mlflow.sklearn import mlflow.sagemaker as mfs # from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split # from sklearn import datasets import time import numpy as np # Load dataset # from sklearn.datasets import load_iris import pickle # Load the pickled model # from matplotlib import pyplot import sys import os import boto3 import subprocess import os.path from os.path import expanduser import platform from pathlib import Path class aionMlopsService: def __init__(self,model,mlflowtosagemakerDeploy,mlflowtosagemakerPushOnly,mlflowtosagemakerPushImageName,mlflowtosagemakerdeployModeluri,experiment_name,mlflow_modelname,awsaccesskey_id,awssecretaccess_key,aws_session_token,mlflow_container_name,aws_region,aws_id,iam_sagemakerfullaccess_arn,sm_app_name,sm_deploy_option,delete_ecr_repository,ecrRepositoryName): try: self.model=model self.mlflowtosagemakerDeploy=mlflowtosagemakerDeploy self.mlflowtosagemakerPushOnly=str(mlflowtosagemakerPushOnly) self.mlflowtosagemakerPushImageName=str(mlflowtosagemakerPushImageName) self.mlflowtosagemakerdeployModeluri=str(mlflowtosagemakerdeployModeluri) self.experiment_name=experiment_name self.mlflow_modelname=mlflow_modelname self.awsaccesskey_id=awsaccesskey_id self.awssecretaccess_key=awssecretaccess_key self.aws_session_token=aws_session_token self.mlflow_container_name=mlflow_container_name self.aws_region=aws_region self.aws_id=aws_id self.iam_sagemakerfullaccess_arn=iam_sagemakerfullaccess_arn self.sm_app_name=sm_app_name self.sm_deploy_option=sm_deploy_option self.delete_ecr_repository=delete_ecr_repository self.ecrRepositoryName=ecrRepositoryName from appbe.dataPath import LOG_LOCATION sagemakerLogLocation = LOG_LOCATION try: os.makedirs(sagemakerLogLocation) except OSError as e: if (os.path.exists(sagemakerLogLocation)): pass else: raise OSError('sagemakerLogLocation error.') self.sagemakerLogLocation=str(sagemakerLogLocation) filename_mlops = 'mlopslog_'+str(int(time.time())) filename_mlops=filename_mlops+'.log' # filename = 'mlopsLog_'+Time() filepath = os.path.join(self.sagemakerLogLocation, filename_mlops) logging.basicConfig(filename=filepath, format='%(message)s',filemode='w') # logging.basicConfig(filename="uq_logging.log", format='%(asctime)s %(message)s',filemode='w') # logging.basicConfig(filename="uq_logging.log", format=' %(message)s',filemode='w') # logging.basicConfig(filename='uq_logging.log', encoding='utf-8', level=logging.INFO) self.log = logging.getLogger('aionMLOps') self.log.setLevel(logging.DEBUG) # mlflow.set_experiment(self.experiment_name) except Exception as e: self.log.info('<!------------- mlflow model INIT Error ---------------> '+str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def mlflowSetPath(self,path): track_dir=os.path.join(path,'mlruns') uri="file:"+str(Path(track_dir)) return uri #Currently not used this delete ecr repository option def ecr_repository_delete(self,rep_name): # import subprocess client = boto3.client('ecr') repositories = client.describe_repositories() ecr_delete_rep=client.delete_repository(registryId=self.aws_id,repositoryName=self.ecrRepositoryName,force=True) mlflow_ecr_delete=subprocess.run(['aws', 'ecr', 'delete-repository','--repository-name',rep_name,'||','true']) self.log.info('Success: deleted aws ecr repository which contains mlops image.') def check_sm_deploy_status(self,app_name): sage_client = boto3.client('sagemaker', region_name=self.aws_region) endpoint_description = sage_client.describe_endpoint(EndpointName=app_name) endpoint_status = endpoint_description["EndpointStatus"] try: failure_reason=endpoint_description["FailureReason"] self.log.info("sagemaker end point creation failure reason is: "+str(failure_reason)) except: pass endpoint_status=str(endpoint_status) return endpoint_status def invoke_sm_endpoint(self,app_name, input_json): client = boto3.session.Session().client("sagemaker-runtime", self.aws_region) response = client.invoke_endpoint( EndpointName=app_name, Body=input_json, ContentType='application/json; format=pandas-split', ) # preds = response['Body'].read().decode("ascii") preds = response['Body'].read().decode("ascii") preds = json.loads(preds) # print("preds: {}".format(preds)) return preds def predict_sm_app_endpoint(self,X_test): #print(X_test) import pandas as pd prediction=None AWS_ACCESS_KEY_ID=str(self.awsaccesskey_id) AWS_SECRET_ACCESS_KEY=str(self.awssecretaccess_key) AWS_SESSION_TOKEN=str(self.aws_session_token) region = str(self.aws_region) #Existing model deploy options # mlflowtosagemakerPushImageName=str(self.mlflowtosagemakerPushImageName) # mlflowtosagemakerdeployModeluri=str(self.mlflowtosagemakerdeployModeluri) try: import subprocess cmd = 'aws configure set region_name '+region os.system(cmd) cmd = 'aws configure set aws_access_key_id '+AWS_ACCESS_KEY_ID os.system(cmd) cmd = 'aws configure set aws_secret_access_key '+AWS_SECRET_ACCESS_KEY os.system(cmd) ''' aws_region=subprocess.run(['aws', 'configure', 'set','region_name',region]) aws_accesskeyid=subprocess.run(['aws', 'configure', 'set','aws_access_key_id',AWS_ACCESS_KEY_ID]) aws_secretaccesskey=subprocess.run(['aws', 'configure', 'set','aws_secret_access_key',AWS_SECRET_ACCESS_KEY]) ''' except: pass #Create a session for aws communication using aws boto3 lib # s3_client = boto3.client('ecr',aws_access_key_id=AWS_ACCESS_KEY_ID,aws_secret_access_key=AWS_SECRET_ACCESS_KEY,aws_session_token=AWS_SESSION_TOKEN,region_name=region) # s3 = boto3.resource('ecr', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key= AWS_SECRET_ACCESS_KEY) session = boto3.Session(aws_access_key_id=AWS_ACCESS_KEY_ID,aws_secret_access_key=AWS_SECRET_ACCESS_KEY,aws_session_token=AWS_SESSION_TOKEN,region_name=region) #X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=2) # query_input = pd.DataFrame(X_test).iloc[[1,5]].to_json(orient="split") try: query_input = pd.DataFrame(X_test).to_json(orient="split") #print(query_input) prediction = self.invoke_sm_endpoint(app_name=self.sm_app_name, input_json=query_input) # self.log.info("sagemaker end point Prediction: \n"+str(prediction)) except Exception as e: print(e) return prediction def deleteSagemakerApp(self,app_name,region): # import mlflow.sagemaker as mfs # region = 'ap-south-1' # app_name = 'aion-demo-app' mfs.delete(app_name=app_name,region_name=region, archive=False,synchronous=True, timeout_seconds=300) # print("AION mlops sagemaker application endpoint is deleted....\n") self.log.info('AION mlops sagemaker application endpoint is deleted, application name is: '+str(app_name)) def deployModel2sagemaker(self,mlflow_container_name,tag_id,model_path): region = str(self.aws_region) aws_id = str(self.aws_id) iam_sagemakerfullaccess_arn = str(self.iam_sagemakerfullaccess_arn) app_name = str(self.sm_app_name) model_uri = str(model_path) app_status=False mlflow_root_dir = None try: os.chdir(str(self.sagemakerLogLocation)) mlflow_root_dir = os.getcwd() self.log.info('mlflow root dir: '+str(mlflow_root_dir)) except: self.log.info("path issue.") try: c_status=self.check_sm_deploy_status(app_name) #if ((c_status == "Failed") or (c_status == "OutOfService")): if ((c_status == "Failed") or (c_status.lower() == "failed")): app_status=False self.log.info("Sagemaker endpoint status: Failed.\n") mfs.delete(app_name=app_name,region_name=region, archive=False,synchronous=True, timeout_seconds=300) elif ((c_status.lower() == "inservice") or (c_status == "InService")): app_status=True self.log.info("Sagemaker endpoint status: InService. Running sagemaker endpoint name: \n"+str(app_name)) else: app_status=False pass except: # print("deploy status error.\n") pass #aws ecr model app_name should contain only [[a-zA-Z0-9-]] import re if app_name: pattern = re.compile("[A-Za-z0-9-]+") # if found match (entire string matches pattern) if pattern.fullmatch(app_name) is not None: #print("Found match: ") pass else: app_name = 'aion-demo-app' else: app_name = 'aion-demo-app' mlflow_image=mlflow_container_name+':'+tag_id image_url = aws_id + '.dkr.ecr.' + region + '.amazonaws.com/' + mlflow_image deploy_option="create" self.log.info('deploy_option: \n'+str(deploy_option)) if (deploy_option.lower() == "create"): # Other deploy modes: mlflow.sagemaker.DEPLOYMENT_MODE_ADD,mlflow.sagemaker.DEPLOYMENT_MODE_REPLACE if not (app_status): try: mfs.deploy(app_name=app_name,model_uri=model_uri,region_name=region,mode="create",execution_role_arn=iam_sagemakerfullaccess_arn,image_url=image_url) self.log.info('sagemaker endpoint created and model deployed. Application name is: \n'+str(app_name)) except: self.log.info('Creating end point application issue.Please check the connection and aws credentials \n') else: self.log.info('Sagemaker application with user endpoint name already running.Please check. Please delete the old endpoint with same name.\n') elif (deploy_option.lower() == "delete"): # import mlflow.sagemaker as mfs # # region = 'ap-south-1' # # app_name = 'aion-demo-app' # mfs.delete(app_name=app_name,region_name=region, archive=False,synchronous=True, timeout_seconds=300) # print("Mlflow sagemaker application endpoint is deleted....\n") # self.log.info('Mlflow sagemaker application endpoint is deleted, application name is: '+str(app_name)) pass elif (deploy_option.lower() == "add"): pass elif (deploy_option.lower() == "replace"): pass else: pass return app_status def mlflow2sagemaker_deploy(self): self.log.info('<!------------- Inside AION mlops to sagemaker communication and deploy process. ---------------> ') deploy_status=False app_name = str(self.sm_app_name) self.log.info('Sagemaker Application Name: '+str(app_name)) uri_mlflow=self.mlflowSetPath(self.sagemakerLogLocation) mlflow.set_tracking_uri(uri_mlflow) mlops_trackuri=mlflow.get_tracking_uri() mlops_trackuri=str(mlops_trackuri) self.log.info('mlops tracking uri: '+str(mlops_trackuri)) localhost_deploy=False try: #Loading aion model to deploy in sagemaker mlflow.set_experiment(self.experiment_name) self.log.info('Endpoint Name: '+str(self.experiment_name)) # Assume, the model already loaded from joblib in aionmlflow2smInterface.py file. aionmodel2deploy=self.model # run_id = None # experiment_id=None # Use the loaded pickled model to make predictions # pred = knn_from_pickle.predict(X_test) with mlflow.start_run(run_name='AIONMLOps') as run: # aionmodel2deploy.fit(X_train, y_train) # predictions = aionmodel2deploy.predict(X_test) mlflow.sklearn.log_model(aionmodel2deploy, self.mlflow_modelname) run_id = run.info.run_uuid experiment_id = run.info.experiment_id self.log.info('AION mlops experiment run_id: '+str(run_id)) self.log.info('AION mlops experiment experiment_id: '+str(experiment_id)) self.log.info('AION mlops experiment model_name: '+str(self.mlflow_modelname)) artifact_uri = {mlflow.get_artifact_uri()} # print("1.artifact_uri: \n",artifact_uri) mlflow.end_run() #If we need, we can check the mlflow experiments. # try: # mlflow_client = mlflow.tracking.MlflowClient('./mlruns') # exp_list = mlflow_client.list_experiments() # except: # pass #print("mlflow exp_list: \n",exp_list) mlflow_modelname=str(self.mlflow_modelname) mlops_trackuri=mlops_trackuri.replace('file:','') mlops_trackuri=str(mlops_trackuri) # mlflow_root_dir = os.getcwd() mlflow_root_dir = None try: os.chdir(str(self.sagemakerLogLocation)) mlflow_root_dir = os.getcwd() self.log.info('mlflow root dir: '+str(mlflow_root_dir)) except: self.log.info("path issue.") model_path = 'mlruns/%s/%s/artifacts/%s' % (experiment_id, run_id,self.mlflow_modelname) # model_path=mlops_trackuri+'\\%s\\%s\\artifacts\\%s' % (experiment_id, run_id,mlflow_modelname) self.log.info("local host aion mlops model_path is: "+str(model_path)) time.sleep(2) #print("Environment variable setup in the current working dir for aws sagemaker cli connection... \n") self.log.info('Environment variable setup in the current working dir for aws sagemaker cli connection... \n ') AWS_ACCESS_KEY_ID=str(self.awsaccesskey_id) AWS_SECRET_ACCESS_KEY=str(self.awssecretaccess_key) AWS_SESSION_TOKEN=str(self.aws_session_token) region = str(self.aws_region) #Existing model deploy options mlflowtosagemakerPushImageName=str(self.mlflowtosagemakerPushImageName) mlflowtosagemakerdeployModeluri=str(self.mlflowtosagemakerdeployModeluri) import subprocess cmd = 'aws configure set region_name '+region os.system(cmd) cmd = 'aws configure set aws_access_key_id '+AWS_ACCESS_KEY_ID os.system(cmd) cmd = 'aws configure set aws_secret_access_key '+AWS_SECRET_ACCESS_KEY os.system(cmd) #Create a session for aws communication using aws boto3 lib # s3_client = boto3.client('ecr',aws_access_key_id=AWS_ACCESS_KEY_ID,aws_secret_access_key=AWS_SECRET_ACCESS_KEY,aws_session_token=AWS_SESSION_TOKEN,region_name=region) # s3 = boto3.resource('ecr', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key= AWS_SECRET_ACCESS_KEY) session = boto3.Session(aws_access_key_id=AWS_ACCESS_KEY_ID,aws_secret_access_key=AWS_SECRET_ACCESS_KEY,aws_session_token=AWS_SESSION_TOKEN,region_name=region) # session = boto3.session.Session( # aws_access_key_id=AWS_ACCESS_KEY_ID, # aws_secret_access_key=AWS_SECRET_ACCESS_KEY, # aws_session_token=AWS_SESSION_TOKEN # ) # awsclient = session.resource('ecr') # s3 = session.resource('s3') self.log.info('aws environment variable setup done... \n') try: os.chdir(mlflow_root_dir) except FileNotFoundError: self.log.info('Directory does not exist. '+str(mlflow_root_dir)) except NotADirectoryError: self.log.info('model_path is not a directory. '+str(mlflow_root_dir)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(mlflow_root_dir)) mlflow_container_name=str(self.mlflow_container_name) mlflow_version=mlflow.__version__ tag_id=mlflow_version if (self.mlflowtosagemakerPushOnly.lower() == "true"): self.log.info('Selected option is <Deploy existing model to sagemaker> \n') aws_id=str(self.aws_id) arn=str(self.iam_sagemakerfullaccess_arn) mlflow_image=mlflow_container_name+':'+tag_id image_url = aws_id+'.dkr.ecr.'+region+'.amazonaws.com/'+mlflow_image # print("image_url:========= \n",image_url) deploy_status=True try: model_path=mlflowtosagemakerdeployModeluri # ##We need to run mlflow docker container command in the artifacts->model directory inside mlruns. self.log.info('Deploy existing model container-Model path given by user: '+str(model_path)) try: os.chdir(model_path) except FileNotFoundError: self.log.info('Directory does not exist. '+str(model_path)) except NotADirectoryError: self.log.info('model_path is not a directory. '+str(model_path)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(model_path)) try: mfs.push_image_to_ecr(image=mlflowtosagemakerPushImageName) deploy_status=True self.log.info('AION mlops pushed the docker container to aws ecr. \n ') except: self.log.info("error in pushing existing container to ecr.\n") deploy_status=False time.sleep(2) #Now,change the working dir to root dir,because now deploy needs full mlruns to model name dir. try: # print(" Changing directory to mlflow root dir....\n") os.chdir(mlflow_root_dir) except FileNotFoundError: self.log.info('model path is not a directory. '+str(mlflow_root_dir)) except NotADirectoryError: self.log.info('model path is not a directory. '+str(mlflow_root_dir)) # print("{0} is not a directory".format(mlflow_root_dir)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(mlflow_root_dir)) # self.deployModel2sagemaker(mlflowtosagemakerPushImageName,tag_id,mlflowtosagemakerdeployModeluri) try: if (deploy_status): self.deployModel2sagemaker(mlflowtosagemakerPushImageName,tag_id,mlflowtosagemakerdeployModeluri) self.log.info('AION creates docker container and push the container into aws ecr.. ') time.sleep(2) except: self.log.info('AION deploy error.check connection and aws config parameters. ') deploy_status=False # self.log.info('model deployed in sagemaker. ') except Exception as e: self.log.info('AION mlops failed to push docker container in aws ecr, check configuration parameters. \n'+str(e)) elif (self.mlflowtosagemakerPushOnly.lower() == "false"): if (self.mlflowtosagemakerDeploy.lower() == "true"): self.log.info('Selected option is <Create and Deploy model> \n') deploy_status=True try: # ##We need to run mlflow docker container command in the artifacts->model directory inside mlruns. try: os.chdir(model_path) except FileNotFoundError: self.log.info('Directory does not exist. '+str(model_path)) except NotADirectoryError: self.log.info('model_path is not a directory. '+str(model_path)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(model_path)) try: mlflow_container_push=subprocess.run(['mlflow', 'sagemaker', 'build-and-push-container','--build','--push','--container',mlflow_container_name]) self.log.info('AION mlops creates docker container and push the container into aws ecr.. ') deploy_status=True time.sleep(2) except: self.log.info('error in pushing aion model container to sagemaker, please check the connection between local host to aws server.') deploy_status=False self.log.info('Now deploying the model container to sagemaker starts....\n ') # Once docker push completes, again going back to mlflow parent dir for deployment #Now,change the working dir to root dir,because now deploy needs full mlruns to model name dir. try: os.chdir(mlflow_root_dir) except FileNotFoundError: self.log.info('model_path does not exist. '+str(mlflow_root_dir)) except NotADirectoryError: self.log.info('model_path is not a directory. '+str(mlflow_root_dir)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(mlflow_root_dir)) # app_name = str(self.sm_app_name) try: if (deploy_status): self.deployModel2sagemaker(mlflow_container_name,tag_id,model_path) except: self.log.info('mlops deploy error.check connection') deploy_status=False except Exception as e: exc = {"status":"FAIL","message":str(e).strip('"')} out_exc = json.dumps(exc) self.log.info('mlflow failed to creates docker container please check the aws iam,ecr permission setup, aws id access_key,secret key values for aion.\n') elif(self.mlflowtosagemakerDeploy.lower() == "false"): deploy_status=False localhost_deploy=True self.log.info('Selected option is <Create AION mlops container in local host .> \n') self.log.info("User selected create-Deploy sagemaker option as False,") self.log.info("Creates the AION mlops-sagemaker container locally starting,but doesn't push into aws ecr and deploy in sagemaker. Check the container in docker repository. ") try: # ##We need to run AION mlops docker container command in the artifacts->model directory inside mlruns. try: os.chdir(model_path) self.log.info('After change to AION mlops model dir, cwd: '+str(model_path)) except FileNotFoundError: self.log.info('Directory does not exist. '+str(model_path)) except NotADirectoryError: self.log.info('model_path is not a directory. '+str(model_path)) except PermissionError: self.log.info('Issue in permissions to change to model dir. '+str(model_path)) # mlflow_container_local=subprocess.run(['AION mlops', 'sagemaker', 'build-and-push-container','--build','--no-push','--container',mlflow_container_name]) try: if not (deploy_status): mlflow_container_local=subprocess.run(['mlflow', 'sagemaker', 'build-and-push-container','--build','--no-push','--container',mlflow_container_name]) self.log.info('AION creates local host bsed docker container and push the container local docker repository. Check with <docker images> command.\n ') localhost_deploy=True time.sleep(2) except: self.log.info('error in pushing aion model container to sagemaker, please check the connection between local host to aws server.') deploy_status=False localhost_deploy=False # print("AION mlops creates docker container and push the container into aws ecr.\n") self.log.info('AION mlops creates docker container and stored locally... ') time.sleep(2) except Exception as e: localhost_deploy=False # print("mlflow failed to creates docker container please check the aws iam,ecr permission setup, aws id access_key,secret key values for aion.\n") self.log.info('AION mlops failed to creates docker container in local machine.\n'+str(e)) else: self.log.info('Deploy option not selected, Please check. ') localhost_deploy=False deploy_status=False else: pass localhost_container_status="Notdeployed" mlflow2sm_deploy_status="Notdeployed" if localhost_deploy: localhost_container_status="success" mlflow2sm_deploy_status="Notdeployed" # print("AION creates local docker container successfully.Please check in docker repository.") self.log.info("AION creates local docker container successfully.Please check in docker repository.") # else: # localhost_container_status="failed" # # print("AION failed to create local docker container successfully.Please check in docker repository.") # self.log.info("AION failed to create local docker container successfully.Please check in docker repository.") if (deploy_status): # Finally checking whether mlops model is deployed to sagemaker or not. app_name = str(self.sm_app_name) deploy_s = self.check_sm_deploy_status(app_name) if (deploy_s == "InService"): # print("AION mlops model is deployed at aws sagemaker, use application name(app_name) and region to access.\n") self.log.info('AION mlops model is deployed at aws sagemaker, use application name(app_name) and region to access.\n'+str(app_name)) mlflow2sm_deploy_status="success" localhost_container_status="Notdeployed" else: # print("AION Mlflow model not able to deploy at aws sagemaker\n") self.log.info('AION mlops model not able to deploy at aws sagemaker.\n') mlflow2sm_deploy_status="failed" localhost_container_status="Notdeployed" # else: # mlflow2sm_deploy_status="None" return mlflow2sm_deploy_status,localhost_container_status except Exception as inst: exc = {"status":"FAIL","message":str(inst).strip('"')} out_exc = json.dumps(exc) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import joblib import pandas as pd import sys import math import time import pandas as pd import numpy as np from sklearn.metrics import confusion_matrix from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, confusion_matrix from sklearn.svm import SVC from sklearn.linear_model import LinearRegression import argparse import json def mltesting(modelfile,datafile,features,target): model = joblib.load(modelfile) ProblemName = model.__class__.__name__ if ProblemName in ['LogisticRegression','SGDClassifier','SVC','DecissionTreeClassifier','RandomForestClassifier','GaussianNB','KNeighborsClassifier','DecisionTreeClassifier','GradientBoostingClassifier','XGBClassifier','LGBMClassifier','CatBoostClassifier']: Problemtype = 'Classification' elif ProblemName in ['LinearRegression','Lasso','Ridge','DecisionTreeRegressor','RandomForestRegressor','GradientBoostingRegressor','XGBRegressor','LGBMRegressor','CatBoostRegressor']: Problemtype = 'Regression' else: Problemtype = 'Unknown' if Problemtype == 'Classification': Params = model.get_params() try: df = pd.read_csv(datafile,encoding='utf-8',skipinitialspace = True) if ProblemName == 'LogisticRegression' or ProblemName == 'DecisionTreeClassifier' or ProblemName == 'RandomForestClassifier' or ProblemName == 'GaussianNB' or ProblemName == 'KNeighborsClassifier' or ProblemName == 'GradientBoostingClassifier' or ProblemName == 'SVC': features = model.feature_names_in_ elif ProblemName == 'XGBClassifier': features = model.get_booster().feature_names elif ProblemName == 'LGBMClassifier': features = model.feature_name_ elif ProblemName == 'CatBoostClassifier': features = model.feature_names_ modelfeatures = features dfp = df[modelfeatures] tar = target target = df[tar] predic = model.predict(dfp) output = {} matrixconfusion = pd.DataFrame(confusion_matrix(predic,target)) matrixconfusion = matrixconfusion.to_json(orient='index') classificationreport = pd.DataFrame(classification_report(target,predic,output_dict=True)).transpose() classificationreport = round(classificationreport,2) classificationreport = classificationreport.to_json(orient='index') output["Precision"] = "%.2f" % precision_score(target, predic,average='weighted') output["Recall"] = "%.2f" % recall_score(target, predic,average='weighted') output["Accuracy"] = "%.2f" % accuracy_score(target, predic) output["ProblemName"] = ProblemName output["Status"] = "Success" output["Params"] = Params output["Problemtype"] = Problemtype output["Confusionmatrix"] = matrixconfusion output["classificationreport"] = classificationreport # import statistics # timearray = [] # for i in range(0,5): # start = time.time() # predic1 = model.predict(dfp.head(1)) # end = time.time() # timetaken = (round((end - start) * 1000,2),'Seconds') # timearray.append(timetaken) # print(timearray) start = time.time() for i in range(0,5): predic1 = model.predict(dfp.head(1)) end = time.time() timetaken = (round((end - start) * 1000,2),'Seconds') # print(timetaken) start1 = time.time() for i in range(0,5): predic2 = model.predict(dfp.head(10)) end1 = time.time() timetaken1 = (round((end1 - start1) * 1000,2) ,'Seconds') # print(timetaken1) start2 = time.time() for i in range(0,5): predic3 = model.predict(dfp.head(100)) end2 = time.time() timetaken2 = (round((end2 - start2) * 1000,2) ,'Seconds') # print(timetaken2) output["onerecord"] = timetaken output["tenrecords"] = timetaken1 output["hundrecords"] = timetaken2 print(json.dumps(output)) except Exception as e: output = {} output['Problemtype']='Classification' output['Status']= "Fail" output["ProblemName"] = ProblemName output["Msg"] = 'Detected Model : {} \\n Problem Type : Classification \\n Error : {}'.format(ProblemName, str(e).replace('"','//"').replace('\n', '\\n')) print(output["Msg"]) print(json.dumps(output)) elif Problemtype == 'Regression': Params = model.get_params() try: df = pd.read_csv(datafile,encoding='utf-8',skipinitialspace = True) if ProblemName == 'LinearRegression' or ProblemName == 'Lasso' or ProblemName == 'Ridge' or ProblemName == 'DecisionTreeRegressor' or ProblemName == 'RandomForestRegressor' or ProblemName == 'GaussianNB' or ProblemName == 'KNeighborsRegressor' or ProblemName == 'GradientBoostingRegressor': features = model.feature_names_in_ elif ProblemName == 'XGBRegressor': features = model.get_booster().feature_names elif ProblemName == 'LGBMRegressor': features = model.feature_name_ elif ProblemName == 'CatBoostRegressor': features = model.feature_names_ modelfeatures = features dfp = df[modelfeatures] tar = target target = df[tar] predict = model.predict(dfp) mse = mean_squared_error(target, predict) mae = mean_absolute_error(target, predict) rmse = math.sqrt(mse) r2 = r2_score(target,predict,multioutput='variance_weighted') output = {} output["MSE"] = "%.2f" % mean_squared_error(target, predict) output["MAE"] = "%.2f" % mean_absolute_error(target, predict) output["RMSE"] = "%.2f" % math.sqrt(mse) output["R2"] = "%.2f" %r2_score(target,predict,multioutput='variance_weighted') output["ProblemName"] = ProblemName output["Problemtype"] = Problemtype output["Params"] = Params output['Status']='Success' start = time.time() predic1 = model.predict(dfp.head(1)) end = time.time() timetaken = (round((end - start) * 1000,2) ,'Seconds') # print(timetaken) start1 = time.time() predic2 = model.predict(dfp.head(10)) end1 = time.time() timetaken1 = (round((end1 - start1) * 1000,2),'Seconds') # print(timetaken1) start2 = time.time() predic3 = model.predict(dfp.head(100)) end2 = time.time() timetaken2 = (round((end2 - start2) * 1000,2) ,'Seconds') # print(timetaken2) output["onerecord"] = timetaken output["tenrecords"] = timetaken1 output["hundrecords"] = timetaken2 print(json.dumps(output)) except Exception as e: output = {} output['Problemtype']='Regression' output['Status']='Fail' output["ProblemName"] = ProblemName output["Msg"] = 'Detected Model : {} \\n Problem Type : Regression \\n Error : {}'.format(ProblemName, str(e).replace('"','//"').replace('\n', '\\n')) print(json.dumps(output)) else: output = {} output['Problemtype']='Unknown' output['Status']='Fail' output['Params'] = '' output["ProblemName"] = ProblemName output["Msg"] = 'Detected Model : {} \\n Error : {}'.format(ProblemName, 'Model not supported') print(json.dumps(output)) return(json.dumps(output)) def baseline_testing(modelFile,csvFile,features,target): features = [x.strip() for x in features.split(',')] return mltesting(modelFile,csvFile,features,target) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import numpy as np import os import datetime, time, timeit from sklearn.model_selection import KFold from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score from sklearn.metrics import classification_report import pickle import logging class recommendersystem(): def __init__(self,features,svd_params): self.features = features self.svd_input = svd_params self.log = logging.getLogger('eion') print ("recommendersystem starts \n") #To extract dict key,values def extract_params(self,dict): self.dict=dict for k,v in self.dict.items(): return k,v def recommender_model(self,df,outputfile): from sklearn.metrics.pairwise import cosine_similarity from utils.file_ops import save_csv USER_ITEM_MATRIX = 'user_item_matrix' ITEM_SIMILARITY_MATRIX = 'item_similarity_matrix' selectedColumns = self.features.split(',') data = pd.DataFrame() for i in range(0,len(selectedColumns)): data[selectedColumns[i]] = df[selectedColumns[i]] dataset = data self.log.info('-------> Top(5) Rows') self.log.info(data.head(5)) start = time.time() self.log.info('\n----------- Recommender System Training Starts -----------') #--------------- Task 11190:recommender system changes Start ---Usnish------------------# # selectedColumns = ['userId', 'movieId', 'rating'] df_eda = df.groupby(selectedColumns[1]).agg(mean_rating=(selectedColumns[2], 'mean'),number_of_ratings=(selectedColumns[2], 'count')).reset_index() self.log.info('-------> Top 10 most rated Items:') self.log.info(df_eda.sort_values(by='number_of_ratings', ascending=False).head(10)) matrix = data.pivot_table(index=selectedColumns[1], columns=selectedColumns[0], values=selectedColumns[2]) relative_file = os.path.join(outputfile, 'data', USER_ITEM_MATRIX + '.csv') matrix.to_csv(relative_file) item_similarity_cosine = cosine_similarity(matrix.fillna(0)) item_similarity_cosine = pd.DataFrame(item_similarity_cosine,columns=pd.Series([i + 1 for i in range(item_similarity_cosine.shape[0])],name='ItemId'),index=pd.Series([i + 1 for i in range(item_similarity_cosine.shape[0])],name='ItemId')) self.log.info('---------> Item-Item Similarity matrix created:') self.log.info(item_similarity_cosine.head(5)) relative_file = os.path.join(outputfile, 'data', ITEM_SIMILARITY_MATRIX + '.csv') save_csv(item_similarity_cosine,relative_file) # --------------- recommender system changes End ---Usnish------------------# executionTime=time.time() - start self.log.info("------->Execution Time: "+str(executionTime)) self.log.info('----------- Recommender System Training End -----------\n') return "filename",matrix,"NA","","" ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import pickle import pandas as pd import sys import time import os from os.path import expanduser import platform from sklearn.preprocessing import binarize import logging import tensorflow as tf from sklearn.model_selection import train_test_split from tensorflow.keras import preprocessing from sklearn.metrics import roc_auc_score from sklearn.metrics import accuracy_score from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.layers import Input, Embedding, LSTM, Lambda import tensorflow.keras.backend as K from tensorflow.keras.models import Model from tensorflow.keras.optimizers import Adam from tensorflow.keras.layers import Concatenate from tensorflow.keras.layers import Input, Dense, Flatten, GlobalMaxPool2D, GlobalAvgPool2D, Concatenate, Multiply, Dropout, Subtract, Add, Conv2D from sklearn.metrics.pairwise import cosine_similarity, cosine_distances import tensorflow.keras.backend as K from tensorflow.keras.models import Model, Sequential from tensorflow.keras import layers, utils, callbacks, optimizers, regularizers ## Keras subclassing based siamese network class siameseNetwork(Model): def __init__(self, activation,inputShape, num_iterations): self.activation=activation self.log = logging.getLogger('eion') super(siameseNetwork, self).__init__() i1 = layers.Input(shape=inputShape) i2 = layers.Input(shape=inputShape) featureExtractor = self.build_feature_extractor(inputShape, num_iterations) f1 = featureExtractor(i1) f2 = featureExtractor(i2) #distance vect distance = layers.Concatenate()([f1, f2]) cosine_loss = tf.keras.losses.CosineSimilarity(axis=1) c_loss=cosine_loss(f1, f2) similarity = tf.keras.layers.Dot(axes=1,normalize=True)([f1,f2]) outputs = layers.Dense(1, activation="sigmoid")(distance) self.model = Model(inputs=[i1, i2], outputs=outputs) ##Build dense sequential layers def build_feature_extractor(self, inputShape, num_iterations): layers_config = [layers.Input(inputShape)] for i, n_units in enumerate(num_iterations): layers_config.append(layers.Dense(n_units)) layers_config.append(layers.Dropout(0.2)) layers_config.append(layers.BatchNormalization()) layers_config.append(layers.Activation(self.activation)) model = Sequential(layers_config, name='feature_extractor') return model def call(self, x): return self.model(x) def euclidean_distance(vectors): (f1, f2) = vectors sumSquared = K.sum(K.square(f1 - f2), axis=1, keepdims=True) return K.sqrt(K.maximum(sumSquared, K.epsilon())) def cosine_similarity(vectors): (f1, f2) = vectors f1 = K.l2_normalize(f1, axis=-1) f2 = K.l2_normalize(f2, axis=-1) return K.mean(f1 * f2, axis=-1, keepdims=True) def cos_dist_output_shape(shapes): shape1, shape2 = shapes return (shape1[0],1) class eion_similarity_siamese: def __init__(self): self.log = logging.getLogger('eion') def siamese_model(self,df,col1,col2,targetColumn,conf,pipe,deployLocation,iterName,iterVersion,testPercentage,predicted_data_file): try: self.log.info('-------> Read Embedded File') home = expanduser("~") if platform.system() == 'Windows': modelsPath = os.path.join(home,'AppData','Local','HCLT','AION','PreTrainedModels','TextSimilarity') else: modelsPath = os.path.join(home,'HCLT','AION','PreTrainedModels','TextSimilarity') if os.path.isdir(modelsPath) == False: os.makedirs(modelsPath) embedding_file_path = os.path.join(modelsPath,'glove.6B.100d.txt') if not os.path.exists(embedding_file_path): from pathlib import Path import urllib.request import zipfile location = modelsPath local_file_path = os.path.join(location,"glove.6B.zip") file_test, header_test = urllib.request.urlretrieve('http://nlp.stanford.edu/data/wordvecs/glove.6B.zip', local_file_path) with zipfile.ZipFile(local_file_path, 'r') as zip_ref: zip_ref.extractall(location) os.unlink(os.path.join(location,"glove.6B.zip")) if os.path.isfile(os.path.join(location,"glove.6B.50d.txt")): os.unlink(os.path.join(location,"glove.6B.50d.txt")) if os.path.isfile(os.path.join(location,"glove.6B.300d.txt")): os.unlink(os.path.join(location,"glove.6B.300d.txt")) if os.path.isfile(os.path.join(location,"glove.6B.200d.txt")): os.unlink(os.path.join(location,"glove.6B.200d.txt")) X = df[[col1,col2]] Y = df[targetColumn] testPercentage = testPercentage self.log.info('\n-------------- Test Train Split ----------------') if testPercentage == 0: xtrain=X ytrain=Y xtest=X ytest=Y else: testSize=testPercentage/100 self.log.info('-------> Split Type: Random Split') self.log.info('-------> Train Percentage: '+str(testSize)) X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=testSize) self.log.info('-------> Train Data Shape: '+str(X_train.shape)+' ---------->') self.log.info('-------> Test Data Shape: '+str(X_test.shape)+' ---------->') self.log.info('-------------- Test Train Split End ----------------\n') self.log.info('\n-------------- Train Validate Split ----------------') X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.20, random_state=42) self.log.info('-------> Train Data Shape: '+str(X_train.shape)+' ---------->') self.log.info('-------> Validate Data Shape: '+str(X_val.shape)+' ---------->') self.log.info('-------------- Train Validate Split End----------------\n') self.log.info('Status:- |... Train / test split done: '+str(100-testPercentage)+'% train,'+str(testPercentage)+'% test') train_sentence1 = pipe.texts_to_sequences(X_train[col1].values) train_sentence2 = pipe.texts_to_sequences(X_train[col2].values) val_sentence1 = pipe.texts_to_sequences(X_val[col1].values) val_sentence2 = pipe.texts_to_sequences(X_val[col2].values) len_vec = [len(sent_vec) for sent_vec in train_sentence1] max_len = np.max(len_vec) len_vec = [len(sent_vec) for sent_vec in train_sentence2] if (max_len < np.max(len_vec)): max_len = np.max(len_vec) train_sentence1 = pad_sequences(train_sentence1, maxlen=max_len, padding='post') train_sentence2 = pad_sequences(train_sentence2, maxlen=max_len, padding='post') val_sentence1 = pad_sequences(val_sentence1, maxlen=max_len, padding='post') val_sentence2 = pad_sequences(val_sentence2, maxlen=max_len, padding='post') y_train = y_train.values y_val = y_val.values activation = str(conf['activation']) model = siameseNetwork(activation,inputShape=train_sentence1.shape[1], num_iterations=[10]) model.compile( loss="binary_crossentropy", optimizer=optimizers.Adam(learning_rate=0.0001), metrics=["accuracy"]) es = callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=1, restore_best_weights=True) rlp = callbacks.ReduceLROnPlateau( monitor='val_loss', factor=0.1, patience=2, min_lr=1e-10, mode='min', verbose=1 ) x_valid=X_val y_valid=y_val n_epoch = int(conf['num_epochs']) batch_size = int(conf['batch_size']) similarityIndex = conf['similarityIndex'] model.fit([train_sentence1,train_sentence2],y_train.reshape(-1,1), epochs = n_epoch,batch_size=batch_size, validation_data=([val_sentence1, val_sentence2],y_val.reshape(-1,1)),callbacks=[es, rlp]) scores = model.evaluate([val_sentence1, val_sentence2], y_val.reshape(-1,1), verbose=0) self.log.info('-------> Model Score Matrix: Accuracy') self.log.info('-------> Model Score (Validate Data) : '+str(scores[1])) self.log.info('Status:- |... Algorithm applied: SIAMESE') test_sentence1 = pipe.texts_to_sequences(X_test[col1].values) test_sentence2 = pipe.texts_to_sequences(X_test[col2].values) test_sentence1 = pad_sequences(test_sentence1, maxlen=max_len, padding='post') test_sentence2 = pad_sequences(test_sentence2, maxlen=max_len, padding='post') prediction = model.predict([test_sentence1, test_sentence2 ]) n_epoch = conf['num_epochs'] batch_size = conf['batch_size'] activation = conf['activation'] similarityIndex = conf['similarityIndex'] self.log.info('-------> similarityIndex : '+str(similarityIndex)) prediction = np.where(prediction > similarityIndex,1,0) rocauc_sco = roc_auc_score(y_test,prediction) acc_sco = accuracy_score(y_test, prediction) predict_df = pd.DataFrame() predict_df['actual'] = y_test predict_df['predict'] = prediction predict_df.to_csv(predicted_data_file) self.log.info('-------> Model Score Matrix: Accuracy') self.log.info('-------> Model Score (Validate Data) : '+str(scores[1])) self.log.info('Status:- |... Algorithm applied: SIAMESE') test_sentence1 = pipe.texts_to_sequences(X_test[col1].values) test_sentence2 = pipe.texts_to_sequences(X_test[col2].values) test_sentence1 = pad_sequences(test_sentence1, maxlen=max_len, padding='post') test_sentence2 = pad_sequences(test_sentence2, maxlen=max_len, padding='post') prediction = model.predict([test_sentence1, test_sentence2 ]) prediction = np.where(prediction > similarityIndex,1,0) rocauc_sco = roc_auc_score(y_test,prediction) acc_sco = accuracy_score(y_test, prediction) predict_df = pd.DataFrame() predict_df['actual'] = y_test predict_df['predict'] = prediction predict_df.to_csv(predicted_data_file) self.log.info("predict_df: \n"+str(predict_df)) sco = acc_sco self.log.info('-------> Test Data Accuracy Score : '+str(acc_sco)) self.log.info('Status:- |... Testing Score: '+str(acc_sco)) self.log.info('-------> Test Data ROC AUC Score : '+str(rocauc_sco)) matrix = '"Accuracy":'+str(acc_sco)+',"ROC AUC":'+str(rocauc_sco) prediction = model.predict([train_sentence1, train_sentence2]) prediction = np.where(prediction > similarityIndex,1,0) train_rocauc_sco = roc_auc_score(y_train,prediction) train_acc_sco = accuracy_score(y_train, prediction) self.log.info('-------> Train Data Accuracy Score : '+str(train_acc_sco)) self.log.info('-------> Train Data ROC AUC Score : '+str(train_rocauc_sco)) trainmatrix = '"Accuracy":'+str(train_acc_sco)+',"ROC AUC":'+str(train_rocauc_sco) model_tried = '{"Model":"SIAMESE","Score":'+str(sco)+'}' saved_model = 'textsimilarity_'+iterName+'_'+iterVersion # filename = os.path.join(deployLocation,'model','textsimilarity_'+iterName+'_'+iterVersion+'.sav') # filename = os.path.join(deployLocation,'model','textsimilarity_'+iterName+'_'+iterVersion+'.h5') ## Because we are using subclassing layer api, please use dir (as below) to store deep learn model instead of .h5 model. filename = os.path.join(deployLocation,'model','textsimilarity_'+iterName+'_'+iterVersion) model.save(filename) # model.save_weights(filename) model_name = 'SIAMESE MODEL' return(model_name,scores[1],matrix,trainmatrix,model_tried,saved_model,filename,max_len,similarityIndex) except Exception as inst: self.log.info("SIAMESE failed " + str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) import itertools import logging from typing import Optional, Dict, Union from nltk import sent_tokenize import torch from transformers import( AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel, PreTrainedTokenizer, ) logger = logging.getLogger(__name__) class QGPipeline: """Poor man's QG pipeline""" def __init__( self, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, ans_model: PreTrainedModel, ans_tokenizer: PreTrainedTokenizer, qg_format: str, use_cuda: bool ): self.model = model self.tokenizer = tokenizer self.ans_model = ans_model self.ans_tokenizer = ans_tokenizer self.qg_format = qg_format self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu" self.model.to(self.device) if self.ans_model is not self.model: self.ans_model.to(self.device) assert self.model.__class__.__name__ in ["T5ForConditionalGeneration", "BartForConditionalGeneration"] if "T5ForConditionalGeneration" in self.model.__class__.__name__: self.model_type = "t5" else: self.model_type = "bart" def __call__(self, inputs: str): inputs = " ".join(inputs.split()) sents, answers = self._extract_answers(inputs) flat_answers = list(itertools.chain(*answers)) if len(flat_answers) == 0: return [] if self.qg_format == "prepend": qg_examples = self._prepare_inputs_for_qg_from_answers_prepend(inputs, answers) else: qg_examples = self._prepare_inputs_for_qg_from_answers_hl(sents, answers) qg_inputs = [example['source_text'] for example in qg_examples] questions = self._generate_questions(qg_inputs) output = [{'answer': example['answer'], 'question': que} for example, que in zip(qg_examples, questions)] return output def _generate_questions(self, inputs): inputs = self._tokenize(inputs, padding=True, truncation=True) outs = self.model.generate( input_ids=inputs['input_ids'].to(self.device), attention_mask=inputs['attention_mask'].to(self.device), max_length=32, num_beams=4, ) questions = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in outs] return questions def _extract_answers(self, context): sents, inputs = self._prepare_inputs_for_ans_extraction(context) inputs = self._tokenize(inputs, padding=True, truncation=True) outs = self.ans_model.generate( input_ids=inputs['input_ids'].to(self.device), attention_mask=inputs['attention_mask'].to(self.device), max_length=32, ) dec = [self.ans_tokenizer.decode(ids, skip_special_tokens=False) for ids in outs] answers = [item.split('<sep>') for item in dec] answers = [i[:-1] for i in answers] return sents, answers def _tokenize(self, inputs, padding=True, truncation=True, add_special_tokens=True, max_length=512 ): inputs = self.tokenizer.batch_encode_plus( inputs, max_length=max_length, add_special_tokens=add_special_tokens, truncation=truncation, padding="max_length" if padding else False, pad_to_max_length=padding, return_tensors="pt" ) return inputs def _prepare_inputs_for_ans_extraction(self, text): sents = sent_tokenize(text) inputs = [] for i in range(len(sents)): source_text = "extract answers:" for j, sent in enumerate(sents): if i == j: sent = "<hl> %s <hl>" % sent source_text = "%s %s" % (source_text, sent) source_text = source_text.strip() if self.model_type == "t5": source_text = source_text + " </s>" inputs.append(source_text) return sents, inputs def _prepare_inputs_for_qg_from_answers_hl(self, sents, answers): inputs = [] for i, answer in enumerate(answers): if len(answer) == 0: continue for answer_text in answer: sent = sents[i] sents_copy = sents[:] answer_text = answer_text.strip() ans_start_idx = 0 # ans_start_idx = sent.index(answer_text) # if answer_text in sent: # ans_start_idx = sent.index(answer_text) # else: # continue sent = f"{sent[:ans_start_idx]} <hl> {answer_text} <hl> {sent[ans_start_idx + len(answer_text): ]}" sents_copy[i] = sent source_text = " ".join(sents_copy) source_text = f"generate question: {source_text}" if self.model_type == "t5": source_text = source_text + " </s>" inputs.append({"answer": answer_text, "source_text": source_text}) return inputs def _prepare_inputs_for_qg_from_answers_prepend(self, context, answers): flat_answers = list(itertools.chain(*answers)) examples = [] for answer in flat_answers: source_text = f"answer: {answer} context: {context}" if self.model_type == "t5": source_text = source_text + " </s>" examples.append({"answer": answer, "source_text": source_text}) return examples class MultiTaskQAQGPipeline(QGPipeline): def __init__(self, **kwargs): super().__init__(**kwargs) def __call__(self, inputs: Union[Dict, str]): if type(inputs) is str: # do qg return super().__call__(inputs) else: # do qa return self._extract_answer(inputs["question"], inputs["context"]) def _prepare_inputs_for_qa(self, question, context): source_text = f"question: {question} context: {context}" if self.model_type == "t5": source_text = source_text + " </s>" return source_text def _extract_answer(self, question, context): source_text = self._prepare_inputs_for_qa(question, context) inputs = self._tokenize([source_text], padding=False) outs = self.model.generate( input_ids=inputs['input_ids'].to(self.device), attention_mask=inputs['attention_mask'].to(self.device), max_length=16, ) answer = self.tokenizer.decode(outs[0], skip_special_tokens=True) return answer class E2EQGPipeline: def __init__( self, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, use_cuda: bool ) : self.model = model self.tokenizer = tokenizer self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu" self.model.to(self.device) assert self.model.__class__.__name__ in ["T5ForConditionalGeneration", "BartForConditionalGeneration"] if "T5ForConditionalGeneration" in self.model.__class__.__name__: self.model_type = "t5" else: self.model_type = "bart" self.default_generate_kwargs = { "max_length": 256, "num_beams": 4, "length_penalty": 1.5, "no_repeat_ngram_size": 3, "early_stopping": True, } def __call__(self, context: str, **generate_kwargs): inputs = self._prepare_inputs_for_e2e_qg(context) # TODO: when overrding default_generate_kwargs all other arguments need to be passsed # find a better way to do this if not generate_kwargs: generate_kwargs = self.default_generate_kwargs input_length = inputs["input_ids"].shape[-1] # max_length = generate_kwargs.get("max_length", 256) # if input_length < max_length: # logger.warning( # "Your max_length is set to {}, but you input_length is only {}. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)".format( # max_length, input_length # ) # ) outs = self.model.generate( input_ids=inputs['input_ids'].to(self.device), attention_mask=inputs['attention_mask'].to(self.device), **generate_kwargs ) prediction = self.tokenizer.decode(outs[0], skip_special_tokens=True) questions = prediction.split("<sep>") questions = [question.strip() for question in questions[:-1]] return questions def _prepare_inputs_for_e2e_qg(self, context): source_text = f"generate questions: {context}" if self.model_type == "t5": source_text = source_text + " </s>" inputs = self._tokenize([source_text], padding=False) return inputs def _tokenize( self, inputs, padding=True, truncation=True, add_special_tokens=True, max_length=512 ): inputs = self.tokenizer.batch_encode_plus( inputs, max_length=max_length, add_special_tokens=add_special_tokens, truncation=truncation, padding="max_length" if padding else False, pad_to_max_length=padding, return_tensors="pt" ) return inputs SUPPORTED_TASKS = { "question-generation": { "impl": QGPipeline, "default": { "model": "valhalla/t5-small-qg-hl", "ans_model": "valhalla/t5-small-qa-qg-hl", } }, "multitask-qa-qg": { "impl": MultiTaskQAQGPipeline, "default": { "model": "valhalla/t5-small-qa-qg-hl", } }, "e2e-qg": { "impl": E2EQGPipeline, "default": { "model": "valhalla/t5-small-e2e-qg", } } } def pipeline( task: str, model: Optional = None, tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None, qg_format: Optional[str] = "highlight", ans_model: Optional = None, ans_tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None, use_cuda: Optional[bool] = True, **kwargs, ): # Retrieve the task if task not in SUPPORTED_TASKS: raise KeyError("Unknown task {}, available tasks are {}".format(task, list(SUPPORTED_TASKS.keys()))) targeted_task = SUPPORTED_TASKS[task] task_class = targeted_task["impl"] # Use default model/config/tokenizer for the task if no model is provided if model is None: model = targeted_task["default"]["model"] # Try to infer tokenizer from model or config name (if provided as str) if tokenizer is None: if isinstance(model, str): tokenizer = model else: # Impossible to guest what is the right tokenizer here raise Exception( "Impossible to guess which tokenizer to use. " "Please provided a PretrainedTokenizer class or a path/identifier to a pretrained tokenizer." ) # Instantiate tokenizer if needed if isinstance(tokenizer, (str, tuple)): if isinstance(tokenizer, tuple): # For tuple we have (tokenizer name, {kwargs}) tokenizer = AutoTokenizer.from_pretrained(tokenizer[0], **tokenizer[1]) else: tokenizer = AutoTokenizer.from_pretrained(tokenizer) # Instantiate model if needed if isinstance(model, str): model = AutoModelForSeq2SeqLM.from_pretrained(model) if task == "question-generation": if ans_model is None: # load default ans model ans_model = targeted_task["default"]["ans_model"] ans_tokenizer = AutoTokenizer.from_pretrained(ans_model) ans_model = AutoModelForSeq2SeqLM.from_pretrained(ans_model) else: # Try to infer tokenizer from model or config name (if provided as str) if ans_tokenizer is None: if isinstance(ans_model, str): ans_tokenizer = ans_model else: # Impossible to guest what is the right tokenizer here raise Exception( "Impossible to guess which tokenizer to use. " "Please provided a PretrainedTokenizer class or a path/identifier to a pretrained tokenizer." ) # Instantiate tokenizer if needed if isinstance(ans_tokenizer, (str, tuple)): if isinstance(ans_tokenizer, tuple): # For tuple we have (tokenizer name, {kwargs}) ans_tokenizer = AutoTokenizer.from_pretrained(ans_tokenizer[0], **ans_tokenizer[1]) else: ans_tokenizer = AutoTokenizer.from_pretrained(ans_tokenizer) if isinstance(ans_model, str): ans_model = AutoModelForSeq2SeqLM.from_pretrained(ans_model) if task == "e2e-qg": return task_class(model=model, tokenizer=tokenizer, use_cuda=use_cuda) elif task == "question-generation": return task_class(model=model, tokenizer=tokenizer, ans_model=ans_model, ans_tokenizer=ans_tokenizer, qg_format=qg_format, use_cuda=use_cuda) else: return task_class(model=model, tokenizer=tokenizer, ans_model=model, ans_tokenizer=tokenizer, qg_format=qg_format, use_cuda=use_cuda) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import logging logging.getLogger('tensorflow').disabled = True import json #from nltk.corpus import stopwords from collections import Counter from numpy import mean from numpy import std from pandas import read_csv from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder from sklearn.compose import ColumnTransformer from learner.machinelearning import machinelearning # from sklearn.dummy import DummyClassifier # create histograms of numeric input variables import sys import os import re import pandas as pd import numpy as np from learner.aion_matrix import aion_matrix import tensorflow as tf tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) import autokeras as ak # load the sonar dataset from sklearn.model_selection import train_test_split # from sklearn.metrics import cohen_kappa_score # from sklearn.metrics import roc_auc_score # from sklearn.metrics import confusion_matrix from sklearn.metrics import roc_curve from math import sqrt from sklearn.metrics import mean_squared_error, explained_variance_score,mean_absolute_error from sklearn import metrics class aionNAS: def __init__(self,nas_class,nas_params,xtrain1,xtest1,ytrain1,ytest1,deployLocation): try: self.dfFeatures=None self.nas_class=nas_class self.nas_params=nas_params self.targetFeature=None self.log = logging.getLogger('eion') self.n_models=int(self.nas_params['n_models']) self.n_epochs=int(self.nas_params['n_epochs']) self.optimizer=self.nas_params['optimizer'] self.metrics=self.nas_params['metrics'] self.tuner=self.nas_params['tuner'] self.seed=int(self.nas_params['seed']) self.xtrain = xtrain1 self.xtest = xtest1 self.ytrain = ytrain1 self.ytest = ytest1 #self.labelMaps = labelMaps self.deployLocation=deployLocation except Exception as e: self.log.info('<!------------- NAS INIT Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def paramCheck(self): try: if not (self.nas_class): self.log.info('<!------------- NAS class input Error ---------------> ') if not (self.nas_params): self.log.info('<!------------- NAS model hyperparameter input Error ---------------> ') if not (self.targetFeature): self.log.info('<!------------- NAS model targetFeature input Error ---------------> ') if (self.n_models < 1): self.n_models=1 if not (self.dfFeatures): self.log.info('<!------------- NAS model features Error ---------------> ') if (self.n_epochs < 1): self.n_models=1 if not (self.optimizer): self.optimizer="adam" if not (self.tuner): self.tuner="greedy" if (self.seed < 1): self.seed=0 if not (self.metrics): self.metrics=None except ValueError: self.log.info('<------------------ NAS config file error. --------------->') def recall_m(self,y_true, y_pred): true_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_true * y_pred, 0, 1))) possible_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + tf.keras.backend.epsilon()) return recall def precision_m(self,y_true, y_pred): true_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_true * y_pred, 0, 1))) predicted_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + tf.keras.backend.epsilon()) return precision def f1_score(self,y_true, y_pred): precision = self.precision_m(y_true, y_pred) recall = self.recall_m(y_true, y_pred) return 2*((precision*recall)/(precision+recall+tf.keras.backend.epsilon())) def nasStructdataPreprocess(self): df=self.data self.paramCheck() target=df[self.targetFeature].values counter = Counter(target) for k,v in counter.items(): per = v / len(target) * 100 self.log.info('autokeras struct Class=%d, Count=%d, Percentage=%.3f%%' % (k, v, per)) # select columns with numerical data types num_ix = df.select_dtypes(include=['int64', 'float64']).columns subset = df[num_ix] last_ix = len(df.columns) - 1 y=df[self.targetFeature] X = df.drop(self.targetFeature, axis=1) #Using Pearson Correlation # plt.figure(figsize=(12,10)) # cor = df.corr() # sns.heatmap(cor, annot=True, cmap=plt.cm.Reds) # plt.show() # select categorical features cat_ix = X.select_dtypes(include=['object', 'bool']).columns # one hot encode cat features only ct = ColumnTransformer([('o',OneHotEncoder(),cat_ix)], remainder='passthrough') X = X.reset_index() X=X.replace(to_replace="NULL",value=0) X = X.dropna(how='any',axis=0) X = ct.fit_transform(X) from sklearn.preprocessing import scale X = scale(X) # label encode the target variable to have the classes 0 and 1 y = LabelEncoder().fit_transform(y) # separate into train and test sets X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=self.test_size,random_state=1) return X_train, X_test, y_train, y_test def nasStructClassification(self,scoreParam): try: objClf = aion_matrix() X_train, X_test, y_train, y_test= self.xtrain, self.xtest, self.ytrain, self.ytest modelName="nas_structdata_classifier" self.log.info("Processing structured data block...\n") s_in = ak.StructuredDataInput() #s_in = Flatten()(s_in) s_out = ak.StructuredDataBlock(categorical_encoding=True)(s_in) self.log.info("Data pipe via autokeras Classification Dense layers ...\n") s_out = ak.ClassificationHead()(s_out) self.log.info("applying autokeras automodel to run different neural models...\n") try: tuner = str(self.tuner).lower() except UnicodeEncodeError: tuner = (self.tuner.encode('utf8')).lower() nasclf = ak.AutoModel( inputs=s_in, outputs=s_out, overwrite=True, tuner=tuner, max_trials=self.n_models, seed=self.seed) # compile the model #nasclf.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc',self.f1_score,self.precision_m, self.recall_m]) nasclf.fit(X_train, y_train, epochs=self.n_epochs) best_model = nasclf.export_model() mpredict=best_model.predict(X_test) mtpredict=best_model.predict(X_train) #loss, accuracy, f1_score, precision, recall = nasclf.evaluate(X_test, y_test, verbose=0) #from sklearn.metrics import classification_report #Classification report y_pred_bool = np.argmax(mpredict, axis=1) y_train_pred_bool = np.argmax(mtpredict, axis=1) score = objClf.get_score(scoreParam,y_test, y_pred_bool) #best_model = nasclf.export_model() best_model_summary=best_model.summary() filename = os.path.join(self.deployLocation,'log','summary.txt') with open(filename,'w') as f: best_model.summary(print_fn=lambda x: f.write(x + '\n')) f.close() #self.log.info("==========") #self.log.info(best_model_summary) self.log.info("NAS struct data classification, best model summary: \n"+str(best_model.summary(print_fn=self.log.info))) #self.log.info("==========") #Save and load model # # #try: # try: # best_model.save("model_class_autokeras", save_format="tf") # except Exception: # best_model.save("model_class_autokeras.h5") # loaded_model = load_model("model_class_autokeras", custom_objects=ak.CUSTOM_OBJECTS) # loadedmodel_predict=loaded_model.predict(X_test) loss,accuracy_m=nasclf.evaluate(X_test, y_test) #mpredict_classes = mpredict.argmax(axis=-1) #accuracy = accuracy_score(y_test.astype(int), mpredict.astype(int)) # precision tp / (tp + fp) #precision = precision_score(y_test.astype(int), mpredict.astype(int),average='macro') # recall: tp / (tp + fn) #recall = recall_score(y_test.astype(int), mpredict.astype(int),average='macro') #f1score=f1_score(y_test.astype(int), mpredict.astype(int) , average="macro") self.log.info("Autokeras struct data classification metrics: \n") except Exception as inst: self.log.info("Error: NAS failed "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) print(inst) return modelName,nasclf,score def nasStructRegressor(self,scoreParam): objClf = aion_matrix() modelName="nas_struct_regressor" #self.paramCheck() X_train, X_test, y_train, y_test= self.xtrain, self.xtest, self.ytrain, self.ytest # Autokeras alg s_in = ak.StructuredDataInput() #tf.keras.layers.GlobalMaxPooling2D()(s_in) s_out = ak.StructuredDataBlock(categorical_encoding=True)(s_in) self.log.info("Data pipe via autokeras Regression Dense layers ...\n") s_out = ak.RegressionHead(loss='mse', metrics=['mae'])(s_out) self.log.info("applying autokeras automodel to evaluate different neural models...\n") try: tuner = str(self.tuner).lower() except UnicodeEncodeError: tuner = (self.tuner.encode('utf8')).lower() nas_reg = ak.AutoModel( inputs=s_in, outputs=s_out, overwrite=True, tuner=tuner, max_trials=self.n_models) nas_reg.fit(X_train, y_train, epochs=self.n_epochs) best_model = nas_reg.export_model() self.log.info("NAS struct data regression best model summary: \n") best_model_summary=best_model.summary(print_fn=self.log.info) self.log.info(best_model_summary) predictm=best_model.predict(X_test) mtpredict=best_model.predict(X_train) score = objClf.get_score(scoreParam,y_test, predictm) self.log.info("Autokeras struct data regression metrics: \n") return modelName,nas_reg,score def nasMain(self,scoreParam): modelName = "" nasclf=None nas_reg=None #text_reg_model=None mse_value=0 reg_rmse=0 mape_reg=0 huber_loss_reg=0 accuracy=0 precision=0 recall=0 #Dummy values to return main for classification problems dummy_score_1=int(0) #dummy_score_2=int(0) try: if ((self.nas_class.lower() == "classification")): modelName,nasclf,score=self.nasStructClassification(scoreParam) self.log.info('NAS Struct Classification score: '+str(score)) best_model_nas = nasclf.export_model() scoredetails = '{"Model":"NAS","Score":'+str(round(score,2))+'}' return best_model_nas,self.nas_params,round(score,2),'NAS',-1,-1,-1 elif (self.nas_class.lower() == "regression"): modelName,nas_reg,score =self.nasStructRegressor(scoreParam) self.log.info('NAS Struct Regression score: '+str(score)) best_model_nas = nas_reg.export_model() ''' filename = os.path.join(self.deployLocation,'model','autoKerasModel') best_model_nas = nas_reg.export_model() try: best_model_nas.save(filename, save_format="tf") modelName = 'autoKerasModel' except Exception: filename = os.path.join(self.deployLocation,'model','autoKerasModel.h5') best_model_nas.save(filename) modelName = 'autoKerasModel.h5' ''' scoredetails = '{"Model":"NAS","Score":'+str(round(score,2))+'}' ''' error_matrix = '"MSE":"'+str(round(mse_value,2))+'","RMSE":"'+str(round(reg_rmse,2))+'","MAPE":"'+str(round(mape_reg,2))+'","MSLE":"'+str(round(msle_reg,2))+'"' ''' return best_model_nas,self.nas_params,score,'NAS' else: pass except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) output = {"status":"FAIL","message":str(inst).strip('"')} output = json.dumps(output) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import socket import os import rsa from os.path import expanduser from pathlib import Path import requests import platform from appbe.dataPath import DATA_DIR import socket import getmac import subprocess import sys import json from datetime import datetime import binascii computername = socket.getfqdn() global_key = ''' -----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAzJcxqRiUpp7CzViyqNlYaeyceDh5y6Ib4SoxoyNkN3+k0q+cr1lb k0KdWTtHIVqH1wsLYofYjpB7X2RN0KYTv8VfwmfQNrpFEbiRz4gcAeuxGCPgGaue N1ttujQMWHWCcY+UH5Voh8YUfkW8P+T3zxvr1d30D+kVBJC59y/31JvTzr3Bw/T+ NYv6xiienYiEYtm9d5ATioEwZOXaQBrtVvRmqcod5A1h4kn1ZauLX2Ph8H4TAuit NLtw6xUCJNumphP7xdU+ca6P6a6eaLprgKhvky+nz16u9/AC2AazRQHKWf8orS6b fw16JDCRs0zU4mTQLCjkUUt0edOaRhUtcQIDAQAB -----END RSA PUBLIC KEY----- ''' quarter_key = ''' -----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAmKzOJxVEV9ulA+cjfxguAduLMD47OWjLcEAEmEuK8vR4O5f6e2h1 08NniGC+nkwqmM00U7JTVBkqnt9S/JgE3pAH2xwfWda2OvXNWisWmOQdqB0+XRHh NXsIG3yRk/sMlDpe7MJIyM5ADSu01PLn9FZTfmMq7lEp32tAf71cuUE/dwuWSvEQ WK2hn1L4D97O43XCd7FHtMSHfgtjdcCFgX9IRgWLKC8Bm3q5qcqF4v3cHuYTj3V9 njxPtRqPg6HJFiJrm9AX5bUEHAvbTcw4wAmsNTRQHPvVB+Lc+yGh5x8crhKjNB01 gdB5I3a4mPO7dKvadR6Mr28trr0Ff5t2HQIDAQAB -----END RSA PUBLIC KEY----- ''' halfYear_key=''' -----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAgrGNwl8CNYQmVxi8/GEgPjfL5aEmyPkDyaJb9h4hZDSZCeeKd7Rv wwhuRTdBBfOp0bQ7QS7NYMg38Xlc3x85I9RnxdQdDKn2nRuvG0hG3wMBFy/DCSXF tXbDjJkLijAhqcBNu8m+a2Gtn14ShC7TbcfY4iVXho3WFUrn0xq6S5ducqWCsLJh R+TNImCaMICqfoAzEDGC3ojO5Hi3vJmmyK5CVp6bt4wLRATQjcp1ujGW4Uv4kEgp 7TR077c226v1KOdKdyZPHJzT1MKwZrG2Gdluk3/Y1apbwyGzYqFdTCOAB+mE73Dn wFXURgDJQmaU2oxxaA13WRcELpnirm+aIwIDAQAB -----END RSA PUBLIC KEY----- ''' oneYear_key=''' -----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEA3GLqn+vkKn3fTNH3Bbb3Lq60pCoe+mn0KPz74Bp7p5OkZAUe14pP Tcf/UqdPwiENhSCseWtfZmfKDK8qYRHJ5xW02+AhHPPdiacS45X504/lGG3q/4SG ZgaFhMDvX+IH/ZH+qqbU3dRQhXJCCrAVAa7MonzM6yPiVeS2SdpMkNg1VDR1oTLB Pn+qSV6CnkK1cYtWCRQ23GH2Ru7fc09r7m8hVcifKJze84orpHC5FX0WScQuR8h/ fs1IbGkxTOxP8vplUj/cd4JjUxgd+w+8R4kcoPhdGZF5UGeZA8xMERzQLvh+4Ui0 KIvz5/iyKB/ozaeSG0OMwDAk3WDEnb1WqQIDAQAB -----END RSA PUBLIC KEY----- ''' full_key=''' -----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAhqfNMuYYLdVrePhkO9rU/qT6FgolzI0YyzIJ2OeJE+++JioYm6nn ohQU32iiE0DZlCCLrHJXOOIAz2Op80goX0lxtngyxVUPsiB5CI77sAC7x6K3anJ0 elpnQCC0+xV2ZL5eIMNQHLe+X6wJl/HGWqkUlxKpWr4/kBEB4EisW60OePfhntIN 4OUJ7iEq+sDdOM5WazJIXeNV1cig4i6057GE3k5ITcQUmw17DZu2+dqkIscckaG+ t5SF7Qnvt4IY8IeQp2htx3yD+CJCV0u2uKwoSFMGJn3OWdaixC3+eojyMXmfAWtQ Ee9NLNNaTCMIvQ8BeItJLQs2Htw3bZNMvwIDAQAB -----END RSA PUBLIC KEY----- ''' def validate_key_Pair(privatepath,publickey): with open(privatepath, 'rb') as privatefile: keydata = privatefile.read() privatefile.close() try: privkey = rsa.PrivateKey.load_pkcs1(keydata,'PEM') data = 'Validate Global License' signature = rsa.sign(data.encode('utf-8'), privkey, 'SHA-1') pubkey = rsa.PublicKey.load_pkcs1(publickey) except: return False try: rsa.verify(data.encode('utf-8'), signature, pubkey) return True except Exception as e: return False def updateDRecord(licensepath): domain_license_path = os.path.join(DATA_DIR,'License','license_domain.lic') if(os.path.isfile(licensepath)): with open(licensepath, 'rb') as f: licensekey = f.read() f.close() with open(domain_license_path, 'wb') as f: f.write(licensekey) f.close() if(validate_key_Pair(domain_license_path,global_key)): return True,'Valid Domain License' else: return False,'Invalid Domain License' else: return False,'File Not Exists' def generateLicenseKey(userKey): record = {'UserKey':userKey} record = json.dumps(record) status = 'Error' url = 'https://qw7e33htlk.execute-api.ap-south-1.amazonaws.com/default/aion_license' try: response = requests.post(url, data=record,headers={"x-api-key":"3cQKRkKA4S57pYrkFp1Dd9jRXt4xnFoB9iqhAQRM","Content-Type":"application/json",}) if response.status_code == 200: outputStr=response.content outputStr = outputStr.decode('utf-8','ignore') outputStr = outputStr.strip() license_dict = json.loads(str(outputStr)) if license_dict['status'] == 'success': status = 'Success' licenseKey = license_dict['msg'] else: status = 'Error' licenseKey = '' else: status = 'Error' licenseKey = '' except Exception as inst: print(inst) status = 'Error' licenseKey = '' msg = {'status':status,'key':userKey,'licenseKey':licenseKey,'link':''} return msg def updateRecord(licensepath): currentDirectory = os.path.dirname(os.path.abspath(__file__)) license_path = os.path.join(currentDirectory,'..','lic','license.lic') if(os.path.isfile(licensepath)): with open(licensepath, 'rb') as f: licensekey = f.read() f.close() with open(license_path, 'wb') as f: f.write(licensekey) f.close() status,msg = check_domain_license() if status: status,msg = getdaysfromstartdate() if status: status,msg = check_days_license(int(msg)) return status,msg else: return False,'File Not Exists' def check_domain_license(): if 'CORP.HCL.IN' in computername: return True,'HCL Domain' else: return True,'HCL Domain' def diff_month(d1, d2): return (d1.year - d2.year) * 12 + d1.month - d2.month def getdaysfromstartdate(): currentDirectory = os.path.dirname(os.path.abspath(__file__)) startdatePath = os.path.join(currentDirectory,'..','lic','startdate.txt') if(os.path.isfile(startdatePath)): with open(startdatePath, "rb") as fl: encrypted_message = fl.read() fl.close() privkey = '''-----BEGIN RSA PRIVATE KEY----- MIIEqwIBAAKCAQEAm75ZwaepuxGJjU1Slk1+IUO2E49Hy8i9dym5FUaBRyTRH6R+ GTF1kcpd+1QinIZDMIdsmAc95Y8pTufxY30QxCkOhVASitSQWHS/IiWQHmsTJwdr 38lqZnQQloOt/iPlhcavbxu/yKFzwBmp+nM+ErDTnCBh6EGCGrw1xWF30T2IBpmp WwMEoqZsFV69RzwQAw39KG1KCxi5uscrB62YPgUdlT2b4Yaa90egQhGLLVdnKvhP ORiGT9omCH90Dkm1oMMQ0Y2JBLezgXa/bunSqtTBxEwzlwUAX2JJcanFYrzKy2OL xzwNRlWUXilZ4R/1RHAgUdNyKbYxZqc24MApoQIDAQABAoIBAQCHZ/i7gNz10qqH 2qkqGlfF7gvYd6MRTwdDGlhbYgA17ZGP9EDaAIFabtpFEAJDmgvCnotQpkMvWcet XcUmHW89TQDd8R8d6u9QqLggpQ3nFGsDbNViLMjAKLrfUb8tjOIZ7ANNE5ArjAuK AgYhxJ48O9bPD+xvtLwip95PHxMMz1CF0vxrpCinvPdeC3HzcnLNZWN3ustbph/4 Tx8mrKDpAVIHVYVbY4CMtm7NbIBYdyR9Lokc4zBg/OTuLo+0QRVJ3GHAN6cGxTwY vLwN9iBBHyn9WBp5NIOSoCdob7+ce8y+X8yHmVhwRCfcrYphzfFNfP7SPNzV1dLs dFybn/h9AoGJALCOC7ss+PBXy5WrWVNRPzFO7KrJDl5q7s/gMk0PkB4i4XOKHDTl MhHZXhxp84HwpphwNxPHvpFe3pVZwwoe8LH1neoodlLOF0Kuk3jENh6cMhKFvcZ+ gxaBxGSCOXF/U307mh0i4AafClhVjxtLgBW5iJSVA9Brc7ZqVwxlUP7aYGzReIE1 uEMCeQDh0vq8NteUlkM/wpNzrHHqgtEzePbTYa+QcTm4xhARHR/cO+E0/mZIfltw 3NVWCIalMia+aKnvRHqHy/cQfEo2Uv/h8oARWnbrvicMRTwYL0w2GrP0f+aG0RqQ msLMzS3kp6szhM7C99reFxdlxJoWBKkp94psOksCgYkApB01zGRudkK17EcdvjPc sMHzfoFryNpPaI23VChuR4UW2mZ797NAypSqRXE7OALxaOuOVuWqP8jW0C9i/Cps hI+SnZHFAw2tU3+hd3Wz9NouNUd6c2MwCSDQ5LikGttHSTa49/JuGdmGLTxCzRVu V0NiMPMfW4I2Sk8o4U3gbzWgwiYohLrhrwJ5ANun/7IB2lIykvk7B3g1nZzRYDIk EFpuI3ppWA8NwOUUoj/zksycQ9tx5Pn0JCMKKgYXsS322ozc3B6o3AoSC5GpzDH4 UnAOwavvC0ZZNeoEX6ok8TP7EL3EOYW8s4zIa0KFgPac0Q0+T4tFhMG9qW+PWwhy Oxeo3wKBiCQ8LEgmHnXZv3UZvwcikj6oCrPy8fnhp5RZl2DPPlaqf3vokE6W5oEo LIKcWKvth3EU7HRKwYgaznj/Mw55aETx31R0FiXMG266B4V7QWPF/KuaR0GBsYfu +edGXQCnLgooKlMtQLdL5mcLXHc9x/0Z0iYEejJtbjcGR87WylSNaCH3hH703iQ= -----END RSA PRIVATE KEY----- ''' privkey = rsa.PrivateKey.load_pkcs1(privkey,'PEM') decrypted_message = rsa.decrypt(encrypted_message, privkey) decrypted_message = decrypted_message.decode() import datetime start_time = datetime.datetime.strptime(decrypted_message, '%Y-%m-%d') current_date = datetime.datetime.today().strftime('%Y-%m-%d') current_date = datetime.datetime.strptime(current_date, '%Y-%m-%d') Months = diff_month(current_date,start_time) return True,Months else: return False,'Start Date Not Exists' def check_days_license(months): currentDirectory = os.path.dirname(os.path.abspath(__file__)) license_path = os.path.join(currentDirectory,'..','lic','license.lic') if(os.path.isfile(license_path)): if(validate_key_Pair(license_path,full_key)): return True,'Valid License' elif(validate_key_Pair(license_path,oneYear_key)): if months <= 12: return True,'Valid License' else: return False,'License for AI.ON has expired. Please contact ERS Research for renewal.' elif(validate_key_Pair(license_path,halfYear_key)): if months <= 6: return True,'Valid License' else: return False,'License for AI.ON has expired. Please contact ERS Research for renewal.' elif(validate_key_Pair(license_path,quarter_key)): if months <= 3: return True,'Valid License' else: return False,'License for AI.ON has expired. Please contact ERS Research for renewal.' else: return False,'Invalid License' else: return False,'License Not exists.Please contact ERS Research for renewal.' def checklicense(): import binascii license_path = os.path.join(DATA_DIR,'License','license.lic') if(os.path.isfile(license_path)): try: with open(license_path, 'r') as privatefile: license_key = privatefile.read() privatefile.close() encrypted_message = binascii.unhexlify(license_key.encode()) privkey = '''-----BEGIN RSA PRIVATE KEY----- MIIEqQIBAAKCAQEAhqfNMuYYLdVrePhkO9rU/qT6FgolzI0YyzIJ2OeJE+++JioY m6nnohQU32iiE0DZlCCLrHJXOOIAz2Op80goX0lxtngyxVUPsiB5CI77sAC7x6K3 anJ0elpnQCC0+xV2ZL5eIMNQHLe+X6wJl/HGWqkUlxKpWr4/kBEB4EisW60OePfh ntIN4OUJ7iEq+sDdOM5WazJIXeNV1cig4i6057GE3k5ITcQUmw17DZu2+dqkIscc kaG+t5SF7Qnvt4IY8IeQp2htx3yD+CJCV0u2uKwoSFMGJn3OWdaixC3+eojyMXmf AWtQEe9NLNNaTCMIvQ8BeItJLQs2Htw3bZNMvwIDAQABAoIBAGGmuRnrYaeDeWAO CmqZxRMyQybOjyDrRgq9rAR/zJoHp8b3ikcBDTkuBQELWVZLFj7k50XU2cono9zC cxI5xwVrNqrUOkV+7VYJVJzPTFkT/xnEt+zbOfstKmmIDpdzthtTLuHlomhhHA83 rPFi5a0Dpynz35suEnm6ONxx4ICONa3xkQ51ALm8EEsdJ+qRQhi2HLTF/OVZMxSa A2DlFd4ChOEbYaN63xVCDxPXe9BfeHd/Rnim9x4xL9i2RL+mhARUy/ZP6LMHIPk7 NxTrGr4TuE/ETg8FZ3cywSnwsMlcplXo8Ar+5ths2XKxbmH1TI/vuQV1r7r0IeqV F4W/xOkCgYkAiDQy7/WyJWuT+rQ+gOjSUumXgWE3HO+vJAsy05cTZFSs+nUE4ctn FnvbBIRuClSr3zhcTtjaEaVnZ2OmGfOoAq0cvaXSlxqEs2456WQBf9oPHnvJEV07 AIqzo2EuDvGUh/bkFN3+djRRL9usNNplYA8jU3OQHGdeaS15ZikT+ZkQLXoHE0Oh vQJ5AP0W9Qouvc9jXRhjNNOWmgt+JiHw/oQts/LUWJ2T4UJ7wKAqGwsmgf0NbF2p aZ6AbMc7dHzCb52iLJRxlmlkJYzg449t0MgQVxTKQ5viIAdjkRBCIY2++GcYXb6k 6tUnF0Vm2kpffYUb5Lx5JoUE6IhMP0mEv3jKKwKBiCmvoC9lCUL+q+m9JKwbldOe fqowcMfAa+AiNUohIORCLjbxfa8Fq+VrvtqhFXS/+WJ2Q3o2UHe6Ie24x+uFcVRw Wy2IBO4ORbMM91iBLRxORvZTeHSCDj7aNKS6Z3hXY9hBLglc8DaJSJfXKdt7RC+k MnGmGuM2l+Sk8FTeGaj4ucTRZjz1JBkCeQDhNSV1GyShv4xeoCCoy1FmOqmZ+EWy vqxqv1PfXHDM5SwCGZWY9XokAGbWbWLjvOmO27QLNEV34pCCwxSR0aCsXI2B2rk2 3Xtvr5A7zRqtGIdEDWSoKjAGJSN9+mhQpglKI3zJQ3GBGdIPeEqzgSud5SNHu01a IaMCgYgyoxtqdWi90iE75/x+uIVGJRdHtWoL2dr8Ixu1bOMjKCR8gjneSRTqI1tA lbRH5K/jg6iccB/pQmBcIPIubF10Nv/ZQV760WK/h6ue2hOCaBLWT8EQEEfBfnp+ 9rfBfNQIQIkBFTfGIHXUUPb9sJgDP1boUxcqxr9bpKUrs1EMkUd+PrvpHIj2 -----END RSA PRIVATE KEY----- ''' privkey = rsa.PrivateKey.load_pkcs1(privkey,'PEM') decrypted_message = rsa.decrypt(encrypted_message, privkey) msg = decrypted_message.decode().split('####') product = msg[0] computernameLicense = msg[1] computername = socket.getfqdn() licenseValid = False if product.lower() == 'aion': if computernameLicense == computername: uuidlicense = msg[3] uuid = guid() if uuidlicense == uuid: current_date = datetime.now() license_expiry_date = msg[5] license_expiry_date = datetime.strptime(license_expiry_date,'%Y-%m-%d %H:%M:%S') if current_date > license_expiry_date: return False,'License Expire' else: return True,'' return False,'License Error' except Exception as e: print(e) return False,'License Error' else: return False,'Generate License' def generate_record_key(product,version): computername = socket.getfqdn() macaddress = getmac.get_mac_address() license_date = datetime.today().strftime('%Y-%m-%d %H:%M:%S') try: user = os.getlogin() except: user = 'NA' uuid = guid() msg = product+'###'+version+'###'+computername+'###'+macaddress+'###'+user+'###'+sys.platform+'###'+uuid+'###'+license_date pkeydata='''-----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAm75ZwaepuxGJjU1Slk1+IUO2E49Hy8i9dym5FUaBRyTRH6R+GTF1 kcpd+1QinIZDMIdsmAc95Y8pTufxY30QxCkOhVASitSQWHS/IiWQHmsTJwdr38lq ZnQQloOt/iPlhcavbxu/yKFzwBmp+nM+ErDTnCBh6EGCGrw1xWF30T2IBpmpWwME oqZsFV69RzwQAw39KG1KCxi5uscrB62YPgUdlT2b4Yaa90egQhGLLVdnKvhPORiG T9omCH90Dkm1oMMQ0Y2JBLezgXa/bunSqtTBxEwzlwUAX2JJcanFYrzKy2OLxzwN RlWUXilZ4R/1RHAgUdNyKbYxZqc24MApoQIDAQAB -----END RSA PUBLIC KEY----- ''' pubkey = rsa.PublicKey.load_pkcs1(pkeydata) encrypted_message = rsa.encrypt(msg.encode(), pubkey) encrypted_message = binascii.hexlify(encrypted_message).decode() return(encrypted_message) def run(cmd): try: return subprocess.run(cmd, shell=True, capture_output=True, check=True, encoding="utf-8").stdout.strip() except Exception as e: print(e) return None def guid(): if sys.platform == 'darwin': return run( "ioreg -d2 -c IOPlatformExpertDevice | awk -F\\\" '/IOPlatformUUID/{print $(NF-1)}'", ) if sys.platform == 'win32' or sys.platform == 'cygwin' or sys.platform == 'msys': return run('wmic csproduct get uuid').split('\n')[2].strip() if sys.platform.startswith('linux'): return run('cat /var/lib/dbus/machine-id') or \ run('cat /etc/machine-id') if sys.platform.startswith('openbsd') or sys.platform.startswith('freebsd'): return run('cat /etc/hostid') or \ run('kenv -q smbios.system.uuid') def updateLicense(licensekey): license_folder = os.path.join(DATA_DIR,'License') license_folder = Path(license_folder) license_folder.mkdir(parents=True, exist_ok=True) license_file = license_folder/'license.lic' with open(license_file, "w") as fl: fl.write(licensekey) fl.close() def enterRecord(version): validLicense,msg = checklicense() if not validLicense: key = generate_record_key('AION',version) msg = {'status':msg,'key':key,'licenseKey':'','link':''} return validLicense,msg ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import json import shutil import logging import sys from AionConfigManager import AionConfigManager from sklearn.externals import joblib class edgeformats: def __init__(self,deploy_path): self.deploy_path = deploy_path self.edge_deploy_path = os.path.join(deploy_path,"edge") os.mkdir(self.edge_deploy_path) def converttoedgedeployment(self,saved_model,edge_format,xtrain,model_type,iterName,iterVersion,features,profiled_data_file): if edge_format == 'onnx': from skl2onnx import convert_sklearn from skl2onnx.common.data_types import FloatTensorType xtrain = xtrain[features] initial_type = [('float_input', FloatTensorType([None, xtrain.shape[1]]))] filename = os.path.join(self.deploy_path,saved_model) loaded_model = joblib.load(filename) onx = convert_sklearn(loaded_model, initial_types=initial_type) onnx_filename = os.path.join(self.edge_deploy_path, model_type + '_' + iterName + '_' + iterVersion + '.onnx') with open(onnx_filename, "wb") as f: f.write(onx.SerializeToString()) self.createedgeruntimeFile(onnx_filename,profiled_data_file,features) def createedgeruntimeFile(self,onnx_filename,datafilepath,features): runtimefilecontent = '' runtimefilecontent += 'import pandas' runtimefilecontent += '\n' runtimefilecontent += 'import numpy' runtimefilecontent += '\n' runtimefilecontent += 'import sys' runtimefilecontent += '\n' runtimefilecontent += 'import onnxruntime as rt' runtimefilecontent += '\n' runtimefilecontent += 'def onnx_runtime_validation():' runtimefilecontent += '\n' runtimefilecontent += ' modelfile = r"'+str(onnx_filename)+'"' runtimefilecontent += '\n' runtimefilecontent += ' datafile = r"'+str(datafilepath)+'"' runtimefilecontent += '\n' runtimefilecontent += ' dataframe = pandas.read_csv(datafile)' runtimefilecontent += '\n' runtimefilecontent += ' dataframe = dataframe['+str(features)+']' runtimefilecontent += '\n' runtimefilecontent += ' df = dataframe.head(8)' runtimefilecontent += '\n' runtimefilecontent += ' dataset = df.values' runtimefilecontent += '\n' runtimefilecontent += ' sess = rt.InferenceSession(modelfile)' runtimefilecontent += '\n' runtimefilecontent += ' input_name = sess.get_inputs()[0].name' runtimefilecontent += '\n' runtimefilecontent += ' label_name = sess.get_outputs()[0].name' runtimefilecontent += '\n' runtimefilecontent += ' inputsize=sess.get_inputs()[0].shape' runtimefilecontent += '\n' runtimefilecontent += ' XYZ = dataset[:,0:inputsize[1]].astype(float)' runtimefilecontent += '\n' runtimefilecontent += ' pred_onx = sess.run([label_name], {input_name: XYZ.astype(numpy.float32)[0:8]})[0]' runtimefilecontent += '\n' runtimefilecontent += ' df[\'predictions\'] = pred_onx' runtimefilecontent += '\n' runtimefilecontent += ' result = df.to_json(orient="records")' runtimefilecontent += '\n' runtimefilecontent += ' return(result)' runtimefilecontent += '\n' runtimefilecontent += 'if __name__ == "__main__":' runtimefilecontent += '\n' runtimefilecontent += ' output = onnx_runtime_validation()' runtimefilecontent += '\n' runtimefilecontent += ' print("predictions:",output)' filename = os.path.join(self.edge_deploy_path,'onnxvalidation.py') f = open(filename, "w") f.write(str(runtimefilecontent)) f.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json from pathlib import Path from AION.prediction_package.imports import importModule from AION.prediction_package import utility from AION.prediction_package.utility import TAB_CHAR from importlib.metadata import version """ This file provide the functionality which is common for most of the problem types deployment. """ def main_code(): return """ class predict(): def __init__(self): self.profiler = inputprofiler() self.selector = selector() self.trainer = trainer() self.formatter = output_format() def run(self, data): try: df = self._parse_data(data) raw_df = df.copy() df = self.profiler.run(df) df = self.selector.run(df) df = self.trainer.run(df) output = self.formatter.run(raw_df, df) print("predictions:",output) return (output) except Exception as e: output = {"status":"FAIL","message":str(e).strip('"')} print("predictions:",json.dumps(output)) return (json.dumps(output)) def _parse_data(self, data): file_path = Path(data) if file_path.suffix == ".tsv": df = pd.read_csv(data,encoding='utf-8',sep='\\t',skipinitialspace = True,na_values=['-','?']) elif file_path.suffix in [".csv", ".dat"]: df=pd.read_csv(data,encoding='utf-8',skipinitialspace = True,na_values=['-','?']) elif file_path.suffix in [".gz"] and file_path.stem.endswith('.csv'): df=pd.read_csv(data,encoding='utf-8',skipinitialspace = True,na_values=['-','?']) elif file_path.suffix == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) df = pd.json_normalize(jsonData) else: jsonData = json.loads(data) df = pd.json_normalize(jsonData) return df import sys if __name__ == "__main__": output = predict().run(sys.argv[1]) """ def profiler_code(params, indent=0): """ This will create the profiler file based on the config file. separated file is created as profiler is required for input drift also. """ imported_modules = [ {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'scipy', 'mod_from': None, 'mod_as': None}, {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None} ] importer = importModule() utility.import_modules(importer, imported_modules) code = """ class inputprofiler(): """ init_code = """ def __init__(self): """ if params.get('text_features'): imported_modules.append({'module':'importlib.util'}) init_code += """ # preprocessing preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if not preprocess_path.exists(): raise ValueError(f'Preprocess model file not found: {preprocess_path}') self.profiler = joblib.load(preprocess_path) """ run_code = """ def run(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) """ if params.get('input_features_type'): imported_modules.append({'module':'dtype','mod_from':'numpy'}) run_code += f""" df = df.astype({params.get('input_features_type')}) """ if params.get('word2num_features'): imported_modules.append({'module':'w2n','mod_from':'word2number'}) run_code += f""" def s2n(value): try: x=eval(value) return x except: try: return w2n.word_to_num(value) except: return np.nan df[{params['word2num_features']}] = df[{params['word2num_features']}].apply(lambda x: s2n(x))""" if params.get('unpreprocessed_columns'): run_code += f""" unpreprocessed_data = df['{params['unpreprocessed_columns'][0]}'] df.drop(['{params['unpreprocessed_columns'][0]}'], axis=1,inplace=True) """ if params.get('force_numeric_conv'): run_code += f""" df[{params['force_numeric_conv']}] = df[{params['force_numeric_conv']}].apply(pd.to_numeric,errors='coerce')""" if params.get('conversion_method','').lower() == 'glove': code_text, modules = __profiler_glove_code(params) imported_modules.extend( modules) init_code += code_text elif params.get('conversion_method','').lower() == 'fasttext': init_code += __profiler_fasttext_code(params) run_code += __profiler_main_code(params) if params.get('unpreprocessed_columns'): run_code += f""" df['{params.get('unpreprocessed_columns')[0]}'] = unpreprocessed_data """ utility.import_modules(importer, imported_modules) import_code = importer.getCode() return import_code + code + init_code + run_code def __profiler_glove_code(params, indent=2): modules = [] modules.append({'module':'load_pretrained','mod_from':'text.Embedding'}) modules.append({'module':'TextProcessing','mod_from':'text'}) code = """ model_path = TextProcessing.checkAndDownloadPretrainedModel('glove') embed_size, pretrained_model = load_pretrained(model_path) self.profiler.set_params(text_process__vectorizer__external_model = pretrained_model) """ return code.replace('\n', '\n'+(indent * TAB_CHAR)), modules def __profiler_fasttext_code(params, indent=2): code = """ def get_pretrained_model_path(): try: from AION.appbe.dataPath import DATA_DIR modelsPath = Path(DATA_DIR)/'PreTrainedModels'/'TextProcessing' except: modelsPath = Path('aion')/'PreTrainedModels'/'TextProcessing' if not modelsPath.exists(): modelsPath.mkdir(parents=True, exist_ok=True) return modelsPath if not importlib.util.find_spec('fasttext'): raise ValueError('fastText not installed') else: import os import fasttext import fasttext.util cwd = os.getcwd() os.chdir(get_pretrained_model_path()) fasttext.util.download_model('en', if_exists='ignore') pretrained_model = fasttext.load_model('cc.en.300.bin') os.chdir(cwd) self.profiler.set_params(text_process__vectorizer__external_model = pretrained_model) self.profiler.set_params(text_process__vectorizer__external_model_type = 'binary') """ return code.replace('\n', '\n'+(indent * TAB_CHAR)) def __profiler_main_code(params, indent=2): code = f""" df = self.profiler.transform(df) columns = {params['output_features']} if isinstance(df, scipy.sparse.spmatrix): df = pd.DataFrame(df.toarray(), columns=columns) else: df = pd.DataFrame(df, columns=columns) return df """ return code.replace('\n', '\n'+(indent * TAB_CHAR)) def feature_selector_code( params, indent=0): modules = [ {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'} ] code = """ class selector(): # this class def __init__(self): pass def run(self, df):""" code +=f""" return df[{params['output_features']}] """ return code, modules def feature_reducer_code( params, indent=0): modules = [ {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None} ] code = f""" class selector(): def __init__(self): reducer_file = (Path(__file__).parent/"model")/"{params['reducer_file']}" if not reducer_file.exists(): raise ValueError(f'Failed to load Feature Engineering model file: {{reducer_file}}') self.model = joblib.load(reducer_file) def run(self, df): reducer_input = {params['input_features']} reducer_output = {params['output_features']} df = self.model.transform(df[reducer_input]) return pd.DataFrame(df,columns=reducer_output) """ if indent: code = code.replace('\n', '\n'+(indent * TAB_CHAR)) return code, modules def create_feature_list(config=None, target_feature=None, deploy_path=None): featurelist = [] if 'profiler' in config: if 'input_features_type' in config['profiler']: input_features = config['profiler']['input_features_type'] for x in input_features: featurelt={} featurelt['feature'] = x if x == target_feature: featurelt['Type'] = 'Target' else: if input_features[x] in ['int','int64','float','float64']: featurelt['Type'] = 'Numeric' elif input_features[x] == 'object': featurelt['Type'] = 'Text' elif input_features[x] == 'category': featurelt['Type'] = 'Category' else: featurelt['Type'] = 'Unknown' featurelist.append(featurelt) featurefile = f""" import json def getfeatures(): try: features = {featurelist} outputjson = {{"status":"SUCCESS","features":features}} output = json.dumps(outputjson) print("Features:",output) return(output) except Exception as e: output = {{"status":"FAIL","message":str(e).strip(\'"\')}} print("Features:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = getfeatures() """ with open( deploy_path/'featureslist.py', 'wb') as f: f.write( str(featurefile).encode('utf8')) def requirement_file(deploy_path,model,textFeatures,learner_type='ML'): modules = ['pandas','numpy','alibi','matplotlib','joblib','shap','ipython','category_encoders','scikit-learn','word2number','flask_restful','evidently','Flask-Cors'] requires = '' for mod in modules: requires += f"{mod}=={version(mod)}\n" if len(textFeatures) > 0: tmodules = ['spacy','nltk','textblob','demoji','beautifulsoup4','text-unidecode','pyspellchecker','contractions','protobuf'] for mod in tmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Extreme Gradient Boosting (XGBoost)': mmodules = ['xgboost'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Light Gradient Boosting (LightGBM)': mmodules = ['lightgbm'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Categorical Boosting (CatBoost)': mmodules = ['catboost'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'arima': mmodules = ['pmdarima'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'fbprophet': mmodules = ['prophet'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'lstm' or model.lower() == 'mlp' or learner_type =='DL': mmodules = ['tensorflow'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() in ['cox', 'kaplanmeierfitter']: #bug 12833 mmodules = ['lifelines'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'sentencetransformer': #bug 12833 mmodules = ['sentence_transformers'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" with open( deploy_path/'requirements.txt', 'wb') as f: f.write(str(requires).encode('utf8')) def create_readme_file(deploy_path,modelfile,features): data = json.dumps([{x:x+'_value'} for x in features]) backslash_data = data.replace('"', '\\"') content = f""" ========== Files Structures ========== {modelfile} ------ Trained Model aion_prediction.py --> Python package entry point script/inputprofiler.py --> Profiling like FillNA and Category to Numeric ========== How to call the model ========== ============== From Windows Terminal ========== python aion_prediction.py "{backslash_data}" ============== From Linux Terminal ========== python aion_prediction.py "{data}" ============== Output ========== {{"status":"SUCCESS","data":[{{"Data1":"Value","prediction":"Value"}}]}} ## for single Row/Record {{"status":"SUCCESS","data":[{{"Data1":"Value","prediction":"Value"}},{{"Data1":"Value","prediction":"Value"}}]}} ## For Multiple Row/Record {{"status":"ERROR","message":"description"}} ## In Case Exception or Error """ filename = deploy_path/'readme.txt' with open(filename, 'w') as f: f.write(content) def create_util_folder(deploy_path): import tarfile ext_path = Path(__file__).parent.parent/'utilities' for x in ext_path.iterdir(): if x.suffix == '.tar': if x.name not in ['scikit_surprise-1.1.1.dist-info.tar','surprise.tar']: my_tar = tarfile.open(x) my_tar.extractall(deploy_path) my_tar.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os,sys import platform import json import shutil import logging from pathlib import Path from prediction_package import production from prediction_package import prediction_transformation as cs class DeploymentManager: def __init__(self): self.requirementfile='' self.modelfile='' self.s2i_environmentfile='' self.selectorfile='' self.profilerfile='' self.readmepackagename='' self.pythonpackage='' self.log = logging.getLogger('eion') def include_import_file(self,learner_type,method,scoreParam,model_type,model): if((learner_type == 'DL') or (learner_type == 'TextDL')): self.modelfile += 'from tensorflow.keras.models import load_model' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras import backend as K' self.modelfile += '\n' self.modelfile += 'import tensorflow as tf' self.modelfile += '\n' if (learner_type == 'ML' and model_type.lower()=='anomaly_detection' and model.lower() == 'autoencoder'): self.modelfile += 'import joblib' self.modelfile += '\n' self.modelfile += 'import os' self.modelfile += '\n' self.modelfile += 'import pandas as pd' self.modelfile += '\n' self.modelfile += 'import numpy as np' self.modelfile += '\n' self.modelfile += 'from pathlib import Path' self.modelfile += '\n' self.modelfile += 'import tensorflow as tf' self.modelfile += '\n' self.modelfile += 'from keras.models import load_model' self.modelfile += '\n' self.modelfile += 'import warnings' self.modelfile += '\n' self.modelfile += 'from sklearn.preprocessing import StandardScaler' self.modelfile += '\n' self.modelfile += 'warnings.filterwarnings("ignore")' self.modelfile += '\n' if(learner_type == 'ImageClassification'): self.modelfile += 'import os' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.models import Sequential' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.layers import Dense, Dropout, Flatten' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.preprocessing import image' self.modelfile += '\n' self.modelfile += 'import numpy as np' self.modelfile += '\n' self.modelfile += 'import tensorflow as tf' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.layers import Input' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.models import Model' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.optimizers import Adam' self.modelfile += '\n' self.modelfile += 'import cv2' self.modelfile += '\n' if(learner_type == 'objectDetection'): self.modelfile += 'import os\n' self.modelfile += 'from object_detection.utils import label_map_util\n' self.modelfile += 'from object_detection.utils import config_util\n' self.modelfile += 'from object_detection.utils import visualization_utils as viz_utils\n' self.modelfile += 'from object_detection.builders import model_builder\n' self.modelfile += 'import tensorflow as tf\n' self.modelfile += 'import numpy as np\n' self.modelfile += 'from PIL import Image\n' self.modelfile += 'import matplotlib.pyplot as plt\n' self.modelfile += 'import pandas as pd\n' self.modelfile += 'from pathlib import Path\n' if(learner_type == 'Text Similarity'): self.modelfile += 'from tensorflow.keras.models import load_model' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras import backend as K' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.preprocessing.sequence import pad_sequences' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.preprocessing.text import Tokenizer' self.modelfile += '\n' self.modelfile += 'import tensorflow as tf' self.modelfile += '\n' if(model == 'Neural Architecture Search'): self.modelfile += 'from tensorflow.keras.models import load_model' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras import backend as K' self.modelfile += '\n' self.modelfile += 'import tensorflow as tf' self.modelfile += '\n' self.modelfile += 'import joblib' self.modelfile += '\n' self.modelfile += 'import os' self.modelfile += '\n' self.modelfile += 'import pandas as pd' self.modelfile += '\n' self.modelfile += 'from sklearn.decomposition import LatentDirichletAllocation\n' self.modelfile += 'import numpy as np\n' self.modelfile += 'from pathlib import Path\n' if model.lower() == 'deep q network' or model.lower() == 'dueling deep q network': self.modelfile += 'from tensorflow import constant' self.modelfile += '\n' self.modelfile += 'from tf_agents.trajectories import time_step' self.modelfile += '\n' self.requirementfile += 'tensorflow==2.5.0' if model.lower() == 'lstm' or model.lower() == 'mlp': self.modelfile += 'from tensorflow.keras.models import load_model' self.modelfile += '\n' self.requirementfile += 'tensorflow==2.5.0' if(learner_type == 'Text Similarity'): self.modelfile += 'def cosine_distance(vests):' self.modelfile += '\n'; self.modelfile += ' x, y = vests' self.modelfile += '\n'; self.modelfile += ' x = K.l2_normalize(x, axis=-1)' self.modelfile += '\n'; self.modelfile += ' y = K.l2_normalize(y, axis=-1)' self.modelfile += '\n'; self.modelfile += ' return -K.mean(x * y, axis=-1, keepdims=True)' self.modelfile += '\n'; self.modelfile += 'def cos_dist_output_shape(shapes):' self.modelfile += '\n'; self.modelfile += ' shape1, shape2 = shapes' self.modelfile += '\n'; self.modelfile += ' return (shape1[0],1)' self.modelfile += '\n'; if(learner_type == 'TextDL' or learner_type == 'DL'): if(scoreParam.lower() == 'recall' or scoreParam.lower() == 'f1_score'): self.modelfile += 'def recall_m(y_true, y_pred):' self.modelfile += '\n'; self.modelfile += ' true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))' self.modelfile += '\n'; self.modelfile += ' possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))' self.modelfile += '\n'; self.modelfile += ' recall = true_positives / (possible_positives + K.epsilon())' self.modelfile += '\n'; self.modelfile += ' return recall' self.modelfile += '\n'; if(scoreParam.lower() == 'precision' or scoreParam.lower() == 'f1_score'): self.modelfile += 'def precision_m(y_true, y_pred):' self.modelfile += '\n'; self.modelfile += ' true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))' self.modelfile += '\n'; self.modelfile += ' predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))' self.modelfile += '\n'; self.modelfile += ' precision = true_positives / (predicted_positives + K.epsilon())' self.modelfile += '\n'; self.modelfile += ' return precision' self.modelfile += '\n'; if(scoreParam.lower() == 'f1_score'): self.modelfile += 'def f1_m(y_true, y_pred):' self.modelfile += '\n'; self.modelfile += ' precision = precision_m(y_true, y_pred)' self.modelfile += '\n'; self.modelfile += ' recall = recall_m(y_true, y_pred)' self.modelfile += '\n'; self.modelfile += ' return 2*((precision*recall)/(precision+recall+K.epsilon()))' self.modelfile += '\n'; if(scoreParam.lower() == 'rmse'): self.modelfile += 'def rmse_m(y_true, y_pred):' self.modelfile += '\n'; self.modelfile += ' return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))' self.modelfile += '\n'; if(scoreParam.lower() =='r2'): self.modelfile += 'def r_square(y_true, y_pred):' self.modelfile += '\n'; self.modelfile += ' SS_res = K.sum(K.square(y_true-y_pred))' self.modelfile += '\n'; self.modelfile += ' SS_tot = K.sum(K.square(y_true-K.mean(y_true)))' self.modelfile += '\n'; self.modelfile += ' return (1 - SS_res/(SS_tot+K.epsilon()))' self.modelfile += '\n'; if(learner_type.lower() in ['similarityidentification','contextualsearch']): self.modelfile += 'from pathlib import Path\n' if model_type == 'BM25': self.modelfile += 'from rank_bm25 import BM25Okapi\n' elif scoreParam == 'VectorDB Cosine': self.modelfile += 'import chromadb\n' else: self.modelfile += 'from sklearn.metrics.pairwise import cosine_similarity\n' self.pythonpackage += '========== Python Packags Requires =========' self.pythonpackage += '\n' self.pythonpackage += 'scikit-learn' self.pythonpackage += '\n' self.pythonpackage += 'scipy' self.pythonpackage += '\n' self.pythonpackage += 'numpy' self.pythonpackage += '\n' if((learner_type == 'DL') or (learner_type =='TextDL')): self.modelfile += 'import numpy as np' self.modelfile += '\n' self.requirementfile += 'scikit-learn==0.21.3' self.requirementfile += '\n' self.requirementfile += 'scipy==1.3.3' self.requirementfile += '\n' self.requirementfile += 'numpy==1.17.4' self.requirementfile += '\n' if(learner_type == 'TextML'): self.requirementfile += 'spacy==2.2.3' self.requirementfile += '\n' self.requirementfile += 'https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz' self.requirementfile += '\n' if(learner_type == 'DL' or learner_type == 'TextDL'): self.requirementfile += 'keras==2.3.1' self.requirementfile += '\n' self.requirementfile += 'tensorflow==2.0.0b1' self.requirementfile += '\n' if(learner_type == 'RecommenderSystem'): self.requirementfile += 'surprise' self.requirementfile += '\n' if(method == 'package'): self.modelfile += 'import surprise' self.modelfile += '\n' self.modelfile += 'import statsmodels' self.modelfile += '\n' self.requirementfile += 'statsmodels==0.10.2' self.requirementfile += '\n' def crate_readme_file(self,deploy_path,modelfile,features,method,single_file=False): self.readme='========== Files Structures ==========' self.readme+='\n' self.readme+=modelfile+' ------ Trained Model' self.readme+='\n' self.readme+='aion_prediction.py --> Python package entry point' self.readme+='\n' if not single_file: self.readme+='script/inputprofiler.py --> Profiling like FillNA and Category to Numeric' self.readme+='\n' self.readme+='script/selector.py --> Feature Selection' self.readme+='\n' self.readme+='script/trained_model.py --> Read the model file and call the prediction' self.readme+='\n' self.readme+='script/output_format.py --> Output formatter file' self.readme+='\n' self.readme+= self.pythonpackage self.readme+= '========== How to call the model ==========' self.readme+='\n' self.readme+= '============== From Windows Terminal ==========' self.readme+='\n' if method == 'optimus_package': self.readme += 'python aion_prediction.py filename.json' self.readme +='\n' self.readme += '========== Embedded Methods ==========' self.readme +='\n' self.readme += 'Function Name: predict_from_json - When input is Json Data' self.readme +='\n' self.readme += 'Function Name: predict_from_file - When input is Json File' self.readme +='\n' else: callpython = 'python aion_prediction.py "[{' for x in features: if(callpython != 'python prediction.py "[{'): callpython += ',' callpython += '\\\"'+str(x)+'\\\"'+':'+'\\\"'+str(x)+'_value'+'\\\"' callpython += '}]"' self.readme += callpython self.readme+='\n' self.readme+= '============== From Linux Terminal ==========' self.readme+='\n' callpython = 'python aion_prediction.py \'[{' temp =callpython for x in features: if(callpython != temp): callpython += ',' callpython += '"'+str(x)+'"'+':'+'"'+str(x)+'_value'+'"' callpython += '}]\'' self.readme += callpython self.readme+='\n' self.readme+= '============== Output ==========' self.readme+='\n' self.readme+= '{"status":"SUCCESS","data":[{"Data1":"Value","prediction":"Value"}]}' ## For Single Row/Record' self.readme+='\n' self.readme+= '{"status":"SUCCESS","data":[{"Data1":"Value","prediction":"Value"},{"Data1":"Value","prediction":"Value"}]} ## For Multiple Row/Record' self.readme+='\n' self.readme+= '{"status":"ERROR","message":"description"} ## In Case Exception or Error' self.readme+='\n' #print(self.readme) filename = os.path.join(deploy_path,'readme.txt') self.log.info('-------> Readme File Location: '+filename) f = open(filename, "wb") f.write(str(self.readme).encode('utf8')) f.close() def create_class(self,classname): #self.modelfile += 'class '+classname+'(object):' self.modelfile += 'class trained_model(object):' self.modelfile += '\n' def profiler_code(self,model_type,model,output_columns, features, text_feature,wordToNumericFeatures=[], deploy={},datetimeFeature=''): profiler = deploy.get('profiler',{}) if isinstance(features, str): features = features.split(',') code = f""" import scipy import joblib import numpy as np import pandas as pd from pathlib import Path """ if text_feature: code += """ import importlib.util\n""" if wordToNumericFeatures: code += """ from word2number import w2n def s2n(value): try: x=eval(value) return x except: try: return w2n.word_to_num(value) except: return np.nan """ if 'code' in deploy.get('preprocess',{}).keys(): code += deploy['preprocess']['code'] if profiler.get('conversion_method','').lower() == 'glove': code += """ class inputprofiler(object): def __init__(self): self.model = None preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if preprocess_path.exists(): self.model = joblib.load(preprocess_path) from text.Embedding import load_pretrained from text import TextProcessing model_path = TextProcessing.checkAndDownloadPretrainedModel('glove') embed_size, loaded_model = load_pretrained(model_path) self.model.set_params(text_process__vectorizer__external_model = loaded_model) else: raise ValueError('Preprocess model not found') def apply_profiler(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) """ elif profiler.get('conversion_method','').lower() == 'fasttext': code += """ def get_pretrained_model_path(): try: from AION.appbe.dataPath import DATA_DIR modelsPath = Path(DATA_DIR)/'PreTrainedModels'/'TextProcessing' except: modelsPath = Path('aion')/'PreTrainedModels'/'TextProcessing' if not modelsPath.exists(): modelsPath.mkdir(parents=True, exist_ok=True) return modelsPath class inputprofiler(object): def __init__(self): self.model = None preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if preprocess_path.exists(): self.model = joblib.load(preprocess_path) if not importlib.util.find_spec('fasttext'): raise ValueError('fastText not installed') else: import os import fasttext import fasttext.util cwd = os.getcwd() os.chdir(get_pretrained_model_path()) fasttext.util.download_model('en', if_exists='ignore') loaded_model = fasttext.load_model('cc.en.300.bin') os.chdir(cwd) self.model.set_params(text_process__vectorizer__external_model = loaded_model) self.model.set_params(text_process__vectorizer__external_model_type = 'binary') else: raise ValueError('Preprocess model not found') def apply_profiler(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) """ else: code += """ class inputprofiler(object): def __init__(self): self.model = None preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if preprocess_path.exists(): self.model = joblib.load(preprocess_path) else: raise ValueError('Preprocess model not found') def apply_profiler(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) """ if 'code' in deploy.get('preprocess',{}).keys(): code += " df = preprocess( df)\n" if wordToNumericFeatures: code += f""" df[{wordToNumericFeatures}] = df[{wordToNumericFeatures}].apply(lambda x: s2n(x))""" if profiler.get('unpreprocessed_columns'): code += f""" unpreprocessed_data = df['{profiler['unpreprocessed_columns'][0]}'] df.drop(['{profiler['unpreprocessed_columns'][0]}'], axis=1,inplace=True) """ if profiler.get('force_numeric_conv'): code += f""" df[{profiler['force_numeric_conv']}] = df[{profiler['force_numeric_conv']}].apply(pd.to_numeric,errors='coerce') """ code += f""" if self.model: df = self.model.transform(df)""" code += f""" columns = {output_columns} if isinstance(df, scipy.sparse.spmatrix): df = pd.DataFrame(df.toarray(), columns=columns) else: df = pd.DataFrame(df, columns=columns) """ ##The below if loop for avoiding unpreprocessed column variable storing which is not used for anomaly detection if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na': pass else: if profiler.get('unpreprocessed_columns'): code += f""" df['{profiler.get('unpreprocessed_columns')[0]}'] = unpreprocessed_data """ if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na': ##This below set_index is wrong, because we drop datetimefeature before profiling and doing set_index. So commented now. # code += f""" # df.set_index('{datetimeFeature}', inplace=True)""" code += f""" return(df,'{datetimeFeature}')\n""" else: code += f""" return(df)""" return code def no_profiling_code(self, features): if isinstance(features, str): features = features.split(',') return f""" import pandas as pd import numpy as np class inputprofiler(object): def apply_profiler(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) return df[{features}] """ def create_profiler_file(self,learner_type,deploy_path,profiler,features,numericToLabel_json,column_merge_flag,text_features,preprocessing_pipe,firstDocFeature,secondDocFeature,normalizer,normFeatures,wordToNumericFeatures,conversion_method,model_type,preprocess_pipe,preprocess_out_columns, label_encoder,model, config=None,datetimeFeature=''): filename = str(Path(deploy_path)/'script'/'inputprofiler.py') if 'profiler' in config: if model_type == 'BM25': code = self.profiler_code(model_type,model,['tokenize'],features, text_features,config['profiler']['word2num_features']) elif model == 'KaplanMeierFitter': code = self.no_profiling_code(features) elif model.lower() in ['arima', 'fbprophet']: #task 12627 code = self.no_profiling_code('noofforecasts') else: code = self.profiler_code(model_type,model,config['profiler']['output_features'],features, text_features,config['profiler']['word2num_features'],config,datetimeFeature) if code: with open(filename,'w',encoding="utf-8") as f: f.write(code) self.log.info('-------> Profiler File Location :'+filename) return self.profilerfile += 'import pandas as pd' self.profilerfile += '\n' self.profilerfile += 'import joblib' self.profilerfile += '\n' self.profilerfile += 'import os' self.profilerfile += '\n' self.profilerfile += 'from word2number import w2n' self.profilerfile += '\n' self.profilerfile += 'import numpy as np' self.profilerfile += '\nfrom pathlib import Path\n' #print("1") #print(profiler) if(learner_type == 'Text Similarity' or len(text_features) > 0): self.profilerfile += 'from text import TextProcessing' self.profilerfile += '\n' self.profilerfile += 'def textCleaning(textCorpus):' self.profilerfile += '\n' self.profilerfile += ' textProcessor = TextProcessing.TextProcessing()' self.profilerfile += '\n' self.profilerfile += ' textCorpus = textProcessor.transform(textCorpus)' self.profilerfile += '\n' self.profilerfile += ' return(textCorpus)' self.profilerfile += '\n' self.profilerfile += 'class inputprofiler(object):' self.profilerfile += '\n' self.profilerfile += ' def s2n(self,value):' self.profilerfile += '\n' self.profilerfile += ' try:' self.profilerfile += '\n' self.profilerfile += ' x=eval(value)' self.profilerfile += '\n' self.profilerfile += ' return x' self.profilerfile += '\n' self.profilerfile += ' except:' self.profilerfile += '\n' self.profilerfile += ' try:' self.profilerfile += '\n' self.profilerfile += ' return w2n.word_to_num(value)' self.profilerfile += '\n' self.profilerfile += ' except:' self.profilerfile += '\n' self.profilerfile += ' return np.nan ' self.profilerfile += '\n' self.profilerfile += ' def apply_profiler(self,df):' self.profilerfile += '\n' if(len(wordToNumericFeatures) > 0): for w2nFeature in wordToNumericFeatures: if w2nFeature not in features: continue self.profilerfile += " df['"+w2nFeature+"']=df['"+w2nFeature+"'].apply(lambda x: self.s2n(x))" self.profilerfile += '\n' self.profilerfile += " df = df.replace(r'^\s*$', np.NaN, regex=True)" self.profilerfile += '\n' self.profilerfile += ' try:' self.profilerfile += '\n' self.profilerfile += ' df.dropna(how="all",axis=1,inplace=True)' self.profilerfile += '\n' self.profilerfile += ' except:' self.profilerfile += '\n' self.profilerfile += ' df.fillna(0)' self.profilerfile += '\n' if model_type.lower() != 'timeseriesforecasting': #task 11997 self.profilerfile += ' preprocess_path = Path(__file__).parent.parent/"model"/"preprocess_pipe.pkl"\n' self.profilerfile += ' if preprocess_path.exists():\n' self.profilerfile += ' model = joblib.load(preprocess_path)\n' if model_type.lower()=='anomaly_detection' and model.lower() == 'autoencoder': self.profilerfile += f" df[{features}] = model.transform(df[{features}])\n" else: self.profilerfile += f" df = model.transform(df)\n" if 'operation' in profiler: y = profiler['operation'] for action in y: feature = action['feature'] #if feature not in features: # continue operation = action['Action'] if(operation == 'Drop'): self.profilerfile += " if '"+feature+"' in df.columns:" self.profilerfile += '\n' self.profilerfile += " df.drop(columns=['"+feature+"'],inplace = True)" self.profilerfile += '\n' if(operation == 'FillValue'): self.profilerfile += " if '"+feature+"' in df.columns:" self.profilerfile += '\n' fvalue = action['value'] self.profilerfile += " df['"+feature+"'] = df['"+feature+"'].fillna(value='"+fvalue+"')" self.profilerfile += '\n' if(operation == 'Encoder'): value = action['value'] value = value.replace("\n", "\\n") self.profilerfile += " if '"+feature+"' in df.columns:" self.profilerfile += '\n' self.profilerfile += " le_dict="+str(value) self.profilerfile += '\n' self.profilerfile += " df['"+feature+"'] = df['"+feature+"'].apply(lambda x: le_dict.get(x,-1))" self.profilerfile += '\n' self.profilerfile += " if -1 in df['"+feature+"'].values:" self.profilerfile += '\n' self.profilerfile += " raise Exception('Category value of "+feature+" not present in training data')" self.profilerfile += '\n' if 'conversion' in profiler: catergoryConverton = profiler['conversion'] #print(catergoryConverton) if (catergoryConverton['categoryEncoding'].lower() in ['targetencoding','onehotencoding']) and ('features' in catergoryConverton): self.profilerfile += " encoder = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','categoryEncoder.pkl'))" self.profilerfile += '\n' self.profilerfile += " CategoryFeatures = "+str(catergoryConverton['features']) self.profilerfile += '\n' if catergoryConverton['categoryEncoding'].lower() == 'onehotencoding': self.profilerfile += " transformed_data = encoder.transform(df[CategoryFeatures]).toarray()" self.profilerfile += '\n' self.profilerfile += " feature_labels = encoder.get_feature_names(CategoryFeatures)" self.profilerfile += '\n' self.profilerfile += " transformed_data = pd.DataFrame(transformed_data,columns=feature_labels) " self.profilerfile += '\n' else: self.profilerfile += " transformed_data = encoder.transform(df[CategoryFeatures])" self.profilerfile += '\n' self.profilerfile += " dataColumns=list(df.columns)" self.profilerfile += '\n' self.profilerfile += " nonNormFeatures=list(set(dataColumns) - set(CategoryFeatures))" self.profilerfile += '\n' self.profilerfile += " dataArray=df[nonNormFeatures]" self.profilerfile += '\n' self.profilerfile += " df = pd.concat([dataArray, transformed_data],axis=1)" self.profilerfile += '\n' y = json.loads(numericToLabel_json) for feature_details in y: feature = feature_details['feature'] if feature not in features: continue label = feature_details['Labels'] bins = feature_details['Bins'] self.profilerfile += " if '"+feature+"' in df.columns:" self.profilerfile += '\n' self.profilerfile += " cut_bins="+str(bins) self.profilerfile += '\n' self.profilerfile += " cut_labels="+str(label) self.profilerfile += '\n' self.profilerfile += " df['"+feature+"'] = pd.cut(df['"+feature+"'],bins=cut_bins,labels=cut_labels)" self.profilerfile += '\n' self.profilerfile += " df['"+feature+"'] = df['"+feature+"'].fillna(value=0)" self.profilerfile += '\n' if(len(text_features) > 0): if(len(text_features) > 1): self.profilerfile += ' merge_features = '+str(text_features) self.profilerfile += '\n' self.profilerfile += ' df[\'combined\'] = df[merge_features].apply(lambda row: \' \'.join(row.values.astype(str)), axis=1)' self.profilerfile += '\n' self.profilerfile += ' features = [\'combined\']' self.profilerfile += '\n' else: self.profilerfile += " features = "+str(text_features) self.profilerfile += '\n' if model_type == 'BM25': self.profilerfile += """\ df_text = df[features[0]] pipe = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','{preprocessing_pipe}')) df['tokenize'] = pipe.transform(df_text)\n""".format(preprocessing_pipe=preprocessing_pipe) elif conversion_method == 'sentenceTransformer': self.profilerfile += """\ df_text = df[features[0]] from sentence_transformers import SentenceTransformer model = SentenceTransformer(\'sentence-transformers/msmarco-distilroberta-base-v2\') df_vect = model.encode(df_text) for empCol in {text_features}: df = df.drop(columns=[empCol]) if isinstance(df_vect, np.ndarray): df1 = pd.DataFrame(df_vect) else: df1 = pd.DataFrame(df_vect.toarray(),columns = pipe.named_steps[\'vectorizer\'].get_feature_names()) df1 = df1.add_suffix(\'_vect\') df = pd.concat([df, df1],axis=1)\n""".format(preprocessing_pipe=preprocessing_pipe, text_features=text_features) else: self.profilerfile += """\ df_text = df[features[0]] pipe = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','{preprocessing_pipe}')) df_vect=pipe.transform(df_text) for empCol in {text_features}: df = df.drop(columns=[empCol]) if isinstance(df_vect, np.ndarray): df1 = pd.DataFrame(df_vect) else: df1 = pd.DataFrame(df_vect.toarray(),columns = pipe.named_steps[\'vectorizer\'].get_feature_names()) df1 = df1.add_suffix(\'_vect\') df = pd.concat([df, df1],axis=1)\n""".format(preprocessing_pipe=preprocessing_pipe, text_features=text_features) if(learner_type == 'Text Similarity'): self.profilerfile += ' df[\''+firstDocFeature+'\'] = textCleaning(df[\''+firstDocFeature+'\'])' self.profilerfile += '\n' self.profilerfile += ' df[\''+secondDocFeature+'\'] = textCleaning(df[\''+secondDocFeature+'\'])' self.profilerfile += '\n' if len(normFeatures) > 0 and normalizer != '': self.profilerfile += " normFeatures = "+str(normFeatures) self.profilerfile += '\n' self.profilerfile += ' normalizepipe = joblib.load(os.path.join(os.path.dirname(os.path.abspath(__file__)),\'..\',\'model\',\''+normalizer+'\'))' self.profilerfile += '\n' self.profilerfile += ' dataColumns=list(df.columns)' self.profilerfile += '\n' self.profilerfile += ' nonNormFeatures=list(set(dataColumns) - set(normFeatures))' self.profilerfile += '\n' self.profilerfile += ' dataframe=df[normFeatures]' self.profilerfile += '\n' self.profilerfile += ' transDf = normalizepipe.transform(dataframe)' self.profilerfile += '\n' self.profilerfile += ' nontransDF=df[nonNormFeatures].values' self.profilerfile += '\n' self.profilerfile += ' dataColumns=normFeatures+nonNormFeatures' self.profilerfile += '\n' self.profilerfile += ' scaledDf = pd.DataFrame(np.hstack((transDf, nontransDF)),columns=dataColumns)' self.profilerfile += '\n' self.profilerfile += ' df=scaledDf' self.profilerfile += '\n' else: self.profilerfile += ' df=df.dropna()\n' self.profilerfile += ' return(df)' filename = os.path.join(deploy_path,'script','inputprofiler.py') self.log.info('-------> Profiler File Location :'+filename) f = open(filename, "w",encoding="utf-8") f.write(str(self.profilerfile)) f.close() def isEnglish(self, s): try: s.encode(encoding='utf-8').decode('ascii') except UnicodeDecodeError: return False else: return True def create_selector_file(self,deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature, model_type,model,config=None): cs.create_selector_file(self,deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature, model_type,model,config) def create_init_function_for_regression(self,modelfile): self.modelfile += ' def __init__(self):' self.modelfile += '\n' self.modelfile += " self.model = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' def create_init_function_for_classification(self,modelfile,classes,learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,modelName,model_type,imageconfig): cs.create_init_function_for_classification(self,modelfile,classes,learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,modelName,model_type,imageconfig) def create_predict_proba(self,learner_type,method): self.modelfile += ' def predict(self,X,features_names):' self.modelfile += '\n' self.modelfile += ' return self.model.predict_proba(X)' def create_forcast(self,method,no_of_prediction): self.modelfile += ' def predict(self,X,features_names):' self.modelfile += '\n' self.modelfile += ' no_of_prediction = '+str(no_of_prediction) self.modelfile += '\n' self.modelfile += ' lag_order = self.model.k_ar' self.modelfile += '\n' self.modelfile += ' return self.model.forecast(X.values[-lag_order:],steps=no_of_prediction)' def create_predict(self,learner_type,method,model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,feature,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scoreParam=None): scorePrm = scoreParam cs.create_predict(self,learner_type,method,model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,feature,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scorePrm) def save_model_deploy(self,outputfolder,modelname): #filename = outputfolder+modelname+'.py' filename = os.path.join(outputfolder,'script','trained_model.py') self.log.info('-------> Model File Location :'+filename) f = open(filename, "w",encoding="utf-8") f.write(str(self.modelfile)) f.close() def create_TextCleaner(self,outputfolder): profilerPath = os.path.join(outputfolder,'profiler') try: os.makedirs(profilerPath) except OSError: self.log.info("ProfilePath Folder Already Exists") try: textprofileFileLocation = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','profiler','textDataProfiler.py') initFileLocation = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','profiler','__init__.py') shutil.copy2(textprofileFileLocation,profilerPath) shutil.copy2(initFileLocation,profilerPath) ''' if(platform.system() == 'Windows'): shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'\\..\\profiler\\textDataProfiler.py',profilerPath) shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'\\..\\profiler\\__init__.py',profilerPath) else: shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'/../profiler/textDataProfiler.py',profilerPath) shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'/../profiler/__init__.py',profilerPath) ''' except OSError: self.log.info("Copy to Profiler Path Failed") def listToString(self,s): str1='[' for feature in s: if(str1 != '['): str1 += ',' str1 += '"'+feature+'"' str1+=']' return str1 def print_files(self): self.log.info(self.modelfile) def create_util_folder(self, deploy_path,learner_type): import tarfile ext_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..','utilities')) for x in os.listdir(ext_path): if x.endswith('.tar'): if x not in ['scikit_surprise-1.1.1.dist-info.tar','surprise.tar']: tarPackage = os.path.join(ext_path, x) my_tar = tarfile.open(tarPackage) my_tar.extractall(deploy_path) my_tar.close() else: if learner_type == 'RecommenderSystem': tarPackage = os.path.join(ext_path, x) my_tar = tarfile.open(tarPackage) my_tar.extractall(deploy_path) my_tar.close() def deploy_model(self,deploy_name,deployJson,learner_type,model_type,model,scoreParam,saved_model,deploy_path,features,profiler,datalocation,output_label,column_merge_flag,textFeatures,numericalFeatures,nonNumericFeatures,preprocessing_pipe,numericToLabel_json,threshold,loss_matrix,optimizer,firstDocFeature,secondDocFeature,padding_length,trained_data_file,dictDiffCount,targetFeature,normalizer,normFeatures,pcaModel_pickle_file,bpca_features,apca_features,optimizationmethod,deployFolder,iterName,iterVersion,wordToNumericFeatures,imageconfig,sessonal_freq,additional_regressors,grouperbyjson,rowfilterexpression,xtrain,profiled_data_file,conversion_method,modelFeatures,indexFeature,lag_order,scalertransformationFile,no_of_prediction,preprocess_pipe,preprocess_out_columns, label_encoder,datetimeFeature,usecaseLocation,config=None): try: serviceName = '{}{}{}'.format(iterName, '_' if iterVersion != '' else '', iterVersion) self.log.info('-------> Deploy Location :'+deploy_path) if production.is_supported(model_type.lower()): if learner_type == 'Text Similarity': coder = production.get_deployer(learner_type) coder.create_file(deploy_path, preprocessing_pipe, saved_model, firstDocFeature, secondDocFeature) elif model_type.lower() in ['classification', 'regression','clustering','timeseriesforecasting']: params = {} params['usecase_name']= iterName params['usecase_ver']= iterVersion params['features']={} params['features']['input_feat'] = config['profiler']['input_features'] params['features']['target_feat'] = targetFeature params['features']['text_feat'] = textFeatures params['paths']={} params['paths']['deploy'] = Path(deploy_path) params['paths']['usecase'] = params['paths']['deploy'].parent params['profiler']=config['profiler'] if 'code' in config.get('preprocess',{}).keys(): params['profiler']['preprocess']=config['preprocess'] params['selector']={} params['selector']['reducer']=True if pcaModel_pickle_file else False params['selector']['reducer_file']=pcaModel_pickle_file if pcaModel_pickle_file: params['selector']['input_features']=bpca_features params['selector']['output_features']=apca_features else: params['selector']['input_features']=config['profiler']['input_features'] params['selector']['output_features']=features params['training']={} params['training']['algo']= model params['training']['model_file']=saved_model if model_type.lower() == 'timeseriesforecasting': if params['training']['algo'] in ['LSTM','MLP','ENCODER_DECODER_LSTM_MVI_UVO']: params['training']['lag_order'] = int(lag_order) params['training']['scaler_file'] = Path(scalertransformationFile).name elif params['training']['algo'] == 'VAR': params['training']['dictDiffCount'] = dictDiffCount params['training']['no_of_prediction'] = no_of_prediction elif params['training']['algo'] == 'FBPROPHET': params['training']['sessonal_freq'] = sessonal_freq params['training']['additional_regressors'] = additional_regressors self.log.info(params) deployer = production.get_deployer(model_type.lower(), params=params) deployer.run( ) self.log.info('Status:- |... Model deployment files created') self.log.info('Status:- |... Model deployment completed') return else: # for output_formatter.py from prediction_package.output_formatter import outputFormatter outputObj = outputFormatter() outputObj.crate_output_format_file(deploy_path, learner_type, model_type, model, output_label, threshold, trained_data_file, dictDiffCount, targetFeature, features,datetimeFeature) #for aion_predict.py from prediction_package.aion_prediction import aionPrediction predictionObj = aionPrediction() # print(deploy_path) predictionObj.create_prediction_file(deploy_name, deploy_path, learner_type, grouperbyjson,rowfilterexpression,model_type,datetimeFeature) # for aion_service.py predictionObj.create_model_service(deploy_path, serviceName, model_type) # for aion_publish.py predictionObj.create_publish_service(usecaseLocation, iterName, iterVersion, model_type) if learner_type.lower()=="recommendersystem": # Task 11190--- #For recommender system from prediction_package.recommender_code import generate_recommender_code generate_recommender_code(deploy_path) return #self.create_TextCleaner(deploy_path) if(len(textFeatures) > 0): self.create_TextCleaner(deploy_path) self.include_import_file(learner_type,deployJson['method'],scoreParam, model_type,model) if((learner_type == 'TS' and model.lower() not in ['lstm','mlp','var']) or learner_type == 'RecommenderSystem'): features=[] self.create_class(deploy_name) if len(bpca_features) != 0: self.create_profiler_file(learner_type,deploy_path,profiler,bpca_features,numericToLabel_json,column_merge_flag,textFeatures,preprocessing_pipe,firstDocFeature,secondDocFeature,normalizer,normFeatures,wordToNumericFeatures,conversion_method,model_type,preprocess_pipe,preprocess_out_columns, label_encoder, model, config,datetimeFeature) else: self.create_profiler_file(learner_type,deploy_path,profiler,features,numericToLabel_json,column_merge_flag,textFeatures,preprocessing_pipe,firstDocFeature,secondDocFeature,normalizer,normFeatures,wordToNumericFeatures,conversion_method,model_type,preprocess_pipe,preprocess_out_columns, label_encoder, model, config,datetimeFeature) self.create_selector_file(deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature,model_type, model,config) self.create_init_function_for_classification(saved_model,'classes',learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,model,model_type,imageconfig) except Exception as e: print(e) import traceback exception_type, exception_object, exception_traceback = sys.exc_info() filename = exception_traceback.tb_frame.f_code.co_filename line_number = exception_traceback.tb_lineno self.log.info("Exception type: ", exception_type) self.log.info("File name: ", filename) self.log.info("Line number: ", line_number) self.log.info("multivariate model build error traceback: \n"+str(traceback.print_exc())) raise Exception(e) #print(model) if(model.lower() == 'var'): self.log.info("Create Forecast Function") self.create_forcast(deployJson['method'],no_of_prediction) else: self.create_predict(learner_type,deployJson['method'],model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,features,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scoreParam) self.save_model_deploy(deploy_path,deploy_name) if(len(textFeatures) > 0): if model_type.lower() == 'classification' or model_type.lower() == 'regression' or model_type.lower() == 'timeseriesforecasting': predictionObj.create_text_drift_file(deploy_path,textFeatures,targetFeature,model_type) if model_type.lower() == 'classification': predictionObj.create_classification_text_performance_file(deploy_path,textFeatures,targetFeature) elif model_type.lower() == 'regression': predictionObj.create_regression_text_performance_file(deploy_path,textFeatures,targetFeature) else: if model_type.lower() == 'classification' or model_type.lower() == 'regression' or model_type.lower() == 'timeseriesforecasting': #task 11997 predictionObj.create_drift_file(deploy_path,features,targetFeature,model_type) if model_type.lower() == 'classification': predictionObj.create_classification_performance_file(deploy_path,features,targetFeature) elif model_type.lower() == 'regression': predictionObj.create_regression_performance_file(deploy_path,features,targetFeature) self.log.info('Status:- |... Model deployment files created') self.crate_readme_file(deploy_path,saved_model,features,deployJson['method']) from prediction_package.requirements import requirementfile requirementfile(deploy_path,model,textFeatures,learner_type) os.chdir(deploy_path) textdata = False if(learner_type == 'Text Similarity' or len(textFeatures) > 0): textdata = True self.create_util_folder(deploy_path,learner_type) self.log.info('Status:- |... Model deployment completed') def deployTSum(self,deploy_path,preTrainedModellocation): def create_predict(preTrainedModellocation): text = f""" import sys import json def predict(data): try: import pandas as pd import numpy as np from pathlib import Path keywordsFile =Path(__file__).parent/'data'/'keywordDataBase.csv' outputSumFile =Path(__file__).parent/'data'/'summarizedOutput.csv' fileName=data #print("fileName---",fileName) inputDataFileFrame = pd.DataFrame() inputDataFileFrame['Sentences']="" rowIndex=0 if fileName.endswith(".pdf"): from pypdf import PdfReader reader = PdfReader(fileName) number_of_pages = len(reader.pages) text="" textOutputForFile="" OrgTextOutputForFile="" for i in range(number_of_pages) : page = reader.pages[i] text1 = page.extract_text() text=text+text1 import nltk tokens = nltk.sent_tokenize(text) for sentence in tokens: sentence=sentence.replace("\\n", " ") if (len(sentence.split()) < 4 ) or (len(str(sentence.split(',')).split()) < 8)or (any(chr.isdigit() for chr in sentence)) : continue inputDataFileFrame.at[rowIndex,'Sentences']=str(sentence.strip()) rowIndex=rowIndex+1 if fileName.endswith(".txt"): data=[] with open(fileName, "r",encoding="utf-8") as f: data.append(f.read()) str1 = "" for ele in data: str1 += ele sentences=str1.split(".") count=0 for sentence in sentences: count += 1 inputDataFileFrame.at[rowIndex,'Sentences']=str(sentence.strip()) rowIndex=rowIndex+1 inputDataFileFrame['LabelByKw']=0 #print(inputDataFileFrame) keywordsFileFrame=pd.read_csv(keywordsFile,encoding='utf-8') Keyword_list = keywordsFileFrame['Keyword'].tolist() for i in inputDataFileFrame.index: for x in Keyword_list: if (str(inputDataFileFrame["Sentences"][i])).lower().find(x) != -1: inputDataFileFrame['LabelByKw'][i]=1 break import pickle from sklearn.preprocessing import LabelEncoder pkl_filename='classificationModel.sav' pkl_filename =Path(__file__).parent/'model'/'classificationModel.sav' with open(pkl_filename, 'rb') as file: pickle_model = pickle.load(file) testsample=inputDataFileFrame[["Sentences"]] labelencoder = LabelEncoder() testsample["Sentences"] = labelencoder.fit_transform(testsample["Sentences"]) y_predicted = pickle_model.predict_proba(testsample) df=pd.DataFrame({{"SectionName":np.nan,"Sentences":np.nan, "Predicted_Prob":y_predicted[:,1]}}) df['LabelByModel']=df['Predicted_Prob'].apply(lambda x: 0 if x <= 0.5 else 1 ) inputDataFileFrame['LabelByModel']= df['LabelByModel'] textToSum="" for i in inputDataFileFrame.index: if (inputDataFileFrame['LabelByModel'][i] or inputDataFileFrame['LabelByKw'][i]) : textToSum=textToSum+" "+inputDataFileFrame["Sentences"][i] stdir=r"{preTrainedModellocation}" stdir = stdir.replace('\\\\', '\\\\\\\\') from transformers import AutoTokenizer, AutoModelForSeq2SeqLM modelbert = AutoModelForSeq2SeqLM.from_pretrained(stdir,local_files_only=True) tokenizer = AutoTokenizer.from_pretrained(stdir,local_files_only=True) inputs = tokenizer("summarize: " + textToSum, return_tensors="pt", max_length=512, truncation=True) outputs = modelbert.generate(inputs["input_ids"], max_length=512, min_length=140, length_penalty=2.0, num_beams=4, early_stopping=True) summarizedOutputOfSection= tokenizer.decode(outputs[0]) summarizedOutputOfSection=summarizedOutputOfSection.replace("</s>","") summarizedOutputOfSection=summarizedOutputOfSection.replace("<s>","") sumDatadata = [summarizedOutputOfSection] df = pd.DataFrame(sumDatadata, columns=['textSum']) df.to_csv(outputSumFile,encoding='utf-8') outputjson = {{"status":"SUCCESS","msg":"Press Download button to download summarized output","data":summarizedOutputOfSection}} print("predictions:",json.dumps(outputjson)) return (json.dumps(outputjson)) except KeyError as e: output = {{"status":"FAIL","message":str(e).strip('"')}} print("predictions:",json.dumps(output)) return (json.dumps(output)) except Exception as e: output = {{"status":"FAIL","message":str(e).strip('"')}} print("predictions:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = predict(sys.argv[1]) """ return text deploy_path = Path(deploy_path) aion_prediction = deploy_path/'aion_predict.py' with open(aion_prediction, 'w') as f: f.write(create_predict(preTrainedModellocation)) #task 11190: Item based Recommender system---Usnish import os def generate_recommender_code(deployPath): code = """ import pandas as pd import numpy as np import os ITEMID = 'itemId' DATA_FOLDER = 'data' USER_ITEM_MATRIX = 'user_item_matrix.csv' ITEM_SIMILARITY_MATRIX = 'item_similarity_matrix.csv' RATING = 'rating' SIMILARITY_SCORE = 'similarity_score' class collaborative_filter(object): def __init__(self): self.matrix = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', DATA_FOLDER, USER_ITEM_MATRIX),index_col=0) self.matrix.index.name = ITEMID self.item_similarity_cosine = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', DATA_FOLDER, ITEM_SIMILARITY_MATRIX)) self.item_similarity_cosine.index.name = ITEMID self.item_similarity_cosine.columns.name = ITEMID def item_based_rec(self,picked_userid, number_of_recommendations,number_of_similar_items=5): import operator if not isinstance(picked_userid,str): picked_userid = str(picked_userid) if picked_userid not in self.matrix.columns: raise KeyError("UserID Does Not Exist") # Movies that the target user has not watched try: picked_userid_unwatched = pd.DataFrame(self.matrix[picked_userid].isna()).reset_index() picked_userid_unwatched = picked_userid_unwatched[picked_userid_unwatched[picked_userid] == True][ITEMID].values.tolist() # Movies that the target user has watched picked_userid_watched = pd.DataFrame(self.matrix[picked_userid].dropna(axis=0, how='all') \ .sort_values(ascending=False)) \ .reset_index() \ .rename(columns={picked_userid: 'rating'}) # Dictionary to save the unwatched movie and predicted rating pair rating_prediction = {} # Loop through unwatched movies for picked_movie in picked_userid_unwatched: if not isinstance(picked_movie,str): picked_movie = str(picked_movie) # Calculate the similarity score of the picked movie with other movies try: picked_movie_similarity_score = self.item_similarity_cosine[[picked_movie]].reset_index().rename( columns={picked_movie: SIMILARITY_SCORE}) # Rank the similarities between the picked user watched movie and the picked unwatched movie. picked_userid_watched_similarity = pd.merge(left=picked_userid_watched, right=picked_movie_similarity_score, on=ITEMID, how='inner') \ .sort_values(SIMILARITY_SCORE, ascending=False)[ :number_of_similar_items] # Calculate the predicted rating using weighted average of similarity scores and the ratings from picked user try: predicted_rating = round(np.average(picked_userid_watched_similarity[RATING],weights=picked_userid_watched_similarity[SIMILARITY_SCORE]), 6) except Exception as e: predicted_rating = 0 # Save the predicted rating in the dictionary rating_prediction[picked_movie] = predicted_rating except Exception as e: rating_prediction[picked_movie] = 0 # Return the top recommended movies return sorted(rating_prediction.items(), key=operator.itemgetter(1), reverse=True)[:number_of_recommendations] except Exception as e: print(e) raise KeyError(str(e)) def predict(self,X): predictions = [] for index,row in X.iterrows(): score = self.item_based_rec(int(row["uid"]),int(row["numberOfRecommendation"])) df = pd.DataFrame(score,columns=['ItemId','Ratings']) predictions.append(df) return predictions""" filename = os.path.join(deployPath, 'script', 'item_recommendation.py') # print(deploy_path) f = open(filename, "wb") f.write(str(code).encode('utf8')) f.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import json import shutil import logging class aionPrediction: def __init__(self): self.log = logging.getLogger('eion') def create_optimus_prediction_file (self,classname,deploy_path,learner_type): self.predictionFile = 'import warnings' self.predictionFile += '\n' self.predictionFile += 'warnings.filterwarnings("ignore")' self.predictionFile += '\n' self.predictionFile += 'import json' self.predictionFile += '\n' self.predictionFile += 'import os' self.predictionFile += '\n' self.predictionFile += 'import sys' self.predictionFile += '\n' self.predictionFile += 'import pandas as pd' self.predictionFile += '\n' self.predictionFile += 'from pandas import json_normalize' self.predictionFile += '\n' self.predictionFile += 'from importlib import import_module' self.predictionFile += '\n' self.predictionFile += 'import importlib.util' self.predictionFile += '\n' self.predictionFile += 'class prediction:' self.predictionFile += '\n' self.predictionFile += ' def predict_from_json(self,json_data):' self.predictionFile += '\n' self.predictionFile += ' data = json.loads(json_data)' self.predictionFile += '\n' self.predictionFile += ' output=self.predict(data)' self.predictionFile += '\n' self.predictionFile += ' print("predictions:",output)' self.predictionFile += '\n' self.predictionFile += '\n' self.predictionFile += ' def predict_from_file(self,filename):' self.predictionFile += '\n' self.predictionFile += ' with open(filename,\'r\',encoding=\'utf-8\') as f:' self.predictionFile += '\n' self.predictionFile += ' data = json.load(f)' self.predictionFile += '\n' self.predictionFile += ' output=self.predict(data)' self.predictionFile += '\n' self.predictionFile += ' print("predictions:",output)' self.predictionFile += '\n' self.predictionFile += '\n' self.predictionFile += ' def predict(self,json_data):' self.predictionFile += '\n' self.predictionFile += ' try:' self.predictionFile += '\n' #self.predictionFile += ' jsonData = json.loads(json_data)' self.predictionFile += ' jsonData=json_data' self.predictionFile += '\n' self.predictionFile += ' model_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/trained_model.py")' self.predictionFile += '\n' self.predictionFile += ' model = importlib.util.module_from_spec(model_obj)' self.predictionFile += '\n' self.predictionFile += ' model_obj.loader.exec_module(model)' self.predictionFile += '\n' #if(learner_type != 'TextML'): self.predictionFile += ' profiler_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/inputprofiler.py")' self.predictionFile += '\n' self.predictionFile += ' inputprofiler = importlib.util.module_from_spec(profiler_obj)' self.predictionFile += '\n' self.predictionFile += ' profiler_obj.loader.exec_module(inputprofiler)' self.predictionFile += '\n' self.predictionFile += ' selector_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/selector.py")' self.predictionFile += '\n' self.predictionFile += ' selector = importlib.util.module_from_spec(selector_obj)' self.predictionFile += '\n' self.predictionFile += ' selector_obj.loader.exec_module(selector)' self.predictionFile += '\n' self.predictionFile += ' output_format_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/output_format.py")' self.predictionFile += '\n' self.predictionFile += ' output_format = importlib.util.module_from_spec(output_format_obj)' self.predictionFile += '\n' self.predictionFile += ' output_format_obj.loader.exec_module(output_format)' self.predictionFile += '\n' self.predictionFile += ' df = json_normalize(jsonData)' self.predictionFile += '\n' self.predictionFile += ' df0 = df.copy()' self.predictionFile += '\n' #if(learner_type != 'TextML'): self.predictionFile += ' profilerobj = inputprofiler.inputprofiler()' self.predictionFile += '\n' self.predictionFile += ' df = profilerobj.apply_profiler(df)' self.predictionFile += '\n' self.predictionFile += ' selectobj = selector.selector()' self.predictionFile += '\n' self.predictionFile += ' df = selectobj.apply_selector(df)' self.predictionFile += '\n' self.predictionFile += ' output = model.trained_model().predict(df,"")' self.predictionFile += '\n' self.predictionFile += ' outputobj = output_format.output_format()' self.predictionFile += '\n' self.predictionFile += ' output = outputobj.apply_output_format(df0,output)' #self.predictionFile += '\n' #self.predictionFile += ' print(output)' self.predictionFile += '\n' self.predictionFile += ' return output' self.predictionFile += '\n' self.predictionFile += ' except KeyError as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' return json.dumps(output)' self.predictionFile += '\n' self.predictionFile += ' except Exception as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' return json.dumps(output)' self.predictionFile += '\n' self.predictionFile += '\n' self.predictionFile += 'if __name__ == "__main__":' self.predictionFile += '\n' self.predictionFile += ' predictobj = prediction()' self.predictionFile += '\n' self.predictionFile += ' predictobj.predict_from_file(sys.argv[1])' self.predictionFile += '\n' filename = os.path.join(deploy_path,'prediction.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_text_drift_file(self,deploy_path,features,target,model_type): #task-14549 self.predictionFile = 'import warnings' self.predictionFile += '\n' self.predictionFile += 'warnings.filterwarnings("ignore")' self.predictionFile += '\n' self.predictionFile += 'import json' self.predictionFile += '\n' self.predictionFile += 'import os' self.predictionFile += '\n' self.predictionFile += 'import sys' self.predictionFile += '\n' self.predictionFile += 'import pandas as pd' self.predictionFile += '\n' self.predictionFile += 'from monitoring import check_drift' self.predictionFile += '\n' self.predictionFile += 'def drift(data):' self.predictionFile += '\n' self.predictionFile += ' try:' self.predictionFile += '\n' self.predictionFile += ' if os.path.splitext(data)[1] == ".json":' self.predictionFile += '\n' self.predictionFile += ' with open(data,\'r\',encoding=\'utf-8\') as f:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.load(f)' self.predictionFile += '\n' self.predictionFile += ' else:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.loads(data)' self.predictionFile += '\n' self.predictionFile += ' jsonData[\'features\'] = \''+",".join([feature for feature in features])+'\'' self.predictionFile += '\n' self.predictionFile += ' jsonData[\'target\'] = \''+target+'\'' self.predictionFile += '\n' if model_type.lower() != 'timeseriesforecasting': #task 11997 self.predictionFile += ' htmlfilepath=evidently_details(jsonData)' self.predictionFile += '\n' else: self.predictionFile += ' htmlfilepath=\'\'' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.dumps(jsonData)' self.predictionFile += '\n' self.predictionFile += ' output = check_drift(jsonData)' self.predictionFile += '\n' self.predictionFile += ' output = json.loads(output)' self.predictionFile += '\n' self.predictionFile += ' output[\'htmlPath\'] = str(htmlfilepath)' self.predictionFile += '\n' self.predictionFile += ' print("drift:", json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return(output)' self.predictionFile += '\n' self.predictionFile += ' except KeyError as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("drift:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' except Exception as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("drift:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' if model_type.lower() != 'timeseriesforecasting': #task 11997 self.predictionFile += 'def evidently_details(deployJson):' self.predictionFile += '\n' self.predictionFile += ' features = deployJson[\'features\'].split(\',\')' self.predictionFile += '\n' self.predictionFile += ' target = deployJson[\'target\']' self.predictionFile += '\n' self.predictionFile += """\ try: from evidently.report import Report from evidently.metrics import TextDescriptorsDriftMetric, ColumnDriftMetric from evidently.pipeline.column_mapping import ColumnMapping from sklearn.preprocessing import LabelEncoder historicaldataFrame=pd.read_csv(deployJson['trainingDataLocation'],skipinitialspace = True,na_values=['-','?']) currentdataFrame=pd.read_csv(deployJson['currentDataLocation'],skipinitialspace = True,na_values=['-','?']) historicaldataFrame.columns = historicaldataFrame.columns.str.strip() currentdataFrame.columns = currentdataFrame.columns.str.strip() hdf = historicaldataFrame.dropna(subset=features) cdf = currentdataFrame.dropna(subset=features) hdf['Text_Features'] = hdf[features].apply("-".join, axis=1) cdf['Text_Features'] = cdf[features].apply("-".join, axis=1) hdf['target'] = historicaldataFrame[target] cdf['target'] = currentdataFrame[target] le = LabelEncoder() le.fit(hdf['target']) hdf['target'] = le.transform(hdf['target']) le.fit(cdf['target']) cdf['target'] = le.transform(cdf['target']) hd = hdf[['Text_Features', 'target']] cd = cdf[['Text_Features', 'target']] column_mapping = ColumnMapping() column_mapping.target = 'target' column_mapping.prediction = 'target' column_mapping.text_features = ['Text_Features'] column_mapping.numerical_features = [] column_mapping.categorical_features = [] performance_report = Report(metrics=[ColumnDriftMetric('target'),TextDescriptorsDriftMetric(column_name='Text_Features')]) performance_report.run(reference_data=hd, current_data=cd,column_mapping=column_mapping) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),"log","My_report.html") performance_report.save_html(report) return(report) except Exception as e: print('Error: ', e) return('NA')""" self.predictionFile += '\n' self.predictionFile += 'if __name__ == "__main__":' self.predictionFile += '\n' self.predictionFile += ' output = drift(sys.argv[1])' filename = os.path.join(deploy_path,'aion_ipdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_drift_file(self,deploy_path,features,target,model_type): self.predictionFile = 'import warnings' self.predictionFile += '\n' self.predictionFile += 'warnings.filterwarnings("ignore")' self.predictionFile += '\n' self.predictionFile += 'import json' self.predictionFile += '\n' self.predictionFile += 'import os' self.predictionFile += '\n' self.predictionFile += 'import sys' self.predictionFile += '\n' self.predictionFile += 'import pandas as pd' self.predictionFile += '\n' self.predictionFile += 'from monitoring import check_drift' self.predictionFile += '\n' self.predictionFile += 'from pandas import json_normalize' self.predictionFile += '\n' self.predictionFile += 'from script.inputprofiler import inputprofiler' self.predictionFile += '\n' self.predictionFile += 'def drift(data):' self.predictionFile += '\n' self.predictionFile += ' try:' self.predictionFile += '\n' self.predictionFile += ' if os.path.splitext(data)[1] == ".json":' self.predictionFile += '\n' self.predictionFile += ' with open(data,\'r\',encoding=\'utf-8\') as f:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.load(f)' self.predictionFile += '\n' self.predictionFile += ' else:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.loads(data)' self.predictionFile += '\n' self.predictionFile += ' jsonData[\'features\'] = \''+",".join([feature for feature in features])+'\'' self.predictionFile += '\n' self.predictionFile += ' jsonData[\'target\'] = \''+target+'\'' self.predictionFile += '\n' if model_type.lower() != 'timeseriesforecasting': #task 11997 self.predictionFile += ' htmlfilepath=evidently_details(jsonData)' self.predictionFile += '\n' else: self.predictionFile += ' htmlfilepath=\'\'' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.dumps(jsonData)' self.predictionFile += '\n' self.predictionFile += ' output = check_drift(jsonData)' self.predictionFile += '\n' self.predictionFile += ' output = json.loads(output)' self.predictionFile += '\n' self.predictionFile += ' output[\'htmlPath\'] = str(htmlfilepath)' self.predictionFile += '\n' self.predictionFile += ' output = json.dumps(output)' self.predictionFile += '\n' self.predictionFile += ' print("drift:",output)' self.predictionFile += '\n' self.predictionFile += ' return(output)' self.predictionFile += '\n' self.predictionFile += ' except KeyError as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("drift:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' except Exception as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("drift:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' if model_type.lower() != 'timeseriesforecasting': #task 11997 self.predictionFile += 'def evidently_details(deployJson):' self.predictionFile += '\n' self.predictionFile += ' features = deployJson[\'features\'].split(\',\')' self.predictionFile += '\n' self.predictionFile += ' target = deployJson[\'target\']' self.predictionFile += '\n' self.predictionFile += """\ try: from evidently.report import Report from evidently.metric_preset import DataDriftPreset historicaldataFrame=pd.read_csv(deployJson['trainingDataLocation'],skipinitialspace = True,na_values=['-','?']) currentdataFrame=pd.read_csv(deployJson['currentDataLocation'],skipinitialspace = True,na_values=['-','?']) historicaldataFrame.columns = historicaldataFrame.columns.str.strip() currentdataFrame.columns = currentdataFrame.columns.str.strip() profilerobj = inputprofiler() historicaldataFramep = profilerobj.run(historicaldataFrame) currentdataFramep = profilerobj.run(currentdataFrame) hdf = historicaldataFramep[features] cdf = currentdataFramep[features] hdf['target'] = historicaldataFrame[target] cdf['target'] = currentdataFrame[target] data_drift_report = Report(metrics = [DataDriftPreset()]) data_drift_report.run(reference_data=hdf,current_data=cdf,column_mapping = None) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','my_report.html') data_drift_report.save_html(report) return(report) except Exception as e: print('Error') return('NA')""" self.predictionFile += '\n' self.predictionFile += 'if __name__ == "__main__":' self.predictionFile += '\n' self.predictionFile += ' output = drift(sys.argv[1])' filename = os.path.join(deploy_path,'aion_ipdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_prediction_file(self,classname,deploy_path,learner_type,grouperbyjson,rowfilterexpression,model_type,datetimeFeature): self.predictionFile = 'import warnings' self.predictionFile += '\n' self.predictionFile += 'warnings.filterwarnings("ignore")' self.predictionFile += '\n' self.predictionFile += 'import json' self.predictionFile += '\n' self.predictionFile += 'import os' self.predictionFile += '\n' self.predictionFile += 'import sys' self.predictionFile += '\n' self.predictionFile += 'import pandas as pd' self.predictionFile += '\n' self.predictionFile += 'from pandas import json_normalize' self.predictionFile += '\n' if(learner_type.lower() != 'recommendersystem'): #task 11190 self.predictionFile += 'from script.selector import selector' self.predictionFile += '\n' self.predictionFile += 'from script.inputprofiler import inputprofiler' self.predictionFile += '\n' #self.predictionFile += 'from '+classname+' import '+classname self.predictionFile += 'from script.trained_model import trained_model' self.predictionFile += '\n' else: self.predictionFile += 'from script.item_recommendation import collaborative_filter' self.predictionFile += '\n' self.predictionFile += 'from script.output_format import output_format' self.predictionFile += '\n' if (learner_type != 'RecommenderSystem'): #task 11190 self.predictionFile += 'profilerobj = inputprofiler()' self.predictionFile += '\n' self.predictionFile += 'selectobj = selector()' self.predictionFile += '\n' self.predictionFile += 'modelobj = trained_model()' self.predictionFile += '\n' else: self.predictionFile += 'colabobj = collaborative_filter()' self.predictionFile += '\n' self.predictionFile += 'outputobj = output_format()' self.predictionFile += '\n' self.predictionFile += 'def predict(data):' self.predictionFile += '\n' self.predictionFile += ' try:' self.predictionFile += '\n' self.predictionFile += ' if os.path.splitext(data)[1] == ".tsv":' self.predictionFile += '\n' self.predictionFile += ' df=pd.read_csv(data,encoding=\'utf-8\',sep=\'\\t\',skipinitialspace = True,na_values=[\'-\',\'?\'])' self.predictionFile += '\n' self.predictionFile += ' elif os.path.splitext(data)[1] == ".csv":' self.predictionFile += '\n' self.predictionFile += ' df=pd.read_csv(data,encoding=\'utf-8\',skipinitialspace = True,na_values=[\'-\',\'?\'])' self.predictionFile += '\n' self.predictionFile += ' elif os.path.splitext(data)[1] == ".dat":' self.predictionFile += '\n' self.predictionFile += ' df=pd.read_csv(data,encoding=\'utf-8\',skipinitialspace = True,na_values=[\'-\',\'?\'])' self.predictionFile += '\n' self.predictionFile += ' else:' self.predictionFile += '\n' self.predictionFile += ' if os.path.splitext(data)[1] == ".json":' self.predictionFile += '\n' self.predictionFile += ' with open(data,\'r\',encoding=\'utf-8\') as f:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.load(f)' self.predictionFile += '\n' self.predictionFile += ' else:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.loads(data)' self.predictionFile += '\n' self.predictionFile += ' df = json_normalize(jsonData)' self.predictionFile += '\n' self.predictionFile += ' df.rename(columns=lambda x: x.strip(), inplace=True)' self.predictionFile += '\n' if str(rowfilterexpression) != '': self.predictionFile += ' filterexpression = "'+rowfilterexpression+'"' self.predictionFile += '\n' self.predictionFile += ' df = df.query(filterexpression)' self.predictionFile += '\n' #print(grouperbyjson) if str(grouperbyjson) != '': datetime = grouperbyjson['datetime'] unit = grouperbyjson['unit'] if unit == '': self.predictionFile += ' df[\'date\'] = pd.to_datetime(df[\''+datetime+'\'])' self.predictionFile += '\n' else: self.predictionFile += ' df[\'date\'] = pd.to_datetime(df[\''+datetime+'\'],unit=\''+unit+'\')' self.predictionFile += '\n' self.predictionFile += ' df = df.reset_index()' self.predictionFile += '\n' self.predictionFile += ' df.set_index(\'date\',inplace=True)' self.predictionFile += '\n' self.predictionFile += ' df = df.'+grouperbyjson['groupbystring'] self.predictionFile += '\n' self.predictionFile += ' df.columns = df.columns.droplevel(0)' self.predictionFile += '\n' self.predictionFile += ' df = df.reset_index()' self.predictionFile += '\n' self.predictionFile += ' df0 = df.copy()' self.predictionFile += '\n' if(learner_type != 'RecommenderSystem'): #task 11190 if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na': self.predictionFile += ' df,datetimeFeature = profilerobj.apply_profiler(df)' self.predictionFile += '\n' else: self.predictionFile += ' df = profilerobj.apply_profiler(df)' self.predictionFile += '\n' self.predictionFile += ' df = selectobj.apply_selector(df)' self.predictionFile += '\n' #self.predictionFile += ' modelobj = '+classname+'()' self.predictionFile += ' output = modelobj.predict(df,"")' self.predictionFile += '\n' else: self.predictionFile += ' output = colabobj.predict(df)' self.predictionFile += '\n' if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na': self.predictionFile += ' output = outputobj.apply_output_format(df0,output,datetimeFeature)' self.predictionFile += '\n' else: self.predictionFile += ' output = outputobj.apply_output_format(df0,output)' self.predictionFile += '\n' self.predictionFile += ' print("predictions:",output)' self.predictionFile += '\n' self.predictionFile += ' return(output)' self.predictionFile += '\n' self.predictionFile += ' except KeyError as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("predictions:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' except Exception as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("predictions:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' self.predictionFile += 'if __name__ == "__main__":' self.predictionFile += '\n' self.predictionFile += ' output = predict(sys.argv[1])' filename = os.path.join(deploy_path,'aion_predict.py') f = open(filename, "w") f.write(str(self.predictionFile)) f.close() def create_classification_text_performance_file(self,deploy_path,features,target): features = ",".join([feature for feature in features]) self.predictionFile = """\ import pandas as pd import warnings warnings.filterwarnings("ignore") import json import os import sys from pandas import json_normalize # from evidently.dashboard import Dashboard # from evidently.tabs import ClassificationPerformanceTab from evidently.pipeline.column_mapping import ColumnMapping from aion_predict import predict from evidently.report import Report from evidently.pipeline.column_mapping import ColumnMapping from evidently.metric_preset import ClassificationPreset def odrift(data): try: """ self.predictionFile += ' features = \''+features+'\'' self.predictionFile += '\n' self.predictionFile += ' target = \''+target+'\'' self.predictionFile += '\n' self.predictionFile +="""\ if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) production = predict().run(jsonData['currentDataLocation']) reference = predict().run(jsonData['trainingDataLocation']) production = json.loads(production) reference = json.loads(reference) if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'): production = production['data'] production = json_normalize(production) reference = reference['data'] reference = json_normalize(reference) production['target'] = production[target] reference['target'] = reference[target] column_mapping = ColumnMapping() column_mapping.target = target column_mapping.prediction = 'prediction' column_mapping.datetime = None column_mapping.text_features = features.split(',') iris_model_performance_dashboard = Report(metrics=[ClassificationPreset()]) iris_model_performance_dashboard.run(reference_data=reference, current_data=production,column_mapping=column_mapping) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html') iris_model_performance_dashboard.save_html(report) metrics_output = iris_model_performance_dashboard.as_dict() output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']} print("drift:",json.dumps(output)) return (json.dumps(output)) except KeyError as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) except Exception as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = odrift(sys.argv[1])""" filename = os.path.join(deploy_path,'aion_opdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_classification_performance_file(self,deploy_path,features,target): features = ",".join([feature for feature in features]) self.predictionFile = """\ import pandas as pd import warnings warnings.filterwarnings("ignore") import json import os import sys from pandas import json_normalize from evidently.report import Report from evidently.metric_preset import ClassificationPreset from evidently.pipeline.column_mapping import ColumnMapping from aion_predict import predict def odrift(data): try: """ self.predictionFile += ' features = \''+features+'\'' self.predictionFile += '\n' self.predictionFile += ' target = \''+target+'\'' self.predictionFile += '\n' self.predictionFile +="""\ if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) production = predict().run(jsonData['currentDataLocation']) reference = predict().run(jsonData['trainingDataLocation']) production = json.loads(production) reference = json.loads(reference) if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'): production = production['data'] production = json_normalize(production) reference = reference['data'] reference = json_normalize(reference) production['target'] = production[target] reference['target'] = reference[target] column_mapping = ColumnMapping() column_mapping.target = target column_mapping.prediction = 'prediction' column_mapping.datetime = None column_mapping.numerical_features = features.split(',') model_performance_dashboard = Report(metrics = [ClassificationPreset()]) model_performance_dashboard.run(reference_data =reference, current_data =production, column_mapping = column_mapping) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html') model_performance_dashboard.save_html(report) metrics_output = model_performance_dashboard.as_dict() output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']} print("drift:",json.dumps(output)) return (json.dumps(output)) else: output = {"status":"SUCCESS","htmlPath":'NA'} print("drift:",json.dumps(output)) return (json.dumps(output)) except KeyError as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) except Exception as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = odrift(sys.argv[1])""" filename = os.path.join(deploy_path,'aion_opdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_model_service(self,deploy_path,serviceName,problemType): filedata = """ from flask import Flask, jsonify, request from flask_restful import Resource, Api from aion_predict import predict""" if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ from aion_xai import local_analysis from aion_ipdrift import drift from aion_opdrift import odrift""" filedata += """ import json import os import pandas as pd import io import argparse from pathlib import Path from flask_cors import CORS, cross_origin app = Flask(__name__) #cross origin resource from system arguments parser = argparse.ArgumentParser() parser.add_argument('-ip', '--ipaddress', help='IP Address') parser.add_argument('-p', '--port', help='Port Number') parser.add_argument("-cors", type=str, required=False) d = vars(parser.parse_args()) modelPath = Path(__file__).parent try: with open( (modelPath/'etc')/'display.json', 'r') as f: disp_data = json.load(f) is_explainable = not disp_data.get('textFeatures') except: disp_data = {} is_explainable = True if "cors" in d.keys(): if d["cors"] != '' and d["cors"] != None: d["cors"] = [s.strip() for s in d["cors"].split(",")] #cors = CORS(app, resources={r"/AION/*": {"origins": ["http://localhost", "http://localhost:5000"]}}) cors = CORS(app, resources={r"/AION/*": {"origins": d["cors"]}}) api = Api(app) class predictapi(Resource): def get(self): features = disp_data.get('modelFeatures') if features: msg=\""" RequestType: POST Content-Type=application/json Body: {displaymsg} \""".format(displaymsg={ x:'Value' for x in features}) else: displaymsg='Data in JSON Format' return jsonify(displaymsg) def post(self): data = request.get_json() output = predict().run(json.dumps(data)) return jsonify(json.loads(output)) class predictfileapi(Resource): def post(self): if 'file' in request.files: file = request.files['file'] urlData = file.read() rawData = pd.read_csv(io.StringIO(urlData.decode('utf-8'))) data = rawData.to_json(orient='records') output = predict().run(data) return jsonify(json.loads(output)) else: displaymsg='File is mising' return jsonify(displaymsg) def get(self): msg=\""" RequestType: POST Body:send file content in body\""" return jsonify(msg) """ if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ class explainapi(Resource): def get(self): features = disp_data.get('modelFeatures') if features: msg=\""" RequestType: POST Content-Type=application/json Body: {displaymsg} \""".format(displaymsg={ x:'Value' for x in features}) else: displaymsg='Data in JSON Format' return jsonify(displaymsg) def post(self): data = request.get_json() if is_explainable: output = local_analysis(json.dumps(data)) else: output = json.dumps({"status":"FAIL","data":"explain api is not supported when text features are used for training"}) return jsonify(json.loads(output)) class monitoringapi(Resource): def get(self): return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'}) def post(self): data = request.get_json() output = drift(json.dumps(data)) return jsonify(json.loads(output)) class performanceapi(Resource): def get(self): return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'}) def post(self): data = request.get_json() output = odrift(json.dumps(data)) return jsonify(json.loads(output)) """ filedata += """ api.add_resource(predictapi, '/AION/{serviceName}/predict')""".format(serviceName=serviceName) filedata += """ api.add_resource(predictfileapi, '/AION/{serviceName}/predict_file')""".format(serviceName=serviceName) if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ api.add_resource(explainapi, '/AION/{serviceName}/explain') api.add_resource(monitoringapi, '/AION/{serviceName}/monitoring') api.add_resource(performanceapi, '/AION/{serviceName}/performance')""".format(serviceName=serviceName) filedata += """ if __name__ == '__main__': args = parser.parse_args() app.run(args.ipaddress,port = args.port,debug = True)""" filename = os.path.join(deploy_path,'aion_service.py') f = open(filename, "wb") f.write(str(filedata).encode('utf8')) f.close() def create_regression_performance_file(self,deploy_path,features,target): features = ",".join([feature for feature in features]) self.predictionFile = """\ import pandas as pd import warnings warnings.filterwarnings("ignore") import json import os import sys from pandas import json_normalize from evidently.report import Report from evidently.metric_preset import RegressionPreset from evidently.pipeline.column_mapping import ColumnMapping from aion_predict import predict def odrift(data): try: """ self.predictionFile += ' features = \''+features+'\'' self.predictionFile += '\n' self.predictionFile += ' target = \''+target+'\'' self.predictionFile += '\n' self.predictionFile +="""\ if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) production = predict().run(jsonData['currentDataLocation']) reference = predict().run(jsonData['trainingDataLocation']) production = json.loads(production) reference = json.loads(reference) if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'): production = production['data'] production = json_normalize(production) reference = reference['data'] reference = json_normalize(reference) production['target'] = production[target] reference['target'] = reference[target] column_mapping = ColumnMapping() column_mapping.target = target column_mapping.prediction = 'prediction' column_mapping.datetime = None column_mapping.numerical_features = features.split(',') iris_model_performance_dashboard = Report(metrics=[RegressionPreset()]) iris_model_performance_dashboard.run(reference_data = reference, current_data = production, column_mapping = column_mapping) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html') iris_model_performance_dashboard.save_html(report) metrics_output = iris_model_performance_dashboard.as_dict() output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']} print("drift:",json.dumps(output)) return (json.dumps(output)) else: output = {"status":"SUCCESS","htmlPath":'NA'} print("drift:",json.dumps(output)) return (json.dumps(output)) except KeyError as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) except Exception as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = odrift(sys.argv[1])""" filename = os.path.join(deploy_path,'aion_opdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_regression_text_performance_file(self,deploy_path,features,target): features = ",".join([feature for feature in features]) self.predictionFile = """\ import pandas as pd import warnings warnings.filterwarnings("ignore") import json import os import sys from pandas import json_normalize from aion_predict import predict from evidently.report import Report from evidently.pipeline.column_mapping import ColumnMapping from evidently.metric_preset import RegressionPreset def odrift(data): try: """ self.predictionFile += ' features = \''+features+'\'' self.predictionFile += '\n' self.predictionFile += ' target = \''+target+'\'' self.predictionFile += '\n' self.predictionFile +="""\ if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) production = predict().run(jsonData['currentDataLocation']) reference = predict().run(jsonData['trainingDataLocation']) production = json.loads(production) reference = json.loads(reference) if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'): production = production['data'] production = json_normalize(production) reference = reference['data'] reference = json_normalize(reference) production['target'] = production[target] reference['target'] = reference[target] column_mapping = ColumnMapping() column_mapping.target = target column_mapping.prediction = 'prediction' column_mapping.datetime = None column_mapping.numerical_features = features.split(',') iris_model_performance_dashboard = Report(metrics=[RegressionPreset()]) iris_model_performance_dashboard.run(reference_data=reference, current_data=production,column_mapping=column_mapping) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html') iris_model_performance_dashboard.save_html(report) metrics_output = iris_model_performance_dashboard.as_dict() output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']} print("drift:",json.dumps(output)) return (json.dumps(output)) else: output = {"status":"SUCCESS","htmlPath":'NA'} print("drift:",json.dumps(output)) return (json.dumps(output)) except KeyError as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) except Exception as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = odrift(sys.argv[1])""" filename = os.path.join(deploy_path,'aion_opdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_publish_service(self,datalocation,usecaseid,version,problemType): filename = os.path.join(datalocation,'aion_publish_service.py') if not os.path.exists(filename): filedata = """ import sys import json import time import sqlite3 import argparse import pandas as pd import io from pathlib import Path from datetime import datetime filename = Path(__file__).parent/'config.json' with open (filename, "r") as f: data = json.loads(f.read()) modelVersion = str(data['version']) modelPath = Path(__file__).parent/modelVersion sys.path.append(str(modelPath)) try: with open( (modelPath/'etc')/'display.json', 'r') as f: disp_data = json.load(f) is_explainable = not disp_data.get('textFeatures') except: disp_data = {} is_explainable = True from flask import Flask, jsonify, request from flask_restful import Resource, Api from flask_cors import CORS, cross_origin from flask import Response from aion_predict import predict """ if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ from aion_ipdrift import drift from aion_opdrift import odrift if is_explainable: from aion_xai import local_analysis """ filedata += """ dataPath = Path(__file__).parent/'data' dataPath.mkdir(parents=True, exist_ok=True) app = Flask(__name__) #cross origin resource from system arguments parser = argparse.ArgumentParser() parser.add_argument('-ip', '--ipaddress', help='IP Address') parser.add_argument('-p', '--port', help='Port Number') parser.add_argument("-cors", type=str, required=False) d = vars(parser.parse_args()) if "cors" in d.keys(): if d["cors"] != '' and d["cors"] != None: d["cors"] = [s.strip() for s in d["cors"].split(",")] #cors = CORS(app, resources={r"/AION/*": {"origins": ["http://localhost", "http://localhost:5000"]}}) cors = CORS(app, resources={r"/AION/*": {"origins": d["cors"]}}) api = Api(app) class sqlite_db(): def __init__(self, location, database_file=None): if not isinstance(location, Path): location = Path(location) if database_file: self.database_name = database_file else: self.database_name = location.stem + '.db' db_file = str(location/self.database_name) self.conn = sqlite3.connect(db_file) self.cursor = self.conn.cursor() self.tables = [] def table_exists(self, name): if name in self.tables: return True elif name: query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';" listOfTables = self.cursor.execute(query).fetchall() if len(listOfTables) > 0 : self.tables.append(name) return True return False def read(self, table_name,condition=''): if condition == '': return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn) else: return pd.read_sql_query(f"SELECT * FROM {table_name} WHERE {condition}", self.conn) def create_table(self,name, columns, dtypes): query = f'CREATE TABLE IF NOT EXISTS {name} (' for column, data_type in zip(columns, dtypes): query += f"'{column}' TEXT," query = query[:-1] query += ');' self.conn.execute(query) return True def update(self,table_name,updates,condition): update_query = f'UPDATE {table_name} SET {updates} WHERE {condition}' self.cursor.execute(update_query) self.conn.commit() return True def write(self,data, table_name): if not self.table_exists(table_name): self.create_table(table_name, data.columns, data.dtypes) tuple_data = list(data.itertuples(index=False, name=None)) insert_query = f'INSERT INTO {table_name} VALUES(' for i in range(len(data.columns)): insert_query += '?,' insert_query = insert_query[:-1] + ')' self.cursor.executemany(insert_query, tuple_data) self.conn.commit() return True def delete(self, name): pass def close(self): self.conn.close()""" filedata += """ app = Flask(__name__) api = Api(app) class predictapi(Resource): def get(self): features = disp_data.get('modelFeatures') if features: msg=\""" RequestType: POST Content-Type=application/json Body: {displaymsg} \""".format(displaymsg={ x:'Value' for x in features}) else: displaymsg='Data in JSON Format' return jsonify(displaymsg) def post(self): sqlite_dbObj = sqlite_db(dataPath,'data.db') if not sqlite_dbObj.table_exists('metrices'): data = {'noOfPredictCalls':'0','noOfDriftCalls':'0',"noOfActualCalls":'0',"mid":'0'} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.create_table('metrices',data.columns, data.dtypes) data = request.get_json() output = predict().run(json.dumps(data)) outputobj = json.loads(output) if outputobj['status'] == 'SUCCESS': try: df2 = pd.read_json(json.dumps(outputobj['data']), orient ='records') if not sqlite_dbObj.table_exists('prodData'): sqlite_dbObj.create_table('prodData',df2.columns, df2.dtypes) sqlite_dbObj.write(df2,'prodData') except: pass try: data = sqlite_dbObj.read('metrices') #print(data) if len(data) == 0: data = [{'mid':'0','noOfPredictCalls':'1','noOfDriftCalls':'0',"noOfActualCalls":'0'}] data = pd.read_json(json.dumps(data), orient ='records') sqlite_dbObj.write(data,'metrices') else: noofPredictCalls = int(data['noOfPredictCalls'].iloc[0])+1 sqlite_dbObj.update('metrices',"noOfPredictCalls = '"+str(noofPredictCalls)+"'","mid = 0") except Exception as e: print(e) pass return jsonify(json.loads(output)) class predictfileapi(Resource): def post(self): sqlite_dbObj = sqlite_db(dataPath,'data.db') if not sqlite_dbObj.table_exists('metrices'): data = {'noOfPredictCalls':'0','noOfDriftCalls':'0',"noOfActualCalls":'0',"mid":'0'} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.create_table('metrices',data.columns, data.dtypes) if 'file' in request.files: file = request.files['file'] urlData = file.read() rawData = pd.read_csv(io.StringIO(urlData.decode('utf-8'))) data = rawData.to_json(orient='records') output = predict().run(data) outputobj = json.loads(output) if outputobj['status'] == 'SUCCESS': try: df2 = pd.read_json(json.dumps(outputobj['data']), orient ='records') if not sqlite_dbObj.table_exists('prodData'): sqlite_dbObj.create_table('prodData',df2.columns, df2.dtypes) sqlite_dbObj.write(df2,'prodData') except: pass try: data = sqlite_dbObj.read('metrices') #print(data) if len(data) == 0: data = [{'mid':'0','noOfPredictCalls':'1','noOfDriftCalls':'0',"noOfActualCalls":'0'}] data = pd.read_json(json.dumps(data), orient ='records') sqlite_dbObj.write(data,'metrices') else: noofPredictCalls = int(data['noOfPredictCalls'].iloc[0])+1 sqlite_dbObj.update('metrices',"noOfPredictCalls = '"+str(noofPredictCalls)+"'","mid = 0") except Exception as e: print(e) pass return jsonify(json.loads(output)) else: output = {'status':'error','msg':'File is missing'} return jsonify(output) """ if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ class explainapi(Resource): def get(self): features = disp_data.get('modelFeatures') if features: msg=\""" RequestType: POST Content-Type=application/json Body: {displaymsg} \""".format(displaymsg={ x:'Value' for x in features}) else: displaymsg='Data in JSON Format' return jsonify(displaymsg) def post(self): data = request.get_json() if is_explainable: output = local_analysis(json.dumps(data)) else: output = json.dumps({"status":"FAIL","data":"explain api is not supported when text features are used for training"}) return jsonify(json.loads(output)) class monitoringapi(Resource): def get(self): return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'}) def post(self): sqlite_dbObj = sqlite_db(dataPath,'data.db') if not sqlite_dbObj.table_exists('monitoring'): data = {'status':'No Drift','Msg':'No Input Drift Found','RecordTime':'Time','version':'1'} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.create_table('monitoring',data.columns, data.dtypes) trainingDataPath = (modelPath/'data')/'preprocesseddata.csv.gz' if not sqlite_dbObj.table_exists('prodData'): return jsonify({'status':'Error','msg':'Prod data not available'}) data = sqlite_dbObj.read('prodData') filetimestamp = str(int(time.time())) dataFile = dataPath/('AION_' + filetimestamp+'.csv') data.to_csv(dataFile, index=False) data = request.get_json() data={'trainingDataLocation':trainingDataPath,'currentDataLocation':dataFile} output = drift(json.dumps(data)) outputData = json.loads(output) status = outputData['status'] if status == 'SUCCESS': Msg = str(outputData['data']) else: Msg = 'Error during drift analysis' now = datetime.now() # current date and time date_time = now.strftime("%m/%d/%Y, %H:%M:%S") data = {'status':status,'Msg':Msg,'RecordTime':date_time,'version':modelVersion} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.write(data,'monitoring') return jsonify(json.loads(output))""" filedata += """ class matricesapi(Resource): def get(self): sqlite_dbObj = sqlite_db(dataPath,'data.db') if sqlite_dbObj.table_exists('metrices'): df1 = sqlite_dbObj.read('metrices') else: df1 = pd.DataFrame() #print(df1) if sqlite_dbObj.table_exists('monitoring'): df2 = sqlite_dbObj.read('monitoring') else: df2 = pd.DataFrame() msg = {'Deployed Version':str(modelVersion)} if df1.shape[0] > 0: msg.update({'noOfPredictCalls':str(df1['noOfPredictCalls'].iloc[0])}) else: msg.update({'noOfPredictCalls':'0'}) driftDetails = [] for idx in reversed(df2.index): driftd = {'version':str(df2.version[idx]),'status':str(df2.status[idx]),'recordTime':str(df2.RecordTime[idx]),'msg':str(df2.Msg[idx])} driftDetails.append(driftd) msg.update({'driftDetails':driftDetails}) return jsonify(msg) class performanceapi(Resource): def get(self): return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'}) def post(self): sqlite_dbObj = sqlite_db(dataPath,'data.db') if not sqlite_dbObj.table_exists('monitoring'): data = {'status':'No Drift','Msg':'No Input Drift Found','RecordTime':'Time','version':'1'} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.create_table('monitoring',data.columns, data.dtypes) trainingDataPath = (modelPath/'data')/'preprocesseddata.csv.gz' if not sqlite_dbObj.table_exists('prodData'): return jsonify({'status':'Error','msg':'Prod data not available'}) data = sqlite_dbObj.read('prodData') filetimestamp = str(int(time.time())) dataFile = dataPath/('AION_' + filetimestamp+'.csv') data.to_csv(dataFile, index=False) data = request.get_json() data={'trainingDataLocation':trainingDataPath,'currentDataLocation':dataFile} output = odrift(json.dumps(data)) return jsonify(json.loads(output)) """ filedata += """ api.add_resource(predictapi, '/AION/{serviceName}/predict') api.add_resource(predictfileapi, '/AION/{serviceName}/predict_file') api.add_resource(matricesapi, '/AION/{serviceName}/metrices')""".format(serviceName=usecaseid) if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ api.add_resource(explainapi, '/AION/{serviceName}/explain') api.add_resource(monitoringapi, '/AION/{serviceName}/monitoring') api.add_resource(performanceapi, '/AION/{serviceName}/performance') """.format(serviceName=usecaseid) filedata += """ if __name__ == '__main__': args = parser.parse_args() app.run(args.ipaddress,port = args.port,debug = True)""" f = open(filename, "wb") f.write(str(filedata).encode('utf8')) f.close() data = {'version':version} filename = os.path.join(datalocation,'config.json') with open(filename, "w") as outfile: json.dump(data, outfile) outfile.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' TAB_CHAR = ' ' * 4 def import_modules(importer, modules_list): for module in modules_list: mod_from = module.get('mod_from',None) mod_as = module.get('mod_as',None) importer.addModule(module['module'], mod_from=mod_from, mod_as=mod_as) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from importlib.metadata import version import sys class importModule(): def __init__(self): self.importModule = {} self.stdlibModule = [] self.localModule = {} def addLocalModule(self,module, mod_from=None, mod_as=None): if module == '*': if module not in self.localModule.keys(): self.localModule[module]= [mod_from] else: self.localModule[module].append(mod_from) elif module not in self.localModule.keys(): self.localModule[module] = {'from':mod_from, 'as':mod_as} def addModule(self, module, mod_from=None, mod_as=None): if module not in self.importModule.keys(): self.importModule[module] = {'from':mod_from, 'as':mod_as} if module in sys.stdlib_module_names: self.stdlibModule.append(module) elif isinstance(self.importModule[module], list): if mod_as not in [x['as'] for x in self.importModule[module]]: self.importModule[module].append({'from':mod_from, 'as':mod_as}) elif mod_as not in [x['from'] for x in self.importModule[module]]: self.importModule[module].append({'from':mod_from, 'as':mod_as}) elif mod_as != self.importModule[module]['as']: as_list = [self.importModule[module]] as_list.append({'from':mod_from, 'as':mod_as}) self.importModule[module] = as_list elif mod_from != self.importModule[module]['from']: as_list = [self.importModule[module]] as_list.append({'from':mod_from, 'as':mod_as}) self.importModule[module] = as_list def getModules(self): return (self.importModule, self.stdlibModule) def getBaseModule(self, extra_importers=[]): modules_alias = { 'sklearn':'scikit-learn', 'genetic_selection':'sklearn-genetic', 'google': 'google-cloud-storage', 'azure':'azure-storage-file-datalake'} local_modules = {'AIX':'/app/AIX-0.1-py3-none-any.whl'} modules = [] require = "" if extra_importers: extra_importers = [importer.importModule for importer in extra_importers if isinstance(importer, importModule)] importers_module = [self.importModule] + extra_importers for importer_module in importers_module: for k,v in importer_module.items(): if v['from']: mod = v['from'].split('.')[0] else: mod = k if mod in modules_alias.keys(): mod = modules_alias[mod] modules.append(mod) modules = list(set(modules)) for mod in modules: try: if mod in local_modules.keys(): require += f"{local_modules[mod]}\n" else: require += f"{mod}=={version(mod)}\n" except : if mod not in sys.stdlib_module_names: raise return require def getCode(self): def to_string(k, v): mod = '' if v['from']: mod += 'from {} '.format(v['from']) mod += 'import {}'.format(k) if v['as']: mod += ' as {} '.format(v['as']) return mod modules = "" local_modules = "" std_lib_modules = "" third_party_modules = "" for k,v in self.importModule.items(): if k in self.stdlibModule: std_lib_modules = std_lib_modules + '\n' + to_string(k, v) elif isinstance(v, dict): third_party_modules = third_party_modules + '\n' + to_string(k, v) elif isinstance(v, list): for alias in v: third_party_modules = third_party_modules + '\n' + to_string(k, alias) for k,v in self.localModule.items(): if k != '*': local_modules = local_modules + '\n' + to_string(k, v) else: for mod_from in v: local_modules = local_modules + '\n' + f'from {mod_from} import {k}' if std_lib_modules: modules = modules + "\n#Standard Library modules" + std_lib_modules if third_party_modules: modules = modules + "\n\n#Third Party modules" + third_party_modules if local_modules: modules = modules + "\n\n#local modules" + local_modules + '\n' return modules def copyCode(self, importer): self.importModule, self.stdlibModule = importer.getModules() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import subprocess import os import glob import sys import python_minifier def encrypt_files(path): cwd = os.getcwd() secure_path = os.path.join(path,'SecuredScripts') try: if not os.path.exists(secure_path): os.mkdir(secure_path) files = [f for f in glob.glob(path + "/*.py")] for file in files: #encrypted_file_details[0] = file #file = files[0] #print(file) #filename_w_dir = os.path.splitext(file) filename_w_ext = os.path.basename(file) filename, file_extension = os.path.splitext(filename_w_ext) file_folder_path = os.path.join(secure_path,filename) #print(file_folder_path) if not os.path.exists(file_folder_path): os.mkdir(file_folder_path) # Minify python source code minify_file = os.path.join(file_folder_path,filename+'_minify.py') pythonfolder,_ = os.path.split(sys.executable) pyminify_script = os.path.join(pythonfolder,'Scripts','pyminify.exe') minify_command = "\""+sys.executable+"\" \""+pyminify_script+ "\" \"" + file + "\" > \"" + minify_file+"\"" subprocess.call(minify_command, shell=True) # Change directory to folder path os.chdir(file_folder_path) # Obfuscate minified file pyarmor_script = os.path.join(pythonfolder,'Scripts','pyarmor.exe') obfusc_commmand = "\""+sys.executable+"\" \""+pyarmor_script+"\" obfuscate \"" + minify_file+"\"" #print(obfusc_commmand) subprocess.call(obfusc_commmand, shell=True) # Change directory to dist path obfusc_file = os.path.join(file_folder_path,'dist',filename+'_minify.py') #print(obfusc_file) chdirpath = os.path.join(file_folder_path,'dist') os.chdir(chdirpath) # Compress obfuscated file compressed_file = os.path.join(file_folder_path,'dist',filename+'_compressed.py') #print(compressed_file) pyminifier_script = os.path.join(pythonfolder,'Scripts','pyminifier.exe') compress_command = "\""+sys.executable+"\" \""+pyminifier_script+"\" --gzip -o \"" +compressed_file + "\" \"" + obfusc_file+"\"" #print(compress_command) subprocess.call(compress_command, shell=True) #compile_command = sys.executable+'-m py_compile "' + compressed_file+'"' #print(compile_command) #subprocess.call(compile_command , shell=True) #encrypted_file_details['compiled_file'] = file #compiled_file = os.path.join(file_folder_path,'dist','__pycache__',filename+'_compressed.cpython-37.pyc') #encrypted_file_details[1] = compiled_file #encrypted_file_list.append(encrypted_file_details) #encrypted_file = filename + '_compressed.cpython-37_encrypted.pyc' #encrypt_command = "python " + cwd + "\\Encrypt_Key_Dcrypt.py " + compiled_file + ' ' + encrypted_file + " --g -e" #print(encrypt_command) #subprocess.call(encrypt_command, shell=True) #encrypted_file_list += ']' #return(encrypted_file_list) os.chdir(path) except OSError as err: print ("Creation of the directory %s failed "+str(err)) # Driver function if __name__=="__main__": path = sys.argv[1] encrypt_files(path) #(base) C:\Himanshu\DataPreprocessing>pyminify DataPreprocessing.py > DataPreprocessing_minify.py #Obfuscate #(base) C:\Himanshu\DataPreprocessing>pyarmor obfuscate C:\Himanshu\DataPreprocessing\DataPreprocessing_minify.py #Compression #(base) C:\Himanshu\DataPreprocessing>pyminifier --gzip -o C:\Himanshu\DataPreprocessing\dist\DataPreprocessing_compressed.py C:\Himanshu\DataPreprocessing\dist\DataPreprocessing_minify.py #(base) C:\Himanshu\DataPreprocessing>cd dist #(base) C:\Himanshu\DataPreprocessing\dist>python DataPreprocessing_compressed.py "DocumentText" "Label" 90 ".csv" "C:\Himanshu\DataAcquisition\ClassificationDataNewBalanced.csv" #Compiling compressed .py to .pyc file #(base) C:\Himanshu\DataPreprocessing\dist>python -m py_compile DataPreprocessing_compressed.py #Encrypt .pyc file #(base) C:\Himanshu\DataPreprocessing\dist>python C:\Himanshu\Encrypt_Key_Dcrypt.py C:\Himanshu\DataPreprocessing\dist\__pycache__\DataPreprocessing_compressed.cpython-36.pyc DataPreprocessing_compressed.cpython-36_encrypted.pyc --g -e #Decrypt file #(base) C:\Himanshu\DataPreprocessing\dist>python C:\Himanshu\Encrypt_Key_Dcrypt.py DataPreprocessing_compressed.cpython-36_encrypted.pyc DataPreprocessing_compressed.cpython-36_decrypted.pyc --d #Run decrypted file #(base) C:\Himanshu\DataPreprocessing\dist>python DataPreprocessing_compressed.cpython-36_decrypted.pyc "DocumentText" "Label" 90 ".csv" "C:\Himanshu\DataAcquisition\ClassificationDataNewBalanced.csv" ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import shutil import subprocess from os.path import expanduser import platform deploymentfolder = os.path.join(os.path.dirname(os.path.abspath(__file__)),'HCLT','AION','target') modelname='AION_12' version='1' def createDockerImage(deploymentfolder,modelname,version,learner_type,textdata): modelPath = os.path.join(deploymentfolder) filename = os.path.join(deploymentfolder,'docker_image') modelservice = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','run_modelService.py') shellscript = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','start_modelservice.sh') aix = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','AIX-0.1-py3-none-any.whl') drift = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','Drift-0.1-py3-none-any.whl') sitepackage = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','site-packages') model_dockerSetup = os.path.join(os.path.dirname(os.path.abspath(__file__)),'dockersetup','docker_'+modelname + '_' + version) docker_setup = os.path.join(model_dockerSetup,modelname + '_' + version) model_sitepackage = os.path.join(model_dockerSetup,'site-packages') model_dockerSetupservicefile = os.path.join(model_dockerSetup,'run_modelService.py') model_dockershellscript = os.path.join(model_dockerSetup,'start_modelservice.sh') model_aix = os.path.join(model_dockerSetup,'AIX-0.1-py3-none-any.whl') model_drift = os.path.join(model_dockerSetup,'Drift-0.1-py3-none-any.whl') try: os.mkdir(model_dockerSetup) except Exception as e: print("Error in creating Setup directpry "+str(e)) pass shutil.copytree(modelPath, docker_setup) if textdata: shutil.copytree(sitepackage, model_sitepackage) modelpretrainpath=os.path.join(model_dockerSetup,'HCLT','AION','PreTrainedModels','TextProcessing') ''' try: os.makedirs(modelpretrainpath, exist_ok=True) except Exception as e: print("Error in creating Setup directpry "+str(e)) pass ''' home = expanduser("~") if platform.system() == 'Windows': hostpretrainpath = os.path.join(home,'AppData','Local','HCLT','AION','PreTrainedModels','TextProcessing') else: hostpretrainpath = os.path.join(home,'HCLT','AION','PreTrainedModels','TextProcessing') shutil.copytree(hostpretrainpath, modelpretrainpath) shutil.copyfile(modelservice, model_dockerSetupservicefile) shutil.copyfile(shellscript, model_dockershellscript) shutil.copyfile(aix, model_aix) shutil.copyfile(drift,model_drift) try: os.mkdir(filename) except: pass requirementfilename = os.path.join(model_dockerSetup,'requirements.txt') installfilename = os.path.join(model_dockerSetup,'install.py') dockerfile = os.path.join(model_dockerSetup,'Dockerfile') dockerdata='FROM python:3.8-slim-buster' dockerdata+='\n' if textdata: dockerdata+='WORKDIR /root' dockerdata+='\n' dockerdata+='COPY HCLT HCLT' dockerdata+='\n' dockerdata+='WORKDIR /app' dockerdata+='\n' dockerdata+='COPY requirements.txt requirements.txt' dockerdata+='\n' dockerdata+='COPY '+modelname+'_'+version+' '+modelname+'_'+version dockerdata+='\n' if textdata: dockerdata+='COPY site-packages site-packages' dockerdata+='\n' dockerdata+='COPY install.py install.py' dockerdata+='\n' dockerdata+='COPY run_modelService.py run_modelService.py' dockerdata+='\n' dockerdata+='COPY AIX-0.1-py3-none-any.whl AIX-0.1-py3-none-any.whl' dockerdata+='\n' dockerdata+='COPY Drift-0.1-py3-none-any.whl Drift-0.1-py3-none-any.whl' dockerdata+='\n' dockerdata+='COPY start_modelservice.sh start_modelservice.sh' dockerdata+='\n' if textdata: dockerdata+='''RUN apt-get update \ && apt-get install -y build-essential manpages-dev \ && python -m pip install --no-cache-dir --upgrade pip \ && python -m pip install --no-cache-dir pandas==1.2.4 \ && python -m pip install --no-cache-dir numpy==1.19.5 \ && python -m pip install --no-cache-dir joblib==1.0.1 \ && python -m pip install --no-cache-dir Cython==0.29.23 \ && mv site-packages/* /usr/local/lib/python3.8/site-packages \ && python -m pip install --no-cache-dir scipy==1.6.3 \ && python -m pip install --no-cache-dir AIX-0.1-py3-none-any.whl \ && python -m pip install --no-cache-dir Drift-0.1-py3-none-any.whl \ && python -m pip install --no-cache-dir scikit-learn==0.24.2 \ && python -m pip install --no-cache-dir spacy==2.2.3 \ && python -m pip install --no-cache-dir nltk==3.6.2 \ && python -m pip install --no-cache-dir textblob==0.15.3 \ && python -m pip install --no-cache-dir gensim==3.8.3 \ && python -m pip install --no-cache-dir demoji==1.1.0 \ && python -m pip install --no-cache-dir lxml==4.6.3 \ && python -m pip install --no-cache-dir Beautifulsoup4==4.9.3 \ && python -m pip install --no-cache-dir Unidecode==1.2.0 \ && python -m pip install --no-cache-dir pyspellchecker==0.6.2 \ && python -m pip install --no-cache-dir pycontractions==2.0.1 \ && python -m pip install --no-cache-dir tensorflow==2.4.1 \ && python -m pip install --no-cache-dir nltk==3.6.2 \ && python -m pip install --no-cache-dir -r requirements.txt \ && python install.py \ && chmod +x start_modelservice.sh ENTRYPOINT ["./start_modelservice.sh"] ''' else: dockerdata+='''RUN apt-get update \ && apt-get install -y build-essential manpages-dev \ && python -m pip install --no-cache-dir --upgrade pip \ && python -m pip install --no-cache-dir pandas==1.2.4 \ && python -m pip install --no-cache-dir numpy==1.19.5 \ && python -m pip install --no-cache-dir joblib==1.0.1 \ && python -m pip install --no-cache-dir Cython==0.29.23 \ && python -m pip install --no-cache-dir scipy==1.6.3 \ && python -m pip install --no-cache-dir AIX-0.1-py3-none-any.whl \ && python -m pip install --no-cache-dir Drift-0.1-py3-none-any.whl \ && python -m pip install --no-cache-dir scikit-learn==0.24.2 \ && python -m pip install --no-cache-dir -r requirements.txt \ && chmod +x start_modelservice.sh ENTRYPOINT ["./start_modelservice.sh"] ''' f = open(dockerfile, "w") f.write(str(dockerdata)) f.close() requirementdata='' requirementdata+='word2number==1.1' if learner_type == 'DL': requirementdata+='\n' requirementdata+='tensorflow==2.5.0' f = open(requirementfilename, "w") f.write(str(requirementdata)) f.close() if textdata: installfile=''' import nltk import ssl try: _create_unverified_https_context = ssl._create_unverified_context except AttributeError: pass else: ssl._create_default_https_context = _create_unverified_https_context nltk.download('punkt') nltk.download('wordnet') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger')''' f = open(installfilename, "w") f.write(str(installfile)) f.close() try: command = 'docker pull python:3.8-slim-buster' os.system(command); #subprocess.check_call(["chmod", "+x", "start_modelservice.sh"], cwd=model_dockerSetup) subprocess.check_call(["docker", "build", "-t",modelname.lower()+":"+version,"."], cwd=model_dockerSetup) subprocess.check_call(["docker", "save", "-o",modelname.lower()+"_"+version+".tar",modelname.lower()+":"+version], cwd=model_dockerSetup) dockerfilepath = os.path.join(model_dockerSetup,modelname.lower()+"_"+version+".tar") shutil.copyfile(dockerfilepath, os.path.join(filename,modelname.lower()+"_"+version+".tar")) shutil.rmtree(model_dockerSetup) return 'Success','SUCCESSFULLY' except Exception as e: print("Error: "+str(e)) shutil.rmtree(model_dockerSetup) return 'Error',str(e) #createDockerImage(deploymentfolder,modelname,version) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from importlib.metadata import version import sys import os def requirementfile(deploy_path,model,textFeatures,learner_type): print('hola', model) modules = ['pandas','numpy','alibi','matplotlib','joblib','shap','ipython','category_encoders','scikit-learn','word2number','flask_restful','evidently','Flask-Cors'] requires = '' for mod in modules: requires += f"{mod}=={version(mod)}\n" if len(textFeatures) > 0: tmodules = ['spacy','nltk','textblob','demoji','beautifulsoup4','text-unidecode','pyspellchecker','contractions','protobuf'] for mod in tmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Extreme Gradient Boosting (XGBoost)': mmodules = ['xgboost'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Light Gradient Boosting (LightGBM)': mmodules = ['lightgbm'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Categorical Boosting (CatBoost)': mmodules = ['catboost'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'arima': mmodules = ['pmdarima'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'fbprophet': mmodules = ['prophet'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'lstm' or model.lower() == 'mlp' or learner_type =='DL': mmodules = ['tensorflow'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() in ['cox', 'kaplanmeierfitter']: #bug 12833 mmodules = ['lifelines'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'sentencetransformer': #bug 12833 mmodules = ['sentence_transformers'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" filename = os.path.join(deploy_path,'requirements.txt') f = open(filename, "wb") f.write(str(requires).encode('utf8')) f.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import sys import subprocess import glob import shutil import time from aion_deployment.EncryptPythonSourceCode import encrypt_files import json def encrypt(alldirs): for dir in alldirs: try: encrypt_files(dir) except Exception as error_obj: print("Exception in encrypting", error_obj) print("-"*50) def replace_by_compressed(alldirs): for dir in alldirs: try: #print("Processing dir", dir) files = [f for f in glob.glob(dir + "/*.py")] secure_path = os.path.join(dir, 'SecuredScripts') time.sleep(6) for file in files: try: filename_w_ext = os.path.basename(file) filename, file_extension = os.path.splitext(filename_w_ext) if filename == "__init__": continue #print("Processing file", file) file_folder_path = os.path.join(secure_path, filename, 'dist') compressed_file_path = os.path.join(file_folder_path, filename+'_compressed.py') shutil.copy(compressed_file_path, dir) os.remove(file) new_compressed_file_path = os.path.join(dir, filename+'_compressed.py') target_file_path = os.path.join(dir, filename_w_ext) os.rename(new_compressed_file_path, target_file_path) if filename == 'aion_prediction': shutil.copytree(os.path.join(file_folder_path, 'pytransform'), os.path.join(dir, 'pytransform')) except Exception as error_obj: print("Exception in file ", error_obj) shutil.rmtree(secure_path) except Exception as error_obj: print("Exception in dir ", error_obj) def start_Obfuscate(path): project_path = path subdirs = [dI for dI in os.listdir(project_path) if os.path.isdir(os.path.join(project_path,dI))] alldirs = [ project_path, ] for subdir in subdirs: if(subdir != 'pytransform'): alldirs.append(os.path.join(project_path, subdir)) encrypt(alldirs) replace_by_compressed(alldirs) if __name__=="__main__": project_path = sys.argv[1] print("project_path", project_path) subdirs = [dI for dI in os.listdir(project_path) if os.path.isdir(os.path.join(project_path,dI))] alldirs = [ project_path, ] for subdir in subdirs: alldirs.append(os.path.join(project_path, subdir)) encrypt(alldirs) print("*"*50) replace_by_compressed(alldirs) # python eion_compress.py "C:\Users\ashwani.s\Desktop\22April\22April\Mohita" "C:\Users\ashwani.s\Desktop\eion\eion" > logfile.log ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from pathlib import Path from AION.prediction_package.imports import importModule from AION.prediction_package.aion_prediction import aionPrediction from AION.prediction_package.utility import TAB_CHAR from AION.prediction_package import utility from AION.prediction_package import common from AION.prediction_package.base import deployer def is_supported(problem_type, algo=None): """ Return True if problem_type supported otherwise False """ supported = ['classification','regression','clustering','timeseriesforecasting','Text Similarity'] return problem_type in supported def get_deployer(problem_type, algo=None, params={}): """ Return deployer class object based on problem type Raise error if no class is associated with problem type """ params['problem_type'] = problem_type if problem_type == 'classification': return classification( params) elif problem_type == 'regression': return regression( params) elif problem_type == 'clustering': return clustering( params) elif problem_type == 'timeseriesforecasting': from AION.prediction_package.time_series import forecasting return forecasting.get_deployer( params) elif problem_type == 'Text Similarity': return textSimilarity( params) else: raise ValueError('deployment is not supported') class classification( deployer): def __init__(self, params={}): super().__init__( params) self.feature_reducer = False if not self.name: self.name = 'classification' def create_idrift(self): obj = aionPrediction() if self.params['features']['text_feat']: obj.create_text_drift_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat'],self.name) else: obj.create_drift_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat'],self.name) def create_odrift(self): obj = aionPrediction() if self.params['features']['text_feat']: obj.create_classification_text_performance_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat']) else: obj.create_classification_performance_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat']) def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') code = f""" class trainer(): """ init_code, run_code = self._get_train_code() return code + init_code + run_code def _get_train_code(self): init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}')""" run_code = f""" def run(self, df):\ """ if self.params['training']['algo'] in ['Neural Network']: self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code += f""" self.model = load_model(model_file) """ run_code += """ df = df.astype(np.float32) return pd.DataFrame(np.argmax(self.model.predict(df),axis=1)) """ elif self.params['training']['algo'] in ['Neural Architecture Search']: self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') self.importer.addModule(module='autokeras',mod_as='ak') init_code += f""" self.model = load_model(model_file,custom_objects=ak.CUSTOM_OBJECTS) """ run_code += """ df = df.astype(np.float32) return pd.DataFrame(self.model.predict(df)) """ elif self.params['training']['algo'] in ['Deep Q Network','Dueling Deep Q Network']: self.importer.addModule('joblib') self.importer.addModule(module='numpy',mod_as='np') self.importer.addModule(module='constant',mod_from='tensorflow') self.importer.addModule(module='time_step',mod_from='tf_agents.trajectories') init_code += f""" self.model = joblib.load(model_file) """ run_code += """ df = df.astype(np.float32) q, _ = self.model(np.array(df), step_type=constant([time_step.StepType.FIRST] * np.array(df).shape[0]), training=False) return pd.DataFrame(q.numpy()) """ elif self.params['training']['algo'] in ['Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)']: self.importer.addModule(module='numpy',mod_as='np') self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code += f""" self.model = load_model(model_file) """ run_code += """ df = np.expand_dims(df, axis=2) df = df.astype(np.float32) return pd.DataFrame(np.argmax(self.model.predict(df),axis=1)) """ else: self.importer.addModule(module='joblib') self.importer.addModule(module='numpy',mod_as='np') init_code += f""" self.model = joblib.load(model_file) """ run_code += """ df = df.astype(np.float32) return pd.DataFrame(self.model.predict_proba(df), columns=self.model.classes_) """ return init_code, run_code def formatter_code(self): self.importer.addModule('json') self.importer.addModule('joblib') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__(self): pass def run(self, raw_df, output): output = round(output,2) encoder_file = (Path(__file__).parent/"model")/"label_encoder.pkl" if encoder_file.exists(): encoder = joblib.load(encoder_file) output.rename(columns=dict(zip(output.columns, encoder.inverse_transform(list(output.columns)))), inplace=True) raw_df['prediction'] = output.idxmax(axis=1) raw_df['probability'] = output.max(axis=1).round(2) raw_df['remarks'] = output.apply(lambda x: x.to_json(double_precision=2), axis=1) outputjson = raw_df.to_json(orient='records',double_precision=5) outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} return(json.dumps(outputjson)) """ class regression( deployer): def __init__(self, params={}): super().__init__( params) self.feature_reducer = False if not self.name: self.name = 'regression' def create_idrift(self): obj = aionPrediction() if self.params['features']['text_feat']: obj.create_text_drift_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat'],self.name) else: obj.create_drift_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat'],self.name) def create_odrift(self): obj = aionPrediction() if self.params['features']['text_feat']: obj.create_regression_text_performance_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat']) else: obj.create_regression_performance_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat']) def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') code = f""" class trainer(): """ init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') """ run_code = f""" def run(self, df):\ """ if self.params['training']['algo'] in ['Neural Architecture Search']: self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') self.importer.addModule(module='autokeras',mod_as='ak') init_code += f""" self.model = load_model(model_file,custom_objects=ak.CUSTOM_OBJECTS) """ run_code += """ df = df.astype(np.float32) return self.model.predict(df).reshape(1, -1) """ elif self.params['training']['algo'] in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)']: self.importer.addModule(module='numpy',mod_as='np') self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code += f""" self.model = load_model(model_file) """ run_code += """ df = np.expand_dims(df, axis=2) df = df.astype(np.float32) return self.model.predict(df).reshape(1, -1) """ else: self.importer.addModule('joblib') init_code += f""" self.model = joblib.load(model_file) """ run_code += """ df = df.astype(np.float32) return self.model.predict(df).reshape(1, -1) """ return code + init_code + run_code def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__(self): pass def run(self, raw_df, output): raw_df['prediction'] = output[0] raw_df['prediction'] = raw_df['prediction'].round(2) outputjson = raw_df.to_json(orient='records',double_precision=5) outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} return(json.dumps(outputjson)) """ class clustering( deployer): def __init__(self, params={}): super().__init__( params) self.feature_reducer = False if not self.name: self.name = 'clustering' def training_code( self): self.importer.addModule('joblib') self.importer.addModule(module='pandas',mod_as='pd') code = f""" class trainer(): """ init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') """ run_code = f""" def run(self, df):\ """ if self.params['training']['algo'] == 'DBSCAN': init_code += f""" self.model = joblib.load(model_file) """ run_code += """ return self.model.fit_predict(df) """ else: init_code += f""" self.model = joblib.load(model_file) """ run_code += """ return self.model.predict(df).reshape(1, -1) """ return code + init_code + run_code def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__(self): pass def run(self, raw_df, output): raw_df['prediction'] = output[0] raw_df['prediction'] = raw_df['prediction'].round(2) outputjson = raw_df.to_json(orient='records',double_precision=2) outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} return(json.dumps(outputjson)) """ return code if __name__ == '__main__': config = {'usecase_name': 'AI0110', 'usecase_ver': '1', 'features': {'input_feat': ['v2'], 'target_feat': 'v1', 'text_feat': ['v2']}, 'paths': {'deploy': r'C:/Users/vashistah/AppData/Local/Programs/HCLTech/AION/data/target/AI0110/1', 'usecase': r'C:/Users/vashistah/AppData/Local/Programs/HCLTech/AION/data/target/AI0110'}, 'profiler': {'input_features': ['v2'], 'output_features': ['07xxxxxxxxx_vect', '08700621170150p_vect', '08702840625comuk_vect', '08718726270150gbpmtmsg18_vect', '1000s_vect', '10am7pm_vect', '10k_vect', '10p_vect', '10pmin_vect', '10ppm_vect', '11mths_vect', '125gift_vect', '12hrs_vect', '12mths_vect', '150p_vect', '150perwksub_vect', '150pm_vect', '150pmin_vect', '150pmsg_vect', '150pmsgrcvdhgsuite3422landsroww1j6hl_vect', '150pmtmsgrcvd18_vect', '150ppm_vect', '150ptone_vect', '150pwk_vect', '150week_vect', '16only_vect', '18only_vect', '1hr_vect', '1minmobsmorelkpobox177hp51fl_vect', '1st_vect', '1x150pwk_vect', '20p_vect', '20pmin_vect', '21st_vect', '220cm2_vect', '24hrs_vect', '25p_vect', '26th_vect', '2day_vect', '2find_vect', '2geva_vect', '2go_vect', '2marrow_vect', '2mrw_vect', '2nd_vect', '2nite_vect', '2optout_vect', '2p_vect', '2u_vect', '2waxsto_vect', '2wks_vect', '300p_vect', '31pmsg_vect', '3510i_vect', '3d_vect', '3g_vect', '3gbp_vect', '3hrs_vect', '3mins_vect', '3qxj9_vect', '3rd_vect', '3ss_vect', '3u_vect', '3uz_vect', '3wk_vect', '40gb_vect', '4a_vect', '4d_vect', '4eva_vect', '4get_vect', '4info_vect', '4mths_vect', '4th_vect', '4u_vect', '50p_vect', '5min_vect', '5pm_vect', '5wb_vect', '5we_vect', '60pmin_vect', '6hrs_vect', '6months_vect', '6pm_vect', '7250i_vect', '7ish_vect', '8am_vect', '8pm_vect', '8th_vect', '8wp_vect', '9ae_vect', '9ja_vect', '9pm_vect', '9t_vect', 'aathi_vect', 'abi_vect', 'ability_vect', 'abiola_vect', 'able_vect', 'abt_vect', 'abta_vect', 'aburo_vect', 'ac_vect', 'academic_vect', 'acc_vect', 'accept_vect', 'access_vect', 'accident_vect', 'accidentally_vect', 'accordingly_vect', 'account_vect', 'ache_vect', 'across_vect', 'acted_vect', 'action_vect', 'activate_vect', 'activities_vect', 'actor_vect', 'actual_vect', 'actually_vect', 'ad_vect', 'adam_vect', 'add_vect', 'added_vect', 'addicted_vect', 'addie_vect', 'address_vect', 'admin_vect', 'administrator_vect', 'admirer_vect', 'admit_vect', 'adore_vect', 'adoring_vect', 'ads_vect', 'adult_vect', 'advance_vect', 'adventure_vect', 'advice_vect', 'advise_vect', 'affair_vect', 'affairs_vect', 'affectionate_vect', 'afraid_vect', 'aft_vect', 'afternoon_vect', 'aftr_vect', 'agalla_vect', 'age_vect', 'age16_vect', 'ages_vect', 'ago_vect', 'agree_vect', 'ah_vect', 'aha_vect', 'ahead_vect', 'ahmad_vect', 'ai_vect', 'aight_vect', 'aint_vect', 'air_vect', 'airport_vect', 'airtel_vect', 'aiya_vect', 'aiyah_vect', 'aiyar_vect', 'aiyo_vect', 'al_vect', 'album_vect', 'alert_vect', 'alex_vect', 'alfie_vect', 'ali_vect', 'allah_vect', 'allow_vect', 'allowed_vect', 'almost_vect', 'alone_vect', 'along_vect', 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'saturday_vect', 'savamob_vect', 'save_vect', 'saved_vect', 'saw_vect', 'say_vect', 'saying_vect', 'says_vect', 'scared_vect', 'scary_vect', 'sch_vect', 'schedule_vect', 'school_vect', 'schools_vect', 'science_vect', 'scold_vect', 'score_vect', 'scores_vect', 'scotland_vect', 'scream_vect', 'screaming_vect', 'scrounge_vect', 'se_vect', 'sea_vect', 'search_vect', 'searching_vect', 'season_vect', 'seat_vect', 'sec_vect', 'second_vect', 'seconds_vect', 'secret_vect', 'secretary_vect', 'secs_vect', 'sed_vect', 'see_vect', 'seeing_vect', 'seem_vect', 'seemed_vect', 'seems_vect', 'seen_vect', 'selected_vect', 'selection_vect', 'self_vect', 'sell_vect', 'selling_vect', 'sells_vect', 'sem_vect', 'semester_vect', 'sen_vect', 'send_vect', 'sender_vect', 'sending_vect', 'sense_vect', 'sent_vect', 'sentence_vect', 'sept_vect', 'series_vect', 'serious_vect', 'seriously_vect', 'service_vect', 'services_vect', 'serving_vect', 'set_vect', 'setting_vect', 'settings_vect', 'settle_vect', 'settled_vect', 'seven_vect', 'several_vect', 'sex_vect', 'sexy_vect', 'sh_vect', 'sha_vect', 'shall_vect', 'share_vect', 'shd_vect', 'sheets_vect', 'shes_vect', 'shesil_vect', 'shining_vect', 'ship_vect', 'shipping_vect', 'shirt_vect', 'shirts_vect', 'shit_vect', 'shld_vect', 'shocking_vect', 'shoot_vect', 'shop_vect', 'shoppin_vect', 'shopping_vect', 'short_vect', 'shorter_vect', 'shortly_vect', 'shot_vect', 'shoving_vect', 'show_vect', 'shower_vect', 'showing_vect', 'shows_vect', 'shu_vect', 'shuhui_vect', 'shy_vect', 'si_vect', 'sick_vect', 'side_vect', 'sighs_vect', 'sight_vect', 'sign_vect', 'signing_vect', 'silence_vect', 'silent_vect', 'silver_vect', 'sim_vect', 'simple_vect', 'simply_vect', 'since_vect', 'sing_vect', 'singing_vect', 'single_vect', 'singles_vect', 'sipix_vect', 'sir_vect', 'sis_vect', 'sister_vect', 'sit_vect', 'site_vect', 'sitting_vect', 'situation_vect', 'siva_vect', 'six_vect', 'size_vect', 'sk3_vect', 'sk38xh_vect', 'skilgme_vect', 'skip_vect', 'sky_vect', 'skype_vect', 'skyped_vect', 'slap_vect', 'slave_vect', 'sleep_vect', 'sleepin_vect', 'sleeping_vect', 'sleepy_vect', 'slept_vect', 'slice_vect', 'slide_vect', 'slightly_vect', 'slip_vect', 'slippers_vect', 'slo_vect', 'slots_vect', 'slow_vect', 'slowly_vect', 'small_vect', 'smashed_vect', 'smile_vect', 'smiles_vect', 'smiling_vect', 'smoke_vect', 'smoking_vect', 'sms_vect', 'smth_vect', 'sn_vect', 'snake_vect', 'snow_vect', 'social_vect', 'sofa_vect', 'soft_vect', 'software_vect', 'sol_vect', 'some1_vect', 'somebody_vect', 'someone_vect', 'somethin_vect', 'something_vect', 'sometimes_vect', 'somewhere_vect', 'song_vect', 'songs_vect', 'sony_vect', 'sonyericsson_vect', 'soon_vect', 'sooner_vect', 'sore_vect', 'sorry_vect', 'sort_vect', 'sorting_vect', 'sound_vect', 'sounds_vect', 'south_vect', 'sp_vect', 'space_vect', 'spanish_vect', 'speak_vect', 'speaking_vect', 'special_vect', 'specialcall_vect', 'specially_vect', 'speed_vect', 'spend_vect', 'spending_vect', 'spent_vect', 'spk_vect', 'spoke_vect', 'spoken_vect', 'spook_vect', 'sport_vect', 'sports_vect', 'spree_vect', 'spring_vect', 'sptv_vect', 'sry_vect', 'st_vect', 'staff_vect', 'stamps_vect', 'stand_vect', 'standard_vect', 'standing_vect', 'star_vect', 'staring_vect', 'start_vect', 'started_vect', 'starting_vect', 'starts_vect', 'starwars3_vect', 'statement_vect', 'station_vect', 'stay_vect', 'stayed_vect', 'staying_vect', 'std_vect', 'steam_vect', 'step_vect', 'steve_vect', 'stick_vect', 'sticky_vect', 'still_vect', 'stock_vect', 'stockport_vect', 'stomach_vect', 'stomps_vect', 'stones_vect', 'stop_vect', 'stopped_vect', 'stops_vect', 'store_vect', 'stores_vect', 'story_vect', 'str_vect', 'straight_vect', 'stranger_vect', 'street_vect', 'stress_vect', 'strike_vect', 'strong_vect', 'strongbuy_vect', 'stuck_vect', 'student_vect', 'study_vect', 'studying_vect', 'stuff_vect', 'stupid_vect', 'style_vect', 'stylish_vect', 'sub_vect', 'subpoly_vect', 'subs_vect', 'subscribe6gbpmnth_vect', 'subscribed_vect', 'subscriber_vect', 'subscription_vect', 'success_vect', 'successful_vect', 'successfully_vect', 'sucks_vect', 'sue_vect', 'sufficient_vect', 'suggest_vect', 'suite_vect', 'suits_vect', 'sum1_vect', 'summer_vect', 'sun_vect', 'sunday_vect', 'sunlight_vect', 'sunny_vect', 'sunshine_vect', 'suntec_vect', 'sup_vect', 'super_vect', 'superb_vect', 'superior_vect', 'supervisor_vect', 'supply_vect', 'support_vect', 'suppose_vect', 'supposed_vect', 'suprman_vect', 'sura_vect', 'sure_vect', 'surely_vect', 'surfing_vect', 'surprise_vect', 'surprised_vect', 'survey_vect', 'sux_vect', 'suzy_vect', 'sw7_vect', 'sw73ss_vect', 'sweet_vect', 'swing_vect', 'system_vect', 'ta_vect', 'tablets_vect', 'tahan_vect', 'take_vect', 'taken_vect', 'takes_vect', 'takin_vect', 'taking_vect', 'talent_vect', 'talk_vect', 'talking_vect', 'tampa_vect', 'tape_vect', 'tariffs_vect', 'tat_vect', 'taunton_vect', 'taylor_vect', 'tb_vect', 'tc_vect', 'tcrw1_vect', 'tcs_vect', 'tea_vect', 'teach_vect', 'teacher_vect', 'teaches_vect', 'team_vect', 'tear_vect', 'tease_vect', 'teasing_vect', 'tech_vect', 'technical_vect', 'tee_vect', 'teeth_vect', 'tel_vect', 'telephone_vect', 'tell_vect', 'telling_vect', 'tells_vect', 'telugu_vect', 'temple_vect', 'ten_vect', 'tenants_vect', 'tenerife_vect', 'tension_vect', 'term_vect', 'terms_vect', 'terrible_vect', 'test_vect', 'testing_vect', 'text_vect', 'texted_vect', 'texting_vect', 'textoperator_vect', 'texts_vect', 'th_vect', 'thangam_vect', 'thank_vect', 'thanks_vect', 'thanksgiving_vect', 'thanx_vect', 'that_vect', 'thats_vect', 'thatåõs_vect', 'the_vect', 'theatre_vect', 'themob_vect', 'theory_vect', 'thesis_vect', 'thgt_vect', 'thing_vect', 'things_vect', 'think_vect', 'thinkin_vect', 'thinking_vect', 'thinks_vect', 'thk_vect', 'thnk_vect', 'tho_vect', 'though_vect', 'thought_vect', 'three_vect', 'throat_vect', 'throw_vect', 'thru_vect', 'tht_vect', 'thts_vect', 'thurs_vect', 'thursday_vect', 'tick_vect', 'ticket_vect', 'tickets_vect', 'tight_vect', 'tihs_vect', 'til_vect', 'till_vect', 'time_vect', 'times_vect', 'timing_vect', 'tired_vect', 'tirunelvali_vect', 'tirupur_vect', 'tissco_vect', 'tkts_vect', 'tm_vect', 'tming_vect', 'tmobile_vect', 'tmr_vect', 'tncs_vect', 'toa_vect', 'toclaim_vect', 'today_vect', 'todays_vect', 'tog_vect', 'together_vect', 'tok_vect', 'told_vect', 'tomarrow_vect', 'tomo_vect', 'tomorrow_vect', 'tone_vect', 'tones_vect', 'tones2youcouk_vect', 'tonight_vect', 'tonite_vect', 'took_vect', 'tool_vect', 'tooo_vect', 'toot_vect', 'top_vect', 'topic_vect', 'torch_vect', 'toshiba_vect', 'tot_vect', 'total_vect', 'totally_vect', 'touch_vect', 'tough_vect', 'tour_vect', 'towards_vect', 'town_vect', 'track_vect', 'trade_vect', 'traffic_vect', 'train_vect', 'training_vect', 'transaction_vect', 'transfer_vect', 'transport_vect', 'travel_vect', 'treat_vect', 'treated_vect', 'tried_vect', 'trip_vect', 'trips_vect', 'trouble_vect', 'true_vect', 'truffles_vect', 'truly_vect', 'trust_vect', 'truth_vect', 'try_vect', 'trying_vect', 'ts_vect', 'tscs_vect', 'tscs087147403231winawk_vect', 'tt_vect', 'ttyl_vect', 'tues_vect', 'tuesday_vect', 'tuition_vect', 'turn_vect', 'turning_vect', 'turns_vect', 'tv_vect', 'twelve_vect', 'twice_vect', 'two_vect', 'txt_vect', 'txtauction_vect', 'txtin_vect', 'txting_vect', 'txtno_vect', 'txts_vect', 'txtstop_vect', 'tyler_vect', 'type_vect', 'tyrone_vect', 'u4_vect', 'ubi_vect', 'ufind_vect', 'ugh_vect', 'uh_vect', 'uk_vect', 'uks_vect', 'ultimatum_vect', 'umma_vect', 'unable_vect', 'uncle_vect', 'understand_vect', 'understanding_vect', 'understood_vect', 'underwear_vect', 'unemployed_vect', 'uni_vect', 'unique_vect', 'university_vect', 'unless_vect', 'unlimited_vect', 'unnecessarily_vect', 'unredeemed_vect', 'unsold_vect', 'unsub_vect', 'unsubscribe_vect', 'upd8_vect', 'update_vect', 'updatenow_vect', 'upgrade_vect', 'upload_vect', 'upset_vect', 'upstairs_vect', 'ur_vect', 'ure_vect', 'urgent_vect', 'urgnt_vect', 'url_vect', 'urn_vect', 'urself_vect', 'us_vect', 'usb_vect', 'use_vect', 'used_vect', 'user_vect', 'usf_vect', 'using_vect', 'usual_vect', 'usually_vect', 'vale_vect', 'valentine_vect', 'valentines_vect', 'valid_vect', 'valid12hrs_vect', 'valuable_vect', 'value_vect', 'valued_vect', 'vary_vect', 've_vect', 'vegas_vect', 'verify_vect', 'version_vect', 'via_vect', 'vid_vect', 'video_vect', 'videochat_vect', 'videophones_vect', 'vijay_vect', 'vikky_vect', 'village_vect', 'violated_vect', 'violence_vect', 'vip_vect', 'virgin_vect', 'visit_vect', 'vivek_vect', 'vl_vect', 'voda_vect', 'vodafone_vect', 'vodka_vect', 'voice_vect', 'voicemail_vect', 'vomit_vect', 'vote_vect', 'voucher_vect', 'vouchers_vect', 'vry_vect', 'vth_vect', 'w45wq_vect', 'wa_vect', 'wah_vect', 'wait_vect', 'waited_vect', 'waitin_vect', 'waiting_vect', 'wake_vect', 'waking_vect', 'wales_vect', 'walk_vect', 'walked_vect', 'walking_vect', 'walmart_vect', 'wan_vect', 'wana_vect', 'want_vect', 'wanted_vect', 'wanting_vect', 'wants_vect', 'wap_vect', 'warm_vect', 'warner_vect', 'waste_vect', 'wasted_vect', 'wat_vect', 'watch_vect', 'watching_vect', 'water_vect', 'wats_vect', 'way_vect', 'wc1n3xx_vect', 'we_vect', 'weak_vect', 'wear_vect', 'wearing_vect', 'weather_vect', 'web_vect', 'website_vect', 'wed_vect', 'wedding_vect', 'wednesday_vect', 'wee_vect', 'weed_vect', 'week_vect', 'weekend_vect', 'weekends_vect', 'weekly_vect', 'weeks_vect', 'weigh_vect', 'weight_vect', 'weird_vect', 'welcome_vect', 'well_vect', 'welp_vect', 'wen_vect', 'went_vect', 'west_vect', 'wet_vect', 'what_vect', 'whatever_vect', 'whats_vect', 'whenever_vect', 'whenevr_vect', 'wherever_vect', 'whether_vect', 'white_vect', 'whn_vect', 'whole_vect', 'whos_vect', 'whose_vect', 'wid_vect', 'widelivecomindex_vect', 'wif_vect', 'wife_vect', 'wil_vect', 'willing_vect', 'win_vect', 'wind_vect', 'wine_vect', 'winner_vect', 'winning_vect', 'wins_vect', 'wipro_vect', 'wisdom_vect', 'wise_vect', 'wish_vect', 'wishes_vect', 'wishing_vect', 'wit_vect', 'within_vect', 'without_vect', 'wiv_vect', 'wk_vect', 'wkend_vect', 'wkg_vect', 'wkly_vect', 'wks_vect', 'wld_vect', 'wml_vect', 'wn_vect', 'wnt_vect', 'wo_vect', 'woke_vect', 'woken_vect', 'woman_vect', 'women_vect', 'wonder_vect', 'wonderful_vect', 'wondering_vect', 'wont_vect', 'woot_vect', 'word_vect', 'words_vect', 'work_vect', 'workin_vect', 'working_vect', 'works_vect', 'world_vect', 'worried_vect', 'worries_vect', 'worry_vect', 'worse_vect', 'worst_vect', 'worth_vect', 'wot_vect', 'would_vect', 'wow_vect', 'write_vect', 'wrong_vect', 'wtf_vect', 'wud_vect', 'wuld_vect', 'wun_vect', 'www4tcbiz_vect', 'wwwcomuknet_vect', 'wwwetlpcoukexpressoffer_vect', 'wwwgetzedcouk_vect', 'wwwldewcom_vect', 'wwwldewcom1win150ppmx3age16_vect', 'wwwmovietriviatv_vect', 'wwwringtonescouk_vect', 'wwwsmsconet_vect', 'wwwtxttowincouk_vect', 'wwwurawinnercom_vect', 'wylie_vect', 'xchat_vect', 'xmas_vect', 'xuhui_vect', 'xx_vect', 'xxx_vect', 'xxxx_vect', 'xxxxx_vect', 'xy_vect', 'ya_vect', 'yahoo_vect', 'yan_vect', 'yar_vect', 'yay_vect', 'yck_vect', 'yeah_vect', 'year_vect', 'years_vect', 'yelling_vect', 'yellow_vect', 'yep_vect', 'yes_vect', 'yest_vect', 'yesterday_vect', 'yet_vect', 'yetunde_vect', 'yijue_vect', 'ym_vect', 'yo_vect', 'yoga_vect', 'yogasana_vect', 'yor_vect', 'you_vect', 'yr_vect', 'yrs_vect', 'yummy_vect', 'yun_vect', 'yuo_vect', 'yup_vect', 'zed_vect', 'zindgi_vect', 'ìï_vect', 'ûò_vect']}, 'training': {'algo': 'Logistic Regression', 'model_file': 'AI0110_1.sav'}} deployer = get_deployer('classification',params=config) deployer.run( ) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import json import shutil import logging class outputFormatter: def __init__(self): self.log = logging.getLogger('eion') self.log.info('========> Inside Output Formatter') def crate_output_format_file(self,deploy_path,learner_type,modelType,model,output_label,threshold,trained_data_file,dictDiffCount,targetFeature,features,datetimeFeature): self.output_formatfile = 'import json' self.output_formatfile += '\n' self.output_formatfile += 'import numpy as np' self.output_formatfile += '\n' self.output_formatfile += 'import pandas as pd' self.output_formatfile += '\n' self.output_formatfile += 'import os' self.output_formatfile += '\n' self.output_formatfile += 'from pathlib import Path' self.output_formatfile += '\n' if((model.lower() in ['autoencoder','dbscan']) and modelType.lower()=="anomaly_detection"): self.output_formatfile += 'from script.aion_granularity import aion_gettimegranularity' self.output_formatfile += '\n' self.output_formatfile += 'class output_format(object):' self.output_formatfile += '\n' if(model == 'VAR'): self.output_formatfile += ' def invertTransformation(self,predictions):' self.output_formatfile += '\n' self.output_formatfile += ' datasetdf = pd.read_csv(os.path.join(os.path.dirname(os.path.abspath(__file__)),"..","data","trainingdata.csv"))' self.output_formatfile += '\n' self.output_formatfile += ' dictDiffCount = '+str(dictDiffCount) self.output_formatfile += '\n' self.output_formatfile += ' targetFeature = "'+str(targetFeature)+'"' self.output_formatfile += '\n' self.output_formatfile += ' columns = targetFeature.split(",")' self.output_formatfile += '\n' self.output_formatfile += ' pred = pd.DataFrame(index=range(0,len(predictions)),columns=columns)' self.output_formatfile += '\n' self.output_formatfile += ' for j in range(0,len(columns)):' self.output_formatfile += '\n' self.output_formatfile += ' for i in range(0, len(predictions)):' self.output_formatfile += '\n' self.output_formatfile += ' pred.iloc[i][j] = round(predictions[i][j],2)' self.output_formatfile += '\n' self.output_formatfile += ' prediction = pred' self.output_formatfile += '\n' self.output_formatfile += ' for col in columns:' self.output_formatfile += '\n' self.output_formatfile += ' if col in dictDiffCount:' self.output_formatfile += '\n' self.output_formatfile += ' if dictDiffCount[col]==2:' self.output_formatfile += '\n' self.output_formatfile += ' prediction[col] = (datasetdf[col].iloc[-1]-datasetdf[col].iloc[-2]) + prediction[col].cumsum()' self.output_formatfile += '\n' self.output_formatfile += ' prediction[col] = datasetdf[col].iloc[-1] + prediction[col].cumsum()' self.output_formatfile += '\n' self.output_formatfile += ' prediction = pred' self.output_formatfile += '\n' self.output_formatfile += ' return(prediction)' self.output_formatfile += '\n' self.log.info("op:modelType: \n"+str(modelType)) if((model.lower() in ['autoencoder','dbscan']) and modelType.lower()=="anomaly_detection"): # if modelType == 'anomaly_detection': self.output_formatfile += ' def find_point_subsequence_anomalies(self,datetime_column,dataframe=None):' self.output_formatfile += '\n' self.output_formatfile += ' try:' self.output_formatfile += '\n' self.output_formatfile += ' dataframe[datetime_column] = pd.to_datetime(dataframe[datetime_column]) ' self.output_formatfile += '\n' self.output_formatfile += ' aion_gettimegranularity_obj=aion_gettimegranularity(dataframe,datetime_column) ' self.output_formatfile += '\n' self.output_formatfile += ' anomaly_info_df=aion_gettimegranularity_obj.get_granularity() ' self.output_formatfile += '\n' self.output_formatfile += ' except Exception as e:' self.output_formatfile += '\n' self.output_formatfile += ' print(f"find_point_subsequence_anomalies,: aion_gettimegranularity err msg:{e} ")\n' self.output_formatfile += ' return anomaly_info_df' self.output_formatfile += '\n' if((model.lower() in ['autoencoder','dbscan']) and modelType.lower()=="anomaly_detection"): if (datetimeFeature!='' and datetimeFeature!='NA'): self.output_formatfile += ' def apply_output_format(self,df,modeloutput,datetimeFeature):' self.output_formatfile += '\n' else: self.output_formatfile += ' def apply_output_format(self,df,modeloutput):' self.output_formatfile += '\n' else: self.output_formatfile += ' def apply_output_format(self,df,modeloutput):' self.output_formatfile += '\n' if modelType.lower() == 'classification': self.output_formatfile += ' modeloutput = round(modeloutput,2)' self.output_formatfile += '\n' if(learner_type == 'ImageClassification'): if(str(output_label) != '{}'): inv_mapping_dict = {v: k for k, v in output_label.items()} self.output_formatfile += ' le_dict = '+ str(inv_mapping_dict) self.output_formatfile += '\n' self.output_formatfile += ' predictions = []' self.output_formatfile += '\n' self.output_formatfile += ' for x in modeloutput:' self.output_formatfile += '\n' self.output_formatfile += ' x = le_dict[x]' self.output_formatfile += '\n' self.output_formatfile += ' predictions.append(x)' self.output_formatfile += '\n' else: self.output_formatfile += ' predictions=modeloutput' self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = predictions' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = df.to_json(orient=\'records\')' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}' self.output_formatfile += '\n' elif(learner_type == 'Text Similarity'): self.output_formatfile += ' df[\'prediction\'] = np.where(modeloutput > '+str(threshold)+',1,0)' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = modeloutput' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = df.to_json(orient=\'records\',double_precision=2)' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}' self.output_formatfile += '\n' elif(learner_type == 'TS'): if(model == 'VAR'): self.output_formatfile += ' modeloutput = self.invertTransformation(modeloutput)' self.output_formatfile += '\n' self.output_formatfile += ' modeloutput = modeloutput.to_json(orient=\'records\',double_precision=2)' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(modeloutput)}' elif(model.lower() == 'fbprophet'): self.output_formatfile += ' modeloutput = modeloutput.to_json(orient=\'records\')' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(modeloutput)}' elif((model.lower() == 'lstm' or model.lower() == 'mlp') and len(features) >= 1): self.output_formatfile += ' modeloutput = modeloutput.round(2)\n' self.output_formatfile += ' modeloutput = modeloutput.to_json(orient=\'records\')\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(modeloutput)}\n' else: self.output_formatfile += ' modeloutput = modeloutput.round(2)' self.output_formatfile += '\n' self.output_formatfile += ' modeloutput = json.dumps(modeloutput.tolist())' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":eval(modeloutput)}' self.output_formatfile += '\n' elif(learner_type in ['RecommenderSystem','similarityIdentification','contextualSearch']): self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = modeloutput' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = df.to_json(orient=\'records\',double_precision=2)' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}' self.output_formatfile += '\n' else: if(modelType == 'Classification' or modelType == 'TLClassification' or modelType == 'anomaly_detection'): if(modelType == 'Classification' or modelType == 'TLClassification' or modelType == 'anomaly_detection'): if(str(output_label) != '{}'): inv_mapping_dict = {v: k for k, v in output_label.items()} self.output_formatfile += ' le_dict = '+ str(inv_mapping_dict) self.output_formatfile += '\n' ''' if(model in ['SGDClassifier']): self.output_formatfile += ' modeloutput = modeloutput.replace({"predict_class": le_dict})' else: self.output_formatfile += ' modeloutput = modeloutput.rename(columns=le_dict)' ''' if modelType != 'anomaly_detection': self.output_formatfile += ' modeloutput = modeloutput.rename(columns=le_dict)' self.output_formatfile += '\n' if(threshold != -1): ''' if(model in ['SGDClassifier']): self.output_formatfile += ' df[\'prediction\'] = np.where(modeloutput[\'probability\'] > '+str(threshold)+',1,0)' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = modeloutput[\'probability\']' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = ""' self.output_formatfile += '\n' else: self.output_formatfile += ' predictedData = modeloutput.iloc[:,1]' self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = np.where(predictedData > '+str(threshold)+',modeloutput.columns[1],modeloutput.columns[0])' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = np.where(df[\'prediction\'] == modeloutput.columns[1],modeloutput.iloc[:,1],modeloutput.iloc[:,0])' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)' self.output_formatfile += '\n' ''' self.output_formatfile += ' predictedData = modeloutput.iloc[:,1]' self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = np.where(predictedData > '+str(threshold)+',modeloutput.columns[1],modeloutput.columns[0])' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = np.where(df[\'prediction\'] == modeloutput.columns[1],modeloutput.iloc[:,1],modeloutput.iloc[:,0])' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)' self.output_formatfile += '\n' else: ''' if(model in ['SGDClassifier']): self.output_formatfile += ' df[\'prediction\'] = modeloutput[\'predict_class\']' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = ""' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = "NA"' self.output_formatfile += '\n' else: self.output_formatfile += ' df[\'prediction\'] = modeloutput.idxmax(axis=1)' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = modeloutput.max(axis=1)' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)' self.output_formatfile += '\n' ''' if modelType == 'anomaly_detection': # if (model.lower()=='autoencoder'): if model.lower() in ['autoencoder']: if (datetimeFeature != '' and datetimeFeature.lower() != 'na'): self.output_formatfile += ' df[modeloutput.columns] = modeloutput\n' self.output_formatfile += ' anomaly_df=df[df[\'anomaly\'] == True]\n' self.output_formatfile += ' anomaly_prediction_df=self.find_point_subsequence_anomalies(datetimeFeature,anomaly_df)\n' self.output_formatfile += ' new_dir = str(Path(__file__).parent.parent/\'data\')\n' self.output_formatfile += ' anomaly_prediction_df.to_csv(f"{new_dir}/anomaly_data.csv")\n' self.output_formatfile += ' try:\n' self.output_formatfile += ' anomaly_prediction_df[datetimeFeature]=pd.to_datetime(anomaly_prediction_df[datetimeFeature])\n' self.output_formatfile += ' df[datetimeFeature]=pd.to_datetime(df[datetimeFeature])\n' self.output_formatfile += ' anomaly_prediction_df.drop("Time_diff",axis=1,inplace=True)\n' self.output_formatfile += ' except:\n' self.output_formatfile += ' pass\n' self.output_formatfile += ' try:\n' self.output_formatfile += ' df_out = pd.merge(df, anomaly_prediction_df, on=df.columns.values.tolist(), how=\'left\')\n' self.output_formatfile += ' df_out[\'anomaly\'].replace([\'None\', \'NaN\', np.nan], "Normal", inplace=True)\n' self.output_formatfile += ' df_out[\'anomalyType\'].replace([\'None\', \'NaN\', np.nan], "Normal", inplace=True)\n' self.output_formatfile += ' df_out.to_csv(f"{new_dir}/overall_ad_output.csv") \n' self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str) \n' self.output_formatfile += ' df_out.drop("time_diff",axis=1,inplace=True)\n' self.output_formatfile += ' except Exception as e:\n' self.output_formatfile += ' print("anomaly data updated issue",e)\n' self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str)\n' self.output_formatfile += ' df=df_out \n' else: self.output_formatfile += ' df[modeloutput.columns] = modeloutput\n' elif (model.lower()=='dbscan'): if (datetimeFeature != '' and datetimeFeature.lower() != 'na'): self.output_formatfile += ' df[\'anomaly\'] = modeloutput[\'cluster\']== -1\n' self.output_formatfile += ' anomaly_df=df[df[\'anomaly\'] == True]\n' self.output_formatfile += ' anomaly_prediction_df=self.find_point_subsequence_anomalies(datetimeFeature,anomaly_df)\n' self.output_formatfile += ' new_dir = str(Path(__file__).parent.parent/\'data\')\n' self.output_formatfile += ' try:\n' self.output_formatfile += ' anomaly_prediction_df[datetimeFeature]=pd.to_datetime(anomaly_prediction_df[datetimeFeature])\n' self.output_formatfile += ' df[datetimeFeature]=pd.to_datetime(df[datetimeFeature])\n' self.output_formatfile += ' except:\n' self.output_formatfile += ' pass\n' self.output_formatfile += ' try:\n' self.output_formatfile += ' df_out = pd.merge(df, anomaly_prediction_df, on=df.columns.values.tolist(), how=\'left\')\n' self.output_formatfile += ' df_out[\'anomaly\'].replace([\'None\', \'NaN\', np.nan], "Normal", inplace=True)\n' self.output_formatfile += ' df_out.to_csv(f"{new_dir}/overall_ad_output.csv") \n' self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str)\n' self.output_formatfile += ' except Exception as e:\n' self.output_formatfile += ' print("anomaly data updated.")\n' self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str)\n' self.output_formatfile += ' df=df_out \n' else: self.output_formatfile += ' df[\'anomaly\'] = modeloutput[\'cluster\']== -1\n' self.output_formatfile += ' df.sort_values(by=[\'anomaly\'], ascending=False, inplace=True)\n' else: self.output_formatfile += ' df[\'prediction\'] = modeloutput' self.output_formatfile += '\n' else: self.output_formatfile += ' df[\'prediction\'] = modeloutput.idxmax(axis=1)' self.output_formatfile += '\n' if learner_type != 'DL': self.output_formatfile += ' df[\'probability\'] = modeloutput.max(axis=1).round(2)' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)' self.output_formatfile += '\n' else: if model == 'COX': self.output_formatfile += '\n' self.output_formatfile += ' modeloutput[0] = modeloutput[0].round(2)' self.output_formatfile += '\n' #self.output_formatfile += ' modeloutput = modeloutput[0].to_json(orient=\'records\',double_precision=2)' #self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = modeloutput' self.output_formatfile += '\n' else: self.output_formatfile += ' df[\'prediction\'] = modeloutput[0]' if(learner_type == 'objectDetection'): self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = df[\'prediction\']' else: self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = df[\'prediction\'].round(2)' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = df.to_json(orient=\'records\',double_precision=2)' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}' self.output_formatfile += '\n' self.output_formatfile += ' return(json.dumps(outputjson))' filename = os.path.join(deploy_path,'script','output_format.py') #print(deploy_path) f = open(filename, "wb") self.log.info('-------> Output Mapping File Location :'+filename) f.write(str(self.output_formatfile).encode('utf8')) f.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import numpy as np import scipy import warnings import scipy.stats as st import logging import json class inputdrift(): def __init__(self,conf): self.log = logging.getLogger('eion') def get_input_drift(self,ndf,hdf,outputfolder): selectedColumns = self.features.split(',') dataalertcount=0 distributionChangeColumns="" distributionChangeMessage=[] for i in range(0,len(selectedColumns)): data1=hdf[selectedColumns[i]] data2=ndf[selectedColumns[i]] if(data1.dtype !="str" and data2.dtype !="str" ): cumulativeData=data1.append(data2) teststaticValue=teststatic(self,data1,data2) if (teststaticValue < 0.05): distributionName1,sse1=DistributionFinder(self,data1) distributionName2,sse2=DistributionFinder(self,data2) if(distributionName1 == distributionName2): dataalertcount = dataalertcount else: dataalertcount = dataalertcount+1 distributionChangeColumns=distributionChangeColumns+selectedColumns[i]+"," changedColumn = {} changedColumn['Feature'] = selectedColumns[i] changedColumn['KS_Training'] = teststaticValue changedColumn['Training_Distribution'] = distributionName1 changedColumn['New_Distribution'] = distributionName2 distributionChangeMessage.append(changedColumn) else : dataalertcount = dataalertcount else : response ="Selected Columns should be Numerical Values" if(dataalertcount == 0): resultStatus="Model is working as expected" else : resultStatus=json.dumps(distributionChangeMessage) return(dataalertcount,resultStatus) def DistributionFinder(self,data): try: distributionName ="" sse =0.0 KStestStatic=0.0 dataType="" if(data.dtype == "float64"): dataType ="Continuous" elif(data.dtype =="int"): dataType="Discrete" elif(data.dtype =="int64"): dataType="Discrete" if(dataType == "Discrete"): distributions= [st.bernoulli,st.binom,st.geom,st.nbinom,st.poisson] index, counts = np.unique(data.astype(int),return_counts=True) if(len(index)>=2): best_sse = np.inf y1=[] total=sum(counts) mean=float(sum(index*counts))/total variance=float((sum(index**2*counts) -total*mean**2))/(total-1) dispersion=mean/float(variance) theta=1/float(dispersion) r=mean*(float(theta)/1-theta) for j in counts: y1.append(float(j)/total) pmf1=st.bernoulli.pmf(index,mean) pmf2=st.binom.pmf(index,len(index),p=mean/len(index)) pmf3=st.geom.pmf(index,1/float(1+mean)) pmf4=st.nbinom.pmf(index,mean,r) pmf5=st.poisson.pmf(index,mean) sse1 = np.sum(np.power(y1 - pmf1, 2.0)) sse2 = np.sum(np.power(y1 - pmf2, 2.0)) sse3 = np.sum(np.power(y1 - pmf3, 2.0)) sse4 = np.sum(np.power(y1 - pmf4, 2.0)) sse5 = np.sum(np.power(y1- pmf5, 2.0)) sselist=[sse1,sse2,sse3,sse4,sse5] for i in range(0,len(sselist)): if best_sse > sselist[i] > 0: best_distribution = distributions[i].name best_sse = sselist[i] elif (len(index) == 1): best_distribution = "Constant Data-No Distribution" best_sse = 0.0 distributionName =best_distribution sse=best_sse elif(dataType == "Continuous"): distributions = [st.uniform,st.expon,st.weibull_max,st.weibull_min,st.chi,st.norm,st.lognorm,st.t,st.gamma,st.beta] best_distribution = st.norm.name best_sse = np.inf datamin=data.min() datamax=data.max() nrange=datamax-datamin y, x = np.histogram(data.astype(float), bins='auto', density=True) x = (x + np.roll(x, -1))[:-1] / 2.0 for distribution in distributions: with warnings.catch_warnings(): warnings.filterwarnings('ignore') params = distribution.fit(data.astype(float)) # Separate parts of parameters arg = params[:-2] loc = params[-2] scale = params[-1] # Calculate fitted PDF and error with fit in distribution pdf = distribution.pdf(x, loc=loc, scale=scale, *arg) sse = np.sum(np.power(y - pdf, 2.0)) if(best_sse >sse > 0): best_distribution = distribution.name best_sse = sse distributionName =best_distribution sse=best_sse except: response = str(sys.exc_info()[0]) message='Job has Failed'+response print(message) return distributionName,sse ##KStestStatic -pvalue finding def teststatic(self,data1,data2): try: teststatic =st.ks_2samp(data1,data2) pValue=0.0 scipyVersion =scipy.__version__ if(scipyVersion <= "0.14.1"): pValue =teststatic[1] else: pValue =teststatic.pvalue except: response = str(sys.exc_info()[0]) print("Input Drift Job Failed "+response) return pValue ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os,sys import platform import json import shutil import logging from pathlib import Path def create_selector_file(self,deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature, model_type,model,config=None): self.selectorfile += 'import pandas as pd' self.selectorfile += '\n' self.selectorfile += 'import joblib' self.selectorfile += '\n' self.selectorfile += 'import os' self.selectorfile += '\n' self.selectorfile += 'import numpy as np' self.selectorfile += '\n' self.selectorfile += 'class selector(object):' self.selectorfile += '\n' self.selectorfile += ' def apply_selector(self,df):' self.selectorfile += '\n' if pcaModel_pickle_file != '': self.selectorfile += " pcaModel = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+pcaModel_pickle_file+"'))" self.selectorfile += '\n' self.selectorfile += ' bpca_features = '+str(bpca_features) self.selectorfile += '\n' self.selectorfile += ' apca_features = '+str(apca_features) self.selectorfile += '\n' self.selectorfile += ' df = pcaModel.transform(df[bpca_features])' self.selectorfile += '\n' self.selectorfile += ' df = pd.DataFrame(df,columns=apca_features)' self.selectorfile += '\n' if(len(features) != 0) and model_type != 'BM25': if model_type.lower()!='anomaly_detection' and model.lower() != 'autoencoder': self.selectorfile += ' df = df['+str(features)+']' self.selectorfile += '\n' self.selectorfile += ' return(df)' filename = os.path.join(deploy_path,'script','selector.py') f = open(filename, "wb") self.log.info('-------> Feature Selector File Location :'+filename) f.write(str(self.selectorfile).encode('utf8')) f.close() featurefile = 'import json' featurefile +='\n' featurefile += 'def getfeatures():' featurefile +='\n' featurefile +=' try:' featurefile +='\n' featurelist = [] if 'profiler' in config: if 'input_features_type' in config['profiler']: inputfeatures = config['profiler']['input_features_type'] for x in inputfeatures: featurelt={} featurelt['feature'] = x print(x,inputfeatures[x]) if x == targetFeature: featurelt['Type'] = 'Target' else: if inputfeatures[x] in ['int','int64','float','float64']: featurelt['Type'] = 'Numeric' elif inputfeatures[x] == 'object': featurelt['Type'] = 'Text' elif inputfeatures[x] == 'category': featurelt['Type'] = 'Category' else: featurelt['Type'] = 'Unknown' featurelist.append(featurelt) featurefile +=' features = '+str(featurelist) featurefile +='\n' featurefile +=' outputjson = {"status":"SUCCESS","features":features}' featurefile +='\n' featurefile +=' output = json.dumps(outputjson)' featurefile +='\n' featurefile +=' print("Features:",output)' featurefile +='\n' featurefile +=' return(output)' featurefile +='\n' featurefile +=' except Exception as e:' featurefile +='\n' featurefile +=' output = {"status":"FAIL","message":str(e).strip(\'"\')}' featurefile +='\n' featurefile +=' print("Features:",json.dumps(output))' featurefile +='\n' featurefile +=' return (json.dumps(output))' featurefile +='\n' featurefile +='if __name__ == "__main__":' featurefile +='\n' featurefile +=' output = getfeatures()' filename = os.path.join(deploy_path,'featureslist.py') f = open(filename, "wb") f.write(str(featurefile).encode('utf8')) f.close() def create_init_function_for_classification(self,modelfile,classes,learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,modelName,model_type,imageconfig): self.modelfile += ' def __init__(self):' self.modelfile += '\n' if (learner_type == 'ML' and model_type.lower()=='anomaly_detection' and modelName.lower()=="autoencoder"): modelfile=modelfile.replace('.sav','') self.modelfile+=" self.model = tf.keras.models.load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif(learner_type == 'TextDL' or learner_type == 'DL'): if modelName.lower() == 'googlemodelsearch': self.modelfile += ' import autokeras as ak' self.modelfile += '\n' self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','modelsearch_rootdir','saved_model_onnx.onnx'))" self.modelfile += '\n' else: if scoreParam == 'recall': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'recall': recall_m},compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[recall_m])' self.modelfile += '\n' elif scoreParam == 'precision': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'precision': precision_m},compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[precision_m])' self.modelfile += '\n' elif scoreParam == 'roc_auc': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[tf.keras.metrics.AUC()])' self.modelfile += '\n' elif scoreParam == 'f1_score': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'f1_score': f1_m},compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[f1_m])' self.modelfile += '\n' elif scoreParam == 'r2': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'r2': r_square},compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[r_square])' self.modelfile += '\n' elif scoreParam == 'rmse': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'rmse': rmse_m},compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[rmse_m])' self.modelfile += '\n' elif scoreParam == 'mse': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif scoreParam == 'mae': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif scoreParam == 'accuracy': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' else: self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif(learner_type == 'Text Similarity'): self.modelfile += " self.preprocessing = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+preprocessing_pipe+"'))" self.modelfile += '\n' self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'), custom_objects={'cosine_distance': cosine_distance, 'cos_dist_output_shape': cos_dist_output_shape})" self.modelfile += '\n' elif(learner_type in ['similarityIdentification','contextualSearch']): if scoreParam == 'VectorDB Cosine': vectorfiledbname = 'trainingdataVecDB' self.modelfile += f"\ \n persist_directory = os.path.join(os.path.dirname(__file__),'..','data')\ \n client = chromadb.PersistentClient(path=persist_directory)\ \n self.collection_name = '{vectorfiledbname}'\ \n self.collection = client.get_collection(self.collection_name)\n" else: self.modelfile += " self.train_input = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','data','trainingdata.csv'))\n\n" elif(learner_type == 'ImageClassification'): self.modelfile += ' self.config='+str(imageconfig) self.modelfile += '\n' if(modelName.lower() == 'densenet'): self.modelfile += ' baseModel = tf.keras.applications.DenseNet121(weights="imagenet", include_top=False, input_tensor=Input(shape=(self.config[\'img_width\'],self.config[\'img_height\'],self.config[\'img_channel\'])))' else: self.modelfile += ' baseModel = tensorflow.keras.applications.InceptionV3(weights="imagenet", include_top=False, input_tensor=Input(shape=(self.config[\'img_width\'],self.config[\'img_height\'],self.config[\'img_channel\'])))' self.modelfile += '\n' self.modelfile += ' headModel = baseModel.output' self.modelfile += '\n' self.modelfile += ' headModel = Flatten(name="flatten")(headModel)' self.modelfile += '\n' self.modelfile += ' headModel = Dense(1024, activation=\'relu\')(headModel)' self.modelfile += '\n' self.modelfile += ' headModel = Dropout(0.5)(headModel)' self.modelfile += '\n' self.modelfile += ' headModel = Dense(2, activation=\'sigmoid\')(headModel)' self.modelfile += '\n' self.modelfile += ' headModel = self.model = Model(inputs=baseModel.input, outputs=headModel)' self.modelfile += '\n' self.modelfile += ' opt = Adam(lr=self.config[\'lr\'])' self.modelfile += '\n' self.modelfile += ' self.model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])' self.modelfile += '\n' self.modelfile += " self.model.load_weights(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif(learner_type == 'objectDetection'): self.modelfile += " self.MODEL_LOCATION = os.path.join(os.path.dirname(__file__),'..','model')\n" self.modelfile += ' PATH_TO_CFG = self.MODEL_LOCATION+"/export/pipeline.config"\n' self.modelfile += ' PATH_TO_CKPT = self.MODEL_LOCATION+"/export/checkpoint/"\n' self.modelfile += ' PATH_TO_LABELS = self.MODEL_LOCATION+"/export/label_map.pbtxt"\n' self.modelfile += ' configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)\n' self.modelfile += ' self.detection_model = model_builder.build(model_config=configs["model"], is_training=False)\n' self.modelfile += ' ckpt = tf.compat.v2.train.Checkpoint(model=self.detection_model)\n' self.modelfile += ' ckpt.restore(os.path.join(PATH_TO_CKPT, "ckpt-0")).expect_partial()\n' self.modelfile += ' self.category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\ use_display_name=True)\n' elif learner_type == 'TS' and (modelName.lower() == 'lstm' or modelName.lower() == 'mlp'): self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif modelName.lower() == 'neural architecture search': self.modelfile += ' import autokeras as ak' self.modelfile += '\n' self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects=ak.CUSTOM_OBJECTS)" self.modelfile += '\n' else: self.modelfile += " self.model = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' def create_predict(self,learner_type,method,model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,feature,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scoreParam=None): self.modelfile += ' def predict(self,X,features_names):' self.modelfile += '\n' if (learner_type == 'ML' and model_type.lower()=='anomaly_detection' and model.lower()=="autoencoder"): self.modelfile += f" X=X[{feature}]\n" self.modelfile += f" X = np.asarray(X).astype('float32')\n" self.modelfile += f" reconstructed = self.model.predict(X)\n" self.modelfile += f" predict_loss = tf.keras.losses.mae(reconstructed,X)\n" self.modelfile += ' max_threshold = np.mean(predict_loss) + 2*np.std(predict_loss)\n' self.modelfile += ' min_threshold = np.mean(predict_loss) - 2*np.std(predict_loss)\n' self.modelfile += ' prediction_df = pd.DataFrame()\n' self.modelfile += ' prediction_df["loss"] = predict_loss\n' self.modelfile += ' prediction_df["max_threshold"] = max_threshold\n' self.modelfile += ' prediction_df["min_threshold"] = min_threshold\n' self.modelfile += ' prediction_df["anomaly"] = np.where((prediction_df["loss"] > prediction_df["max_threshold"]) | (prediction_df["loss"] <= prediction_df["min_threshold"]), True, False)\n' self.modelfile += ' return prediction_df\n' elif(learner_type == 'RecommenderSystem'): self.modelfile += ' predictions = []' self.modelfile += '\n' self.modelfile += ' for index,row in X.iterrows():' self.modelfile += '\n' self.modelfile += ' score = self.model.predict(int(row["uid"]),int(row["iid"]))' self.modelfile += '\n' self.modelfile += ' predictions.append(score.est)' self.modelfile += '\n' self.modelfile += ' return predictions' elif(learner_type in ['similarityIdentification','contextualSearch']): tfeatures = list(modelFeatures.split(",")) if indexFeature != '' and indexFeature != 'NA': ifeatures = indexFeature.split(",") for ifes in ifeatures: if ifes not in tfeatures: tfeatures.append(ifes) if model_type == 'BM25': self.modelfile += f"\n\ tokenized_corpus =[doc.split(' ') for doc in self.train_input.tokenize]\n\ bm25 = BM25Okapi(tokenized_corpus)\n\ tokenized_query = [doc.split(' ') for doc in X.tokenize]\n\ logcnt = 5\n\ output = []\n\ for query in tokenized_query:\n\ doc_scores = bm25.get_scores(query)\n\ related_docs_indices = np.argsort(doc_scores)[::-1][:logcnt]\n\ x = self.train_input[{tfeatures}].loc[self.train_input.index[related_docs_indices]]\n\ x['Score'] = doc_scores[related_docs_indices]\n\ x['Score'] = round(x['Score'],2).astype(str)+'%'\n\ output.append(x)\n\ return output\n" elif scoreParam == 'VectorDB Cosine': featuresVecDB = modelFeatures.split(",") self.modelfile += ' logcnt = 5\n' self.modelfile += f" columns = {featuresVecDB}\n" self.modelfile += f"\ \n output = []\ \n for rowindex, row in X.iterrows():\ \n queryembedding = X.iloc[rowindex:rowindex+1].to_numpy()\ \n results = self.collection.query(\ \n query_embeddings=queryembedding.tolist(),\ \n n_results=logcnt\ \n )\ \n x = pd.DataFrame(columns=columns)\ \n for i in range(0, len(results['ids'][0])):\ \n documentAry = results['documents'][0][i]\ \n documentAry = documentAry.split(' ~&~ ')\ \n for j in range(0, len(documentAry)):\ \n x.at[i,columns[j]] = documentAry[j]\ \n x.at[i,'Score'] = results['distances'][0][i]\ \n output.append(x)\ \n return output" else: self.modelfile += ' columns = self.train_input.columns.tolist()\n' self.modelfile += ' logcnt = 5\n' self.modelfile += f" train_input = self.train_input[{tfeatures}]\n" for tf in tfeatures: self.modelfile += f" columns.remove('{tf}')\n" self.modelfile += f"\ \n results = cosine_similarity(self.train_input[columns],X)\ \n output = []\ \n for i in range(results.shape[1]):\ \n related_docs_indices = results[:,i].argsort(axis=0)[:-(int(logcnt) + 1):-1]\ \n x=self.train_input[{tfeatures}].loc[self.train_input.index[related_docs_indices]]\ \n scores = []\ \n for j in range(0,logcnt):\ \n scores.append(str(round((results[related_docs_indices][j][i])*100))+'%')\ \n x['Score'] = scores\ \n output.append(x)\ \n return output" elif(learner_type == 'Text Similarity'): self.modelfile += ' X["'+firstDocFeature+'"] = X["'+firstDocFeature+'"].astype(str)' self.modelfile += '\n' self.modelfile += ' X["'+secondDocFeature+'"] = X["'+secondDocFeature+'"].astype(str)' self.modelfile += '\n' self.modelfile += ' test_sentence1 = self.preprocessing.texts_to_sequences(X["'+firstDocFeature+'"].values)' self.modelfile += '\n' self.modelfile += ' test_sentence2 = self.preprocessing.texts_to_sequences(X["'+secondDocFeature+'"].values)' self.modelfile += '\n' self.modelfile += ' test_sentence1 = pad_sequences(test_sentence1, maxlen='+str(padding_length)+', padding=\'post\')' self.modelfile += '\n' self.modelfile += ' test_sentence2 = pad_sequences(test_sentence2, maxlen='+str(padding_length)+', padding=\'post\')' self.modelfile += '\n' self.modelfile += ' prediction = self.model.predict([test_sentence1, test_sentence2 ])' self.modelfile += '\n' self.modelfile += ' return(prediction)' self.modelfile += '\n' elif(learner_type == 'ImageClassification'): self.modelfile += ' predictions = []' self.modelfile += '\n' self.modelfile += ' for index, row in X.iterrows(): ' self.modelfile += '\n' self.modelfile += ' img = cv2.imread(row[\'imagepath\'])' self.modelfile += '\n' self.modelfile += ' img = cv2.resize(img, (self.config[\'img_width\'],self.config[\'img_height\']))' self.modelfile += '\n' self.modelfile += ' img = image.img_to_array(img)' self.modelfile += '\n' self.modelfile += ' img = np.expand_dims(img, axis=0)' self.modelfile += '\n' self.modelfile += ' img = img/255' self.modelfile += '\n' self.modelfile += ' prediction = self.model.predict(img)' self.modelfile += '\n' self.modelfile += ' prediction = np.argmax(prediction,axis=1)' self.modelfile += '\n' self.modelfile += ' predictions.append(prediction[0])' self.modelfile += '\n' self.modelfile += ' return(predictions)' self.modelfile += '\n' elif(learner_type == 'objectDetection'): self.modelfile += ' @tf.function\n' self.modelfile += ' def detect_fn(image):\n' self.modelfile += ' image, shapes = self.detection_model.preprocess(image)\n' self.modelfile += ' prediction_dict = self.detection_model.predict(image, shapes)\n' self.modelfile += ' detections = self.detection_model.postprocess(prediction_dict, shapes)\n' self.modelfile += ' return detections\n' self.modelfile += ' def load_image_into_numpy_array(path):\n' self.modelfile += ' return np.array(Image.open(path))\n' self.modelfile += ' imageLocation = []\n' self.modelfile += ' for i, row in X.iterrows():\n' self.modelfile += ' if ("confidance" in row) and row["confidance"] <= 1.0:\n' self.modelfile += ' confidance = row["confidance"]\n' self.modelfile += ' else:\n' self.modelfile += ' confidance = 0.8\n' self.modelfile += ' imageName = str(Path(row["imagepath"]).stem)+"_output"+str(Path(row["imagepath"]).suffix)\n' self.modelfile += ' image_np = load_image_into_numpy_array(row["imagepath"])\n' self.modelfile += ' input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n' self.modelfile += ' detections = detect_fn(input_tensor)\n' self.modelfile += ' num_detections = int(detections.pop("num_detections"))\n' self.modelfile += ' detections = {key: value[0, :num_detections].numpy()\n\ for key, value in detections.items()}\n' self.modelfile += ' detections["num_detections"] = num_detections\n' self.modelfile += ' detections["detection_classes"] = detections["detection_classes"].astype(np.int64)\n' self.modelfile += ' label_id_offset = 1\n' self.modelfile += ' image_np_with_detections = image_np.copy()\n' self.modelfile += ' viz_utils.visualize_boxes_and_labels_on_image_array(\n\ image_np_with_detections,\n\ detections["detection_boxes"],\n\ detections["detection_classes"]+label_id_offset,\n\ detections["detection_scores"],\n\ self.category_index,\n\ use_normalized_coordinates=True,\n\ max_boxes_to_draw=200,\n\ min_score_thresh=confidance,\n\ agnostic_mode=False)\n' self.modelfile += ' plt.figure()\n' self.modelfile += ' plt.imsave(os.path.join(self.MODEL_LOCATION,imageName), image_np_with_detections)\n' self.modelfile += ' imageLocation.append(os.path.join(self.MODEL_LOCATION,imageName))\n' self.modelfile += ' plt.show()\n' self.modelfile += ' return imageLocation\n' else: if(learner_type == 'DL' and model != 'Neural Network'): self.modelfile += ' X = np.expand_dims(X, axis=2)' self.modelfile += '\n' if(learner_type == 'TextDL'): self.modelfile += ' return pd.DataFrame(np.argmax(self.model.predict(X),axis=1))' self.modelfile += '\n' elif(learner_type == 'TextML'): self.modelfile += ' return pd.DataFrame(self.model.predict_proba(X),columns=self.model.classes_)' self.modelfile += '\n' elif(learner_type == 'DL' and model_type == 'Classification'): self.modelfile += ' X = X.astype(np.float32)' self.modelfile += '\n' self.modelfile += ' return pd.DataFrame(np.argmax(self.model.predict(X),axis=1))' self.modelfile += '\n' else: if(model_type == 'Classification' or model_type == 'TLClassification'): if model == 'Neural Architecture Search': self.modelfile += ' X = X.astype(np.float32)' self.modelfile += '\n' self.modelfile += ' return pd.DataFrame(self.model.predict(X))' self.modelfile += '\n' else: if optimizationmethod == 'genetic': self.modelfile += '\n' self.modelfile += ' try:' self.modelfile += '\n' self.modelfile += ' return pd.DataFrame(self.model.predict_proba(X))' self.modelfile += '\n' self.modelfile += ' except:' self.modelfile += '\n' self.modelfile += ' return pd.DataFrame(self.model.predict(X))' else: self.modelfile += ' X = X.astype(np.float32)' self.modelfile += '\n' if model.lower() == 'deep q network' or model.lower() == 'dueling deep q network': self.modelfile += ' q, _ = self.model(np.array(X), step_type=constant([time_step.StepType.FIRST] * np.array(X).shape[0]), training=False)' self.modelfile += '\n' self.modelfile += ' return pd.DataFrame(q.numpy())' else: self.modelfile += ' return pd.DataFrame(self.model.predict_proba(X), columns=self.model.classes_)' self.modelfile += '\n' elif model_type == 'Regression' and model == 'NAS': self.modelfile += \ """ X = X.astype(np.float32) return self.model.predict(X) """ elif(learner_type == 'TS'): if model.lower() == 'fbprophet': self.modelfile += ' sessonal_freq="'+str(sessonal_freq)+'"' self.modelfile += '\n' self.modelfile += ' ts_prophet_future = self.model.make_future_dataframe(periods=int(X["noofforecasts"][0]),freq=sessonal_freq,include_history = False)' self.modelfile += '\n' if (additional_regressors): self.modelfile += '\n' self.modelfile += ' additional_regressors='+str(additional_regressors) self.modelfile += '\n' self.modelfile += ' ts_prophet_future[additional_regressors] = dataFrame[additional_regressors]' self.modelfile += '\n' self.modelfile += ' ts_prophet_future.reset_index(drop=True)' self.modelfile += '\n' self.modelfile += ' ts_prophet_future=ts_prophet_future.dropna()' self.modelfile += '\n' self.modelfile += ' train_forecast = self.model.predict(ts_prophet_future)' self.modelfile += '\n' self.modelfile += ' prophet_forecast_tail=train_forecast[[\'ds\', \'yhat\', \'yhat_lower\',\'yhat_upper\']].tail( int(X["noofforecasts"][0]))' self.modelfile += '\n' self.modelfile += ' return(prophet_forecast_tail)' elif model.lower() == 'lstm' or model.lower() == 'mlp': self.modelfile += ' lag_order='+str(lag_order) self.modelfile += '\n' self.modelfile += ' xt = X.values' self.modelfile += '\n' scalertransformationFile = scalertransformationFile.split('\\')[-1] self.modelfile += ' loaded_scaler_model = joblib.load(os.path.join(os.path.dirname(__file__),\'..\',\'model\',\''+scalertransformationFile+'\'))' self.modelfile += '\n' self.modelfile += ' xt = xt.astype(\'float32\')' self.modelfile += '\n' self.modelfile += ' xt = loaded_scaler_model.transform(xt)' self.modelfile += '\n' self.modelfile += ' noOfPredictions = 10' self.modelfile += '\n' self.modelfile += ' pred_data = xt' self.modelfile += '\n' self.modelfile += ' y_future = []' self.modelfile += '\n' self.modelfile += ' for i in range(noOfPredictions):' self.modelfile += '\n' if len(feature) == 1: self.modelfile += ' pred_data = pred_data[-lag_order:]' self.modelfile += '\n' if model.lower() == 'mlp': self.modelfile += ' pred_data = pred_data.reshape((1,lag_order))' else: self.modelfile += ' pred_data = pred_data.reshape((1,lag_order,1))' self.modelfile += '\n' self.modelfile += ' pred = self.model.predict(pred_data)' self.modelfile += '\n' self.modelfile += ' predoutput = loaded_scaler_model.inverse_transform(pred) ' self.modelfile += '\n' self.modelfile += ' y_future.append(predoutput.flatten()[-1])' self.modelfile += '\n' self.modelfile += ' pred_data = np.append(pred_data,pred)' self.modelfile += '\n' self.modelfile += ' pred = pd.DataFrame(index=range(0,len(y_future)),columns='+str(feature)+')' self.modelfile += '\n' self.modelfile += ' for i in range(0, len(y_future)):' self.modelfile += '\n' self.modelfile += ' pred.iloc[i] = y_future[i]' self.modelfile += '\n' self.modelfile += ' return pred' else: self.modelfile += ' pdata = pred_data[-lag_order:]' self.modelfile += '\n' self.modelfile += ' pdata = pdata.reshape((1,lag_order,'+str(len(feature))+'))' self.modelfile += '\n' self.modelfile += ' pred = self.model.predict(pdata)' self.modelfile += '\n' self.modelfile += ' predoutput = loaded_scaler_model.inverse_transform(pred) ' self.modelfile += '\n' self.modelfile += ' y_future.append(predoutput)' self.modelfile += '\n' self.modelfile += ' pred_data = np.append(pred_data,pred,axis=0)' self.modelfile += '\n' self.modelfile += ' pred = pd.DataFrame(index=range(0,len(y_future)),columns='+str(feature)+')' self.modelfile += '\n' self.modelfile += ' for i in range(0, len(y_future)):' self.modelfile += '\n' self.modelfile += ' pred.iloc[i] = y_future[i]' self.modelfile += '\n' self.modelfile += ' return pred' else: self.modelfile += ' return self.model.predict(n_periods=int(X["noofforecasts"][0]))' else: if model == 'KaplanMeierFitter': self.modelfile += '\n' self.modelfile += ' res = self.model.predict(X[\''+feature[0]+'\'].astype(int))' self.modelfile += '\n' self.modelfile += ' if isinstance(res, pd.DataFrame):\n' self.modelfile += ' return res.values.reshape(1,-1)\n' self.modelfile += ' else:\n' self.modelfile += ' return np.array([res])\n' elif model == 'COX': self.modelfile += ' res = []\n' self.modelfile += ' for idx,row in X.iterrows():\n' self.modelfile += ' res.append(self.model.predict_survival_function(X, times=row[self.model.duration_col])[idx].values[0])\n' self.modelfile += ' return pd.DataFrame(res)' #self.modelfile += ' return self.model.predict_survival_function(X, times=X[self.model.duration_col])' self.modelfile += '\n' elif(learner_type == 'DL' and model_type in ['Classification','Regression']): self.modelfile += ' X = X.astype(np.float32)' self.modelfile += '\n' self.modelfile += ' return self.model.predict(X).reshape(1, -1)' self.modelfile += '\n' elif (model_type == 'Clustering' and model == 'DBSCAN'): self.modelfile += ' return self.model.fit_predict(X)' elif(model_type.lower() == 'anomaly_detection' and model.lower() == 'dbscan'): self.modelfile += " pred=self.model.fit_predict(X)\n" self.modelfile += " X.loc[:,'cluster'] = self.model.labels_ \n" self.modelfile += ' return X\n' elif model_type.lower() == 'anomaly_detection': self.modelfile += ' X = X.astype(np.float32)\n' self.modelfile += ' return self.model.predict(X)' else: if model_type != 'Clustering': self.modelfile += ' X = X.astype(np.float32)' self.modelfile += '\n' #self.modelfile += ' return self.model.predict(X).reshape(1, -1)' self.modelfile += \ """ if isinstance(self.model, LatentDirichletAllocation): output = np.matrix(self.model.transform(X)).argmax(axis=1) return output.flatten().tolist() return self.model.predict(X).reshape(1, -1) """ ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from pathlib import Path from AION.prediction_package.imports import importModule from AION.prediction_package.aion_prediction import aionPrediction from AION.prediction_package.utility import TAB_CHAR from AION.prediction_package import utility from AION.prediction_package import common def file_header( usecase=''): return '' class deployer(): """ base deployer class which can be used to generate the deployemnt code. This class will be inherited by deployer specific to problem type. """ def __init__(self, params={}): if not params['paths']['deploy']: raise ValueError('Deploy path is not provided') self.deploy_path = Path(params['paths']['deploy']) if not self.deploy_path.exists(): self.deploy_path.mkdir(parents=True, exist_ok=True) self.name = params.get('problem_type', '') self.params = params self.importer = importModule() self.feature_reducer = False def profiler_code(self): return common.profiler_code(self.params['profiler']) def feature_engg_code(self): if self.params['selector'].get('reducer',False): code, modules = common.feature_reducer_code(self.params['selector']) else: code, modules = common.feature_selector_code(self.params['selector']) utility.import_modules(self.importer, modules) return code def training_code(self): return common.training_code(self.params['training']) def formatter_code(self): return '' def run(self): """ run function will be called to start the deployment process. This function will create following files inputprofiler.py for preprocessing the input aion_predict.py for prediction model service file """ code = self.predict_code( ) with open(self.deploy_path/'aion_predict.py', 'w') as f: f.write(code) profiler_code = self.profiler_code() with open(self.deploy_path/'script'/'inputprofiler.py', 'w') as f: f.write(profiler_code) self.create_model_service( ) self.create_publish_service() self.create_idrift() self.create_odrift() common.create_feature_list(self.params, self.params['features']['target_feat'], self.deploy_path) common.requirement_file(self.deploy_path,self.params['training']['algo'],self.params['features']['text_feat']) common.create_readme_file(self.deploy_path, self.params['training']['model_file'], self.params['features']['input_feat']) self.create_utils_folder() def predict_code(self): imported_modules = [ {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None} ] utility.import_modules(self.importer, imported_modules) self.importer.addLocalModule(module='inputprofiler',mod_from='script.inputprofiler') code_text = "" code_text += self.feature_engg_code() code_text += self.training_code() code_text += self.formatter_code() code_text += common.main_code() code = file_header() code += self.importer.getCode() return code + code_text def create_model_service(self): service_name = '{}{}{}'.format(self.params['usecase_name'], '_' if self.params['usecase_ver'] != '' else '', self.params['usecase_ver']) obj = aionPrediction() obj.create_model_service(self.deploy_path, service_name, self.name) def create_publish_service(self): obj = aionPrediction() obj.create_publish_service(self.params['paths']['usecase'], self.params['usecase_name'],self.params['usecase_ver'], self.name) def create_idrift(self): pass def create_odrift(self): pass def create_utils_folder(self): common.create_util_folder(self.deploy_path) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from pathlib import Path from AION.prediction_package.imports import importModule from AION.prediction_package.aion_prediction import aionPrediction from AION.prediction_package.utility import TAB_CHAR from AION.prediction_package import utility from AION.prediction_package.base import deployer from AION.prediction_package import common import numpy as np def get_deployer( params): if params['training']['algo'] == 'ARIMA': return arima(params) elif params['training']['algo'] == 'LSTM': return lstm(params) elif params['training']['algo'] == 'ENCODER_DECODER_LSTM_MVI_UVO': return lstmencdec_mviuvo(params) elif params['training']['algo'] == 'MLP': return mlp(params) elif params['training']['algo'] == 'VAR': return var(params) elif params['training']['algo'] == 'FBPROPHET': return fbprophet(params) else: raise ValueError(f"Algorithm {params['training']['algo']} for time series forecasting is not supported") def _profiler_code(params, importer): """ This will create the profiler file based on the config file. separated file is created as profiler is required for input drift also. """ imported_modules = [ {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'scipy', 'mod_from': None, 'mod_as': None}, {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None} ] utility.import_modules(importer, imported_modules) if 'code' in params['profiler'].get('preprocess',{}).keys(): code = params['profiler']['preprocess']['code'] else: code = "" code += """ class inputprofiler(): """ init_code = """ def __init__(self): """ init_code += """ # preprocessing preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if not preprocess_path.exists(): raise ValueError(f'Preprocess model file not found: {preprocess_path}') self.profiler = joblib.load(preprocess_path) """ run_code = """ def run(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) """ if 'code' in params['profiler'].get('preprocess',{}).keys(): run_code += """ df = preprocess( df)""" if params['profiler'].get('unpreprocessed_columns'): run_code += f""" unpreprocessed_data = df['{params['profiler']['unpreprocessed_columns'][0]}'] df.drop(['{params['profiler']['unpreprocessed_columns'][0]}'], axis=1,inplace=True) """ if params['profiler'].get('force_numeric_conv'): run_code += f""" df[{params['profiler']['force_numeric_conv']}] = df[{params['profiler']['force_numeric_conv']}].apply(pd.to_numeric,errors='coerce')""" run_code += _profiler_main_code(params) if params['profiler'].get('unpreprocessed_columns'): run_code += f""" df['{params['profiler'].get('unpreprocessed_columns')[0]}'] = unpreprocessed_data """ run_code += """ return df """ utility.import_modules(importer, imported_modules) import_code = importer.getCode() return import_code + code + init_code + run_code def _profiler_main_code(params): code = f""" df = self.profiler.transform(df) columns = {params['profiler']['output_features']} if isinstance(df, scipy.sparse.spmatrix): df = pd.DataFrame(df.toarray(), columns=columns) else: df = pd.DataFrame(df, columns=columns) """ return code class arima( deployer): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' def profiler_code( self): imported_modules = [ {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, ] importer = importModule() utility.import_modules(importer, imported_modules) code = """ class inputprofiler(): def __init__(self): pass def run( self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) return df[['noofforecasts']] """ return importer.getCode() + code def feature_engg_code(self): self.importer.addModule(module='pandas',mod_as='pd') return f""" class selector(): def __init__(self): pass def run(self, df): return df """ def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') self.importer.addModule(module='Path',mod_from='pathlib') self.importer.addModule(module='numpy',mod_as='np') self.importer.addModule(module='joblib') return f""" class trainer(): def __init__(self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = joblib.load(model_file) def run(self,df): return self.model.predict(n_periods=int(df["noofforecasts"][0])) """ def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__( self): pass def run(self,raw_df,df): df = df.round(2) df = json.dumps(df.tolist()) outputjson = {"status":"SUCCESS","data":eval(df)} return(json.dumps(outputjson)) """ class lstm( deployer): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' def profiler_code(self): importer = importModule() return _profiler_code( self.params, importer) def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') self.importer.addModule(module='Path',mod_from='pathlib') code = f""" class trainer(): """ init_code, run_code = self._get_train_code() return code + init_code + run_code def _get_train_code(self): self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = load_model(model_file) """ run_code = f""" def run(self, df): lag_order={self.params['training']['lag_order']} xt = df.values scaler_file = (Path(__file__).parent/"model")/"{self.params['training']['scaler_file']}" if not scaler_file.exists(): raise ValueError(f'Scaling file not found: {{scaler_file}}') loaded_scaler_model = joblib.load(scaler_file) xt = xt.astype('float32') xt = loaded_scaler_model.transform(xt) noOfPredictions = 10 pred_data = xt y_future = [] for i in range(noOfPredictions): """ if len(self.params['selector']['output_features']) == 1: run_code += f""" pred_data = pred_data[-lag_order:] pred_data = pred_data.reshape((1,lag_order,1)) pred = self.model.predict(pred_data) predoutput = loaded_scaler_model.inverse_transform(pred) y_future.append(predoutput.flatten()[-1]) pred_data = np.append(pred_data,pred) pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']}) for i in range(0, len(y_future)): pred.iloc[i] = y_future[i] return pred """ else: run_code += f""" pdata = pred_data[-lag_order:] pdata = pdata.reshape((1,lag_order,{len(self.params['selector']['output_features'])})) pred = self.model.predict(pdata) predoutput = loaded_scaler_model.inverse_transform(pred) y_future.append(predoutput) pred_data = np.append(pred_data,pred,axis=0) pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']}) for i in range(0, len(y_future)): pred.iloc[i] = y_future[i] return pred """ return init_code, run_code def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__( self): pass def run(self,raw_df,df): df = df.round(2) df = df.to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(df)} return(json.dumps(outputjson)) """ class lstmencdec_mviuvo( deployer): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' outputFeatrues = params['profiler']['output_features'] self.targetColIndx = outputFeatrues.index(params['features']['target_feat']) selectedColDict = params['selector']['output_features'] self.selectedCols = list() for col in selectedColDict: self.selectedCols.append(col) def profiler_code(self): importer = importModule() return _profiler_code( self.params, importer) def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') self.importer.addModule(module='Path',mod_from='pathlib') code = f""" class trainer(): """ init_code, run_code = self._get_train_code() return code + init_code + run_code def _get_train_code(self): self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = load_model(model_file) """ run_code = f""" def run(self, df): targetColIndx = {self.targetColIndx} lag_order={self.params['training']['lag_order']} xt = df.values scaler_file = (Path(__file__).parent/"model")/"{self.params['training']['scaler_file']}" if not scaler_file.exists(): raise ValueError(f'Scaling file not found: {{scaler_file}}') loaded_scaler_model = joblib.load(scaler_file) xt = xt.astype('float32') xt = loaded_scaler_model.transform(xt) noOfPredictions = 10 pred_data = xt y_future = [] pdata = pred_data[-lag_order:] pdata = pdata.reshape((1,lag_order,{len(self.params['selector']['output_features'])})) pred = self.model.predict(pdata) pred_1d = pred.ravel() pdata_2d = pdata.ravel().reshape(len(pdata) * lag_order, {len(self.params['selector']['output_features'])}) pdata_2d[:,targetColIndx] = pred_1d pred_2d_inv = loaded_scaler_model.inverse_transform(pdata_2d) predout = pred_2d_inv[:, targetColIndx] predout = predout.reshape(len(pred_1d),1) pred = pd.DataFrame(index=range(0,len(predout)),columns=['{self.params['features']['target_feat']}']) for i in range(0, len(predout)): pred.iloc[i] = predout[i] return pred """ return init_code, run_code def feature_engg_code(self): self.importer.addModule(module='pandas',mod_as='pd') return f""" class selector(): def __init__(self): pass def run(self, df): return df[{self.selectedCols}] """ def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__( self): pass def run(self,raw_df,df): df = df.round(2) df = df.to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(df)} return(json.dumps(outputjson)) """ class mlp( lstm): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') self.importer.addModule(module='Path',mod_from='pathlib') code = f""" class trainer(): """ init_code, run_code = self._get_train_code() return code + init_code + run_code def _get_train_code(self): self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = load_model(model_file)""" run_code = f""" def run(self, df): lag_order={self.params['training']['lag_order']} xt = df.values scaler_file = (Path(__file__).parent/"model")/"{self.params['training']['scaler_file']}" if not scaler_file.exists(): raise ValueError(f'Scaling file not found: {{scaler_file}}') loaded_scaler_model = joblib.load(scaler_file) xt = xt.astype('float32') xt = loaded_scaler_model.transform(xt) noOfPredictions = 10 pred_data = xt y_future = [] for i in range(noOfPredictions): """ if len(self.params['selector']['output_features']) == 1: run_code += f""" pred_data = pred_data[-lag_order:] pred_data = pred_data.reshape((1,lag_order)) pred = self.model.predict(pred_data) predoutput = loaded_scaler_model.inverse_transform(pred) y_future.append(predoutput.flatten()[-1]) pred_data = np.append(pred_data,pred) pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']}) for i in range(0, len(y_future)): pred.iloc[i] = y_future[i] return pred """ else: run_code += f""" pdata = pred_data[-lag_order:] pdata = pdata.reshape((1,lag_order,{len(self.params['selector']['output_features'])})) pred = self.model.predict(pdata) predoutput = loaded_scaler_model.inverse_transform(pred) y_future.append(predoutput) pred_data = np.append(pred_data,pred,axis=0) pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']}) for i in range(0, len(y_future)): pred.iloc[i] = y_future[i] return pred """ return init_code, run_code class var( deployer): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' def profiler_code(self): importer = importModule() code = _profiler_code( self.params, importer) return code def feature_engg_code(self): self.importer.addModule(module='pandas',mod_as='pd') return f""" class selector(): def __init__(self): pass def run(self, df): return df[{self.params['selector']['output_features']}] """ def training_code( self): self.importer.addModule(module='joblib') return f""" class trainer(): def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = joblib.load(model_file) def run(self,df): lag_order = self.model.k_ar return self.model.forecast(df.values[-lag_order:],steps={self.params['training']['no_of_prediction']}) """ def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return f""" class output_format(): def __init__( self): pass def invertTransformation(self,predictions): datasetdf = pd.read_csv((Path(__file__).parent/"data")/"trainingdata.csv") dictDiffCount = {self.params['training']['dictDiffCount']} target_features = "{self.params['features']['target_feat']}" columns = target_features.split(',') pred = pd.DataFrame(index=range(0,len(predictions)),columns=columns) for j in range(0,len(columns)): for i in range(0, len(predictions)): pred.iloc[i][j] = round(predictions[i][j],2) prediction = pred for col in columns: if col in dictDiffCount: if dictDiffCount[col]==2: prediction[col] = (datasetdf[col].iloc[-1]-datasetdf[col].iloc[-2]) + prediction[col].cumsum() prediction[col] = datasetdf[col].iloc[-1] + prediction[col].cumsum() prediction = pred return(prediction) def run(self,raw_df,df): df = self.invertTransformation(df) df = df.to_json(orient='records',double_precision=2) outputjson = {{"status":"SUCCESS","data":json.loads(df)}} return(json.dumps(outputjson)) """ class fbprophet( deployer): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' def profiler_code( self): imported_modules = [ {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, ] importer = importModule() utility.import_modules(importer, imported_modules) code = """ class inputprofiler(): def __init__(self): pass def run( self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) return df[['noofforecasts']] """ return importer.getCode() + code def feature_engg_code(self): self.importer.addModule(module='pandas',mod_as='pd') return f""" class selector(): def __init__(self): pass def run(self, df): return df """ def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') self.importer.addModule(module='Path',mod_from='pathlib') self.importer.addModule(module='joblib') code = f""" class trainer(): def __init__(self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = joblib.load(model_file) """ code += f""" def run(self,df): sessonal_freq = '{self.params['training']['sessonal_freq']}' ts_prophet_future = self.model.make_future_dataframe(periods=int(df["noofforecasts"][0]),freq=sessonal_freq,include_history = False) """ if (self.params['training']['additional_regressors']): code += f""" additional_regressors={self.params['training']['additional_regressors']} ts_prophet_future[additional_regressors] = dataFrame[additional_regressors] ts_prophet_future.reset_index(drop=True) ts_prophet_future=ts_prophet_future.dropna() """ code += """ train_forecast = self.model.predict(ts_prophet_future) prophet_forecast_tail=train_forecast[[\'ds\', \'yhat\', \'yhat_lower\',\'yhat_upper\']].tail( int(df["noofforecasts"][0])) return(prophet_forecast_tail)""" return code def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__( self): pass def run(self,raw_df,df): df = df.to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(df)} return(json.dumps(outputjson)) """ import docker import json import logging def read_json(file_path): data = None with open(file_path,'r') as f: data = json.load(f) return data def run_pipeline(inputconfig): inputconfig = json.loads(inputconfig) logfilepath = inputconfig['logfilepath'] logging.basicConfig(level=logging.INFO,filename =logfilepath) usecasename = inputconfig['usecase'] logging.info("UseCaseName :"+str(usecasename)) version = inputconfig['version'] logging.info("version :"+str(version)) config = inputconfig['dockerlist'] persistancevolume = inputconfig['persistancevolume'] logging.info("PersistanceVolume :"+str(persistancevolume)) datasetpath = inputconfig['datasetpath'] logging.info("DataSet Path :"+str(datasetpath)) config = read_json(config) client = docker.from_env() inputconfig = {'modelName':usecasename,'modelVersion':str(version),'dataLocation':datasetpath} inputconfig = json.dumps(inputconfig) inputconfig = inputconfig.replace('"', '\\"') logging.info("===== Model Monitoring Container Start =====") outputStr = client.containers.run(config['ModelMonitoring'],'python code.py -i'+datasetpath,volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('ModelMonitoring: '+str(outputStr)) print('ModelMonitoring: '+str(outputStr)) logging.info("===== ModelMonitoring Stop =====") logging.info("===== Data Ingestion Container Start =====") outputStr = client.containers.run(config['DataIngestion'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('DataIngestion: '+str(outputStr)) print('DataIngestion: '+str(outputStr)) logging.info("===== Data Ingestion Container Stop =====") outputStr = outputStr.strip() decoded_data = json.loads(outputStr) status = decoded_data['Status'] if status != 'Success': output = {'Status':'Error','Msg':'Data Ingestion Fails'} logging.info("===== Transformation Container Start =====") outputStr = client.containers.run(config['DataTransformation'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('Data Transformations: '+str(outputStr)) print('Data Transformations: '+str(outputStr)) logging.info("===== Transformation Container Done =====") outputStr = outputStr.strip() decoded_data = json.loads(outputStr) status = decoded_data['Status'] if status != 'Success': output = {'Status':'Error','Msg':'Data Transformations Fails'} logging.info("===== Feature Engineering Container Start =====") outputStr = client.containers.run(config['FeatureEngineering'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('FeatureEngineering: '+str(outputStr)) print('FeatureEngineering: '+str(outputStr)) logging.info("===== Feature Engineering Container Done =====") outputStr = outputStr.strip() decoded_data = json.loads(outputStr) status = decoded_data['Status'] modeltraining = config['ModelTraining'] for mt in modeltraining: logging.info("===== Training Container Start =====") outputStr = client.containers.run(mt['Training'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('ModelTraining: '+str(outputStr)) print('ModelTraining: '+str(outputStr)) logging.info("===== Training Container Done =====") outputStr = outputStr.strip() try: decoded_data = json.loads(outputStr) status = decoded_data['Status'] except Exception as inst: logging.info(inst) logging.info("===== Model Registry Start =====") outputStr = client.containers.run(config['ModelRegistry'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('ModelRegistry: '+str(outputStr)) print('ModelRegistry: '+str(outputStr)) logging.info("===== ModelRegistry Done =====") logging.info("===== ModelServing Start =====") outputStr = client.containers.run(config['ModelServing'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('Prediction: '+str(outputStr)) print('Prediction: '+str(outputStr)) logging.info("===== ModelServing Done =====") import os import shutil import sys import subprocess from os.path import expanduser import platform import json def createDockerImage(model_name,model_version,module,folderpath): command = 'docker pull python:3.8-slim-buster' os.system(command); subprocess.check_call(["docker", "build", "-t",module+'_'+model_name.lower()+":"+model_version,"."], cwd=folderpath) def local_docker_build(config): print(config) config = json.loads(config) model_name = config['usecase'] model_version = config['version'] mlaac__code_path = config['mlacPath'] docker_images = {} docker_images['ModelMonitoring'] = 'modelmonitoring'+'_'+model_name.lower()+':'+model_version dataset_addr = os.path.join(mlaac__code_path,'ModelMonitoring') createDockerImage(model_name,model_version,'modelmonitoring',dataset_addr) docker_images['DataIngestion'] = 'dataingestion'+'_'+model_name.lower()+':'+model_version dataset_addr = os.path.join(mlaac__code_path,'DataIngestion') createDockerImage(model_name,model_version,'dataingestion',dataset_addr) transformer_addr = os.path.join(mlaac__code_path,'DataTransformation') docker_images['DataTransformation'] = 'datatransformation'+'_'+model_name.lower()+':'+model_version createDockerImage(model_name,model_version,'datatransformation',transformer_addr) featureengineering_addr = os.path.join(mlaac__code_path,'FeatureEngineering') docker_images['FeatureEngineering'] = 'featureengineering'+'_'+model_name.lower()+':'+model_version createDockerImage(model_name,model_version,'featureengineering',featureengineering_addr) from os import listdir arr = [filename for filename in os.listdir(mlaac__code_path) if filename.startswith("ModelTraining")] docker_training_images = [] for x in arr: dockertraing={} dockertraing['Training'] = str(x).lower()+'_'+model_name.lower()+':'+model_version docker_training_images.append(dockertraing) training_addri = os.path.join(mlaac__code_path,x) createDockerImage(model_name,model_version,str(x).lower(),training_addri) docker_images['ModelTraining'] = docker_training_images docker_images['ModelRegistry'] = 'modelregistry'+'_'+model_name.lower()+':'+model_version deploy_addr = os.path.join(mlaac__code_path,'ModelRegistry') createDockerImage(model_name,model_version,'modelregistry',deploy_addr) docker_images['ModelServing'] = 'modelserving'+'_'+model_name.lower()+':'+model_version deploy_addr = os.path.join(mlaac__code_path,'ModelServing') createDockerImage(model_name,model_version,'modelserving',deploy_addr) outputjsonFile = os.path.join(mlaac__code_path,'dockerlist.json') with open(outputjsonFile, 'w') as f: json.dump(docker_images, f) f.close() output = {'Status':'Success','Msg':outputjsonFile} output = json.dumps(output) print("aion_build_container:",output) import os import sys import json from pathlib import Path import subprocess import shutil import argparse def create_and_save_yaml(git_storage_path, container_label,usecasepath): file_name_prefix = 'gh-acr-' yaml_file = f"""\ name: gh-acr-{container_label} on: push: branches: main paths: {container_label}/** workflow_dispatch: jobs: gh-acr-build-push: runs-on: ubuntu-latest steps: - name: 'checkout action' uses: actions/checkout@main - name: 'azure login' uses: azure/login@v1 with: creds: ${{{{ secrets.AZURE_CREDENTIALS }}}} - name: 'build and push image' uses: azure/docker-login@v1 with: login-server: ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}} username: ${{{{ secrets.REGISTRY_USERNAME }}}} password: ${{{{ secrets.REGISTRY_PASSWORD }}}} - run: | docker build ./{container_label}/ModelMonitoring -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelmonitoring:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelmonitoring:{container_label} docker build ./{container_label}/DataIngestion -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/dataingestion:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/dataingestion:{container_label} docker build ./{container_label}/DataTransformation -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/datatransformation:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/datatransformation:{container_label} docker build ./{container_label}/FeatureEngineering -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/featureengineering:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/featureengineering:{container_label} docker build ./{container_label}/ModelRegistry -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelregistry:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelregistry:{container_label} docker build ./{container_label}/ModelServing -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelserving:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelserving:{container_label} """ arr = [filename for filename in os.listdir(usecasepath) if filename.startswith("ModelTraining")] for x in arr: yaml_file+=' docker build ./'+container_label+'/'+x+' -t ${{ secrets.REGISTRY_LOGIN_SERVER }}/'+x.lower()+':'+container_label yaml_file+='\n' yaml_file+=' docker push ${{ secrets.REGISTRY_LOGIN_SERVER }}/'+x.lower()+':'+container_label yaml_file+='\n' with open(Path(git_storage_path)/(file_name_prefix + container_label + '.yaml'), 'w') as f: f.write(yaml_file) def run_cmd(cmd): try: subprocess.check_output(cmd, stderr=subprocess.PIPE) except subprocess.CalledProcessError as e: if e.stderr: if isinstance(e.stderr, bytes): err_msg = e.stderr.decode(sys.getfilesystemencoding()) else: err_msg = e.stderr elif e.output: if isinstance(e.output, bytes): err_msg = e.output.decode(sys.getfilesystemencoding()) else: err_msg = e.output else: err_msg = str(e) return False, err_msg return True, "" def validate_config(config): non_null_keys = ['url','username', 'token', 'location', 'gitFolderLocation', 'email', 'modelName'] missing_keys = [k for k in non_null_keys if k not in config.keys()] if missing_keys: raise ValueError(f"following fields are missing in config file: {missing_keys}") for k,v in config.items(): if k in non_null_keys and not v: raise ValueError(f"Please provide value for '{k}' in config file.") def upload(config): validate_config(config) url_type = config.get('url_type','https') if url_type == 'https': https_str = "https://" url = https_str + config['username'] + ":" + config['token'] + "@" + config['url'][len(https_str):] else: url = config['url'] model_location = Path(config['location']) git_folder_location = Path(config['gitFolderLocation']) git_folder_location.mkdir(parents=True, exist_ok=True) (git_folder_location/'.github'/'workflows').mkdir(parents=True, exist_ok=True) if not model_location.exists(): raise ValueError('Trained model data not found') os.chdir(str(git_folder_location)) (git_folder_location/config['modelName']).mkdir(parents=True, exist_ok=True) shutil.copytree(model_location, git_folder_location/config['modelName'], dirs_exist_ok=True) create_and_save_yaml((git_folder_location/'.github'/'workflows'), config['modelName'],config['location']) if (Path(git_folder_location)/'.git').exists(): first_upload = False else: first_upload = True if first_upload: cmd = ['git','init'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','config','user.name',config['username']] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','config','user.email',config['email']] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','add', '-A'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','commit','-m',f"commit {config['modelName']}"] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','branch','-M','main'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) if first_upload: cmd = ['git','remote','add','origin', url] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','push','-f','-u','origin', 'main'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) else: cmd = ['git','push'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) return json.dumps({'Status':'SUCCESS'}) if __name__ == '__main__': try: if shutil.which('git') is None: raise ValueError("git is not installed on this system") parser = argparse.ArgumentParser() parser.add_argument('-c', '--config', help='Config file location or as a string') args = parser.parse_args() if Path(args.config).is_file() and Path(args.config).suffix == '.json': with open(args.config,'r') as f: config = json.load(f) else: config = json.loads(args.config) print(upload(config)) except Exception as e: status = {'Status':'Failure','msg':str(e)} print(json.dumps(status)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from kafka import KafkaConsumer from json import loads import pandas as pd import json import os,sys import time import multiprocessing from os.path import expanduser import platform import datetime modelDetails = {} class Process(multiprocessing.Process): def __init__(self, modelSignature,jsonData,predictedData,modelpath): super(Process, self).__init__() self.config = jsonData self.modelSignature = modelSignature self.data = predictedData self.modelpath = modelpath def run(self): #data = pd.json_normalize(self.data) minotoringService = self.config['minotoringService']['url'] trainingdatalocation = self.config['trainingDataLocation'][self.modelSignature] #filetimestamp = 'AION_'+str(int(time.time()))+'.csv' #data.to_csv(dataFile, index=False) inputFieldsJson = {"trainingDataLocation":trainingdatalocation,"currentDataLocation":self.data} inputFieldsJson = json.dumps(inputFieldsJson) ser_url = minotoringService+self.modelSignature+'/monitoring' driftTime = datetime.datetime.now() import requests try: response = requests.post(ser_url, data=inputFieldsJson,headers={"Content-Type":"application/json",}) outputStr=response.content outputStr = outputStr.decode('utf-8') outputStr = outputStr.strip() decoded_data = json.loads(outputStr) print(decoded_data) status = decoded_data['status'] msg = decoded_data['data'] except Exception as inst: if 'Failed to establish a new connection' in str(inst): status = 'Fail' msg = 'AION Service needs to be started' else: status = 'Fail' msg = 'Error during Drift Analysis' statusFile = os.path.join(self.modelpath,self.modelSignature+'_status.csv') df = pd.DataFrame(columns = ['dateTime', 'status', 'msg']) df = df.append({'dateTime' : driftTime, 'status' : status, 'msg' : msg},ignore_index = True) print(df) if (os.path.exists(statusFile)): df.to_csv(statusFile, mode='a', header=False,index=False) else: df.to_csv(statusFile, header=True,index=False) def launch_kafka_consumer(): from appbe.dataPath import DATA_DIR configfile = os.path.join(os.path.dirname(__file__),'..','config','kafkaConfig.conf') with open(configfile,'r',encoding='utf-8') as f: jsonData = json.load(f) f.close() kafkaIP=jsonData['kafkaCluster']['ip'] kafkaport = jsonData['kafkaCluster']['port'] topic = jsonData['kafkaCluster']['topic'] kafkaurl = kafkaIP+':'+kafkaport if jsonData['database']['csv'] == 'True': database = 'csv' elif jsonData['database']['mySql'] == 'True': database = 'mySql' else: database = 'csv' kafkaPath = os.path.join(DATA_DIR,'kafka') if not (os.path.exists(kafkaPath)): try: os.makedirs(kafkaPath) except OSError as e: pass consumer = KafkaConsumer(topic,bootstrap_servers=[kafkaurl],auto_offset_reset='earliest',enable_auto_commit=True,group_id='my-group',value_deserializer=lambda x: loads(x.decode('utf-8'))) for message in consumer: message = message.value data = message['data'] data = pd.json_normalize(data) modelname = message['usecasename'] version = message['version'] modelSignature = modelname+'_'+str(version) modelpath = os.path.join(kafkaPath,modelSignature) try: os.makedirs(modelpath) except OSError as e: pass secondsSinceEpoch = time.time() if modelSignature not in modelDetails: modelDetails[modelSignature] = {} modelDetails[modelSignature]['startTime'] = secondsSinceEpoch if database == 'csv': csvfile = os.path.join(modelpath,modelSignature+'.csv') if (os.path.exists(csvfile)): data.to_csv(csvfile, mode='a', header=False,index=False) else: data.to_csv(csvfile, header=True,index=False) modelTimeFrame = jsonData['timeFrame'][modelSignature] currentseconds = time.time() print(currentseconds - modelDetails[modelSignature]['startTime']) if (currentseconds - modelDetails[modelSignature]['startTime']) >= float(modelTimeFrame): csv_path = os.path.join(modelpath,modelSignature+'.csv') #predictedData = pd.read_csv(csv_path) ##predictedData = predictedData.to_json(orient="records") index = Process(modelSignature,jsonData,csv_path,modelpath) index.start() modelDetails[modelSignature]['startTime'] = secondsSinceEpoch ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import numpy as np import sys import math import markov_clustering as mc import os import networkx as nx import logging import json ## How far you'd like your random-walkers to go (bigger number -> more walking) EXPANSION_POWER = 2 ## How tightly clustered you'd like your final picture to be (bigger number -> more clusters) INFLATION_POWER = 2 ## If you can manage 100 iterations then do so - otherwise, check you've hit a stable end-point. ITERATION_COUNT = 100 def normalize(matrix): return matrix/np.sum(matrix, axis=0) def expand(matrix, power): return np.linalg.matrix_power(matrix, power) def inflate(matrix, power): for entry in np.nditer(matrix, op_flags=['readwrite']): entry[...] = math.pow(entry, power) return matrix class pattern: def __init__(self,modelFeatures,targetFeature): self.modelFeatures = modelFeatures.split(',') self.targetFeature = targetFeature self.log = logging.getLogger('eion') def training(self,df,outputLocation): df["code"] = df[self.targetFeature].astype("category") df['code'] = df.code.cat.codes df2 = df[[self.targetFeature,'code']] df2 = df2.drop_duplicates() code_book = df2.to_dict('records') size = len(code_book) if self.targetFeature in self.modelFeatures: self.modelFeatures.remove(self.targetFeature) df['prev_code'] = df.groupby(self.modelFeatures)['code'].shift() df['prev_activity'] = df.groupby(self.modelFeatures)[self.targetFeature].shift() print(self.modelFeatures) df = df.dropna(axis=0, subset=['prev_code']) df['prev_code'] = df['prev_code'].astype('int32') matrix = np.zeros((size, size),float) np.set_printoptions(suppress=True) for index, row in df.iterrows(): matrix[int(row['prev_code'])][int(row['code'])] += 1 np.fill_diagonal(matrix, 1) matrix = normalize(matrix) pmatrix = matrix i = 0 records = [] for row in matrix: j = 0 for val in row: for event in code_book: if event['code'] == i: page = event[self.targetFeature] if event['code'] == j: nextpage = event[self.targetFeature] record = {} record['State'] = page record['NextState'] = nextpage record['Probability'] = round(val,2) records.append(record) j = j+1 i = i+1 df_probability = pd.DataFrame(records) self.log.info('Status:- |... StateTransition Probability Matrix') for _ in range(ITERATION_COUNT): matrix = normalize(inflate(expand(matrix, EXPANSION_POWER), INFLATION_POWER)) result = mc.run_mcl(matrix) # run MCL with default parameters c = 0 clusters = mc.get_clusters(matrix) # get clusters self.log.info('Status:- |... StateTransition Algorithm applied: MarkovClustering') clusterrecords = [] for cluster in clusters: clusterid = c clusterlist = '' for pageid in cluster: for event in code_book: if event['code'] == pageid: page = event[self.targetFeature] if clusterlist != '': clusterlist = clusterlist+',' clusterlist = clusterlist+page record = {} record['clusterid'] = c record['clusterlist'] = clusterlist clusterrecords.append(record) c = c+1 df_cluster = pd.DataFrame(clusterrecords) probabilityoutputfile = os.path.join(outputLocation, 'stateTransitionProbability.csv') self.log.info('-------> State Transition Probability Matrix:' + probabilityoutputfile) df_probability.to_csv(probabilityoutputfile,index=False) clusteringoutputfile = os.path.join(outputLocation, 'stateClustering.csv') self.log.info('-------> State Transition Probability Grouping:' + clusteringoutputfile) df_cluster.to_csv(clusteringoutputfile,index=False) datadetailsfile = os.path.join(outputLocation, 'datadetails.json') dataanalytics = {} dataanalytics['activity'] = self.targetFeature dataanalytics['sessionid'] = self.modelFeatures[0] updatedConfig = json.dumps(dataanalytics) with open(datadetailsfile, "w") as fpWrite: fpWrite.write(updatedConfig) fpWrite.close() evaulatemodel = '{"Model":"MarkovClustering","Score":0}' return(evaulatemodel,probabilityoutputfile,clusteringoutputfile) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' class aionRunTimeUtility: # def __init__(self): # print("AI.ON ConversionUtility function init...") def executeOnRuntime(self,inputModelName,inputDataSet): # print("AI.ON ConversionUtility function starts...") RuntimeType = inputModelName.rsplit('.', 1)[1] inputDataType = inputDataSet.rsplit('.', 1)[1] if((RuntimeType == 'ONNX' or RuntimeType == 'onnx') and (inputDataType.lower()=='json')): # print("Inference through ONNX Runtime started [ML]") import pandas import json with open(inputDataSet) as datafile: data = json.load(datafile) dataframe = pandas.DataFrame(data,index=[0]) import numpy import onnxruntime as rt sess = rt.InferenceSession(inputModelName) input_name = sess.get_inputs()[0].name label_name = sess.get_outputs()[0].name inputsize=sess.get_inputs()[0].shape first_n_column = dataframe.iloc[: , :inputsize[1]] dataset = first_n_column.values if(inputsize[1]!=len(dataframe.columns)): print("Error : Input Data size does not match") return 0 pred_onx = sess.run([label_name], {input_name: dataset.astype(numpy.float32)[0:1]})[0] # for i in range(0, 1): #print("ONNX Runtime Prediction [csv]: ",pred_onx) output = numpy.squeeze(pred_onx) predictions = numpy.squeeze(output) prediction = numpy.argmax(predictions) return(prediction) # print("Inference through ONNX modelcompleted ") if((RuntimeType == 'ONNX' or RuntimeType == 'onnx') and (inputDataType!='json')): import numpy as np import onnxruntime as rt from tensorflow.keras.preprocessing import image sess = rt.InferenceSession(inputModelName) input_name = sess.get_inputs()[0].name label_name = sess.get_outputs()[0].name inputsize=sess.get_inputs()[0].shape img = image.load_img(inputDataSet, target_size=(inputsize[1], inputsize[2])) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) pred_onx = sess.run([label_name], {input_name: x.astype(np.float32)[0:1]})[0] output = np.squeeze(pred_onx) predictions = np.squeeze(output) return(pred_onx) if((RuntimeType == 'TFLITE' or RuntimeType == 'tflite')and (inputDataType=='json')): import numpy as np import tensorflow as tf import pandas from numpy import asarray interpreter = tf.lite.Interpreter(model_path=inputModelName) interpreter.allocate_tensors() input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() input_shape = input_details[0]['shape'] import pandas import json with open(inputDataSet) as datafile: data = json.load(datafile) dataframe = pandas.DataFrame(data,index=[0]) dataset = dataframe.values XYZ = dataset[:,0:input_shape[1]].astype(float) input_data = asarray(XYZ[0]).reshape((1, input_shape[1])) for i in range(0, 1): input_data = asarray(XYZ[i]).reshape((1,input_shape[1])) interpreter.set_tensor(input_details[0]['index'], input_data.astype(np.float32)[0:1]) interpreter.invoke() output_data = interpreter.get_tensor(output_details[0]['index']) predictions = np.squeeze(output_data) prediction = np.argmax(predictions) return(prediction) if((RuntimeType == 'TFLITE' or RuntimeType == 'tflite') and (inputDataType!='json')): import numpy as np from tensorflow.keras.preprocessing import image import os import tensorflow as tf import pandas from numpy import asarray interpreter = tf.lite.Interpreter(model_path=inputModelName) interpreter.allocate_tensors() input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() input_shape = input_details[0]['shape'] img = image.load_img(inputDataSet, target_size=(input_shape[1], input_shape[2])) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) interpreter.set_tensor(input_details[0]['index'], x.astype(np.float32)[0:1]) interpreter.invoke() output_data = interpreter.get_tensor(output_details[0]['index']) predictions = np.squeeze(output_data) prediction = np.argmax(predictions) return(prediction) def runTimeTesting(inputModelName,inputDataSet): objRunTimeUtility=aionRunTimeUtility() return(objRunTimeUtility.executeOnRuntime(inputModelName,inputDataSet)) import os import sys import logging import json import joblib from pathlib import Path import platform from datetime import datetime as dt import time import argparse log = None def get_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def convert_keras2onnx(input_model, output_file): import tensorflow as tf import tf2onnx from tensorflow.keras.models import load_model model = load_model(input_model) config = model.get_config() modelInputShape=config["layers"][0]["config"]["batch_input_shape"] spec = (tf.TensorSpec(modelInputShape, tf.float32, name="input"),) model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=13, output_path=output_file) def convert_sklearn2onnx(input_model, file_path, input_shape=1): #print('Instead convert_sklearn2onnx') from skl2onnx import convert_sklearn #print('Instead convert_sklearn2onnx') from skl2onnx.common.data_types import FloatTensorType sklearn_model = joblib.load(input_model) sklearn_model_name= str(type(sklearn_model)).split(".")[-1][:-2] initial_type = [('float_input', FloatTensorType([None, input_shape]))] model = convert_sklearn(sklearn_model, initial_types=initial_type) with open(file_path, "wb") as f: f.write(model.SerializeToString()) def convert_xgboost2onnx(input_model, file_path, input_shape=1): from onnxmltools.convert import convert_xgboost from onnxmltools.convert.common.data_types import FloatTensorType xgBoost_model = joblib.load(input_model) if not xgBoost_model.n_estimators: xgBoost_model.n_estimators = xgBoost_model.get_num_boosting_rounds() n_features = xgBoost_model.n_features_in_ xgBoost_model.get_booster().feature_names = [f'f{x}' for x in range(n_features)] initial_type = [('float_input', FloatTensorType([None, xgBoost_model.n_features_in_]))] model = convert_xgboost(xgBoost_model, initial_types=initial_type) with open(file_path, "wb") as f: f.write(model.SerializeToString()) def convert_lightgbm2onnx(input_model, file_path): from onnxmltools.convert import convert_lightgbm from onnxmltools.convert.common.data_types import FloatTensorType lightGBM_model = joblib.load(input_model) initial_type = [('float_input', FloatTensorType([None, lightGBM_model.n_features_in_]))] model = convert_lightgbm(lightGBM_model, initial_types=initial_type, zipmap=False) with open(file_path, "wb") as f: f.write(model.SerializeToString()) def convert_coreml2onnx(input_model, file_path): import coremltools import onnxmltools coreml_model = coremltools.utils.load_spec(input_model) onnx_model = onnxmltools.convert_coreml(coreml_model) onnxmltools.utils.save_model(onnx_model, file_path) def convert_tflite2onnx(input_model, file_path): cmd = f"{sys.executable} -m tf2onnx.convert --opset 13 --tflite {str(input_model)} --output {str(file_path)}" os.system(cmd) def convert_tensorflow2onnx(input_model, file_path): import subprocess cmd = [sys.executable, '-m','tf2onnx.convert','--saved-model',str(input_model),'--output',str(file_path)] result = subprocess.check_output(cmd) result = result.decode('utf-8') def convert_libsvm2onnx(input_model, file_path): import onnxmltools import libsvm.svmutil as svmutil from onnxmltools.convert.libsvm import convert from onnxmltools.convert.common.data_types import FloatTensorType loaded_model=svmutil.svm_load_model(str(input_model)) model = convert(loaded_model, "node", [('input', FloatTensorType())]) onnxmltools.utils.save_model(model, file_path) def optimize_onnx(onnx_model_file, output_file_path): from onnxruntime.quantization import quantize_dynamic, QuantType quantize_dynamic(onnx_model_file, output_file_path, weight_type=QuantType.QUInt8) return True def convert_keras2tflite(input_model, file_path, optimized=False): import tensorflow as tf converter = tf.compat.v1.lite.TFLiteConverter.from_keras_model_file(input_model) if optimized: converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.target_spec.supported_types = [tf.float16] model = converter.convert() with open(file_path, 'wb') as f: f.write(model) def convert_tensorflow2tflite(input_model, file_path, optimized=False): import tensorflow as tf modelpath=str(input_model) #converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model(input_model) converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model(modelpath) if optimized: converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.target_spec.supported_types = [tf.float16] model = converter.convert() with open(file_path, 'wb') as f: f.write(model) class model_converter(): def __init__(self, model_path, output_path,input_format,output_format, shape=None): if not self.is_conversion_supported(input_format,output_format): raise ValueError(f"{input_format} to {output_format} is not supported") if not Path(model_path).exists(): raise ValueError(f"Model doen't exists at: {model_path}") self.model_path = Path(model_path) self.output_path = Path(output_path) self.output_path.mkdir(parents=True, exist_ok=True) self.input_format = input_format self.output_format = output_format self.shape = shape def is_conversion_supported(self, model_format, output_format): onnx_formats = ['onnx_standard','onnx_optimized'] tflite_formats = ['tflite_standard','tflite_optimized'] sagemaker_formats = ['sagemaker'] all_formats = onnx_formats + tflite_formats + sagemaker_formats formats = {'sklearn':onnx_formats + sagemaker_formats, 'keras':onnx_formats + tflite_formats, 'tensorflow':onnx_formats + tflite_formats, 'tflite':onnx_formats, 'lightgbm':onnx_formats, 'xgboost':onnx_formats, 'libsvm':onnx_formats,'coreml':['onnx_standard'] } if model_format in list(formats.keys()) and output_format in all_formats: if output_format in formats[model_format]: return True return False def convert(self): if self.output_format == 'onnx_standard': output_file = self.output_path/(self.model_path.stem + '.onnx') if self.input_format == 'sklearn': model = convert_sklearn2onnx(self.model_path, output_file, self.shape) elif self.input_format == 'keras': convert_keras2onnx(self.model_path, output_file) elif self.input_format == 'lightgbm': convert_lightgbm2onnx(self.model_path, output_file) elif self.input_format == 'xgboost': convert_xgboost2onnx(self.model_path, output_file) elif self.input_format == 'libsvm': convert_libsvm2onnx(self.model_path, output_file) elif self.input_format == 'coreml': convert_coreml2onnx(self.model_path, output_file) elif self.input_format == 'tflite': convert_tflite2onnx(self.model_path, output_file) elif self.input_format == 'tensorflow': convert_tensorflow2onnx(self.model_path, output_file) elif self.output_format == 'onnx_optimized': onnx_std_file = self.output_path/(self.model_path.stem + '_unquant.onnx') if onnx_std_file.exists(): onnx_std_file.unlink() output_file = self.output_path/(self.model_path.stem + 'Opt.onnx') if self.input_format == 'sklearn': convert_sklearn2onnx(self.model_path, onnx_std_file, self.shape) elif self.input_format == 'keras': convert_keras2onnx(self.model_path, onnx_std_file) elif self.input_format == 'lightgbm': convert_lightgbm2onnx(self.model_path, onnx_std_file) elif self.input_format == 'xgboost': convert_xgboost2onnx(self.model_path, onnx_std_file) elif self.input_format == 'libsvm': convert_libsvm2onnx(self.model_path, onnx_std_file) elif self.input_format == 'tflite': convert_tflite2onnx(self.model_path, onnx_std_file) elif self.input_format == 'tensorflow': convert_tensorflow2onnx(self.model_path, onnx_std_file) if onnx_std_file.exists(): try: optimize_onnx(onnx_std_file, output_file) except Exception as e: raise finally: onnx_std_file.unlink() temp_file = onnx_std_file.parent/(onnx_std_file.stem + '-opt.onnx') if temp_file.exists(): temp_file.unlink() elif self.output_format in ['tflite_standard', 'tflite_optimized']: if self.output_format == 'tflite_optimized': output_file = self.output_path/(self.model_path.stem + 'Opt.tflite') optimized = True else: output_file = self.output_path/(self.model_path.stem + '.tflite') optimized = False if self.input_format == 'keras': convert_keras2tflite(self.model_path, output_file, optimized) elif self.input_format == 'tensorflow': convert_tensorflow2tflite(self.model_path, output_file, optimized) def run(model_path, output_path, input_format, output_format, input_shape=None): from appbe.dataPath import LOG_LOCATION input_format = input_format.lower() output_format = output_format.lower() log_file_path = Path(LOG_LOCATION) log_file_path.mkdir(parents=True, exist_ok=True) time_stamp = dt.fromtimestamp(time.time()).strftime('%Y-%m-%d-%H-%M-%S') fileName='modelConversion_'+time_stamp+'.log' filehandler = logging.FileHandler(log_file_path/fileName, 'w','utf-8') formatter = logging.Formatter('%(message)s') filehandler.setFormatter(formatter) log = logging.getLogger('modelConversionUtility') log.propagate = False for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): log.removeHandler(hdlr) log.addHandler(filehandler) log.setLevel(logging.INFO) log.info('------------------ModelConversionUtility---------------------') log.info(f'Input model path: {model_path}') log.info(f'Output model path: {output_path}') log.info(f'Input model format: {input_format}') log.info(f'Output model format: {output_format}') log.info(f'\nConverting {input_format} to {output_format} start:') output ={} output['logfiles'] = str(log_file_path/fileName) log.info(f"\nExecution Start Time: {dt.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')}") try: start_time = time.time() objConvUtility=model_converter(model_path,output_path,input_format,output_format,input_shape) objConvUtility.convert() end_time = time.time() log.info(f"Time required for conversion: {end_time - start_time} sec") log.info(f'\nConverting {input_format} to {output_format} Successful') output['Convert'] = "Success" except Exception as e: output['Convert'] = "Error" log.info('Error: ' + str(e)) log.error(e, exc_info=True) if 'not supported' in str(e): output['sub error'] = "Not supported" output = json.dumps(output) log.info(f'Output: {output}') return output def convert(config_file): with open(config_file, 'r') as f: config = json.load(f) model_path = config['advance']['aionConversionUtility']['modelpath'] output_path = config['advance']['aionConversionUtility']['deployedlocation'] input_format = get_true_option(config['advance']['aionConversionUtility']['inputModelType'],'').lower() output_format = get_true_option(config['advance']['aionConversionUtility']['outputModelType'],'').lower() if input_format=="keras": input_shape = int(config['advance']['aionConversionUtility']['inputShape']) if input_format!="keras": input_shape = config['advance']['aionConversionUtility']['numberoffeatures'] input_shape = int(input_shape) if input_shape else 0 #input_shape = int(config['advance']['aionConversionUtility']['numberoffeatures']) output = run(model_path, output_path, input_format, output_format, input_shape) print(output) import pandas import numpy import sys import onnxruntime as rt def onnx_runtime_validation(modelfile,datafile): dataframe = pandas.read_csv(datafile) df = dataframe.head(8) dataset = df.values sess = rt.InferenceSession(modelfile) input_name = sess.get_inputs()[0].name label_name = sess.get_outputs()[0].name inputsize=sess.get_inputs()[0].shape XYZ = dataset[:,0:inputsize[1]].astype(float) pred_onx = sess.run([label_name], {input_name: XYZ.astype(numpy.float32)[0:8]})[0] print("Prediction of AION generated/converted model on ONNX runtime for 8 sets of data") for i in range(0, 8): output = numpy.squeeze(pred_onx[i]) predictions = numpy.squeeze(output) prediction = numpy.argmax(predictions) df['predictions'] = predictions result = df.to_json(orient="records") return(result) if __name__ == "__main__": output = onnx_runtime_validation(sys.argv[1],sys.argv[2]) print("predictions:",output) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import logging logging.getLogger('tensorflow').disabled = True import json #from nltk.corpus import stopwords from collections import Counter from numpy import mean from numpy import std from pandas import read_csv from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder from sklearn.compose import ColumnTransformer from learner.machinelearning import machinelearning # from sklearn.dummy import DummyClassifier # create histograms of numeric input variables import sys import os import re import pandas as pd import numpy as np from learner.aion_matrix import aion_matrix import tensorflow as tf tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) import autokeras as ak # load the sonar dataset from sklearn.model_selection import train_test_split # from sklearn.metrics import cohen_kappa_score # from sklearn.metrics import roc_auc_score # from sklearn.metrics import confusion_matrix from sklearn.metrics import roc_curve from math import sqrt from sklearn.metrics import mean_squared_error, explained_variance_score,mean_absolute_error from sklearn import metrics class aionNAS: def __init__(self,nas_class,nas_params,xtrain1,xtest1,ytrain1,ytest1,deployLocation): try: self.dfFeatures=None self.nas_class=nas_class self.nas_params=nas_params self.targetFeature=None self.log = logging.getLogger('eion') self.n_models=int(self.nas_params['n_models']) self.n_epochs=int(self.nas_params['n_epochs']) self.optimizer=self.nas_params['optimizer'] self.metrics=self.nas_params['metrics'] self.tuner=self.nas_params['tuner'] self.seed=int(self.nas_params['seed']) self.xtrain = xtrain1 self.xtest = xtest1 self.ytrain = ytrain1 self.ytest = ytest1 #self.labelMaps = labelMaps self.deployLocation=deployLocation except Exception as e: self.log.info('<!------------- NAS INIT Error ---------------> ') exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def paramCheck(self): try: if not (self.nas_class): self.log.info('<!------------- NAS class input Error ---------------> ') if not (self.nas_params): self.log.info('<!------------- NAS model hyperparameter input Error ---------------> ') if not (self.targetFeature): self.log.info('<!------------- NAS model targetFeature input Error ---------------> ') if (self.n_models < 1): self.n_models=1 if not (self.dfFeatures): self.log.info('<!------------- NAS model features Error ---------------> ') if (self.n_epochs < 1): self.n_models=1 if not (self.optimizer): self.optimizer="adam" if not (self.tuner): self.tuner="greedy" if (self.seed < 1): self.seed=0 if not (self.metrics): self.metrics=None except ValueError: self.log.info('<------------------ NAS config file error. --------------->') def recall_m(self,y_true, y_pred): true_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_true * y_pred, 0, 1))) possible_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + tf.keras.backend.epsilon()) return recall def precision_m(self,y_true, y_pred): true_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_true * y_pred, 0, 1))) predicted_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + tf.keras.backend.epsilon()) return precision def f1_score(self,y_true, y_pred): precision = self.precision_m(y_true, y_pred) recall = self.recall_m(y_true, y_pred) return 2*((precision*recall)/(precision+recall+tf.keras.backend.epsilon())) def nasStructdataPreprocess(self): df=self.data self.paramCheck() target=df[self.targetFeature].values counter = Counter(target) for k,v in counter.items(): per = v / len(target) * 100 self.log.info('autokeras struct Class=%d, Count=%d, Percentage=%.3f%%' % (k, v, per)) # select columns with numerical data types num_ix = df.select_dtypes(include=['int64', 'float64']).columns subset = df[num_ix] last_ix = len(df.columns) - 1 y=df[self.targetFeature] X = df.drop(self.targetFeature, axis=1) #Using Pearson Correlation # plt.figure(figsize=(12,10)) # cor = df.corr() # sns.heatmap(cor, annot=True, cmap=plt.cm.Reds) # plt.show() # select categorical features cat_ix = X.select_dtypes(include=['object', 'bool']).columns # one hot encode cat features only ct = ColumnTransformer([('o',OneHotEncoder(),cat_ix)], remainder='passthrough') X = X.reset_index() X=X.replace(to_replace="NULL",value=0) X = X.dropna(how='any',axis=0) X = ct.fit_transform(X) from sklearn.preprocessing import scale X = scale(X) # label encode the target variable to have the classes 0 and 1 y = LabelEncoder().fit_transform(y) # separate into train and test sets X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=self.test_size,random_state=1) return X_train, X_test, y_train, y_test def nasStructClassification(self,scoreParam): try: objClf = aion_matrix() X_train, X_test, y_train, y_test= self.xtrain, self.xtest, self.ytrain, self.ytest modelName="nas_structdata_classifier" self.log.info("Processing structured data block...\n") s_in = ak.StructuredDataInput() #s_in = Flatten()(s_in) s_out = ak.StructuredDataBlock(categorical_encoding=True)(s_in) self.log.info("Data pipe via autokeras Classification Dense layers ...\n") s_out = ak.ClassificationHead()(s_out) self.log.info("applying autokeras automodel to run different neural models...\n") try: tuner = str(self.tuner).lower() except UnicodeEncodeError: tuner = (self.tuner.encode('utf8')).lower() nasclf = ak.AutoModel( inputs=s_in, outputs=s_out, overwrite=True, tuner=tuner, max_trials=self.n_models, seed=self.seed) # compile the model #nasclf.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc',self.f1_score,self.precision_m, self.recall_m]) nasclf.fit(X_train, y_train, epochs=self.n_epochs) best_model = nasclf.export_model() mpredict=best_model.predict(X_test) mtpredict=best_model.predict(X_train) #loss, accuracy, f1_score, precision, recall = nasclf.evaluate(X_test, y_test, verbose=0) #from sklearn.metrics import classification_report #Classification report y_pred_bool = np.argmax(mpredict, axis=1) y_train_pred_bool = np.argmax(mtpredict, axis=1) score = objClf.get_score(scoreParam,y_test, y_pred_bool) #best_model = nasclf.export_model() best_model_summary=best_model.summary() filename = os.path.join(self.deployLocation,'log','summary.txt') with open(filename,'w') as f: best_model.summary(print_fn=lambda x: f.write(x + '\n')) f.close() #self.log.info("==========") #self.log.info(best_model_summary) self.log.info("NAS struct data classification, best model summary: \n"+str(best_model.summary(print_fn=self.log.info))) #self.log.info("==========") #Save and load model # # #try: # try: # best_model.save("model_class_autokeras", save_format="tf") # except Exception: # best_model.save("model_class_autokeras.h5") # loaded_model = load_model("model_class_autokeras", custom_objects=ak.CUSTOM_OBJECTS) # loadedmodel_predict=loaded_model.predict(X_test) loss,accuracy_m=nasclf.evaluate(X_test, y_test) #mpredict_classes = mpredict.argmax(axis=-1) #accuracy = accuracy_score(y_test.astype(int), mpredict.astype(int)) # precision tp / (tp + fp) #precision = precision_score(y_test.astype(int), mpredict.astype(int),average='macro') # recall: tp / (tp + fn) #recall = recall_score(y_test.astype(int), mpredict.astype(int),average='macro') #f1score=f1_score(y_test.astype(int), mpredict.astype(int) , average="macro") self.log.info("Autokeras struct data classification metrics: \n") except Exception as inst: self.log.info("Error: NAS failed "+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) print(inst) return modelName,nasclf,score def nasStructRegressor(self,scoreParam): objClf = aion_matrix() modelName="nas_struct_regressor" #self.paramCheck() X_train, X_test, y_train, y_test= self.xtrain, self.xtest, self.ytrain, self.ytest # Autokeras alg s_in = ak.StructuredDataInput() #tf.keras.layers.GlobalMaxPooling2D()(s_in) s_out = ak.StructuredDataBlock(categorical_encoding=True)(s_in) self.log.info("Data pipe via autokeras Regression Dense layers ...\n") s_out = ak.RegressionHead(loss='mse', metrics=['mae'])(s_out) self.log.info("applying autokeras automodel to evaluate different neural models...\n") try: tuner = str(self.tuner).lower() except UnicodeEncodeError: tuner = (self.tuner.encode('utf8')).lower() nas_reg = ak.AutoModel( inputs=s_in, outputs=s_out, overwrite=True, tuner=tuner, max_trials=self.n_models) nas_reg.fit(X_train, y_train, epochs=self.n_epochs) best_model = nas_reg.export_model() self.log.info("NAS struct data regression best model summary: \n") best_model_summary=best_model.summary(print_fn=self.log.info) self.log.info(best_model_summary) predictm=best_model.predict(X_test) mtpredict=best_model.predict(X_train) score = objClf.get_score(scoreParam,y_test, predictm) self.log.info("Autokeras struct data regression metrics: \n") return modelName,nas_reg,score def nasMain(self,scoreParam): modelName = "" nasclf=None nas_reg=None #text_reg_model=None mse_value=0 reg_rmse=0 mape_reg=0 huber_loss_reg=0 accuracy=0 precision=0 recall=0 #Dummy values to return main for classification problems dummy_score_1=int(0) #dummy_score_2=int(0) try: if ((self.nas_class.lower() == "classification")): modelName,nasclf,score=self.nasStructClassification(scoreParam) self.log.info('NAS Struct Classification score: '+str(score)) best_model_nas = nasclf.export_model() scoredetails = '{"Model":"NAS","Score":'+str(round(score,2))+'}' return best_model_nas,self.nas_params,round(score,2),'NAS',-1,-1,-1 elif (self.nas_class.lower() == "regression"): modelName,nas_reg,score =self.nasStructRegressor(scoreParam) self.log.info('NAS Struct Regression score: '+str(score)) best_model_nas = nas_reg.export_model() ''' filename = os.path.join(self.deployLocation,'model','autoKerasModel') best_model_nas = nas_reg.export_model() try: best_model_nas.save(filename, save_format="tf") modelName = 'autoKerasModel' except Exception: filename = os.path.join(self.deployLocation,'model','autoKerasModel.h5') best_model_nas.save(filename) modelName = 'autoKerasModel.h5' ''' scoredetails = '{"Model":"NAS","Score":'+str(round(score,2))+'}' ''' error_matrix = '"MSE":"'+str(round(mse_value,2))+'","RMSE":"'+str(round(reg_rmse,2))+'","MAPE":"'+str(round(mape_reg,2))+'","MSLE":"'+str(round(msle_reg,2))+'"' ''' return best_model_nas,self.nas_params,score,'NAS' else: pass except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) output = {"status":"FAIL","message":str(inst).strip('"')} output = json.dumps(output) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * '''# -*- coding: utf-8 -*- """ @author: satish_k """ import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import statistics from sklearn.impute import SimpleImputer from sklearn.model_selection import train_test_split from lifelines import KaplanMeierFitter, CoxPHFitter from lifelines.statistics import logrank_test from scipy import stats import logging class SurvivalAnalysis(object): def __init__(self, df, method, event_column, duration_column, fitter_param=None, df_negate=None ): pd.options.display.width = 30 self.df = df self.fitter_param = fitter_param self.method = method self.event_column = event_column self.duration_column = duration_column self.models = [] self.train = df.drop_duplicates().reset_index() self.test = None if isinstance(df_negate, pd.DataFrame): self.df_n = df_negate.drop_duplicates().reset_index() else: self.df_n = None self.log = logging.getLogger('eion') self.plots = [] def learn(self): self.log.info('\n---------- SurvivalAnalysis learner has started ----------') self.log.info('\n---------- SurvivalAnalysis learner method is "%s" ----------'%self.method) lifelines_univariate_models = ["AalenJohansenFitter", "BreslowFlemingHarringtonFitter", "ExponentialFitter", "GeneralizedGammaFitter", "KaplanMeierFitter", "LogLogisticFitter", "LogNormalFitter", "MixtureCureFitter", "NelsonAalenFitter", "PiecewiseExponentialFitter", "SplineFitter", "WeibullFitter"] lifelines_regression_models = ["AalenAdditiveFitter", "CRCSplineFitter", "CoxPHFitter", "CoxTimeVaryingFitter", "GeneralizedGammaRegressionFitter", "LogLogisticAFTFitter", "LogNormalAFTFitter", "PiecewiseExponentialRegressionFitter", "WeibullAFTFitter"] if self.method.lower() in ['kaplanmeierfitter','kaplanmeier','kaplan-meier','kaplan meier','kaplan','km','kmf']: self.log.info('\n---------- SurvivalAnalysis learner method "%s" has started ----------'%self.method) #from lifelines.utils import find_best_parametric_model #m,s = find_best_parametric_model(event_times=self.df[self.duration_column]) if not isinstance(self.df_n, pd.DataFrame): kmf = KaplanMeierFitter() self.log.info('\n Shape of training data - %s'%str(self.train.shape)) T = self.train[self.duration_column] E = self.train[self.event_column] self.log.info('\n T : \n%s'%str(T)) self.log.info('\n E : \n%s'%str(E)) K = kmf.fit(T, E) ax = plt.subplot(111) kmf_sf = K.survival_function_ ax = kmf_sf.plot(ax=ax) kmf_sf_json = self.survival_probability_to_json(kmf_sf) self.models.append(K) plt.title("KM Survival Functions") self.plots.append(plt) self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------'%self.method) self.log.info('\n---------- SurvivalAnalysis learner has ended ----------') return kmf_sf_json else: kmf1 = KaplanMeierFitter() kmf2 = KaplanMeierFitter() T1 = self.train[self.duration_column] E1 = self.train[self.event_column] #self.df_n = self.df_n.drop('fin', axis=1) T2 = self.df_n[self.duration_column] E2 = self.df_n[self.event_column] ax = plt.subplot(111) plt.title("KM Survival Functions - Filter vs Negation") self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for filter expression has started----------'%self.method) kmf1.fit(T1, E1) ax = kmf1.plot(ax=ax, label='%s'%self.fitter_param) self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for filter expression has ended----------'%self.method) self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for negation has started----------'%self.method) kmf2.fit(T2, E2) ax = kmf2.plot(ax=ax, label='~%s'%self.fitter_param) self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for negation has ended----------'%self.method) self.models.extend([kmf1,kmf2]) kmf1_sf = kmf1.survival_function_ kmf2_sf = kmf2.survival_function_ kmf1_sf_json = self.survival_probability_to_json(kmf1_sf) self.plots.append(plt) self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------'%self.method) self.log.info('\n---------- SurvivalAnalysis learner has ended ----------') return kmf1_sf_json elif self.method.lower() in ['coxphfitter','coxregression','cox-regression','cox regression','coxproportionalhazard','coxph','cox','cph']: self.log.info('\n---------- SurvivalAnalysis learner method "%s" has started ----------'%self.method) #from lifelines.utils import k_fold_cross_validation if not isinstance(self.df_n, pd.DataFrame): cph = CoxPHFitter() C = cph.fit(self.train, self.duration_column, self.event_column, show_progress=True) self.models.append(C) cph_sf = C.baseline_survival_ ax = plt.subplot(111) ax = C.plot(ax=ax) cph_sf_json = self.survival_probability_to_json(cph_sf) self.log.info('\n Summary : \n%s'%str(C.summary)) plt.title("COX hazard ratio") self.plots.append(plt) self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------'%self.method) self.log.info('\n---------- SurvivalAnalysis learner has ended ----------') #plt.show() return cph_sf_json else: cph1 = CoxPHFitter(penalizer=0.0001) cph2 = CoxPHFitter(penalizer=0.0001) ax = plt.subplot(211) plt.title("COX hazard ratio - [%s](Top) vs [~(%s)](Bottom)"%(self.fitter_param,self.fitter_param)) #self.train = self.train.drop('fin',axis=1) self.df_n = self.drop_constant_features(self.df_n) self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for filter expression has started----------'%self.method) cph1.fit(self.train, self.duration_column, self.event_column, show_progress=True, step_size=0.4) ax = cph1.plot(ax=ax, label='%s'%self.fitter_param) self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for filter expression has ended----------'%self.method) self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for negation has started----------'%self.method) cph2.fit(self.df_n, self.duration_column, self.event_column, show_progress=True, step_size=0.4) ax = plt.subplot(212) ax = cph2.plot(ax=ax, label='~%s'%self.fitter_param) self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for negation has ended----------'%self.method) self.models.extend([cph1,cph2]) cph1_sf = cph1.baseline_survival_ cph2_sf = cph2.baseline_survival_ cph1_sf_json = self.survival_probability_to_json(cph1_sf) #plt.show() plt.tight_layout() self.plots.append(plt) self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------'%self.method) self.log.info('\n---------- SurvivalAnalysis learner has ended ----------') return cph1_sf_json def survival_probability_to_json(self, sf): ''' sf = Survival function i.e. KaplanMeierFitter.survival_function_ or CoxPHFitter.baseline_survival_ returns json of survival probabilities ''' sf = sf[sf.columns[0]].apply(lambda x: "%4.2f"%(x*100)) self.log.info('\n Survival probabilities : \n%s'%str(sf)) sf = sf.reset_index() sf = sf.sort_values(sf.columns[0]) sf_json = sf.to_json(orient='records') self.log.info('\n Survival prbability json : \n%s'%str(sf_json)) return sf_json def drop_constant_features(self, df): for col in df.columns: if len(df[col].unique()) == 1: df.drop(col,inplace=True,axis=1) return df def predict(self): if self.method == 'KaplanMeierFitter': return self.model.predict(self.test[self.duration_column]) #kmf.predict() #kmf.median_survival_time_ #from lifelines.utils import median_survival_times #median_ci = median_survival_times(kmf.confidence_interval_) elif self.method == 'CoxPHFitter': #print('train score',self.model.score(self.train)) #print('test score',self.model.score(self.test)) return self.model.predict_survival_function(self.test) #cph.predict_cumulative_hazard() #cph.predict_expectation() #cph.predict_log_partial_hazard() #cph.predict_median() #cph.predict_partial_hazard() #cph.predict_percentile() #cph.predict_survival_function() #cph.predict_hazard() #cph.score() #cph.summary() #if __name__ == "__main__": # data_file = r"C:\Users\satish_k\Desktop\Work\input\echocardiogram.csv" # #data_file = r"C:\Users\satish_k\Desktop\Work\input\lymphoma.csv" # method = "CoxPHFitter" # event_column = "alive" # duration_column = "survival" # sa = SurvivalAnalysis(data_file, method, event_column, duration_column) # sa.profiler() # model = sa.learn() # print(sa.predict()) #print(model.survival_function_) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from sklearn.preprocessing import MinMaxScaler, LabelEncoder import numpy as np import logging logging.getLogger('tensorflow').disabled = True import aif360 from aif360.datasets import StandardDataset from aif360.algorithms.preprocessing.reweighing import Reweighing from aif360.algorithms.preprocessing import DisparateImpactRemover class DebiasingManager: def __init__(self): self.data = '' # ------------------------------- Debiasing Changes ------------------------------- def get_attributes(self, data, selected_attr=None): unprivileged_groups = [] privileged_groups = [] if selected_attr == None: selected_attr = data.protected_attribute_names for attr in selected_attr: idx = data.protected_attribute_names.index(attr) privileged_groups.append({attr:data.privileged_protected_attributes[idx]}) unprivileged_groups.append({attr:data.unprivileged_protected_attributes[idx]}) return privileged_groups, unprivileged_groups # ------------------------------- ------------------------------- def Bias_Mitigate(self, dataFrame, protected_feature, privileged_className, target_feature, algorithm): # log = logging.getLogger('eion') # log.propagate = False data_encoded = dataFrame.copy() categorical_names = {} encoders = {} dataFrame = dataFrame.replace('Unknown', 'NA') dataFrame = dataFrame.replace(np.nan, 'NA') try: # Label-Encoding for feature in dataFrame.columns: le = LabelEncoder() le.fit(data_encoded[feature]) data_encoded[feature] = le.transform(data_encoded[feature]) categorical_names[feature] = le.classes_ encoders[feature] = le privileged_class = np.where(categorical_names[protected_feature] == privileged_className)[0] target_feature_count = len(data_encoded[target_feature].value_counts()) # Check if it's BinaryLabel if target_feature_count == 2: binaryLabelDataset = aif360.datasets.BinaryLabelDataset( favorable_label='1', unfavorable_label='0', df=data_encoded, label_names=[target_feature], protected_attribute_names=[protected_feature]) data_orig = binaryLabelDataset # Check if it's Non-BinaryLabel if target_feature_count > 2: data_orig = StandardDataset(data_encoded, label_name=target_feature, favorable_classes=[1], protected_attribute_names=[protected_feature], privileged_classes=[privileged_class]) if algorithm == 'DIR': DIR = DisparateImpactRemover(repair_level=0.9) data_transf_train = DIR.fit_transform(data_orig) # log.info('Status:-|... DIR applied on input dataset') else: privileged_groups, unprivileged_groups = self.get_attributes(data_orig, selected_attr=[protected_feature]) RW = Reweighing(unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) data_transf_train = RW.fit_transform(data_orig) # log.info('Status:-|... Reweighing applied on input dataset') transf_dataFrame = data_transf_train.convert_to_dataframe()[0] data_decoded = transf_dataFrame.copy().astype('int') for column in data_decoded.columns: data_decoded[column] = encoders[column].inverse_transform(data_decoded[column]) debiased_dataFrame = data_decoded except Exception as e: print(e) debiased_dataFrame = dataFrame return debiased_dataFrame ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pyodbc as pyodbc import pandas as pd import json def simple_select(c, sql_query, bind_params=None, display_sql=False): """where c is a cursor""" if bind_params is None: c.execute(sql_query) else: if display_sql: c.execute(sql_query, bind_params) headers = [] if c.description is not None: # We have a SELECT statement for x in c.description: headers.append(x[0]) row_count = 0 row = c.fetchone() data=[] while row: row_count += 1 xrow={} for i in range(len(row)): xrow[headers[i]] = row[i] data.append(xrow) row = c.fetchone() #df = pd.DataFrame(data) return(data) def validatequery(request,query): resultdata = [] try: server_url = request.session['server_url'] username_actian = request.session['username'] password_actian = request.session['password'] database_actian = request.session['database'] conn = get_connection(server_url,username_actian,password_actian,database_actian) sql_text = query cur = conn.cursor() resultdata = simple_select(cur, query) cur.close() if len(resultdata) > 0: return "Query executed successfully" else: return "No rows returned" except Exception as e: print(e) return str(e) def executequery(request,query): resultdata = [] try: server_url = request.session['server_url'] username_actian = request.session['username'] password_actian = request.session['password'] database_actian = request.session['database'] conn = get_connection(server_url,username_actian,password_actian,database_actian) sql_text = query cur = conn.cursor() resultdata = simple_select(cur, query) cur.close() return(resultdata) except Exception as e: print(e) return(resultdata) def list_tables_fields(request,table_list): table_field_obj = {} table_field_obj['data'] = [] try: server_url = request.session['server_url'] username_actian = request.session['username'] password_actian = request.session['password'] database_actian = request.session['database'] table_list = json.loads(table_list) conn = get_connection(server_url,username_actian,password_actian,database_actian) for table in table_list: tf_obj = {} tf_obj['TableName'] = str(table).strip() tf_obj['Fields']= [] field_list = [] sql_text = "SELECT column_name, false as is_select FROM iicolumns WHERE table_name='"+table+"'" cur = conn.cursor() field_list = simple_select(cur, sql_text) cur.close() print(field_list) tf_obj['Fields'] = field_list table_field_obj['data'].append(tf_obj) print("----------------------") print(table_field_obj) print(json.dumps(table_field_obj)) print("----------------------") return json.dumps(table_field_obj) except Exception as e: print("Something went wrong "+str(e)) return table_field_obj def list_tables(request): server_url = request.session['server_url'] username_actian = request.session['username'] password_actian = request.session['password'] database_actian = request.session['database'] dt_list = [] try: conn = get_connection(server_url,username_actian,password_actian,database_actian) sql_text = "select table_name from iitables where table_type='T' and table_owner='"+username_actian+"'" cur = conn.cursor() dt_list = simple_select(cur, sql_text) cur.close() return dt_list except: print("Something went wrong") return dt_list def get_connection(server_url,username_actian,password_actian,database_actian): conn = pyodbc.connect("driver=Ingres;servertype=ingres;server=@"+str(server_url)+",tcp_ip,VW;uid="+str(username_actian)+";pwd="+str(password_actian)+";database="+str(database_actian)) print("connected") return conn def getDataFromActianAvalanche(request): server_url = request.POST.get('server_url') username_actian = request.POST.get('username') password_actian = request.POST.get('password') database_actian = request.POST.get('database') table_actian = request.POST.get('table') conn = get_connection(server_url,username_actian,password_actian,database_actian) c = conn.cursor() sql_text = "select * from "+str(table_actian) data = simple_select(c, sql_text) df = pd.DataFrame(data) return(df) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os from os.path import expanduser import platform DEFAULT_FILE_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)),'conf') cur_dir = os.path.dirname(os.path.abspath(__file__)) home = expanduser("~") if platform.system() == 'Windows': DATA_DIR = os.path.normpath(os.path.join(cur_dir,'..','..','..','..','..','..','data')) DATA_FILE_PATH = os.path.join(DATA_DIR,'storage') CONFIG_FILE_PATH = os.path.join(DATA_DIR,'config') DEPLOY_LOCATION = os.path.join(DATA_DIR,'target') LOG_LOCATION = os.path.join(DATA_DIR,'logs') LOG_FILE = os.path.join(DATA_DIR,'logs','ux.log') else: DATA_DIR = os.path.join(home,'HCLT','data') DATA_FILE_PATH = os.path.join(DATA_DIR,'storage') CONFIG_FILE_PATH = os.path.join(DATA_DIR,'config') DEPLOY_LOCATION = os.path.join(DATA_DIR,'target') LOG_FILE = os.path.join(DATA_DIR,'logs','ux.log') LOG_LOCATION = os.path.join(DATA_DIR,'logs') ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os,sys from appbe import compute from appbe.aion_config import kafka_setting from appbe.aion_config import running_setting from records import pushrecords from appbe import service_url import json import time import pandas as pd from django.db.models import Max, F from os.path import expanduser import platform from appbe.data_io import sqlite_db import subprocess from appbe.dataPath import DEFAULT_FILE_PATH from appbe.dataPath import DATA_FILE_PATH from appbe.dataPath import CONFIG_FILE_PATH from appbe.dataPath import DEPLOY_LOCATION from appbe.dataPath import DATA_DIR DEPLOY_DATABASE_PATH = os.path.join(DATA_DIR,'sqlite') def pushRecordForTraining(): from appbe.pages import getversion AION_VERSION = getversion() try: status,msg = pushrecords.enterRecord(AION_VERSION) except Exception as e: print("Exception", e) status = False msg = str(e) return status,msg def getversion(): configFolder = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','config') version = 'NA' for file in os.listdir(configFolder): if file.endswith(".var"): version = file.rsplit('.', 1) version = version[0] break return version def getusercasestatus(request): if 'UseCaseName' in request.session: selected_use_case = request.session['UseCaseName'] else: selected_use_case = 'Not Defined' if 'ModelVersion' in request.session: ModelVersion = request.session['ModelVersion'] else: ModelVersion = 0 if 'ModelStatus' in request.session: ModelStatus = request.session['ModelStatus'] else: ModelStatus = 'Not Trained' return selected_use_case,ModelVersion,ModelStatus def getMLModels(configSettingsJson): mlmodels ='' dlmodels = '' problem_type = "" problemtypes = configSettingsJson['basic']['analysisType'] for k in problemtypes.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break sc = "" if problemtypes in ['classification','regression','survivalAnalysis']: scoringCreteria = configSettingsJson['basic']['scoringCriteria'][problem_type] for k in scoringCreteria.keys(): if configSettingsJson['basic']['scoringCriteria'][problem_type][k] == 'True': sc = k break else: sc = 'NA' if problem_type in ['classification','regression']: algorihtms = configSettingsJson['basic']['algorithms'][problem_type] #print(algorihtms) for k in algorihtms.keys(): #print(configSettingsJson['basic']['algorithms'][problem_type][k]) if configSettingsJson['basic']['algorithms'][problem_type][k] == 'True': if k in ['SNN','RNN','CNN']: if dlmodels != '': dlmodels += ', ' dlmodels += k else: if mlmodels != '': mlmodels += ', ' mlmodels += k elif problem_type in ['videoForecasting','imageClassification','objectDetection']: algorihtms = configSettingsJson['basic']['algorithms'][problem_type] for k in algorihtms.keys(): if configSettingsJson['basic']['algorithms'][problem_type][k] == 'True': if dlmodels != '': dlmodels += ', ' dlmodels += k else: algorihtms = configSettingsJson['basic']['algorithms'][problem_type] for k in algorihtms.keys(): if configSettingsJson['basic']['algorithms'][problem_type][k] == 'True': if mlmodels != '': mlmodels += ', ' mlmodels += k displayProblemType = problem_type selected_model_size = '' if problem_type.lower() == 'llmfinetuning': displayProblemType = 'LLM Fine-Tuning' supported_model_types = configSettingsJson['basic']['modelSize'][problem_type][mlmodels] for k in supported_model_types.keys(): if configSettingsJson['basic']['modelSize'][problem_type][mlmodels][k] == 'True': selected_model_size = k break #print(selected_model_size) if mlmodels == 'TF_IDF': mlmodels = 'TF-IDF' if mlmodels == 'LatentSemanticAnalysis': mlmodels = 'Latent Semantic Analysis (LSA)' if mlmodels == 'SentenceTransformer_distilroberta': mlmodels = 'SentenceTransformer (DistilRoBERTa)' if mlmodels == 'SentenceTransformer_miniLM': mlmodels = 'SentenceTransformer (MiniLM)' if mlmodels == 'SentenceTransformer_mpnet': mlmodels = 'SentenceTransformer (MPNet)' return(problem_type,displayProblemType,sc,mlmodels,dlmodels,selected_model_size) def get_usecase_page(request,usecasedetails,Existusecases,usecaseId = None,search_text=None): try: x = request.build_absolute_uri().split("http://") y = x[1].split("/") url = y[0].split(":") tacking_url = url[0] except: tacking_url = '127.0.0.1' computeinfrastructure = compute.readComputeConfig() ruuningSetting = running_setting() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) status = 'SUCCESS' ser_url = service_url.read_service_url_params(request) hosturl =request.get_host() hosturl = hosturl.split(':') hosturl = hosturl[0] packagetip=''' Call From Command Line 1. Click AION Shell 2. python {packageAbsolutePath}/aion_prediction.py {json_data} Call As a Package 1. Go To package_path\WHEELfile 2. python -m pip install {packageName}-py3-none-any.whl Call the predict function after wheel package installation 1. from {packageName} import aion_prediction as p1 2. p1.predict({json_data})''' models = Existusecases.objects.filter(Status='SUCCESS').order_by('-id') usecase = usecasedetails.objects.all().order_by('-id') usecase = landing_page(usecasedetails,Existusecases,hosturl,usecaseId,search_text) if len(usecase) > 0: nouc = usecasedetails.objects.latest('id') nouc = (nouc.id)+1 nouc = str(nouc).zfill(4) else: nouc = 1 nouc = str(nouc).zfill(4) description_text = 'This is a usecase for AI' + str(nouc) context = {'description_text':description_text,'usecasedetail': usecase, 'nouc': nouc, 'models': models, 'selected_use_case': selected_use_case,'ser_url':ser_url,'packagetip':packagetip,'tacking_url':tacking_url, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'computeinfrastructure':computeinfrastructure,'ruuningSetting':ruuningSetting} return status,context,'usecases.html' def checkText(configPath): isText='False' with open(configPath) as config: data = json.load(config) for feature in data['advance']['profiler']['featureDict'] : if feature['type']=='text': isText = 'True'; break; return isText # For Task ID 12393 # For BUG ID 13161 def checkFE(configPath): isFE = 'False' with open(configPath) as config: data = json.load(config) is_selection_method = data.get('advance', {}).get('selector', {}).get('selectionMethod', {}).get('featureEngineering','False') feature_dict= data.get('advance', {}).get('selector', {}).get('featureEngineering', {}) if 'null' in feature_dict.keys(): feature_dict.pop('null') if is_selection_method == 'True' or 'True' in list(feature_dict.values()): isFE = 'True' return isFE def get_model(Existusecases,usercaseid,version=-1): from django.core import serializers if version == -1: models = Existusecases.objects.filter(ModelName=usercaseid).order_by('-id') else: models = Existusecases.objects.filter(ModelName=usercaseid,Version=version).order_by('-id') for model in models: model.scoringCreteria = 'NA' model.score = 'NA' model.deploymodel = 'NA' model.problemType = 'NA' model.maacsupport = 'False' model.flserversupport = 'False' model.onlinelerningsupport = 'False' model.oltrainingdetails='' model.xplain = 'True' model.isText = 'False' problemTypeNames = {'topicmodelling':'TopicModelling','anomalydetection':'AnomalyDetection'} if model.Status == 'SUCCESS': if os.path.isdir(str(model.DeployPath)): modelPath = os.path.join(str(model.DeployPath),'etc','output.json') try: with open(modelPath) as file: outputconfig = json.load(file) file.close() if outputconfig['status'] == 'SUCCESS': model.scoringCreteria = outputconfig['data']['ScoreType'] model.score = outputconfig['data']['BestScore'] model.deploymodel = outputconfig['data']['BestModel'] model.problemType = outputconfig['data']['ModelType'] if model.problemType in ['topicmodelling','anomalydetection']: model.problemType = problemTypeNames[model.problemType] model.featuresused = outputconfig['data']['featuresused'] model.targetFeature = outputconfig['data']['targetFeature'] if 'params' in outputconfig['data']: model.modelParams = outputconfig['data']['params'] model.modelType = outputconfig['data']['ModelType'] model.isText = checkText(str(model.ConfigPath)) model.isFeatureEng = checkFE(str(model.ConfigPath))#task id 12393 model.dataPath = os.path.join(str(model.DeployPath),'data', 'postprocesseddata.csv.gz') mlacSupportedModel = ["Logistic Regression","Naive Bayes","Decision Tree","Random Forest", "Support Vector Machine","K Nearest Neighbors","Gradient Boosting","Extreme Gradient Boosting (XGBoost)","Light Gradient Boosting (LightGBM)", "Categorical Boosting (CatBoost)","Linear Regression","Lasso","Ridge","MLP","LSTM"] if model.problemType.lower() in ['classification','regression','timeseriesforecasting']: #task 11997 if model.deploymodel in mlacSupportedModel: model.maacsupport = 'True' if model.problemType.lower() not in ['classification','regression']: model.xplain = 'False' elif model in ["Neural Architecture Search"]: model.xplain = 'False' model.flserversupport = 'False' model.onlinelerningsupport = 'False' supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"] if model.deploymodel in supportedmodels: model.flserversupport = 'True' else: model.flserversupport = 'False' supportedmodels = ["Extreme Gradient Boosting (XGBoost)"] if model.deploymodel in supportedmodels: model.encryptionsupport = 'True' else: model.encryptionsupport = 'False' supportedmodels = ["Online Decision Tree Classifier","Online Logistic Regression","Online Linear Regression","Online Decision Tree Regressor","Online KNN Regressor","Online Softmax Regression","Online KNN Classifier"] if model.deploymodel in supportedmodels: model.onlinelerningsupport = 'True' onlineoutputPath = os.path.join(str(model.DeployPath),'production','Config.json') with open(onlineoutputPath) as file: onlineoutputPath = json.load(file) file.close() details = {'Score' :onlineoutputPath['metricList'],'DataSize':onlineoutputPath['trainRowsList']} dfonline = pd.DataFrame(details) model.oltrainingdetails = dfonline else: model.onlinelerningsupport = 'False' except Exception as e: print(e) pass return models def landing_page(usecasedetails,Existusecases,hosturl,usecaseId = None,search_text=None): sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db') if usecaseId: usecase = usecasedetails.objects.filter(id=usecaseId) else: if search_text: usecase = usecasedetails.objects.filter(UsecaseName__contains=str(search_text)).order_by('-id') else: #usecase = usecasedetails.objects.all().order_by('-id')[:100] #top 100 records usecase = usecasedetails.objects.all().order_by('-id') #top 100 records usecaselist=[] if not usecaseId: for x in usecase: problemType= 'NA' publish_url = '' otherModel = {} models = Existusecases.objects.filter(Status='SUCCESS',publishStatus='Published',ModelName=x.id).order_by('-id') if len(models) > 0: #print(models[0]) version = models[0].Version if os.path.isdir(str(models[0].DeployPath)): modelPath = os.path.join(str(models[0].DeployPath),'etc','output.json') with open(modelPath) as file: outputconfig = json.load(file) problemType = outputconfig['data']['ModelType'] #print(problemType.lower()) if problemType.lower() == "llm fine-tuning": cloudconfig = os.path.normpath( os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'compute_conf.json')) print(cloudconfig) from appbe.models import get_instance hypervisor,instanceid,region,image,status = get_instance(x.usecaseid+ '_' + str(version)) from llm.llm_inference import get_ip instanceip = get_ip(cloudconfig,instanceid,hypervisor,region,image) #usnish__ server maynot running if instanceip != '': publish_url = 'http://' + instanceip + ':' + '8000' + '/generate' else: publish_url = 'service not available' else: publish_url = 'http://'+hosturl+':'+str(models[0].portNo)+'/AION/'+x.usecaseid+'/predict' publish_status = 'Published' #publish_url = 'http://'+hosturl+':'+str(models[0].portNo)+'/AION/'+x.usecaseid+'/predict' parentModel = get_model(Existusecases,x.id,int(version)) else: models = Existusecases.objects.filter(Status='SUCCESS',ModelName=x.id).order_by('-id') if len(models) > 0: publish_status = 'Trained' version = models[0].Version parentModel = get_model(Existusecases,x.id,int(version)) else: models = Existusecases.objects.filter(ModelName=x.id).order_by('-id') if len(models)==0: publish_status= 'Not Trained' version = -1 else: if models[0].Status == 'FAIL': publish_status= 'Failed' elif models[0].Status == 'Running': publish_status = 'Running' else: publish_status='Not Trained' problemType = models[0].ProblemType version = models[0].Version parentModel={} usecasedetails = {'uuid':x.id,'description':x.Description,'usecaseid':x.usecaseid,'usecase':x.UsecaseName,'status':publish_status,'publish_url':publish_url,'version':version,'parentModel':parentModel,'otherModel':otherModel,'problemType':problemType} usecaselist.append(usecasedetails) else: for x in usecase: otherModel = get_model(Existusecases,x.id) problemType = otherModel[0].problemType usecasedetails = {'uuid':x.id,'description':x.Description,'usecase':x.UsecaseName,'status':'','version':'','parentModel':{},'otherModel':otherModel,'problemType':problemType} usecaselist.append(usecasedetails) return usecaselist def get_landing_model(Existusecases): models = Existusecases.objects.filter(Status='SUCCESS').order_by('-id') for model in models: model.scoringCreteria = 'NA' model.score = 'NA' model.deploymodel = 'NA' if os.path.isdir(str(model.DeployPath)): modelPath = os.path.join(str(model.DeployPath),'etc','output.json') try: with open(modelPath) as file: outputconfig = json.load(file) file.close() if outputconfig['status'] == 'SUCCESS': model.scoringCreteria = outputconfig['data']['ScoreType'] model.score = outputconfig['data']['BestScore'] model.deploymodel = outputconfig['data']['BestModel'] model.problemType = outputconfig['data']['ModelType'] model.maacsupport = 'True' model.flserversupport = 'False' model.onlinelerningsupport = 'False' supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"] if model.deploymodel in supportedmodels: model.flserversupport = 'True' else: model.flserversupport = 'False' supportedmodels = ["Extreme Gradient Boosting (XGBoost)"] if model.deploymodel in supportedmodels: model.encryptionsupport = 'True' else: model.encryptionsupport = 'False' supportedmodels = ["Online Decision Tree Classifier","Online Logistic Regression"] if model.deploymodel in supportedmodels: model.onlinelerningsupport = 'True' onlineoutputPath = os.path.join(str(model.DeployPath),'production','Config.json') with open(onlineoutputPath) as file: onlineoutputPath = json.load(file) file.close() details = {'Score' :onlineoutputPath['metricList'],'DataSize':onlineoutputPath['trainRowsList']} dfonline = pd.DataFrame(details) model.oltrainingdetails = dfonline else: model.onlinelerningsupport = 'False' except Exception as e: pass return models def usecase_page(request,usecasedetails,Existusecases,usecaseid,search_text): try: from appbe import read_service_url_params tacking_url = read_service_url_params(request) except: tacking_url = '127.0.0.1' hosturl =request.get_host() hosturl = hosturl.split(':') hosturl = hosturl[0] computeinfrastructure = compute.readComputeConfig() from appbe.aion_config import settings usecasetab = settings() kafkaSetting = kafka_setting() ruuningSetting = running_setting() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) status,msg = pushRecordForTraining() if status == False: context = {'msg':msg} context['selected'] = 'License' return status,context,'licenseexpired.html' ser_url = service_url.read_service_url_params(request) packagetip=''' Call From Command Line 1. Click AION Shell 2. python {packageAbsolutePath}/aion_predict.py {json_data} Call As a Package 1. Go To package_path\publish\package 2. python -m pip install {packageName}-py3-none-any.whl Call the predict function after wheel package installation 1. from {packageName} import aion_predict as p1 2. p1.predict({json_data})''' if request.method == "POST": usecasename = request.POST.get('UsecaseName') description = request.POST.get('Description') usecaseid = request.POST.get('usecaseid') #print('1',usecasename) if (usecasename == ''): usecase = landing_page(usecasedetails,Existusecases,hosturl) if len(usecase) > 0: nouc = usecasedetails.objects.latest('id') nouc = (nouc.id)+1 else: nouc = 1 nouc = str(nouc).zfill(4) description_text = 'This is a usecase for AI' + str(nouc) context = {'description_text':description_text,'usecase':'usecase','Notallowed':'Usecasename is mandatory','ser_url':ser_url,'packagetip':packagetip,'usecasedetail': usecase,'nouc':nouc, 'ser_url':ser_url,'packagetip':packagetip, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'tacking_url':tacking_url,'usecasetab':usecasetab, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting} return status,context,'usecases.html' else: usecase_count = usecasedetails.objects.filter(usecaseid=usecaseid).count() usecasename_count = usecasedetails.objects.filter(UsecaseName=usecasename).count() usecase = landing_page(usecasedetails,Existusecases,hosturl) if (usecase_count > 0) or (usecasename_count > 0): nouc = usecasedetails.objects.latest('id') nouc = (nouc.id)+1 nouc = str(nouc).zfill(4) Msg = 'Error in usecase creating, try again' if usecase_count > 0: Msg = 'Error in usecase creating, try again' if usecasename_count > 0: Msg = 'There is already a use case with same name, please provide unique name' description_text = 'This is a usecase for AI' + str(nouc) context = {'description_text':description_text,'usecasedetail': usecase, 'nouc': nouc,'Status':'error','Msg': Msg,'tacking_url':tacking_url,'usecasetab':usecasetab,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ser_url':ser_url,'packagetip':packagetip, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting} return status,context,'usecases.html' else: clusteringModels = Existusecases.objects.filter(Status='SUCCESS',ProblemType='unsupervised').order_by('-id') from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage p = usecasedetails(UsecaseName=usecasename,usecaseid=usecaseid,Description=description) p.save() s1 = Existusecases.objects.filter(ModelName=p.id).annotate(maxver=Max('ModelName__existusecases__Version')) config_list = s1.filter(Version=F('maxver')) if config_list.count() > 0: Version = config_list[0].Version Version = Version + 1 else: Version = 1 ps = Existusecases(DataFilePath='', DeployPath='', Status='Not Trained',ConfigPath='', Version=Version, ModelName=p,TrainOuputLocation='') ps.save() request.session['ModelName'] = p.id request.session['UseCaseName'] = usecasename request.session['usecaseid'] = usecaseid request.session['ModelVersion'] = Version request.session['ModelStatus'] = 'Not Trained' request.session['currentstate'] = 0 request.session['finalstate'] = 0 selected_use_case = usecasename model_status = 'Not Trained' ModelVersion = Version from appbe.telemetry import UseCaseCreated UseCaseCreated(usecaseid+'-'+str(Version)) if len(usecase) > 0: nouc = usecasedetails.objects.latest('id') nouc = (nouc.id)+1 else: nouc = 1 nouc = str(nouc).zfill(4) description_text = 'This is a usecase for AI' + str(nouc) context = {'description_text':description_text,'usecasedetail': usecase, 'nouc': nouc, 'newusercase': usecasename,'tacking_url':tacking_url,'finalstate':request.session['finalstate'], 'description': description,'selected_use_case': selected_use_case,'ser_url':ser_url,'packagetip':packagetip,'clusteringModels':clusteringModels,'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'usecasetab':usecasetab,'azurestorage':get_azureStorage(), 'ModelStatus': model_status, 'ModelVersion': ModelVersion, 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure} return status,context,'upload.html' else: models = get_landing_model(Existusecases) usecase = landing_page(usecasedetails,Existusecases,hosturl,usecaseid,search_text) if len(usecase) > 0: nouc = usecasedetails.objects.latest('id') nouc = (nouc.id)+1 else: nouc = 1 nouc = str(nouc).zfill(4) description_text = 'This is a usecase for AI' + str(nouc) context = {'description_text':description_text,'usecasedetail': usecase, 'nouc': nouc, 'models': models, 'selected_use_case': selected_use_case,'ser_url':ser_url,'packagetip':packagetip,'tacking_url':tacking_url,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting,'usecasetab':usecasetab} if usecaseid: context.update({'ucdetails':'True'}) return status,context,'usecases.html' def index_page(request,usecasedetails,Existusecases): if 'ModelVersion' in request.session: del request.session['ModelVersion'] if 'UseCaseName' in request.session: del request.session['UseCaseName'] if 'ModelStatus' in request.session: del request.session['ModelStatus'] if 'currentstate' in request.session: del request.session['currentstate'] if 'finalstate' in request.session: del request.session['finalstate'] return usecases_page(request,usecasedetails,Existusecases) def usecases_page(request,usecasedetails,Existusecases,usecaseid=None,substring=None): return usecase_page(request,usecasedetails,Existusecases,usecaseid,substring) def mllite_page(request): from appbe.aion_config import settings usecasetab = settings() status,msg = pushRecordForTraining() if status == False: context = {'selected':'mllite','lerror':msg} return context configFile = os.path.join(DEFAULT_FILE_PATH, 'model_converter.json') f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) context = {} context = {'selected':'mllite','sagemaker':configSettingsJson,'usecasetab':usecasetab} return context def mltesting_page(request): from appbe.aion_config import settings usecasetab = settings() status,msg = pushRecordForTraining() if status == False: context = {'lerror':msg} return context if request.method == "POST": models = request.POST['model'] datap = request.POST['data'] if(os.path.isfile(models) and os.path.isfile(datap)): request.session['datalocation'] = datap df = pd.read_csv(datap,encoding='utf-8',skipinitialspace = True,encoding_errors= 'replace') trainfea = df.columns.tolist() featurs = request.POST.getlist('Training') feature = ",".join(featurs) filetimestamp = str(int(time.time())) settingconfig = os.path.join(CONFIG_FILE_PATH, 'MLTest_' + filetimestamp + '.json') request.session['MLTestResult'] = settingconfig mltestresult={} mltestresult['models'] = models mltestresult['datap'] = datap mltestresult['feature'] = feature # features = ['PetalLengthCm','PetalWidthCm'] targ = request.POST['Target'] tar =[targ] mltestresult['target'] = targ mltestresult = json.dumps(mltestresult) with open(settingconfig, "w") as fpWrite: fpWrite.write(mltestresult) fpWrite.close() from pathlib import Path mltest={} if Path(models).is_file() and Path(datap).is_file(): try: from mltest import baseline outputStr = baseline.baseline_testing(models,datap, feature, targ) #scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','bin','run_mltest.py')) #print(scriptPath, models, datap, feature, targ) #outputStr = subprocess.check_output([sys.executable, scriptPath, models, datap, feature, targ]) #print(outputStr) #outputStr = outputStr.decode('utf-8') #outputStr= outputStr.replace('\'','\"') #print('ou',outputStr) #outputStr = outputStr.strip() mltest = json.loads(outputStr) Problemtype= mltest['Problemtype'] with open(request.session['MLTestResult'], 'r+') as f: mltestresult = json.load(f) f.close() mltestresult['Problemtype'] = Problemtype mltestresult['ProblemName'] = mltest['ProblemName'] status = mltest['Status'] if status == 'Fail': errormsg= mltest['Msg'] context = {'error':errormsg,'mltest':'mltest'} else: if Problemtype == 'Classification': mltestresult['Score'] = mltest['Accuracy'] mltestresult['Params'] = mltest['Params'] Problem= mltest['ProblemName'] Parameters= mltest['Params'] round_params = {} for key, value in Parameters.items(): if isinstance(value, float): round_params[key] = round(value,2) else: round_params[key] = value matrixconfusion = mltest['Confusionmatrix'] classificationreport = mltest['classificationreport'] classificationreport = json.loads(classificationreport) matrixconfusion = json.loads(matrixconfusion) indexName =[] columnName = [] for i in matrixconfusion.keys(): indexName.append("act:"+str(i)) for j in matrixconfusion[i].keys(): columnName.append("pre:"+str(j)) df3 = pd.DataFrame.from_dict(classificationreport) df = df3.transpose() df2 = pd.DataFrame.from_dict(matrixconfusion) df1 = pd.DataFrame(df2.values,index=indexName,columns=columnName) report = df.to_html() report1 = df1.to_html() recordone = mltest['onerecord'] recordsten = mltest['tenrecords'] recordshund = mltest['hundrecords'] context = {'modelname': models,'datapath':datap,'features':featurs,'target':tar,'Problemtype':Problem,'modeltype':Problemtype,'Parameter':round_params,'Onerecord':recordone,'Tenrecords':recordsten,'Hundrecords':recordshund,'matrixconfusion':report1,'classificationreport':report,'classification':'classification','df':df,'df1':df1,'basemltest':'basemltest','success':'success','trainfea':trainfea,'selected':'mltesting','usecasetab':usecasetab} elif Problemtype == 'Regression': Problem= mltest['ProblemName'] mltestresult['Params'] = mltest['Params'] mltestresult['Score'] = mltest['R2'] Parameters= mltest['Params'] round_params = {} for key, value in Parameters.items(): if isinstance(value, float): round_params[key] = round(value,2) else: round_params[key] = value Mse = mltest['MSE'] Mae = mltest['MAE'] Rmse = mltest['RMSE'] R2 = mltest['R2'] recordone = mltest['onerecord'] recordsten = mltest['tenrecords'] recordshund = mltest['hundrecords'] context = {'modelname': models,'datapath':datap,'features':featurs,'target':tar, 'Problemtype':Problem,'Parameter':round_params,'Onerecord':recordone,'Tenrecords':recordsten,'Hundrecords':recordshund,'Mse':Mse,'Mae':Mae,'Rmse':Rmse,'R2Score':R2,'regression':'regression','success':"success",'selected': 'mltest','basemltest':'basemltest','usecasetab':usecasetab} else: errormsg= mltest['Msg'] context = {'error':errormsg,'mltest':'mltest'} mltestresult = json.dumps(mltestresult) with open(settingconfig, "w") as fpWrite: fpWrite.write(mltestresult) fpWrite.close() except Exception as e: print("-------------"+str(e)+'=================') e = str(e).replace('\'','') errormsg = 'Error: Exception '+str(e) context = {'error':errormsg,'mltest':'mltest'} else: if not (Path(models).is_file() and Path(datap).is_file()): context = {'error':"Please Check ModelPath & Datapath Format","result":"result",'selected':'mltesting','usecasetab':usecasetab} elif not Path(models).is_file(): context = {'error':"Please Check ModelPath Format","result":"result",'selected':'mltesting','usecasetab':usecasetab} elif not Path(datap).is_file(): context = {'error':"Please Check DataPath Format","result":"result",'selected':'mltesting','usecasetab':usecasetab} else: context = {'error':'Either model path or data path does not exist','mltest':'mltest','usecasetab':usecasetab} else: context = {'selected':'mltesting','usecasetab':usecasetab} return context ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os,sys import json import platform import subprocess def kafka_setting(): file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','kafkaConfig.conf')) f = open(file_path, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) from os.path import expanduser home = expanduser("~") if platform.system() == 'Windows': DEPLOY_LOCATION = os.path.join(home,'AppData','Local','HCLT','AION','target','kafka') else: DEPLOY_LOCATION = os.path.join(home,'HCLT','AION','target','kafka') configSettingsJson['kafkalocation'] = DEPLOY_LOCATION return(configSettingsJson) def start_tracking(): from appbe.dataPath import DEPLOY_LOCATION import platform mlflowpath = os.path.normpath(os.path.join(os.path.dirname(__file__),'..','..','..','..','Scripts','mlflow.exe')) script_path = os.path.normpath(os.path.join(os.path.dirname(__file__),'..','..','..','..','Scripts')) #Updating path for system environment; Bug-13835 os.environ['PATH']= os.environ['PATH']+ ';'+ str(script_path) DEPLOY_LOCATION = os.path.join(DEPLOY_LOCATION,'mlruns') if platform.system() == 'Windows': subprocess.Popen([sys.executable, mlflowpath,"ui", "--backend-store-uri","file:///"+DEPLOY_LOCATION]) else: subprocess.Popen(['mlflow',"ui","-h","0.0.0.0","--backend-store-uri","file:///"+DEPLOY_LOCATION]) def aion_tracking(): status = 'Success' import requests try: response = requests.get('http://localhost:5000') if response.status_code != 200: status = 'Error' except Exception as inst: print(inst) status = 'Error' return status def aion_service(): try: if platform.system() == 'Windows': nooftasks = getrunningstatus('AION_Service') else: nooftasks = getrunningstatus('run_service') if len(nooftasks): status = 'Running' else: if platform.system() == 'Windows': servicepath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','sbin','AION_Service.bat')) os.system('start cmd /c "'+servicepath+'"') else: servicepath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','bin','run_service.py')) subprocess.Popen([sys.executable,servicepath]) status = 'Started' except Exception as inst: print(inst) status = 'Error' return status def getrunningstatus(name): try: taskdetails = [] if platform.system() == 'Windows': r = ([line.split() for line in subprocess.check_output('tasklist /v /FI "IMAGENAME eq conhost.exe"').decode('UTF-8').splitlines()]) r.append([line.split() for line in subprocess.check_output('tasklist /v /FI "IMAGENAME eq cmd.exe"').decode('UTF-8').splitlines()]) else: r = ([line.split() for line in subprocess.check_output("ps -ef | grep .py",shell=True).decode('UTF-8').splitlines()]) for i in range(len(r)): s = r[i] if any(name in j for j in s): taskdetails.append('Yes') break return (taskdetails) except Exception as inst: print(inst) status = 'Error' return status def getTasks(mlflow,consumer,service): mlflowlist = [] consumerlist=[] servicelist = [] #r = os.popen('tasklist /v').read().strip().split('\n') try: if platform.system() == 'Windows': r = ([line.split() for line in subprocess.check_output('tasklist /v /FI "IMAGENAME eq conhost.exe"').decode('UTF-8').splitlines()]) r.append([line.split() for line in subprocess.check_output('tasklist /v /FI "IMAGENAME eq cmd.exe"').decode('UTF-8').splitlines()]) else: r = ([line.split() for line in subprocess.check_output("ps -ef | grep .py",shell=True).decode('UTF-8').splitlines()]) except Exception as e: print(e) r = [] #print(r) #print ('# of tasks is %s' % (len(r))) for i in range(len(r)): s = r[i] if any(mlflow in j for j in s): mlflowlist.append('Yes') if any(consumer in j for j in s): consumerlist.append('Yes') if any(service in j for j in s): servicelist.append('Yes') return (mlflowlist,consumerlist,servicelist) def running_setting(): otherApps = {} if platform.system() == 'Windows': mlflowlist,consumerlist,servicelist = getTasks('AION_MLFlow','AION_Consumer','AION_Service') else: mlflowlist,consumerlist,servicelist = getTasks('run_mlflow','AION_Consumer','run_service') if len(mlflowlist): otherApps['modeltracking'] = 'Running' else: otherApps['modeltracking'] = 'Not Running' #nooftasks = getTasks('AION_Consumer') if len(consumerlist): otherApps['consumer'] = 'Running' else: otherApps['consumer'] = 'Not Running' #nooftasks = getTasks('AION_Service') if len(servicelist): otherApps['service'] = 'Running' else: otherApps['service'] = 'Not Running' return(otherApps) #EDA Performance change # ---------------------------- def eda_setting(): configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','config','eda.config') sample_size='' try: if(os.path.isfile(configfilepath)): file = open(configfilepath, "r") read = file.read() file.close() for line in read.splitlines(): if 'sample_size=' in line: sample_size = line.split('=',1)[1] except Exception as inst: pass return(sample_size) def get_telemetryoptout(): telemetryoptuout = "No" from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') try: if sqlite_obj.table_exists('settings'): data = sqlite_obj.read_data('settings') for values in data: telemetryoptuout = values[7] else: telemetryoptuout = 'No' except Exception as e: print(e) telemetryoptuout ='No' return telemetryoptuout def get_edafeatures(): No_of_Permissible_Features_EDA = "" from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') try: if sqlite_obj.table_exists('settings'): data = sqlite_obj.read_data('settings') for values in data: No_of_Permissible_Features_EDA = values[3] else: configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'aion.config') if (os.path.isfile(configfilepath)): file = open(configfilepath, "r") read = file.read() file.close() for line in read.splitlines(): if 'No_of_Permissible_Features_EDA=' in line: No_of_Permissible_Features_EDA = line.split('=', 1)[1] except Exception as e: print(e) No_of_Permissible_Features_EDA =20 return No_of_Permissible_Features_EDA def get_graviton_data(): graviton_url = "" graviton_userid = "" from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') try: if sqlite_obj.table_exists('settings'): data = sqlite_obj.read_data('settings') for values in data: graviton_url = values[0] graviton_userid = values[1] else: configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'aion.config') if (os.path.isfile(configfilepath)): file = open(configfilepath, "r") read = file.read() file.close() for line in read.splitlines(): if 'graviton_url=' in line: graviton_url = line.split('=', 1)[1] if 'graviton_userid=' in line: graviton_userid = line.split('=', 1)[1] except Exception as e: print(e) graviton_url = "" graviton_userid = "" return graviton_url,graviton_userid def get_llm_data(): apiKeyIdLLM = "" apiUrlLLM = "" from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') try: if sqlite_obj.table_exists('openai'): data = sqlite_obj.read_data('openai')[0] param_keys = ['api_type','api_key','api_base','api_version'] openai_data = dict((x,y) for x,y in zip(param_keys,data)) return openai_data['api_key'],openai_data['api_base'],openai_data['api_type'],openai_data['api_version'] except Exception as e: print(e) apiKeyIdLLM = "" apiUrlLLM = "" return apiKeyIdLLM,apiUrlLLM,"","" def settings(): configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','config','aion.config') usecase='disable' graviton_url = '' graviton_userid = '' apiKeyIdLLM = '' apiUrlLLM = '' No_of_Permissible_Features_EDA = '' try: from appbe.sqliteUtility import sqlite_db import pandas as pd from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') if sqlite_obj.table_exists('settings'): column_names = sqlite_obj.column_names('settings') data = sqlite_obj.read_data('settings') if 'telemetryOptOut' not in column_names: query = 'Alter Table settings ADD telemetryOptOut TEXT' sqlite_obj.execute_query(query) if 'No_of_Permissible_Features_EDA' not in column_names or 'apiKeyIdLLM' not in column_names: sqlite_obj.drop_table('settings') configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'aion.config') file = open(configfilepath, "r") dataread = file.read() for line in dataread.splitlines(): if 'usecase=' in line: cusecase = line.split('=', 1)[1] if 'graviton_url=' in line: cgraviton_url = line.split('=', 1)[1] if 'graviton_userid=' in line: cgraviton_userid = line.split('=', 1)[1] if 'No_of_Permissible_Features_EDA=' in line: cNo_of_Permissible_Features_EDA = line.split('=', 1)[1] if 'apiKeyIdLLM=' in line: capiKeyIdLLM = '' if 'apiUrlLLM=' in line: capiUrlLLM = '' file.close() if 'apiKeyIdLLM' not in column_names: apiKeyIdLLM = capiKeyIdLLM if 'apiUrlLLM' not in column_names: apiUrlLLM = capiUrlLLM if 'No_of_Permissible_Features_EDA' not in column_names: No_of_Permissible_Features_EDA = cNo_of_Permissible_Features_EDA newdata = {} newdata.update({'graviton_url':[data[0][0]],'graviton_userid': [data[0][1]],'usecase': [data[0][2]],'No_of_Permissible_Features_EDA':[No_of_Permissible_Features_EDA],'settingsid':['1'],'apiKeyIdLLM' :apiKeyIdLLM,'apiUrlLLM':apiUrlLLM,'telemetryOptOut':telemetryOptOut}) sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'settings') data = sqlite_obj.read_data('settings') for values in data: graviton_url = values[0] graviton_userid = values[1] usecase = values[2] No_of_Permissible_Features_EDA = values[3] telemetryOptOut = values[7] else: configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'aion.config') if (os.path.isfile(configfilepath)): file = open(configfilepath, "r") read = file.read() file.close() apiKeyIdLLM = '' apiUrlLLM = '' for line in read.splitlines(): if 'usecase=' in line: usecase = line.split('=', 1)[1] if 'graviton_url=' in line: graviton_url = line.split('=', 1)[1] if 'graviton_userid=' in line: graviton_userid = line.split('=', 1)[1] if 'No_of_Permissible_Features_EDA=' in line: No_of_Permissible_Features_EDA = line.split('=', 1)[1] newdata = {} newdata.update({'graviton_url':[graviton_url],'graviton_userid': [graviton_userid],'usecase': [usecase],'No_of_Permissible_Features_EDA':[No_of_Permissible_Features_EDA],'settingsid':['1'],'apiKeyIdLLM' :'','apiUrlLLM':'','telemetryOptOut':['No']}) # --------else create table and update the data, write data will create a table if it does nt exists----- sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'settings') return(usecase) except Exception as e: print(e) configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','config','aion.config') try: if(os.path.isfile(configfilepath)): file = open(configfilepath, "r") read = file.read() file.close() for line in read.splitlines(): if 'usecase=' in line: usecase = line.split('=',1)[1] if 'graviton_url=' in line: graviton_url = line.split('=',1)[1] if 'graviton_userid=' in line: graviton_userid = line.split('=',1)[1] if 'No_of_Permissible_Features_EDA=' in line: No_of_Permissible_Features_EDA = line.split('=', 1)[1] if 'apiKeyIdLLM=' in line: apiKeyIdLLM = line.split('=', 1)[1] if 'apiUrlLLM=' in line: apiUrlLLM = line.split('=', 1)[1] except Exception as inst: pass external_system = 'enable' semantico = 'enable' return(usecase) def addKafkaModel(request,datalocation): file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','kafkaConfig.conf')) f = open(file_path, "r+") configSettings = f.read() configSettingsJson = json.loads(configSettings) modelSignature = request.POST.get('modelsignature') timeframe = request.POST.get('timeframe') command = request.POST.get('kafkasubmit') if command.lower() == 'configure': configSettingsJson['timeFrame'][modelSignature] = str(timeframe) configSettingsJson['trainingDataLocation'][modelSignature] = datalocation elif command.lower() == 'unconfigure': del configSettingsJson['timeFrame'][modelSignature] updatedConfigSettingsJson = json.dumps(configSettingsJson) f.seek(0) f.write(updatedConfigSettingsJson) f.truncate() f.close() def saveopenaisettings(request): try: from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR import pandas as pd file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') if sqlite_obj.table_exists('openai'): updated_data = 'api_type="'+request.POST.get('api_type')+'",api_key="'+request.POST.get('apiKeyIdLLM')+'",api_base="'+request.POST.get('apiUrlLLM')+'",api_version="'+request.POST.get('api_version')+'"' sqlite_obj.update_data(updated_data,'','openai') else: newdata = {} newdata.update({'api_type':['azure'],'api_key': [request.POST.get('apiKeyIdLLM')],'api_base': [request.POST.get('apiUrlLLM')],'api_version':[request.POST.get('api_version')]}) sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'openai') except Exception as e: print(e) def savegravitonconfig(request): try: from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR import pandas as pd file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') updated_data = 'graviton_url="'+request.POST.get('graviton_url')+'",graviton_userid="'+request.POST.get('graviton_userid')+'"' sqlite_obj.update_data(updated_data,'settingsid=1','settings') except Exception as e: print(e) def saveconfigfile(request): try: from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR import pandas as pd file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') updated_data = 'usecase="'+request.POST.get('usecasetab')+'",No_of_Permissible_Features_EDA="'+request.POST.get('edefeatures')+'",telemetryOptOut="'+request.POST.get('telemetryOptOut')+'"' print(updated_data) sqlite_obj.update_data(updated_data,'settingsid=1','settings') return request.POST.get('usecasetab') except Exception as e: print(e) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import numpy as np import json import os def downloadtrainingfile(request,Existusecases): usename = request.session['UseCaseName'].replace(" ", "_") + '_' + str(request.session['ModelVersion']) updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r+", encoding="utf-8") configSettingsData = f.read() configSettingsJson = json.loads(configSettingsData) modelName = request.session['UseCaseName'] modelVersion = request.session['ModelVersion'] modelStatus = request.session['ModelStatus'] model = Existusecases.objects.get(ModelName=request.session['ModelName'],Version=request.session['ModelVersion']) output_train_json_filename = str(model.TrainOuputLocation) f = open(output_train_json_filename, "r+") training_output = f.read() f.close() dict = {'Attribute':[], 'Value':[] } training_output = json.loads(training_output) dfdashbord = pd.DataFrame(dict) dfdashbord.loc[len(dfdashbord.index)] = ['UseCaseName',modelName] dfdashbord.loc[len(dfdashbord.index)] = ['ProblemType',training_output['data']['ModelType']] dfdashbord.loc[len(dfdashbord.index)] = ['Version',str(modelVersion)] dfdashbord.loc[len(dfdashbord.index)] = ['Status',modelStatus] if 'vmDetails' in training_output['data']: dfdashbord.loc[len(dfdashbord.index)] = ['DeployLocation', training_output['data']['vmDetails']] else: dfdashbord.loc[len(dfdashbord.index)] = ['DeployLocation',training_output['data']['deployLocation']] dfdashbord.loc[len(dfdashbord.index)] = ['BestModel',training_output['data']['BestModel']] dfdashbord.loc[len(dfdashbord.index)] = ['BestScore',training_output['data']['BestScore']] dfdashbord.loc[len(dfdashbord.index)] = ['ScoringParam',training_output['data']['ScoreType']] if training_output['data']['ModelType'] != 'LLM Fine-Tuning': dfdashbord.loc[len(dfdashbord.index)] = ['Test%',configSettingsJson['advance']['testPercentage']] dfdashbord.loc[len(dfdashbord.index)] = ['FeaturesUsed',training_output['data']['featuresused']] from io import BytesIO as IO excel_file = IO() edaFileName = usename + '_training.xlsx' excel_writer = pd.ExcelWriter(excel_file, engine="xlsxwriter") dfdashbord.to_excel(excel_writer, sheet_name='Dashboard',index=False) if training_output['data']['ModelType'].lower() != 'multimodellearning' and training_output['data']['ModelType'].lower() != 'multilabelprediction': EvaluatedModels = training_output['data']['EvaluatedModels'] EvaluatedModels = pd.DataFrame(EvaluatedModels) EvaluatedModels.to_excel(excel_writer, sheet_name='EvaluatedModels',startrow=0 , startcol=0) if training_output['data']['ModelType'].lower() == 'classification': #print(training_output['data']['matrix']) row1 = 10 row2 = 10 if 'ConfusionMatrix' in training_output['data']['matrix']: confusionMatrix = training_output['data']['matrix']['ConfusionMatrix'] confusionMatrix = pd.DataFrame(confusionMatrix) confusionMatrix.to_excel(excel_writer, sheet_name='Testing Matrix',startrow=0 , startcol=0) row1 =confusionMatrix.shape[0]+5 if 'ConfusionMatrix' in training_output['data']['trainmatrix']: confusionMatrix = training_output['data']['trainmatrix']['ConfusionMatrix'] confusionMatrix = pd.DataFrame(confusionMatrix) confusionMatrix.to_excel(excel_writer, sheet_name='Training Matrix',startrow=0 , startcol=0) if 'ClassificationReport' in training_output['data']['matrix']: confusionMatrix = training_output['data']['matrix']['ClassificationReport'] confusionMatrix = pd.DataFrame(confusionMatrix) confusionMatrix.to_excel(excel_writer, sheet_name='Testing Matrix',startrow=row1 , startcol=0) if 'ClassificationReport' in training_output['data']['trainmatrix']: confusionMatrix = training_output['data']['trainmatrix']['ClassificationReport'] confusionMatrix = pd.DataFrame(confusionMatrix) confusionMatrix.to_excel(excel_writer, sheet_name='Training Matrix',startrow=row2 , startcol=0) if training_output['data']['ModelType'].lower() == 'regression': dict = {'Attribute':[],'Value':[]} testingDF = pd.DataFrame(dict) try: testingDF.loc[len(testingDF.index)] = ['MAE',training_output['data']['matrix']['MAE']] testingDF.loc[len(testingDF.index)] = ['R2Score',training_output['data']['matrix']['R2Score']] testingDF.loc[len(testingDF.index)] = ['MSE',training_output['data']['matrix']['MSE']] testingDF.loc[len(testingDF.index)] = ['MAPE',training_output['data']['matrix']['MAPE']] testingDF.loc[len(testingDF.index)] = ['RMSE',training_output['data']['matrix']['RMSE']] except: pass testingDF.to_excel(excel_writer, sheet_name='Testing Matrix',startrow=0 , startcol=0) trainingDF = pd.DataFrame(dict) try: trainingDF.loc[len(trainingDF.index)] = ['MAE',training_output['data']['trainmatrix']['MAE']] trainingDF.loc[len(trainingDF.index)] = ['R2Score',training_output['data']['trainmatrix']['R2Score']] trainingDF.loc[len(trainingDF.index)] = ['MSE',training_output['data']['trainmatrix']['MSE']] trainingDF.loc[len(trainingDF.index)] = ['MAPE',training_output['data']['trainmatrix']['MAPE']] trainingDF.loc[len(trainingDF.index)] = ['RMSE',training_output['data']['trainmatrix']['RMSE']] except: pass trainingDF.to_excel(excel_writer, sheet_name='Training Matrix',startrow=0 , startcol=0) if training_output['data']['ModelType'].lower() == 'clustering': dict = {'Attribute':[],'Value':[]} trainingDF = pd.DataFrame(dict) try: trainingDF.loc[len(trainingDF.index)] = ['SilHouette_Avg',round(training_output['data']['trainmatrix']['SilHouette_Avg'],2)] trainingDF.loc[len(trainingDF.index)] = ['DaviesBouldinScore',round(training_output['data']['trainmatrix']['DaviesBouldinScore'],2)] trainingDF.loc[len(trainingDF.index)] = ['CalinskiHarabazScore',round(training_output['data']['trainmatrix']['CalinskiHarabazScore'],2)] except: pass trainingDF.to_excel(excel_writer, sheet_name='Training Matrix',startrow=0 , startcol=0) centroidpath = os.path.join(training_output['data']['deployLocation'],'centers.csv') if(os.path.isfile(centroidpath)): df_center = pd.read_csv(centroidpath) df_center = df_center.rename(columns={"Unnamed: 0": "Cluster"}) df_center.to_excel(excel_writer, sheet_name='Centroid',startrow=0 , startcol=0) if training_output['data']['ModelType'].lower() == 'timeseriesforecasting': #task 11997 if training_output['data']['BestModel'].lower() == 'var': dict = {'Features':[],'Attribute':[],'Value':[]} trainingDF = pd.DataFrame(dict) FeaturesMatrix = training_output['data']['matrix'] for x in FeaturesMatrix: try: trainingDF.loc[len(trainingDF.index)] = [x['Features'],'MAE',x['MAE']] trainingDF.loc[len(trainingDF.index)] = [x['Features'],'MSE',x['MSE']] trainingDF.loc[len(trainingDF.index)] = [x['Features'],'MAPE',x['MAPE']] trainingDF.loc[len(trainingDF.index)] = [x['Features'],'RMSE',x['RMSE']] except: pass trainingDF.to_excel(excel_writer, sheet_name='Testing Matrix',startrow=0 , startcol=0) else: dict = {'Attribute':[],'Value':[]} trainingDF = pd.DataFrame(dict) try: trainingDF.loc[len(trainingDF.index)] = ['MAE',training_output['data']['matrix']['MAE']] trainingDF.loc[len(trainingDF.index)] = ['MSE',training_output['data']['matrix']['MSE']] trainingDF.loc[len(trainingDF.index)] = ['MAPE',training_output['data']['matrix']['MAPE']] trainingDF.loc[len(trainingDF.index)] = ['RMSE',training_output['data']['matrix']['RMSE']] except: pass trainingDF.to_excel(excel_writer, sheet_name='Testing Matrix',startrow=0 , startcol=0) workbook = excel_writer.book #excel_writer.save() excel_writer.close() excel_file.seek(0) return edaFileName,excel_file ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' def analysis_images(folder_path): from AIX import image_eda qualityscore = image_eda.img_MeasureImageQuality(folder_path) eda_result = image_eda.img_EDA(folder_path) #Image Duplicate Finder duplicate_img = image_eda.img_duplicatefinder(folder_path) color_plt = image_eda.img_plot_colour_hist(folder_path) return qualityscore,eda_result,duplicate_img,color_plt ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os.path from pathlib import Path import time import subprocess import sys import shutil from appbe.aion_config import kafka_setting from appbe.aion_config import running_setting from appbe.publish import chech_publish_info from llm.llm_tuning import update_sqllite_data from appbe.data_io import sqlite_db from appbe.dataPath import DATA_DIR from appbe import installPackage from appbe import compute import json import os import signal from os.path import expanduser import platform import pandas as pd LOG_FILE_PATH = os.path.join(DATA_DIR,'logs') GITHUB_FILE_PATH = os.path.join(DATA_DIR,'github') PUBLISH_PATH = os.path.join(DATA_DIR,'target') DEPLOY_DATABASE_PATH = os.path.join(DATA_DIR,'sqlite') os.makedirs(LOG_FILE_PATH, exist_ok=True) ''' def check_publish_info(usecase,version): sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db') if sqlite_dbObj.table_exists('publish'): publishState= 'Published' ''' def get_instance(modelID): from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') if sqlite_obj.table_exists("LLMTuning"): data = sqlite_obj.get_data('LLMTuning','usecaseid',modelID) if len(data) > 0: return (data[3],data[2],data[5],data[6],data[4]) else: return '','','','','' else: return '','','','','' def startServices(request,usecasedetails,Existusecases): try: models = Existusecases.objects.filter(publishStatus='Published') print(models) if len(models) > 0: for model in models: try: portNo = model.portNo ppid = model.publishPID if ppid == 0: continue try: os.kill(int(model.publishPID), signal.SIGTERM) except Exception as e: print(e) scriptPath = os.path.join(PUBLISH_PATH,model.ModelName.usecaseid,'aion_publish_service.py') if os.path.exists(scriptPath): outputStr = subprocess.Popen([sys.executable, scriptPath,'-ip','0.0.0.0','-p',str(portNo)]) model.publishStatus = 'Published' model.publishPID = outputStr.pid model.portNo = portNo model.save() else: print("Pass") pass except Exception as e: print(e) except Exception as e: print(e) def publishmodel(request,usecaseid,version,Existusecases,usecasedetails): portNo=0 usecased = usecasedetails.objects.get(usecaseid=usecaseid) models = Existusecases.objects.filter(ModelName=usecased,publishStatus='Published') if len(models) > 0: for model in models: try: portNo = model.portNo try: os.kill(int(model.publishPID), signal.SIGTERM) except Exception as e: print(e) mod = Existusecases.objects.get(id=model.id) mod.publishStatus = '' mod.publishPID = 0 mod.portNo = 0 mod.save() except Exception as e: print(e) pass missingNumbers = [] if portNo == 0: models = Existusecases.objects.filter(publishStatus='Published') usedPortNo=[] for model in models: usedPortNo.append(model.portNo) startPortNo = 8091 endPortNo = 8091+5 missingNumbers = [ i for i in range(startPortNo,endPortNo) if i not in usedPortNo] if len(missingNumbers) > 0: portNo = missingNumbers[0] if portNo != 0: scriptPath = os.path.join(PUBLISH_PATH,usecaseid,'aion_publish_service.py') model = Existusecases.objects.get(ModelName=usecased,Version=version) isExist = os.path.exists(scriptPath) if isExist: configfile = os.path.join(PUBLISH_PATH,usecaseid,'config.json') configdata = {'version': str(version)} with open(configfile, "w") as outfile: json.dump(configdata, outfile) outfile.close() outputStr = subprocess.Popen([sys.executable, scriptPath,'-ip','0.0.0.0','-p',str(portNo)]) model.publishStatus = 'Published' model.publishPID = outputStr.pid model.portNo = portNo model.save() Status = 'SUCCESS' hosturl =request.get_host() hosturl = hosturl.split(':') url = 'http://'+hosturl[0]+':'+str(portNo)+'/AION/'+str(usecaseid)+'/predict' Msg = 'Model Published Successfully' else: Status = 'Error' Msg = 'Model Published Error' url = '' else: Status = 'Error' Msg = 'All ports are utilized' url='' return Status,Msg,url def get_published_models(instanceid): from appbe.sqliteUtility import sqlite_db file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') if sqlite_obj.table_exists("LLMTuning"): condition = f'"instance"=="{instanceid}" AND "status"=="Published"' datas = sqlite_obj.read_data('LLMTuning',condition) if len(datas)>0: return True,datas[0][0] return False,'' def maac_command(request,Existusecases,usecasedetails): command = request.POST.get('maacsubmit') kafkaSetting = kafka_setting() ruuningSetting = running_setting() computeinfrastructure = compute.readComputeConfig() modelID = request.POST.get('modelID') Version = request.POST.get('Version') p = Existusecases.objects.get(id=modelID,Version=Version) usecasename = p.ModelName.usecaseid #bugid 13339 usecaseid = p.ModelName.id # runningStatus,pid,ip,port = installPackage.checkModelServiceRunning(usecasename) # installationStatus,modelName,modelVersion=installPackage.checkInstalledPackge(usecasename) usecasedetail = usecasedetails.objects.get(id=p.ModelName.id) usecase = usecasedetails.objects.all() problemType = p.ProblemType score = 0 scoreType = '' deployedModel = '' deployedModelVersion = p.Version models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS') computeinfrastructure = compute.readComputeConfig() for model in models: model.scoringCreteria = 'NA' model.score = 'NA' model.deploymodel = 'NA' if os.path.isdir(str(model.DeployPath)): modelPath = os.path.join(str(model.DeployPath),'etc','output.json') try: with open(modelPath) as file: outputconfig = json.load(file) file.close() if outputconfig['status'] == 'SUCCESS': if deployedModelVersion == model.Version: problemType = outputconfig['data']['ModelType'] scoreType = outputconfig['data']['ScoreType'] score = outputconfig['data']['BestScore'] deployedModel = outputconfig['data']['BestModel'] model.scoringCreteria = outputconfig['data']['ScoreType'] model.score = outputconfig['data']['BestScore'] model.deploymodel = outputconfig['data']['BestModel'] model.maacsupport = 'True' model.flserversupport = 'False' supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"] if model.deploymodel in supportedmodels: model.flserversupport = 'True' else: model.flserversupport = 'False' supportedmodels = ["Extreme Gradient Boosting (XGBoost)"] if model.deploymodel in supportedmodels: model.encryptionsupport = 'True' else: model.encryptionsupport = 'False' except Exception as e: print(e) pass MLaaC_output = '' if command == 'generatemaac': deployPath = str(p.DeployPath) codeconfig = os.path.join(deployPath,'etc','code_config.json') if os.path.isfile(codeconfig): with open(codeconfig,'r') as f: cconfig = json.load(f) f.close() dbserver = request.POST.get('productiondb') db_config = {} if dbserver.lower() == 'influxdb': cconfig['prod_db_type'] = 'influx' db_config['host'] = request.POST.get('influxdbhost') db_config['port'] = request.POST.get('influxdbportno') db_config['user'] = request.POST.get('influxdbuser') db_config['password'] = request.POST.get('influxpassword') db_config['database'] = 'production' db_config['measurement'] = usecasename tags = {} db_config['tags']=tags cconfig['db_config'] = db_config else: cconfig['prod_db_type'] = 'sqlite' cconfig['db_config'] = db_config dbserver = request.POST.get('mlflowserver') mlflow_config = {} if dbserver.lower() == 'local': cconfig['mlflow_config'] = mlflow_config else: mlflow_config['tracking_uri_type'] = request.POST.get('mlflowserverurl') mlflow_config['tracking_uri'] = request.POST.get('mlflowserverurl') mlflow_config['registry_uri'] = request.POST.get('mlflowserverurl') mlflow_config['artifacts_uri'] = request.POST.get('mlflowserverurl') cconfig['mlflow_config'] = mlflow_config with open(codeconfig,'w') as f: json.dump(cconfig, f) f.close() from bin.aion_mlac import generate_mlac_code outputStr = generate_mlac_code(codeconfig) output = json.loads(outputStr) from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'MLaC','Yes') if output['Status'] == 'SUCCESS': Status = 'SUCCESS' MLaaC_output = output['MLaC_Location'].replace('\\', '\\\\') Msg = 'MLaC code successfully generated' else: Status = 'Failure' Msg = output['msg'] else: Status = 'Failure' Msg = 'Code Config Not Present' if command == 'buildContainer': deployPath = str(p.DeployPath) maac_path = os.path.join(deployPath,'publish','MLaC') if os.path.isdir(maac_path): config={'usecase':str(usecasename),'version':str(p.Version),'mlacPath':maac_path} config = json.dumps(config) scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','aion.py')) if platform.system() == 'Windows': outputStr = subprocess.Popen([sys.executable, scriptPath,'-m','buildMLaCContainerLocal' ,'-j',config],creationflags = subprocess.CREATE_NEW_CONSOLE) else: outputStr = subprocess.Popen([sys.executable, scriptPath,'-m','buildMLaCContainerLocal' ,'-j',config]) #cmd = scriptPath+" "+str(usecasename)+" "+str(p.Version)+" "+str(maac_path) #subprocess.Popen(cmd,shell=True) Status = 'SUCCESS' Msg = 'Build Container Started' else: Status = 'Failure' Msg = 'Run Code Generator' if command == 'runpipeline': deployPath = str(p.DeployPath) dockerlist = os.path.join(deployPath,'publish','MLaC','dockerlist.json') if os.path.isfile(dockerlist): persistancevolume = request.POST.get('persistancevolume') datasetpath = request.POST.get('dataset') filetimestamp = str(int(time.time())) logfilepath = os.path.join(LOG_FILE_PATH,'AIONPipeline_'+str(filetimestamp)+'.log') config={'usecase':str(usecasename),'version':str(p.Version),'persistancevolume':persistancevolume,'datasetpath':datasetpath,'dockerlist':str(dockerlist),'logfilepath':logfilepath} config = json.dumps(config) scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','aion.py')) if platform.system() == 'Windows': outputStr = subprocess.Popen([sys.executable, scriptPath,'-m','runpipelinelocal','-j',config],creationflags = subprocess.CREATE_NEW_CONSOLE) else: outputStr = subprocess.Popen([sys.executable, scriptPath, str(usecasename),str(p.Version),persistancevolume,datasetpath,str(dockerlist),logfilepath]) Status = 'SUCCESS' Msg = 'Pipeline Started' MLaaC_output = 'Check log file for pipeline execution status: ' + str(logfilepath) else: Status = 'Failure' Msg = 'Not found container information' if command == 'generateyaml': deployPath = str(p.DeployPath) maac_path = os.path.join(deployPath,'publish','MLaC') if os.path.isdir(maac_path): persistancevolume = request.POST.get('persistancevolume') datasetpath = request.POST.get('dataset') supported_urls_starts_with = ('gs://','https://','http://') if datasetpath.startswith(supported_urls_starts_with): datasetpath = request.POST.get('dataset') else: datasetpath = '/aion/'+request.POST.get('dataset') serviceport = request.POST.get('serviceport') scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','bin','run_generateyaml.py')) outputStr = subprocess.check_output([sys.executable, scriptPath, str(usecasename),str(p.Version),persistancevolume,datasetpath,maac_path,serviceport]) outputStr = outputStr.decode('utf-8') outputStr=outputStr.strip() print(outputStr) output = json.loads(outputStr) if output['Status'] == 'SUCCESS': Status = 'SUCCESS' MLaaC_output = output['location'] Msg = 'MLaaC dockerfile successfully generated' else: Status = 'Failure' Msg = output['msg'] else: Status = 'Failure' Msg = 'Execute generate code first' if command == 'githubupload': if shutil.which('git') is None: Status = 'Failure' Msg = 'Git is not installed, Please install Git first.' else: try: deployPath = str(p.DeployPath) maac_path = os.path.join(deployPath,'publish','MLaC') if os.path.isdir(maac_path): githuburl = request.POST.get('githuburl') githubusername = request.POST.get('githubusername') githubtoken = request.POST.get('githubtoken') githubemail = request.POST.get('githubemail') githubconfig = {"url_type":"https","url":githuburl,"username":githubusername,"email":githubemail,"token":githubtoken,"location":maac_path,"modelName":usecasename,"gitFolderLocation":GITHUB_FILE_PATH} from mlops import git_upload outputStr = git_upload.upload(githubconfig) print(outputStr) output = json.loads(outputStr) if output['Status'] == 'SUCCESS': Status = 'SUCCESS' MLaaC_output = githuburl Msg = 'Code Uploaded to GitHub Successfully' else: Status = 'Failure' Msg = output['msg'] else: Status = 'Failure' Msg = 'GitHub Upload failed' except Exception as e: print(e) Status = 'Failure' Msg = 'GitHub Upload failed' if command == 'unpublishmodel': try: models = Existusecases.objects.filter(ModelName=usecasedetail,publishStatus='Published') if len(models) > 0: for model in models: try: if problemType.lower() == "llm fine-tuning": cloudconfig = os.path.normpath( os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'compute_conf.json')) modelid = usecasename + '_' + str(Version) usecasename = usecasename.replace(" ", "_") hypervisor,instanceid,region,image,status = get_instance(usecasename + '_' + str(Version)) from llm.llm_inference import kill_inference_server kill_inference_server(cloudconfig,instanceid,hypervisor,region,image) update_sqllite_data(modelid,'status','Success') else: try: os.kill(int(model.publishPID), signal.SIGTERM) mod.publishPID = 0 except Exception as e: print(e) mod = Existusecases.objects.get(id=model.id) mod.publishStatus = '' mod.portNo = 0 mod.save() Status = 'SUCCESS' Msg = 'Model Unpublished Successfully' except Exception as e: print(e) Status = 'Error' Msg = 'Model Unpublished Error' except Exception as e: exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) print(e) pass if command == 'publishmodel': try: portNo=0 models = Existusecases.objects.filter(ModelName=usecasedetail,publishStatus='Published') if len(models) > 0: for model in models: try: portNo = model.portNo try: os.kill(int(model.publishPID), signal.SIGTERM) except Exception as e: print(e) mod = Existusecases.objects.get(id=model.id) mod.publishStatus = '' mod.publishPID = 0 mod.portNo = 0 mod.save() except Exception as e: print(e) pass missingNumbers = [] if problemType.lower() == "llm fine-tuning": model = Existusecases.objects.get(ModelName=usecasedetail,Version=Version) try: usecasename = usecasename.replace(" ", "_") hypervisor,instanceid,region,image,status = get_instance(usecasename + '_' + str(Version)) if status.lower() in ['published','success'] : if status.lower() == 'published': from llm.llm_inference import kill_inference_server kill_inference_server('',instanceid, hypervisor, region, image) update_sqllite_data(usecasename + '_' + str(Version), 'status', 'Success') already_published,published_usecase = get_published_models(instanceid) if already_published: Status = 'Error' Msg = f'{published_usecase} is published at the same id, Please Unpublish mentioned model to proceed.' else: if not region: region = '' cloudconfig = os.path.normpath( os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'compute_conf.json')) usecase = usecasename + '_' + str(Version) #modelid = usecasename + '_' + str(Version) scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'aion.py')) cmd = [sys.executable, scriptPath, '-m', 'llmpublish', '-cc', cloudconfig, '-i',instanceid,'-hv',hypervisor,'-md',deployedModel,'-uc',usecase,'-r',region,'-im',image ] outputStr = subprocess.Popen(cmd) model.publishStatus = 'Published' model.publishPID = 0 model.portNo = 8000 model.save() Status = 'SUCCESS' from llm.llm_inference import get_ip instanceip = get_ip(cloudconfig,instanceid,hypervisor,region,image) print(instanceip) url = 'http://' + instanceip + ':' + str(model.portNo) + '/generate' Msg = 'Model Published Successfully, Server will take few minutes to be ready for Inferencing. URL: ' + url update_sqllite_data(usecase,'status','Published') else: Status = 'Error' Msg = 'Only Trained models are availble for Publish.' except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) Status = 'Error' Msg = 'Model Published Error' else: if portNo == 0: models = Existusecases.objects.filter(publishStatus='Published') usedPortNo=[] for model in models: usedPortNo.append(model.portNo) startPortNo = 8091 endPortNo = 8091+5 missingNumbers = [ i for i in range(startPortNo,endPortNo) if i not in usedPortNo] if len(missingNumbers) > 0: portNo = missingNumbers[0] if portNo != 0: model = Existusecases.objects.get(ModelName=usecasedetail,Version=Version) scriptPath = os.path.join(PUBLISH_PATH,usecasename,'aion_publish_service.py') isExist = os.path.exists(scriptPath) if isExist: configfile = os.path.join(PUBLISH_PATH,usecasename,'config.json') configdata = {'version': str(Version)} with open(configfile, "w") as outfile: json.dump(configdata, outfile) outfile.close() outputStr = subprocess.Popen([sys.executable, scriptPath,'-ip','0.0.0.0','-p',str(portNo)]) model.publishStatus = 'Published' model.publishPID = outputStr.pid model.portNo = portNo model.save() Status = 'SUCCESS' hosturl =request.get_host() hosturl = hosturl.split(':') url = 'http://'+hosturl[0]+':'+str(portNo)+'/AION/'+str(usecasename)+'/predict' Msg = 'Model Published Successfully URL: '+url else: Status = 'Error' Msg = 'Model Published Error' else: Status = 'Error' Msg = 'All ports are utilized' except Exception as e: exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) print(e) pass if command == 'generatekubeflowyaml': try: if problemType.lower() == 'timeseriesforecasting': #task 11997 from appbe.aionpipelinets import aionpipelinets else: from appbe.aionpipeline import aionpipeline deployPath = str(p.DeployPath) codeconfig = os.path.join(deployPath,'etc','code_config.json') featuresmapping = {'modelBased':'mlbased','statisticalBased':'statisticalBased'} if os.path.isfile(codeconfig): with open(codeconfig,'r') as f: codeconfig = json.load(f) f.close() modelsarray=[] for featureselection in codeconfig['feature_selector']: for algo in codeconfig['algorithms'].keys(): if problemType.lower() == 'timeseriesforecasting': #task 11997 modelname = 'modeltraining_'+algo.lower() else: modelname = 'modeltraining_'+algo.lower()+'_'+featuresmapping[featureselection] modelx = {'modelname':modelname} modelsarray.append(modelx) modelsjson = {'models':modelsarray} kubeflowhost= request.POST.get('kubeflowhost') containerregistry= request.POST.get('containerregistry') containerlabel= request.POST.get('containerlabel') containersecret= request.POST.get('containersecret') if problemType.lower() == 'timeseriesforecasting': #task 11997 ap = aionpipelinets(modelsjson,containerregistry,containerlabel,containersecret) else: ap = aionpipeline(modelsjson,containerregistry,containerlabel,containersecret) ap.aion_mlops() ap.compilepl() ap.executepl(kubeflowhost) Status = 'SUCCESS' MLaaC_output = '' Msg = 'MLOps pipeline executed successfully' except Exception as e: print(e) Status = 'Failure' Msg = 'Error in pipeline execution' from appbe.pages import get_usecase_page if command in ['publishmodel','unpublishmodel']: status,context,action = get_usecase_page(request,usecasedetails,Existusecases,usecaseid) context['Status'] = Status context['MLaaC_output'] = MLaaC_output context['Msg'] = Msg return(context,'usecasedetails.html') else: status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['Status'] = Status context['MLaaC_output'] = MLaaC_output context['Msg'] = Msg return(context,'usecases.html') def getusercasestatus(request): if 'UseCaseName' in request.session: selected_use_case = request.session['UseCaseName'] else: selected_use_case = 'Not Defined' if 'ModelVersion' in request.session: ModelVersion = request.session['ModelVersion'] else: ModelVersion = 0 if 'ModelStatus' in request.session: ModelStatus = request.session['ModelStatus'] else: ModelStatus = 'Not Trained' return selected_use_case,ModelVersion,ModelStatus ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json import time import os import subprocess import base64 import sys import re from appbe.dataIngestion import getcommonfields from appbe.dataIngestion import getusercasestatus def startSummarization(request,DEFAULT_FILE_PATH,CONFIG_PATH,DATA_FILE_PATH): try: if request.FILES: Datapath = request.FILES['summarypath'] ext = str(Datapath).split('.')[-1] filetimestamp = str(int(time.time())) if ext.lower() in ['txt','pdf','doc','docs']: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext) else: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp) with open(dataFile, 'wb+') as destination: for chunk in Datapath.chunks(): destination.write(chunk) destination.close() configFile = os.path.join(DEFAULT_FILE_PATH,'aion_textSummerization.json') filetimestamp = str(int(time.time())) config_json_filename = os.path.join(CONFIG_PATH, 'AION_' + filetimestamp + '.json') f = open(configFile) data = json.load(f) f.close() data['basic']['dataLocation'] = dataFile type = request.POST.get('type') model = request.POST.get('model') slength = request.POST.get('length') types = data['basic']['analysisAproach']['textSummarization'] for x in list(types.keys()): data['basic']['analysisAproach']['textSummarization'][x] = 'False' data['basic']['analysisAproach']['textSummarization'][type] = 'True' format = request.POST.get('format') algorithm = data['basic']['algorithms']['textSummarization'] for x in list(algorithm.keys()): data['basic']['algorithms']['textSummarization'][x] = 'False' data['basic']['algorithms']['textSummarization'][model]='True' length = data['advance']['textSummarization']['summaryLength'] for x in list(types.keys()): data['advance']['textSummarization']['summaryLength'][x] = 'False' data['advance']['textSummarization']['summaryLength'][slength] = 'True' with open(config_json_filename, "w") as outfile: json.dump(data, outfile) outfile.close() from bin.aion_text_summarizer import aion_textsummary outputStr = aion_textsummary(config_json_filename) #scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','bin','aion_text_summarizer.py')) #outputStr = subprocess.check_output([sys.executable, scriptPath, config_json_filename]) #outputStr = outputStr.decode('utf-8') #outputStr = re.search(r'Summary:(.*)', str(outputStr), re.IGNORECASE).group(1) predict_dict = json.loads(str(outputStr)) summary = predict_dict['summary'] except Exception as e: print(e) summary = str(e) context = getcommonfields() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) context.update({'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion}) context.update({'summary':summary}) return context ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import sqlite3 from pathlib import Path import json import os import rsa import boto3 #usnish import pandas as pd import time class sqlite_db(): def __init__(self, location, database_file=None): if not isinstance(location, Path): location = Path(location) if database_file: self.database_name = database_file else: self.database_name = location.stem db_file = str(location/self.database_name) self.conn = sqlite3.connect(db_file) self.cursor = self.conn.cursor() def table_exists(self, name): query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';" listOfTables = self.cursor.execute(query).fetchall() return len(listOfTables) > 0 def read_data(self, table_name): query = f"SELECT * FROM {table_name}" row = self.cursor.execute(query).fetchall() return list(row) #return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn) def create_table(self,name, columns, dtypes): query = f'CREATE TABLE IF NOT EXISTS {name} (' for column, data_type in zip(columns, dtypes): query += f"'{column}' TEXT," query = query[:-1] query += ');' self.conn.execute(query) return True def delete_record(self,table_name,col_name, col_value): try: query = f"DELETE FROM {table_name} WHERE {col_name}='{col_value}'" self.conn.execute(query) self.conn.commit() return 'success' except Exception as e : print(str(e)) print("Deletion Failed") return 'error' def get_data(self,table_name,col_name,col_value): query = f"SELECT * FROM {table_name} WHERE {col_name}='{col_value}'" row = self.cursor.execute(query).fetchone() if(row == None): return [] return list(row) def write_data(self,data, table_name): if not self.table_exists(table_name): self.create_table(table_name, data.columns, data.dtypes) tuple_data = list(data.itertuples(index=False, name=None)) insert_query = f'INSERT INTO {table_name} VALUES(' for i in range(len(data.columns)): insert_query += '?,' insert_query = insert_query[:-1] + ')' self.cursor.executemany(insert_query, tuple_data) self.conn.commit() return True def close(self): self.conn.close() def add_new_s3bucket(request): try: from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') if request.POST["aionreferencename"] =='' or request.POST["s3bucketname"] == '' or request.POST["awsaccesskey"] == '' : return 'error' pkeydata='''-----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAxIHM1FphEMMwViUrG0b2Bqf8tOxbhUWlnmjgFt5A25qbY1AfnrMv fVx8+7iCcZ/3TY9Jv2I584SOc1tvsgESCke/t6+o/u2esPBsnDFzV62l3Zvw0m4e wQeKlFC8EoOblyIXRbZdelSJinzlr9lOiKuid/xPvXHou6jxF1A2W7a89A2PM4Re n0W9YkjB7dRGW1sSrpruHdVJvgHhGZFZ7sCTue0jVOnc5sT3Tq5saLfEDqHyKxlq i/mcThmcTfisRIYFH5pyt/Ysr4VVP924QlcoqPOyg3RMCS3G0VjstSoVwNhxWrs/ lujDuCnpxvWzNpq21OWmF66GXxwiq+6W0wIDAQAB -----END RSA PUBLIC KEY-----''' pubkey = rsa.PublicKey.load_pkcs1(pkeydata) awssecretaccesskey = rsa.encrypt(request.POST["awssecretaccesskey"].encode(), pubkey) newdata = {} newdata['Name'] = [request.POST["aionreferencename"]] newdata['AWSAccessKeyID'] = [request.POST["awsaccesskey"]] newdata['AWSSecretAccessKey'] = [str(awssecretaccesskey)] newdata['S3BucketName'] = [request.POST["s3bucketname"]] name = request.POST["aionreferencename"] if sqlite_obj.table_exists("s3bucket"): if(len(sqlite_obj.get_data("s3bucket","Name",name)) > 0): return 'error1' sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'s3bucket') except Exception as e: print(e) return 'error' def get_s3_bucket(): try: from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') temp_data = sqlite_obj.read_data('s3bucket') data = [] for x in temp_data: data_dict = {} data_dict['Name'] = x[0] data_dict['AWSAccessKeyID'] = x[1] data_dict['AWSSecretAccessKey'] = x[2] data_dict['S3BucketName'] = x[3] data.append(data_dict) except Exception as e: print(e) data = [] return data def remove_s3_bucket(name): from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') return sqlite_obj.delete_record('s3bucket','Name',name) def read_s3_bucket(name,filename,DATA_FILE_PATH): privkey = '''-----BEGIN RSA PRIVATE KEY----- MIIEqQIBAAKCAQEAxIHM1FphEMMwViUrG0b2Bqf8tOxbhUWlnmjgFt5A25qbY1Af nrMvfVx8+7iCcZ/3TY9Jv2I584SOc1tvsgESCke/t6+o/u2esPBsnDFzV62l3Zvw 0m4ewQeKlFC8EoOblyIXRbZdelSJinzlr9lOiKuid/xPvXHou6jxF1A2W7a89A2P M4Ren0W9YkjB7dRGW1sSrpruHdVJvgHhGZFZ7sCTue0jVOnc5sT3Tq5saLfEDqHy Kxlqi/mcThmcTfisRIYFH5pyt/Ysr4VVP924QlcoqPOyg3RMCS3G0VjstSoVwNhx Wrs/lujDuCnpxvWzNpq21OWmF66GXxwiq+6W0wIDAQABAoIBAC/VbNfQPEqJSO3f VFPqfR73q2MbGdgiMQOTgeDvLxiF1QdizJ+j/I5mgiIAMviXuOpPU+NbdMHbZZWd D15kNlD8UCXVg6yyiOuHStjmjK4uHe8I86E1nxTb0hbyZCWZlbk/WizlDHInu+dT KdIZcq2AIidU6tAxtwA0ingHaRSoXDlSGwOTEigNqmWOKnDTVg0SMscoHOD7siXF DHm1/lkvD3uvcZk6c7fGxC8SgNX2dj6n/Nbuy0Em+bJ0Ya5wq4HFdLJn3EHZYORF ODUDYoGaSxeXqYsGg/KHJBc8J7xW9FdN9fGbHfw1YplrmiGL3daATtArjMmAh0EQ H8Sj7+ECgYkA3oWMCHi+4t8txRPkg1Fwt8dcqYhGtqpAus3NESVurAdi0ZPqEJcQ 4cUbflwQPhX0TOaBlkgzdP8DMdcW/4RalxHsAh5N8ezx/97PQMb3Bht0WsQUBeYJ xLV7T2astjTRWactGCG7dwTaUYRtU3FqL6//3CysmA12B5EMX0udNBOTKwmaYKww AwJ5AOISS7f12Q0fgTEVY0H8Zu5hHXNOA7DN92BUzf99iPx+H+codLet4Ut4Eh0C cFmjA3TC78oirp5mOOQmYxwaFaxlZ7Rs60dlPFrhz0rsHYPK1yUOWRr3RcXWSR13 r+kn+f+8k7nItfGi7shdcQW+adm/EqPfwTHM8QKBiQCIPEMrvKFBzVn8Wt2A+I+G NOyqbuC8XSgcNnvij4RelncN0P1xAsw3LbJTfpIDMPXNTyLvm2zFqIuQLBvMfH/q FfLkqSEXiPXwrb0975K1joGCQKHxqpE4edPxHO+I7nVt6khVifF4QORZHDbC66ET aTHA3ykcPsGQiGGGxoiMpZ9orgxyO3l5Anh92jmU26RNjfBZ5tIu9dhHdID0o8Wi M8c3NX7IcJZGGeCgywDPEFmPrfRHeggZnopaAfuDx/L182pQeJ5MEqlmI72rz8bb JByJa5P+3ZtAtzc2RdqNDIMnM7fYU7z2S279U3nZv0aqkk3j9UDqNaqvsZMq73GZ y8ECgYgoeJDi+YyVtqgzXyDTLv6MNWKna9LQZlbkRLcpg6ELRnb5F/dL/eB/D0Sx QpUFi8ZqBWL+A/TvgrCrTSIrfk71CKv6h1CGAS02dXorYro86KBLbJ0yp1T/WJUj rHrGHczglvoB+5stY/EpquNpyca03GcutgIi9P2IsTIuFdnUgjc7t96WEQwL -----END RSA PRIVATE KEY-----''' try: from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') data = sqlite_obj.get_data("s3bucket",'Name',name) except: data = [] awssecretaccesskey = '' found = False if len(data)!=0: aws_access_key_id = data[1] awssecretaccesskey = data[2] bucketName = data[3] found = True if found: privkey = rsa.PrivateKey.load_pkcs1(privkey,'PEM') awssecretaccesskey = eval(awssecretaccesskey) awssecretaccesskey = rsa.decrypt(awssecretaccesskey, privkey) awssecretaccesskey = awssecretaccesskey.decode('utf-8') #awssecretaccesskey = 'SGcyJavYEQPwTbOg1ikqThT+Op/ZNsk7UkRCpt9g'#rsa.decrypt(awssecretaccesskey, privkey) client_s3 = boto3.client('s3', aws_access_key_id=aws_access_key_id, aws_secret_access_key=str(awssecretaccesskey)) #print(bucketName,filename) try: response = client_s3.get_object(Bucket=bucketName, Key=filename) df = pd.read_csv(response['Body']) except Exception as e: print(str(e))#usnish return 'Error',str(e), pd.DataFrame() #return 'Error', pd.DataFrame() return 'Success','',df return 'Error',"Please check bucket configuration", pd.DataFrame() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from pathlib import Path import sqlite3 class sqlite_db(): def __init__(self, location, database_file=None): if not isinstance(location, Path): location = Path(location) if database_file: self.database_name = database_file else: self.database_name = location.stem db_file = str(location / self.database_name) self.conn = sqlite3.connect(db_file) self.cursor = self.conn.cursor() def table_exists(self, name): query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';" listOfTables = self.cursor.execute(query).fetchall() return len(listOfTables) > 0 def read_data(self, table_name, condition = None): if condition: query = f"SELECT * FROM {table_name} WHERE "+condition else: query = f"SELECT * FROM {table_name}" row = self.cursor.execute(query).fetchall() return list(row) def column_names(self, table_name): query = f"SELECT * FROM {table_name}" row = self.cursor.execute(query).fetchall() column_names = list(map(lambda x:x[0],self.cursor.description)) return column_names # return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn) def create_table(self, name, columns, dtypes): query = f'CREATE TABLE IF NOT EXISTS {name} (' for column, data_type in zip(columns, dtypes): query += f"'{column}' TEXT," query = query[:-1] query += ');' self.conn.execute(query) return True def delete_record(self, table_name, col_name, col_value): try: query = f"DELETE FROM {table_name} WHERE {col_name}='{col_value}'" self.conn.execute(query) self.conn.commit() return 'success' except Exception as e: print(str(e)) print("Deletion Failed") return 'error' def drop_table(self,table_name): query = f"DROP TABLE {table_name}" self.cursor.execute(query) print("Table dropped... ") # Commit your changes in the database self.conn.commit() def get_data(self, table_name, col_name, col_value): query = f"SELECT * FROM {table_name} WHERE {col_name}='{col_value}'" row = self.cursor.execute(query).fetchone() if (row == None): return [] return list(row) def execute_query(self,query): self.cursor.execute(query) self.conn.commit() def write_data(self, data, table_name): if not self.table_exists(table_name): self.create_table(table_name, data.columns, data.dtypes) tuple_data = list(data.itertuples(index=False, name=None)) insert_query = f'INSERT INTO {table_name} VALUES(' for i in range(len(data.columns)): insert_query += '?,' insert_query = insert_query[:-1] + ')' self.cursor.executemany(insert_query, tuple_data) self.conn.commit() return True def update_dict_data(self,data:dict,condition,table_name): if not data: return if not table_name: raise ValueError('Database table name is not provided') updates = '' #TODO validation of keys for i,kv in enumerate(data.items()): if i: updates += ',' updates += f'"{kv[0]}"="{kv[1]}"' if condition == '': update_query = f'UPDATE {table_name} SET {updates}' else: update_query = f'UPDATE {table_name} SET {updates} WHERE {condition}' self.cursor.execute(update_query) self.conn.commit() def update_data(self,updates,condition,table_name): if condition == '': update_query = f'UPDATE {table_name} SET {updates}' else: update_query = f'UPDATE {table_name} SET {updates} WHERE {condition}' self.cursor.execute(update_query) self.conn.commit() def close(self): self.conn.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import logging from appbe.dataIngestion import getcommonfields from appbe.dataIngestion import getusercasestatus from appbe import service_url import json from appbe.dataIngestion import delimitedsetting import os,sys import pandas as pd from django.http import HttpResponse import time from appbe.dataPath import LOG_LOCATION from appbe.log_ut import logg def get_instance_id(modelID): from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') if sqlite_obj.table_exists("LLMTuning"): data = sqlite_obj.get_data('LLMTuning','usecaseid',modelID) print(data) if len(data) > 0: return (data[3]+' instance '+data[2]) else: return 'Instance ID not available' else: return 'Instance ID not available' def get_instance(modelID): from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') if sqlite_obj.table_exists("LLMTuning"): data = sqlite_obj.get_data('LLMTuning','usecaseid',modelID) if len(data) > 0: return (data[3],data[2],data[5],data[6]) else: return '','','','' else: return '','','','' def getprompt(promptfeature,contextFeature,responseFeature,promptFriendlyName,responseFriendlyName,data): if contextFeature != '': promptData = data[promptfeature].replace('\n','') inputData = data[contextFeature].replace('\n','') prompt = ( f"Below is an {promptFriendlyName} that describes a task, paired with an Input that provides further context. " f"Write a {responseFriendlyName} that appropriately completes the request.\n\n" f"### {promptFriendlyName}:\n{promptData}\n\n### Input:\n{inputData}\n\n### {responseFriendlyName}:\n") else: promptData = data[promptfeature].replace('\n','') prompt=( f"Below is an {promptFriendlyName} that describes a task. " f"Write a {responseFriendlyName} that appropriately completes the request.\n\n" f"### {promptFriendlyName}:\n{promptData}\n\n### {responseFriendlyName}:\n") return prompt def getDataInstance(problem_type,mlmodels,configSettingsJson): log = logging.getLogger('log_ux') delimiters,textqualifier = delimitedsetting(configSettingsJson['basic']['fileSettings']['delimiters'],configSettingsJson['basic']['fileSettings']['textqualifier']) if problem_type == 'timeSeriesForecasting': #task 11997 inputFieldsDict = {'noofforecasts': 10} elif problem_type == 'recommenderSystem' and mlmodels =='ItemRating': inputFieldsDict = {"uid": 1, "iid": 31, "rating": 0} elif problem_type == 'stateTransition': inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] inputFeaturesList = inputFeatures.split(',') inputFieldsDict = {inputFeatures:'session',targetFeature:'Activity'} else: inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] inputFeaturesList = inputFeatures.split(',') if targetFeature in inputFeaturesList: inputFeaturesList.remove(targetFeature) if problem_type == 'survivalAnalysis': inputFeaturesList.insert(0,configSettingsJson['basic']['dateTimeFeature']) dataFilePath = str(configSettingsJson['basic']['dataLocation']) if os.path.isfile(dataFilePath): df = pd.read_csv(dataFilePath,encoding='utf8',nrows=2,sep=delimiters,quotechar=textqualifier,encoding_errors= 'replace') try: singleInstanceData = df.loc[0, inputFeaturesList] except: singleInstanceData = pd.Series(0, index =inputFeaturesList) inputFieldsDict = singleInstanceData.to_dict() else: inputFieldsDict = {"File":"EnterFileContent"} inputFields = [] inputFields.append(inputFieldsDict) return inputFields def createInstanceFeatures(configSettingsJson,problem_type,mlmodels,usecaseid,version,ser_url): delimiters,textqualifier = delimitedsetting(configSettingsJson['basic']['fileSettings']['delimiters'],configSettingsJson['basic']['fileSettings']['textqualifier']) inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] if inputFeatures != '': inputFeaturesList = inputFeatures.split(',') else: inputFeaturesList = [] if targetFeature in inputFeaturesList: inputFeaturesList.remove(targetFeature) if configSettingsJson['basic']['contextFeature'] != '': inputFeaturesList.append(configSettingsJson['basic']['contextFeature']) if problem_type == 'llmFineTuning': inputFeaturesList.append('Temperature') inputFeaturesList.append('Max Tokens') if problem_type in ['survivalAnalysis','anomalyDetection', 'timeSeriesAnomalyDetection']: #task 11997 if configSettingsJson['basic']['dateTimeFeature'] != '' and configSettingsJson['basic']['dateTimeFeature'] != 'na': inputFeaturesList.insert(0,configSettingsJson['basic']['dateTimeFeature']) dataFilePath = str(configSettingsJson['basic']['dataLocation']) if problem_type == 'timeSeriesForecasting': #task 11997 inputFieldsDict = {'noofforecasts': 10} elif problem_type == 'recommenderSystem' and mlmodels=='ItemRating': inputFieldsDict = {"uid": 1, "numberOfRecommendation":10} elif problem_type == 'stateTransition': inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] if inputFeatures != '': inputFeaturesList = inputFeatures.split(',') else: inputFeaturesList = [] inputFieldsDict = {inputFeatures:'session',targetFeature:'Activity'} elif problem_type != 'llmFineTuning': if os.path.isfile(dataFilePath): df = pd.read_csv(dataFilePath,encoding='utf8',nrows=2,sep=delimiters,quotechar=textqualifier,skipinitialspace = True,encoding_errors= 'replace') try: inputFieldsDict = df.to_dict(orient='index')[0] except: inputFieldsDict = pd.Series(0, index =inputFeaturesList).to_dict() else: inputFieldsDict = {"File":"EnterFileContent"} else: inputFieldsDict = pd.Series('', index =inputFeaturesList).to_dict() inputFieldsDict['Temperature'] = '0.1' hypervisor,instanceid,region,image = get_instance(usecaseid+'_'+str(version)) if hypervisor.lower() == 'AWS': inputFieldsDict['Max Tokens'] = '1024' else: inputFieldsDict['Max Tokens'] = '4096' inputFields = [] inputFields.append(inputFieldsDict) if problem_type == 'llmFineTuning': ser_url = get_instance_id(usecaseid+'_'+str(version)) elif problem_type == 'stateTransition': ser_url = ser_url+'pattern_anomaly_predict?usecaseid='+usecaseid+'&version='+str(version) else: ser_url = ser_url+'predict?usecaseid='+usecaseid+'&version='+str(version) return inputFields,ser_url def singleInstancePredict(request, Existusecases, usecasedetails): log = logging.getLogger('log_ux') modelType='' context = getcommonfields() submittype = request.POST.get('predictsubmit') selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) t1 = time.time() try: try: model = Existusecases.objects.get(ModelName=request.session['ModelName'], Version=request.session['ModelVersion']) output_train_json_filename = str(model.TrainOuputLocation) f = open(output_train_json_filename, "r+") training_output = f.read() f.close() training_output = json.loads(training_output) featureused = training_output['data']['featuresused'] except: featureused = [] from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'Prediction','Yes') usecasename = request.session['usecaseid'].replace(" ", "_") context.update({'usecasename':usecasename}) updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r", encoding = "utf-8") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] if inputFeatures != '': inputFeaturesList = inputFeatures.split(',') else: inputFeaturesList = [] if targetFeature in inputFeaturesList: inputFeaturesList.remove(targetFeature) if configSettingsJson['basic']['contextFeature'] != '': inputFeaturesList.append(configSettingsJson['basic']['contextFeature']) problemtypes = configSettingsJson['basic']['analysisType'] problem_type = '' modelSize = '' for k in problemtypes.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break if problem_type == 'llmFineTuning': inputFeaturesList.append('Temperature') inputFeaturesList.append('Max Tokens') mlmodels ='' algorihtms = configSettingsJson['basic']['algorithms'][problem_type] for k in algorihtms.keys(): if configSettingsJson['basic']['algorithms'][problem_type][k] == 'True': if mlmodels != '': mlmodels += ', ' mlmodels += k if problem_type == 'llmFineTuning': ser_url = get_instance_id(usecasename+'_'+str(request.session['ModelVersion'])) if 'modelSize' in configSettingsJson['basic']: selectedModelSize = configSettingsJson['basic']['modelSize']['llmFineTuning'][mlmodels] for k in selectedModelSize.keys(): if configSettingsJson['basic']['modelSize']['llmFineTuning'][mlmodels][k] == 'True': modelSize = k break elif problem_type == 'stateTransition': ser_url = service_url.read_service_url_params(request) ser_url = ser_url+'pattern_anomaly_predict?usecaseid='+usecasename+'&version='+str(request.session['ModelVersion']) else: ser_url = service_url.read_service_url_params(request) ser_url = ser_url+'predict?usecaseid='+usecasename+'&version='+str(request.session['ModelVersion']) if submittype.lower() == 'predict': inputFieldsDict = {} if problem_type == 'timeSeriesForecasting': #task 11997 inputFieldsDict['noofforecasts'] = int(request.POST.get('noofforecasts')) elif problem_type == 'stateTransition': inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] sessionid = request.POST.get('SessionID') activity = request.POST.get(targetFeature) inputFieldsDict[inputFeatures] = request.POST.get(inputFeatures) inputFieldsDict[targetFeature] = request.POST.get(targetFeature) elif problem_type == 'recommenderSystem' and mlmodels == 'ItemRating': inputFieldsDict['uid'] = request.POST.get('uid') inputFieldsDict['numberOfRecommendation'] = int(request.POST.get('numberOfRecommendation')) #Task 11190 else: if problem_type in ['survivalAnalysis','anomalyDetection', 'timeSeriesAnomalyDetection']: #task 11997 if configSettingsJson['basic']['dateTimeFeature'] != '' and configSettingsJson['basic']['dateTimeFeature'] != 'na': inputFeaturesList.insert(0,configSettingsJson['basic']['dateTimeFeature']) for feature in inputFeaturesList: inputFieldsDict[feature] = request.POST.get(feature) if problem_type.lower() not in ['contextualsearch','similarityidentification']: for key, value in inputFieldsDict.items(): if value == 'nan': inputFieldsDict[key] = '' if value == '': if key in featureused: context.update({'tab': 'predict','ser_url':ser_url, 'error': ' Error : Mandatory field(s) are empty', 'selected': 'prediction', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion}) return context inputFieldsJson = json.dumps(inputFieldsDict) if problem_type == 'llmFineTuning': modelType = request.POST.get('modelTypeforInferencing') x = inputFieldsDict.keys() from appbe.dataPath import DATA_DIR prompt = inputFieldsDict[configSettingsJson['basic']['trainingFeatures']] promptobj = {'prompt':prompt} if configSettingsJson['basic']['contextFeature'] != '': inputData = inputFieldsDict[configSettingsJson['basic']['contextFeature']] promptobj.update({'input':inputData}) filetimestamp = str(int(time.time())) file_path = os.path.join(DATA_DIR,'logs',filetimestamp+'.json') f= open(file_path,"w",encoding="utf-8") #print(promptobj) json.dump(promptobj,f) f.close() from llm.llm_inference import LLM_predict cloudconfig = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','config','compute_conf.json')) hypervisor,instanceid,region,image = get_instance(usecasename+'_'+str(request.session['ModelVersion'])) if hypervisor and instanceid: if modelSize != '': mlmodels = mlmodels+'-'+modelSize cachepath = os.path.join(DATA_DIR,'sqlite','cachePrompt.db') import sqlite3 conn = sqlite3.connect(cachepath) from llm.llm_cache import CachePrompt cachepromptObj = CachePrompt(conn) searchFlag,result = cachepromptObj.selectFromCache(prompt,usecasename+'_'+str(request.session['ModelVersion']),modelType,temperature=inputFieldsDict['Temperature'],max_token=inputFieldsDict['Max Tokens']) if searchFlag: buf = LLM_predict(cloudconfig,instanceid,file_path,hypervisor,mlmodels,usecasename+'_'+str(request.session['ModelVersion']),region,image,inputFieldsDict['Temperature'],inputFieldsDict['Max Tokens'],modelType) import re outputStr = buf.split('ModelOutput:')[1] cachepromptObj.insertRecord(prompt,outputStr,usecasename+'_'+str(request.session['ModelVersion']),modelType,temperature=inputFieldsDict['Temperature'],max_token=inputFieldsDict['Max Tokens']) else: outputStr = result if configSettingsJson['basic']['folderSettings']['fileType'].lower() != 'llm_document': outputStr = outputStr.split('### '+configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['response']+':')[1] singlePredictionResults = [] singlePredictionsummary="" Results={} Results['Response'] = outputStr singlePredictionResults.append(Results) else: context.update( {'tab': 'tabconfigure', 'error': 'Prediction Error: Instance ID not found ', 'selected': 'prediction', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'mlmodels':mlmodels}) log.info('Predict Instance :' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Prediction Error, Instance ID not found') return context else: try: import requests #response = requests.post(ser_url,auth=(aion_service_username,aion_service_password),data=inputFieldsJson,headers={"Content-Type":"application/json",}) response = requests.post(ser_url,data=inputFieldsJson,headers={"Content-Type":"application/json",}) if response.status_code != 200: outputStr=response.content context.update({'tab': 'tabconfigure', 'error': outputStr.decode('utf-8'), 'selected': 'prediction'}) log.info('Predict Instance : '+str(selected_use_case) + ' : ' + str(ModelVersion) + ' : '+'0 '+'sec'+' : '+'Error : '+str(outputStr.decode('utf-8'))) return context except Exception as inst: if 'Failed to establish a new connection' in str(inst): context.update({'tab': 'tabconfigure', 'error': 'AION service need to be started', 'selected': 'prediction', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion}) log.info('Predict Instance :'+str(selected_use_case) + ' : ' + str(ModelVersion) + ' : '+'0'+' sec'+' : '+'Error : AION service need to be started, '+str(inst)) return context else: context.update({'tab': 'tabconfigure', 'error': 'Prediction Error '+str(inst),'selected': 'prediction', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion}) log.info('Predict Instance :'+str(selected_use_case) + ' : ' + str(ModelVersion) + ' : '+'0 '+'sec'+' : '+'Error : Prediction Error, '+str(inst)) return context outputStr=response.content outputStr = outputStr.decode('utf-8','ignore') outputStr = outputStr.strip() predict_dict = json.loads(str(outputStr)) #print(predict_dict) singlePredictionsummary="" if (predict_dict['status'] == 'SUCCESS'): data = predict_dict['data'] singlePredictionResults = [] Results = {} if problem_type == 'multiModalLearning': data = data[0] Results['prediction'] = data['predict'] singlePredictionResults.append(Results) if problem_type == 'textSummarization': data = data[0] Results['msg'] = predict_dict['msg'] singlePredictionResults.append(Results) Results['prediction'] = predict_dict['data'] singlePredictionResults.append(Results) Results1 = {} Results1['prediction'] = predict_dict['data'] print("prdata------------",predict_dict['data']) singlePredictionsummary=predict_dict['data'] print("singlePredictionsummary",singlePredictionsummary) t2 = time.time() elif problem_type == 'multiLabelPrediction': prediction = '' for x in data: for y in x: if 'predict' in y: if prediction != '': prediction += ',' prediction += str(y)+':'+str(x[y]) Results['prediction'] = prediction singlePredictionResults.append(Results) elif problem_type == 'timeSeriesForecasting': #task 11997 Results['prediction'] = json.dumps(data) singlePredictionResults.append(Results) elif problem_type == 'stateTransition': if str(data['Anomaly']) == 'False': Results['prediction'] = 'No Anomaly' else: Results['prediction'] = str(data['Remarks']) singlePredictionResults.append(Results) elif problem_type.lower() in ['similarityidentification','contextualsearch']: data = data[0] prediction = data['prediction'] i = 1 for x in prediction: te = '' for y in x: info = (str(x[y])[:50] + '...') if len(str(x[y])) > 50 else str(x[y]) te += y+': '+info+'\n\n' Results[i] = te i = i+1 singlePredictionResults.append(Results) else: data = data[0] if 'prediction' in data: Results['prediction'] = data['prediction'] if 'probability' in data: Results['probability'] = data['probability'] if 'remarks' in data: Results['remarks'] = json.loads(data['remarks']) singlePredictionResults.append(Results) t2 = time.time() log.info('Predict Instance : '+str(selected_use_case) + ' : ' + str(ModelVersion) + ' : '+str(round(t2-t1))+' sec'+' : '+'Success') else: context.update({'tab': 'tabconfigure', 'error': 'Prediction Error '+str(predict_dict['message']), 'selected': 'prediction','selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion}) log.info('Predict Instance : '+str(selected_use_case) + ' : ' + str(ModelVersion) + ' : '+'0 '+'sec'+' : '+'Error : Prediction Error') return context inputFields = [] inputFields.append(inputFieldsDict) ##Below added by sjayaram for llm langkit evaluation metrics Task:17109 prompt_response_results = '' if problem_type == 'llmFineTuning': try: response_msg = outputStr prompt_msg = prompt except: response_msg = '' prompt_msg = '' from appbe.evaluate_prompt import evaluate_prompt_response_inputs final_output_json,prompt_response_results = evaluate_prompt_response_inputs(prompt_msg,response_msg) #ser_url = service_url.read_service_url_params(request) #ser_url = ser_url+'predict?usecaseid='+usecasename+'&version='+str(ModelVersion) context.update({'tab': 'predict','mlmodels':mlmodels,'fineTunedModelType':modelType,'ser_url':ser_url, 'inputFields': inputFields,'singlePredictionResults': singlePredictionResults,'singlePredictionsummary':singlePredictionsummary,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion, 'selected': 'prediction', 'prompt_response_results':prompt_response_results}) return context elif submittype.lower() == 'script': scriptdata="'''\n" scriptdata+="* =============================================================================\n" scriptdata+="* COPYRIGHT NOTICE\n" scriptdata+="* =============================================================================\n" scriptdata+="* @ Copyright HCL Technologies Ltd. 2021, 2022, 2023\n" scriptdata+="* Proprietary and confidential. All information contained herein is, and\n" scriptdata+="* remains the property of HCL Technologies Limited. Copying or reproducing the\n" scriptdata+="* contents of this file, via any medium is strictly prohibited unless prior\n" scriptdata+="* written permission is obtained from HCL Technologies Limited.\n" scriptdata+="'''\n" scriptdata+='import sys\n' scriptdata+='import json\n' scriptdata+='import requests\n' scriptdata+='import pandas as pd\n' scriptdata+='from pandas import json_normalize\n' scriptdata+='ser_url ="'+ser_url+'"\n\n' scriptdata+="def predict(data):\n" scriptdata+=" if data.endswith('.tsv'):\n" scriptdata+=" df=pd.read_csv(data,encoding='utf-8',encoding_errors= 'replace',sep='\\t')\n" scriptdata+=" else:\n" scriptdata+=" df=pd.read_csv(data,encoding='utf-8',encoding_errors= 'replace')\n" scriptdata+=' features = "'+",".join([feature for feature in inputFeaturesList])+'"\n' scriptdata+=" features = features.split(',')\n" scriptdata+=" df = df[features]\n" scriptdata+=" data = df.to_json(orient='records')\n" scriptdata+=" try:\n" scriptdata+=' response = requests.post(ser_url,data=data,headers={"Content-Type":"application/json",})\n' scriptdata+=" if response.status_code == 200:\n" scriptdata+=" outputStr=response.content\n" scriptdata+=" outputStr = outputStr.decode('utf-8')\n" scriptdata+=" outputStr = outputStr.strip()\n" scriptdata+=" predict_dict = json.loads(str(outputStr))\n" scriptdata+=" print(predict_dict)\n" scriptdata+=" except Exception as e:\n" scriptdata+=' print(e)\n' scriptdata+='\nif __name__ == "__main__":\n' scriptdata+=' predict(sys.argv[1])' response = HttpResponse() response['content_type'] = 'text/plain' response['Content-Disposition'] = 'attachment; filename=prediction.py' response.write(scriptdata) return response except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) context.update({'tab': 'tabconfigure', 'error': 'Failed To perform prediction','selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion, 'selected': 'prediction'}) log.info('Predict Instance :' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + ' 0 ' + 'sec' + ' : ' + 'Error : Failed To perform prediction, '+ str(inst)) return context ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os.path import time import subprocess import sys from appbe.aion_config import kafka_setting from appbe.aion_config import running_setting from appbe import installPackage from appbe import compute from appbe.models import getusercasestatus import json import pandas as pd from os.path import expanduser import ntpath import shutil import platform from pathlib import Path home = expanduser("~") if platform.system() == 'Windows': LOG_FILE_PATH = os.path.join(home,'AppData','Local','HCLT','AION','logs') else: LOG_FILE_PATH = os.path.join(home,'HCLT','AION','logs') def convert(obj): if isinstance(obj, bool): return str(obj).capitalize() if isinstance(obj, (list, tuple)): return [convert(item) for item in obj] if isinstance(obj, dict): return {convert(key):convert(value) for key, value in obj.items()} return obj def fl_command(request,Existusecases,usecasedetails): command = request.POST.get('flsubmit') print(command) #kafkaSetting = kafka_setting() ruuningSetting = running_setting() computeinfrastructure = compute.readComputeConfig() modelID = request.POST.get('modelID') p = Existusecases.objects.get(id=modelID) usecasename = p.ModelName.UsecaseName runningStatus,pid,ip,port = installPackage.checkModelServiceRunning(usecasename) installationStatus,modelName,modelVersion=installPackage.checkInstalledPackge(usecasename) usecasedetail = usecasedetails.objects.get(id=p.ModelName.id) usecase = usecasedetails.objects.all() models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS') for model in models: model.scoringCreteria = 'NA' model.score = 'NA' model.deploymodel = 'NA' if os.path.isdir(str(model.DeployPath)): modelPath = os.path.join(str(model.DeployPath),'etc','output.json') try: with open(modelPath) as file: outputconfig = json.load(file) file.close() if outputconfig['status'] == 'SUCCESS': model.scoringCreteria = outputconfig['data']['ScoreType'] model.score = outputconfig['data']['BestScore'] model.deploymodel = outputconfig['data']['BestModel'] model.modelType = outputconfig['data']['ModelType'] model.featuresused = eval(outputconfig['data']['featuresused']) model.targetFeature = outputconfig['data']['targetFeature'] model.modelParams = outputconfig['data']['params'] model.dataPath = os.path.join(str(model.DeployPath),'data', 'postprocesseddata.csv.gz') model.maacsupport = 'True' model.flserversupport = 'False' supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"] if model.deploymodel in supportedmodels: model.flserversupport = 'True' else: model.flserversupport = 'False' supportedmodels = ["Logistic Regression", "Naive Bayes","Decision Tree","Support Vector Machine","K Nearest Neighbors","Gradient Boosting","Random Forest","Linear Regression","Lasso","Ridge","Extreme Gradient Boosting (XGBoost)","Light Gradient Boosting (LightGBM)","Categorical Boosting (CatBoost)"] if model.deploymodel in supportedmodels: model.maacsupport = 'True' else: model.maacsupport = 'False' supportedmodels = ["Extreme Gradient Boosting (XGBoost)"] if model.deploymodel in supportedmodels: model.encryptionsupport = 'True' else: model.encryptionsupport = 'False' except Exception as e: pass flserver = os.path.join(str(p.DeployPath),'publish','FedLearning') if command == 'startServer': flservicefile = os.path.join(flserver,'fedServer','aionfls.py') confilefile = os.path.join(flserver,'fedServer','config.json') if platform.system() == 'Windows': outputStr = subprocess.Popen([sys.executable, flservicefile,confilefile],creationflags = subprocess.CREATE_NEW_CONSOLE) else: outputStr = subprocess.Popen([sys.executable, flservicefile,confilefile]) Status = 'SUCCESS' Msg = 'Federated Learning Server Started' if command == 'saveflconfig': #print(command) fedserverPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','fedServer')) shutil.rmtree(flserver, ignore_errors=True) Path(flserver).mkdir(parents=True, exist_ok=True) flcopypath = os.path.join(flserver,'fedServer') shutil.copytree(fedserverPath,flcopypath) fedserverDataPath = os.path.join(flcopypath,'data') shutil.copy2(request.POST.get('flserver_datalocation'),fedserverDataPath) flcon = {} AlgorithmNames={'Logistic Regression':'LogisticRegression','Neural Network':'deeplearning','Linear Regression':'LinearRegression'} flcon['server_IP'] = request.POST.get('flserver_ipaddress') flcon['server_port'] = request.POST.get('flserver_port') flcon['model_name'] = AlgorithmNames[request.POST.get('flserver_model')] flcon['version'] = request.POST.get('flserver_Version') flcon['model_hyperparams'] = convert(eval(request.POST.get('flserver_params'))) dataLocation = request.POST.get('flserver_datalocation') dataPath,datafile = ntpath.split(dataLocation) flcon['data_location'] = 'data/'+datafile flcon['selected_feature'] = ",".join([model for model in eval(request.POST.get('flserver_trainingfeatures'))]) flcon['target_feature'] = request.POST.get('flserver_targetfeature') flcon['problem_type'] = request.POST.get('flserver_modelType') flcon['min_available_clients'] = request.POST.get('flserver_noofclient') flcon['min_fit_clients'] = 2 flcon['fl_round'] = request.POST.get('flserver_trainround') flcon['evaluation_required'] = request.POST.get('flserver_evaluation') flcon['model_store'] = "" flconfigfile = os.path.join(flcopypath,'config.json') flconjson = json.dumps(flcon) f = open(flconfigfile, "w+") f.seek(0) f.write(flconjson) f.truncate() f.close() nouc = 0 Status = 'Success' Msg = 'Federated Learning Server Configured' if command =='startClient': flconfigfile = os.path.join(str(model.DeployPath),'publish','FedLearning','fedServer','config.json') if os.path.isfile(flconfigfile): with open(flconfigfile) as file: flconfig = json.load(file) file.close() numberofclient = flconfig['min_available_clients'] for x in range(int(numberofclient)): flclientdirectory = os.path.join(str(model.DeployPath),'publish','FedLearning','fedClient_'+str(x+1)) flclientpath = os.path.join(str(model.DeployPath),'publish','FedLearning','fedClient_'+str(x+1),'fedClient.bat') if platform.system() == 'Windows': outputStr = subprocess.Popen([flclientpath],creationflags = subprocess.CREATE_NEW_CONSOLE,cwd=flclientdirectory) else: outputStr = subprocess.Popen([flclientpath],cwd=flclientdirectory) Status = 'SUCCESS' Msg = 'Federated Learning Client Started' if command == 'generateClient': flconfigfile = os.path.join(str(model.DeployPath),'publish','FedLearning','fedServer','config.json') if os.path.isfile(flconfigfile): with open(flconfigfile) as file: flconfig = json.load(file) file.close() numberofclient = flconfig['min_available_clients'] trainingDataLocation = os.path.join(str(p.DeployPath),'data','postprocesseddata.csv.gz') from utils.file_ops import read_df_compressed status,df = read_df_compressed(trainingDataLocation,encoding='utf8') for x in range(int(numberofclient)): flclientpath = os.path.join(str(model.DeployPath),'publish','FedLearning','fedClient_'+str(x+1)) logPath = os.path.join(flclientpath,'logs') modelsPath = os.path.join(flclientpath,'models') Path(flclientpath).mkdir(parents=True, exist_ok=True) Path(logPath).mkdir(parents=True, exist_ok=True) Path(modelsPath).mkdir(parents=True, exist_ok=True) flclientor = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','fedClient','aionflc.py')) shutil.copy2(flclientor,flclientpath) flclientor = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','fedClient','utils.py')) shutil.copy2(flclientor,flclientpath) flclientor = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','fedClient','dl_model.py')) shutil.copy2(flclientor,flclientpath) subset = df.sample(frac=0.8) dataPath = os.path.join(flclientpath,'data') Path(dataPath).mkdir(parents=True, exist_ok=True) datafile = os.path.join(dataPath,'data.dat') subset.to_csv(datafile, index=False) flclient = {} flclient['server_IP'] = flconfig['server_IP'] flclient['server_port'] = flconfig['server_port'] flclient['model_name'] = flconfig['model_name'] flclient['problem_type'] = flconfig['problem_type'] flclient['version'] = flconfig['version'] flclient['model_hyperparams'] = flconfig['model_hyperparams'] flclient['data_location'] = 'data\data.dat' flclient['selected_feature'] = flconfig['selected_feature'] flclient['target_feature'] = flconfig['target_feature'] flclient['train_size'] = 80 #flclient['deploy_location'] = flconfig['deploy_location'] flclient['num_records_per_round'] = request.POST.get('flserver_recordperround') flclient['wait_time'] = request.POST.get('flserver_roundtime') flclient['model_overwrite'] = request.POST.get('model_overwritelabel') configPath = os.path.join(flclientpath,'config') Path(configPath).mkdir(parents=True, exist_ok=True) configFile = os.path.join(configPath,'config.json') flconjson = json.dumps(flclient) f = open(configFile, "w+") f.seek(0) f.write(flconjson) f.truncate() f.close() locate_python = sys.exec_prefix bathfilePath = os.path.join(flclientpath,'fedClient.bat') batfilecontent=''' @ECHO OFF GOTO weiter :setenv SET "Path={python_path}\;%Path%;" GOTO :EOF :weiter IF "%1" EQU "setenv" ( ECHO. ECHO Setting environment for AION Federated Learning Client. CALL :setenv python %CD%\\aionflc.py %CD%\config\config.json ) ELSE ( SETLOCAL EnableDelayedExpansion TITLE ION Federated Learning Client PROMPT %username%@%computername%$S$P$_#$S IF EXIST aion.config (FOR /F "delims=" %%A IN (aion.config) DO SET "%%A") START "" /B %COMSPEC% /K "%~f0" setenv ) '''.format(python_path=str(locate_python)) f = open(bathfilePath, "w",encoding="utf-8") f.write(str(batfilecontent)) f.close() Status = 'Success' Msg = 'Federated Learning Client Code Generated' nouc = 0 #selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) from appbe.pages import get_usecase_page status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['Status'] = Status context['Msg'] = Msg return(context) from langkit import textstat from whylogs.experimental.core.udf_schema import udf_schema import pandas as pd import whylogs as why from langkit import light_metrics from whylogs.experimental.core.udf_schema import udf_schema from whylogs.experimental.core.udf_schema import register_dataset_udf import whylogs as why import json from sentence_transformers import SentenceTransformer, util from langkit import lang_config, response_column def evaluate_prompt_metrics(prompt_msg: any): """ Evaluate prompt only information.""" text_schema = udf_schema() llm_schema = light_metrics.init() df = pd.DataFrame({ "prompt": [ prompt_msg ]}) results = why.log(df, schema=udf_schema()) # .profile() view = results.view() automated_readability_index_prompt = view.get_column("prompt.automated_readability_index").to_summary_dict() automated_readability_index_prompt_mean = automated_readability_index_prompt['distribution/mean'] arip_m = lambda x:1 if x < 1 else (14 if x > 14 else x) automated_readability_index_prompt_mean = arip_m(automated_readability_index_prompt_mean) automated_readability_index_prompt_value = get_readability_index_range_value(automated_readability_index_prompt_mean) flesch_reading_ease_prompt = view.get_column("prompt.flesch_reading_ease").to_summary_dict() flesch_reading_ease_prompt_mean = flesch_reading_ease_prompt['distribution/mean'] frep_m = lambda x:1 if x < 1 else (100 if x > 100 else x) flesch_reading_ease_prompt_mean = frep_m(flesch_reading_ease_prompt_mean) flesch_reading_ease_prompt_value = get_flesch_reading_ease_prompt_value(flesch_reading_ease_prompt_mean) prompt_results = {'prompt_readability_score': str(automated_readability_index_prompt_mean), 'prompt_readability_value': automated_readability_index_prompt_value, 'prompt_reading_ease': str(flesch_reading_ease_prompt_mean), 'prompt_reading_ease_value': flesch_reading_ease_prompt_value} prompt_results_json = json.dumps(prompt_results, indent=4) return prompt_results_json,prompt_results model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') @register_dataset_udf(["prompt", "response"], "response.relevance_to_prompt") def similarity_MiniLM_L6_v2(text): x = text["prompt"] y = text["response"] embedding_1 = model.encode(x, convert_to_tensor=True) embedding_2 = model.encode(y, convert_to_tensor=True) similarity = util.pytorch_cos_sim(embedding_1, embedding_2) result = similarity.item() return result def get_readability_index_range_value(readability_value): if readability_value <= 1: ## Grade level Kindergarden to fourth grade return "Kindergarten" elif 1 < readability_value <= 2: ## Grade level Kindergarden to fourth grade return "First Grade" elif 2 < readability_value <= 3: ## Grade level Fifth grade to Ninth grade return "Second Grade" elif 3 < readability_value <= 4: ## Grade level Fifth grade to Ninth grade return "Third Grade" elif 4 < readability_value <= 5: ## Grade level Fifth grade to Ninth grade return "Fourth Grade" elif 5 < readability_value <= 6: ## Grade level Fifth grade to Ninth grade return "Fifth Grade" elif 6 < readability_value <= 7: ## Grade level Fifth grade to Ninth grade return "Sixth Grade" elif 7 < readability_value <= 8: ## Grade level Fifth grade to Ninth grade return "Seventh Grade" elif 8 < readability_value <= 9: ## Grade level Fifth grade to Ninth grade return "Eighth Grade" elif 9 < readability_value <=10: ## Grade level Fifth grade to Ninth grade return "Ninth Grade" elif 10 < readability_value <=11: ## Grade level Fifth grade to Ninth grade return "Tenth Grade" elif 11 < readability_value <=12: ## Grade level Fifth grade to Ninth grade return "Eleventh Grade" elif 12 < readability_value <= 13: ## Grade level Fifth grade to Ninth grade return "Twelfth Grade" elif readability_value > 13: ## Grade level Fifth grade to Ninth grade return "College Grade" else: return "College Grade" def get_flesch_reading_ease_prompt_value(readability_value): """ Get flesch readability score range approximation""" if readability_value <= 29: return "Very Confusing" elif 29 < readability_value <= 49: return "Difficult" elif 49 < readability_value <= 59: return "Fairly Difficult" elif 59 < readability_value <= 69: return "Standard" elif 69 < readability_value <= 79: return "Fairly Easy" elif 79 < readability_value <= 89: return "Easy" elif 89 < readability_value <= 100: return "Very Easy" else: return "Very Easy" def get_relevence_to_response_value(similarity_score): """ To findout relevence to response results based on similarity score.""" if similarity_score <=0.3: return "Low" elif 0.3 < similarity_score <= 0.5: return "Average" elif 0.5 < similarity_score <= 0.8: return "Good" elif similarity_score > 0.8: return "High" def evaluate_prompt_response_inputs (prompt_msg:any, response_msg:any)->str: """ Predict the text quality, text relevence for both prompt and response messages.""" df = pd.DataFrame({ "prompt": [prompt_msg], "response": [response_msg]}) results = why.log(df, schema=udf_schema()) view = results.view() automated_readability_index_prompt = view.get_column("prompt.automated_readability_index").to_summary_dict() automated_readability_index_prompt_mean = automated_readability_index_prompt['distribution/mean'] arip_m = lambda x:1 if x < 1 else (14 if x > 14 else x) automated_readability_index_prompt_mean = arip_m(automated_readability_index_prompt_mean) automated_readability_index_prompt_value = get_readability_index_range_value(automated_readability_index_prompt_mean) flesch_reading_ease_prompt = view.get_column("prompt.flesch_reading_ease").to_summary_dict() flesch_reading_ease_prompt_mean = flesch_reading_ease_prompt['distribution/mean'] frep_m = lambda x:1 if x < 1 else (100 if x > 100 else x) flesch_reading_ease_prompt_mean = frep_m(flesch_reading_ease_prompt_mean) flesch_reading_ease_prompt_value = get_flesch_reading_ease_prompt_value(flesch_reading_ease_prompt_mean) automated_readability_index_response = view.get_column("response.automated_readability_index").to_summary_dict() automated_readability_index_response_mean = automated_readability_index_response['distribution/mean'] arir_m = lambda x:1 if x < 1 else (14 if x > 14 else x) automated_readability_index_response_mean = arir_m(automated_readability_index_response_mean) automated_readability_index_response_value = get_readability_index_range_value(automated_readability_index_response_mean) flesch_reading_ease_response = view.get_column("response.flesch_reading_ease").to_summary_dict() flesch_reading_ease_response_mean = flesch_reading_ease_response['distribution/mean'] frer_m = lambda x:1 if x < 1 else (100 if x > 100 else x) flesch_reading_ease_response_mean = frer_m(flesch_reading_ease_response_mean) flesch_reading_ease_response_value = get_flesch_reading_ease_prompt_value(flesch_reading_ease_response_mean) relevance_to_response = view.get_column("response.relevance_to_prompt").to_summary_dict() relevance_to_response_mean = relevance_to_response['distribution/mean'] r2r_m = lambda x:0 if x < 0 else (1 if x > 1 else x) relevance_to_response_mean = r2r_m(relevance_to_response_mean) relevance_to_response_value = get_relevence_to_response_value(relevance_to_response_mean) sentence_count_response = view.get_column("response.sentence_count").to_summary_dict() sentence_count_response_mean = sentence_count_response['distribution/mean'] word_count_response = view.get_column("response.lexicon_count").to_summary_dict() word_count_response_mean = word_count_response['distribution/mean'] prompt_response_results = {'prompt_readability_score': str(automated_readability_index_prompt_mean), 'prompt_readability_value': automated_readability_index_prompt_value, 'prompt_reading_ease': str(flesch_reading_ease_prompt_mean), 'prompt_reading_ease_value': flesch_reading_ease_prompt_value, 'response_readability': str(automated_readability_index_response_mean), 'response_readability_value': str(automated_readability_index_response_value), 'response_reading_ease': str(flesch_reading_ease_response_mean), 'response_reading_ease_value': str(flesch_reading_ease_response_value), 'response_sentence_count': str(sentence_count_response_mean), 'response_word_count_response': str(word_count_response_mean), 'relevance_to_response': str(relevance_to_response_mean), 'relevance_to_response_value': relevance_to_response_value } final_output_json = json.dumps(prompt_response_results, indent=4) return final_output_json,prompt_response_results if __name__ == "__main__": ##Test only prompt message information option = 'predict' if option == 'evaluate': prompt_only_response_msg = "A large language model is an advanced artificial intelligence (AI) system designed to process, understand, and generate human-like text based on massive amounts of data. These models are typically built using deep learning techniques, such as neural networks, and are trained on extensive datasets that include text from a broad range, such as books and websites, for natural language processing.Fine-tuning a large language model involves adjusting and adapting a pre-trained model to perform specific tasks or to cater to a particular domain more effectively. The process usually entails training the model further on a smaller, targeted dataset that is relevant to the desired task or subject matter.Few-shot learning (FSL) can be considered as a meta-learning problem where the model learns how to learn to solve the given problem. In this approach, the model is provided with a very limited number of examples (i.e., “few shots”) from the new task, and it uses this information to adapt and perform well on that task. Adapter Training: Adapter training is a method that involves training lightweight modules that are plugged into the pre-trained model, allowing for fine-tuning on a specific task without affecting the original model’s performance on other tasks.Multi-task Learning: Multi-task learning is a method where the pre-trained model is fine-tuned on multiple tasks simultaneously. This approach enables the model to learn and leverage the shared representations across different tasks, leading to better generalization and performance. Task-specific Fine-tuning: Task-specific fine-tuning is a method where the pre-trained model is fine-tuned on a specific task or domain using a task-specific dataset. This method requires more data and time than transfer learning but can result in higher performance on the specific task. Sequential Fine-tuning: Sequential fine-tuning is a method where a pre-trained model is fine-tuned on multiple related tasks or domains sequentially. This allows the model to learn more nuanced and complex language patterns across different tasks, leading to better generalization and performance.A noteworthy avenue of research within LLM fine-tuning explores strategies to reduce the expenses associated with updating model parameters. This endeavor is the essence of parameter-efficient fine-tuning (PEFT), a collection of techniques aiming to curtail the number of parameters requiring adjustments.Various PEFT techniques exist, and one prominent example is a low-rank adaptation (LoRA), a technique gaining popularity among open-source language models." prompt_res = evaluate_prompt_metrics(prompt_only_response_msg) elif option == 'predict': prompt_msg = "What is AION?" response_msg = "AION (Artificial Intelligence ONline) is an open -source software platform for building, deploying and operating the entire lifecycle of AI applications. It supports various use cases such as predictive analytics , machine learning and deep learning . Key features: 1. Data Ingestion : Supports multiple data sources like text files, excel sheet, database etc." evaluation_metrics_json = evaluate_prompt_response_inputs(prompt_msg,response_msg) print("evaluation_metrics_json: \n",evaluation_metrics_json) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import kfp import kfp.dsl as dsl import json from pathlib import Path class aionpipeline(): containerRegistry = str() containerLabel = str() containerSecret = str() pipelineName = 'AION MLOps Pipeline {0}' exeCmd = 'python' codeFile = 'aionCode.py' mntPoint = '/aion' inputArg = '-i' msIP = '0.0.0.0' port = '8094' cachingStrategy = 'P0D' deafultVolume = '1Gi' volName = 'aion-pvc' volMode = 'ReadWriteMany' fileExt = '.tar.gz' fileName = 'aion_mlops_pipeline_{0}' containerMM = 'modelmonitoring' containerDI = 'dataingestion' containerDT = 'datatransformation' containerFE = 'featureengineering' containerMR = 'modelregistry' containerMS = 'modelserving' containerImage = '{0}/{1}:{2}' models = {} nameSeprator = '-' modelsLiteral = 'models' modelNameLiteral = 'modelname' msTemplate = '{"apiVersion": "v1", "kind": "Pod", "metadata": {"name": "{{workflow.name}}-{0}"}, "spec": {"containers": [{"name": "{0}", "image": "{1}", "command": ["python"], "args": ["aionCode.py", "-ip", "{2}", "-pn", "{3}"],"volumeMounts": [{"name": "aion-pvc", "mountPath": "{4}"}], "ports": [{"name": "http", "containerPort": {3}, "protocol": "TCP"}]}], "imagePullSecrets": [{"name": "{5}"}], "volumes": [{"name": "aion-pvc", "persistentVolumeClaim": {"claimName": "{{workflow.name}}-{6}"}}]}}' def __init__(self, models, containerRegistry, containerLabel, containerSecret=str()): self.models = models self.containerRegistry = containerRegistry self.containerLabel = containerLabel self.containerSecret = containerSecret @dsl.pipeline( name=pipelineName.format(containerLabel), description=pipelineName.format(containerLabel), ) def aion_mlops(self, inputUri=str(), volSize=deafultVolume): vop = dsl.VolumeOp( name=self.volName + self.nameSeprator + self.containerLabel, resource_name=self.volName, modes=[self.volMode], size=volSize ) mm = dsl.ContainerOp( name=self.containerMM, image=self.containerImage.format(self.containerRegistry,self.containerMM,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, self.inputArg, inputUri, ], pvolumes={self.mntPoint: vop.volume} ) mm.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy di = dsl.ContainerOp( name=self.containerDI, image=self.containerImage.format(self.containerRegistry,self.containerDI,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, ], pvolumes={self.mntPoint: mm.pvolume} ) di.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy dt = dsl.ContainerOp( name=self.containerDT, image=self.containerImage.format(self.containerRegistry,self.containerDT,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, ], pvolumes={self.mntPoint: di.pvolume} ) dt.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy fe = dsl.ContainerOp( name=self.containerFE, image=self.containerImage.format(self.containerRegistry,self.containerFE,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, ], pvolumes={self.mntPoint: dt.pvolume} ) fe.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy dictMT = {} listMTOps = [] for model in self.models[self.modelsLiteral]: modelName = model[self.modelNameLiteral] mt=dsl.ContainerOp( name=modelName, image=self.containerImage.format(self.containerRegistry,modelName,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, ], pvolumes={self.mntPoint: fe.pvolume}) mt.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy listMTOps.append(mt) dictMT[self.mntPoint]=mt.pvolume mr = dsl.ContainerOp( name=self.containerMR, image=self.containerImage.format(self.containerRegistry,self.containerMR,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, ], pvolumes=dictMT ).after(*tuple(listMTOps)) mr.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy msJson = self.msTemplate.replace(str({0}),self.containerMS).replace(str({1}),self.containerImage.format(self.containerRegistry,self.containerMS,self.containerLabel)).replace(str({2}),self.msIP).replace(str({3}),self.port).replace(str({4}),self.mntPoint).replace(str({5}),self.containerSecret).replace(str({6}),self.volName) ms = dsl.ResourceOp( name=self.containerMS + self.nameSeprator + self.containerLabel, k8s_resource=json.loads(msJson), ) ms.after(mr) def compilepl(self, targetPath=str()): filePath = self.fileName.format(self.containerLabel.lower()) + self.fileExt if targetPath != str(): filePath = Path(targetPath, filePath) kfp.compiler.Compiler().compile(self.aion_mlops, str(filePath)) def executepl(self, kfhost=str()): client = kfp.Client(kfhost) client.create_run_from_pipeline_func(self.aion_mlops,arguments={}) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import requests import json import os from datetime import datetime import socket import getmac from appbe.sqliteUtility import sqlite_db import pandas as pd from appbe.dataPath import DATA_DIR def TelemetryCreateSyncState(state): try: newdata = {} file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'telemetry.db') now = datetime.now() SyncingTime = int(datetime.timestamp(now)) newdata.update({'ID':['1'],'state':[state],'syncingTime':[SyncingTime]}) sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'syncState') except Exception as e: print(e) pass def TelemetryUpdateSyncState(state): try: newdata = {} file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'telemetry.db') now = datetime.now() SyncingTime = int(datetime.timestamp(now)) updated_data = '"state"="'+state+'","syncingTime"="'+str(SyncingTime)+'"' sqlite_obj.update_data(updated_data,'ID="1"','syncState') except Exception as e: print(e) pass def checkTelemtry(): import subprocess import sys scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','aion.py')) if os.path.exists(scriptPath): outputStr = subprocess.Popen([sys.executable,scriptPath,'-m','pushtelemetry']) def SyncTelemetry(): try: newdata = {} file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'telemetry.db') if sqlite_obj.table_exists('syncState'): data = sqlite_obj.read_data('syncState')[0] param_keys = ['ID','state','syncingTime'] sync_data = dict((x,y) for x,y in zip(param_keys,data)) #print(sync_data['state'],sync_data['syncingTime']) if sync_data['state'].lower() != 'syncing': sync_time = sync_data['syncingTime'] now = datetime.now() currTime = datetime.timestamp(now) diffTime = int(float(currTime)) - int(float(sync_time)) #print(diffTime) if int(diffTime) > 86400: TelemetryUpdateSyncState('Syncing') SendTelemetryUpdate(sync_time) TelemetryUpdateSyncState('Done') else: TelemetryCreateSyncState('Initialize') except Exception as e: print(e) pass def UseCaseCreated(Usecase): try: file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'telemetry.db') newdata = {} now = datetime.now() ID = datetime.timestamp(now) record_date = int(datetime.timestamp(now)) computername = socket.getfqdn() macaddress = getmac.get_mac_address() try: user = os.getlogin() except: user = 'NA' newdata.update({'ID':[str(int(ID))],'RecordDate': [record_date],'Usecase': [Usecase],'Operation':['Created'],'User':[str(user)],'HostName' :[computername],'MACAddress':[macaddress],'ProblemType':[''],'Algorithms':[''],'EDA':['No'],'Prediction':['No'],'MLaC':['No'],'Drift':['No'],'TrustedAI':['No']}) sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'logs') except Exception as e: print(e) pass def UpdateTelemetry(Usecase,operation,value): try: file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'telemetry.db') data = sqlite_obj.read_data('logs','Usecase="'+Usecase+'"') #print(data) if sqlite_obj.table_exists('logs'): updated_data = operation+'="'+value+'"' now = datetime.now() ID = datetime.timestamp(now) record_date = int(datetime.timestamp(now)) updated_data += ',"RecordDate"="'+str(record_date)+'"' sqlite_obj.update_data(updated_data,'Usecase="'+Usecase+'"','logs') except Exception as e: print(e) pass def SendTelemetryUpdate(sync_time): file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'telemetry.db') if sqlite_obj.table_exists('logs'): ddata = sqlite_obj.read_data("logs","RecordDate >= '"+str(sync_time)+"'") #print(ddata) keys = sqlite_obj.column_names('logs') for data in ddata: now = datetime.now() ID = datetime.timestamp(now) item = {} item['ID'] = str(int(ID)) item['RecordID'] = data[ keys.index('ID')] item['RecordDate'] = data[ keys.index('RecordDate')] item['Usecase'] = data[ keys.index('Usecase')] item['Operation'] = data[ keys.index('Operation')] item['User'] = data[ keys.index('User')] item['HostName'] = data[ keys.index('HostName')] item['MACAddress'] = data[ keys.index('MACAddress')] item['Algorithms'] = data[ keys.index('Algorithms')] item['ProblemType'] = data[ keys.index('ProblemType')] item['EDA'] = data[ keys.index('EDA')] item['Prediction'] = data[ keys.index('Prediction')] item['MLaC'] = data[ keys.index('MLaC')] item['Drift'] = data[ keys.index('Drift')] item['TrustedAI'] = data[ keys.index('TrustedAI')] url = 'https://l5m119j6v9.execute-api.ap-south-1.amazonaws.com/default/aion_telemetry' record = {} record['TableName'] = 'AION_LOGS' record['Item'] = item record = json.dumps(record) #print(record) try: response = requests.post(url, data=record,headers={"x-api-key":"Obzt8ijfOT3dgBYma9JCt1tE3W6tzHaV8rVuQdMK","Content-Type":"application/json",}) except Exception as e: print(e) def telemetry_data(operation,Usecase,data): now = datetime.now() ID = datetime.timestamp(now) record_date = now.strftime("%y-%m-%d %H:%M:%S") computername = socket.getfqdn() macaddress = getmac.get_mac_address() try: user = os.getlogin() except: user = 'NA' item = {} item['ID'] = str(int(ID)) item['record_date'] = record_date item['UseCase'] = Usecase item['operation'] = operation item['remarks'] = data item['user'] = str(user) item['hostname'] = computername item['macaddress'] = macaddress url = 'https://l5m119j6v9.execute-api.ap-south-1.amazonaws.com/default/aion_telemetry' record = {} record['TableName'] = 'AION_OPERATION' record['Item'] = item record = json.dumps(record) try: response = requests.post(url, data=record,headers={"x-api-key":"Obzt8ijfOT3dgBYma9JCt1tE3W6tzHaV8rVuQdMK","Content-Type":"application/json",}) check_telemetry_file() except Exception as inst: filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),'telemetry.txt') f=open(filename, "a+") f.write(record+'\n') f.close() def check_telemetry_file(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),'telemetry.txt') if(os.path.isfile(file_path)): f = open(file_path, 'r') url = 'https://l5m119j6v9.execute-api.ap-south-1.amazonaws.com/default/aion_telemetry' file_content = f.read() f.close() matched_lines = file_content.split('\n') write_lines = [] for record in matched_lines: try: response = requests.post(url, data=record,headers={"x-api-key":"Obzt8ijfOT3dgBYma9JCt1tE3W6tzHaV8rVuQdMK","Content-Type":"application/json",}) except: write_lines.append(record) f = open(file_path, "a") f.seek(0) f.truncate() for record in write_lines: f.write(record+'\n') f.close() else: return True ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json import os import pandas as pd import numpy as np import subprocess import sys import re import plotly.graph_objects as go import plotly.figure_factory as ff def global_explain(request): try: selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) problemType = 'classification' for key in configSettingsJson['basic']['analysisType']: if configSettingsJson['basic']['analysisType'][key] == 'True': problemType = key break if problemType.lower() != 'classification' and problemType.lower() != 'regression': return 'Problem Type Error','Explainable AI only available for classification and regression problem','NA','NA','NA','NA',0,0,'NA','NA','NA','NA',0,'NA','NA',0,'NA','NA','NA','NA','NA','NA' displaypath = os.path.join( request.session['deploypath'],'etc','display.json') with open(displaypath) as file: config = json.load(file) file.close() inputFeatures = configSettingsJson['basic']['trainingFeatures'] targetFeature = configSettingsJson['basic']['targetFeature'] inputFeatures = inputFeatures.split(',') if targetFeature in inputFeatures: inputFeatures.remove(targetFeature) dataFilePath = str(configSettingsJson['basic']['dataLocation']) from utils.file_ops import read_df_compressed status,df = read_df_compressed(config['postprocessedData'],encoding='utf8',nrows=10) #print(df) df.rename(columns=lambda x: x.strip(), inplace=True) df = df[inputFeatures] #print(df) singleInstanceData = df.loc[5, inputFeatures] inputFieldsDict = singleInstanceData.to_dict() inputFields = [] inputFields.append(inputFieldsDict) if 'nrows' in config: nrows = config['nrows'] else: nrows = 'Not Available' if 'ncols' in config: ncols = config['ncols'] else: ncols = 'Not Available' if 'targetFeature' in config: targetFeature = config['targetFeature'] else: targetFeature = '' labelMaps = config['labelMaps'] modelfeatures = config['modelFeatures'] mfcount = len(modelfeatures) df_proprocessed = pd.read_csv(dataFilePath) if 'targetFeature' != '': target_classes = df_proprocessed[targetFeature].unique() numberofclasses = len(target_classes) else: target_classes = [] numberofclasses = 'Not Available' dataPoints = df_proprocessed.shape[0] df_proprocessed = df_proprocessed.head(5) df_proprocessed = df_proprocessed.to_json(orient="records") df_proprocessed = json.loads(df_proprocessed) expainableAIPath = os.path.join(request.session['deploypath'],'aion_xai.py') outputStr = subprocess.check_output([sys.executable,expainableAIPath,'global']) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_ai_explanation:(.*)',str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() ale_json = json.loads(str(outputStr)) ale_json = ale_json['data'] ale_view = ale_json['data'] sentences = ale_json['sentences'] scoreMessage = '' feature_importance = ale_json['feature_importance'] dfimp = pd.DataFrame.from_dict(feature_importance) dfimp = dfimp.sort_values(by=['values'],ascending=False).reset_index() yaxis_data = dfimp['values'].tolist() xaxis_data = dfimp['labels'].tolist() cfig = go.Figure() cfig.add_trace(go.Bar(x=xaxis_data,y=yaxis_data,name='Feature Importance')) cfig.update_layout(barmode='stack',xaxis_title='Features') bargraph = cfig.to_html(full_html=False, default_height=450,default_width=1000) dftoprecords = dfimp.head(2) topTwoFeatures = dfimp['labels'].tolist() topFeaturesMsg = [] for i in range(0,len(dfimp)): value = round(dfimp.loc[i, "values"],2)*100 value = round(value,2) tvalue = str(dfimp.loc[i, "labels"])+' contributing to '+ str(value)+'%' topFeaturesMsg.append(tvalue) most_influencedfeature = ale_json['most_influencedfeature'] interceppoint = ale_json['interceptionpoint'] anchorjson = ale_json['anchorjson'] return 'Success','Success',ale_view,sentences,bargraph,inputFields,nrows,ncols,targetFeature,dataPoints,target_classes,df_proprocessed,numberofclasses,modelfeatures,problemType,mfcount,topTwoFeatures,topFeaturesMsg,most_influencedfeature,interceppoint,anchorjson,labelMaps except Exception as Inst: print(Inst) return 'Error','Exception: '+str(Inst),'NA','NA','NA','NA',0,0,'NA','NA','NA','NA',0,'NA','NA',0,'NA','NA','NA','NA','NA','NA' # -*- coding: utf-8 -*- import os # import glob from glob import glob import ast from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.feature_extraction.text import HashingVectorizer from sklearn.metrics.pairwise import cosine_similarity import numpy as np import pandas as pd import json import time import logging from datetime import datetime """ Code clone detection based on user input files. """ class codeCloneDetectionSklearn: """ Detect code clones based on sklearn text vectorizer modules. Input params: files_dir: python files folder, deply_dir: logs,resultant dataframe stored location. chunk_size: max size split for the input text or code function. return values: report_dict which contains clone type, path and clones. """ def __init__(self,files_dir,deploy_dir, chunk_size): self.files_dir = files_dir self.deploy_dir = deploy_dir self.chunk_size = chunk_size try: self.ccdreportpath = os.path.join(self.deploy_dir, "codeCloneReport") os.makedirs(self.ccdreportpath, exist_ok = True) except OSError as error: print("Directory 'codeCloneReport' cann't be created.Error msg: ",error) try: current_datetime = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") str_current_datetime = str(current_datetime) log_file_name = 'codeclonelog_sklearn'+f"_{str_current_datetime}"+".log" logpath = os.path.join(self.ccdreportpath,log_file_name) logging.basicConfig(level=logging.INFO,filename=logpath,filemode='w',format='%(message)s') self.log = logging.getLogger() except Exception as e: print("code clone log object creation error.",e) pass def get_function_names(self,filename): """ Get the function names from python files """ try: with open(filename, 'r') as file: content = file.read() tree = ast.parse(content) function_names = [] for node in ast.walk(tree): if isinstance(node, ast.FunctionDef): function_names.append(node.name) except Exception as e: self.log.info("function name read error: "+str(e)) return function_names def get_function_code(self,filename, function_name): """ To get the function codes """ try: with open(filename, 'r') as file: content = file.read() tree = ast.parse(content) function_code = "" for node in ast.walk(tree): if isinstance(node, ast.FunctionDef) and node.name == function_name: function_code = ast.unparse(node) except Exception as e: self.log.info("function name read error: "+str(e)) return function_code def get_python_files(self,root_dir): """ Walk thru the directory user given, get all py files. """ try: code_files = [y for x in os.walk(root_dir) for y in glob(os.path.join(x[0], '*.py'))] except Exception as e: self.log.info("Python file read error: "+str(e)) return code_files def chunk_functions(self,function_code, chunk_size): """ Check the function size based on chunk size. """ try: if (len(function_code) > 20): chunks = [function_code[i:i + chunk_size] for i in range(0, len(function_code), chunk_size)] else: chunks = list((function_code,)) except Exception as e: self.log.info("function chunk based on chunk_size error: "+str(e)) total_tokens = round(len(function_code)/4) return chunks,total_tokens def get_clone(self): """ Main code clone detection function using sklearn tfidf_vectorizer and cosine_similarity. return values:report_dict which contains total_clones, """ try: start_time = time.time() chunk_size = int(self.chunk_size) ccdreportpath = os.path.join(self.deploy_dir, "codeCloneReport") python_files = self.get_python_files(self.files_dir) total_files = len(python_files) # print('python_files: \n',python_files) function_codes = [] function_n = [] file_name=[] # files_info=[] total_tokens_used = [] for file in python_files: function_names = self.get_function_names(file) for i,function_name in enumerate(function_names): file_name.append(file) function_n.append(function_name) function_code = self.get_function_code(file, function_name) chunks,total_tokens = self.chunk_functions(function_code, chunk_size) total_tokens_used.append(total_tokens) function_codes.extend(chunks) total_functions = len(function_n) files_info=list(zip(file_name, function_n,function_codes)) tfidf_vectorizer = TfidfVectorizer() ## we can use other vectorizer models also. # tfidf_vectorizer = HashingVectorizer() tfidf_matrix = tfidf_vectorizer.fit_transform(function_codes) similarity_matrix = cosine_similarity(tfidf_matrix) #Uncomment if you want to send two different code clonne blocks at a time for similarity comparison # similarity_matrix = cosine_similarity(tfidf_matrix, tfidf_matrix) clone_d = dict() total_clones = 0 final_report=list() #getting funtion and next function for comparison for i in range(len(similarity_matrix)): for j in range(i + 1, len(similarity_matrix)): if(similarity_matrix[i, j] >= 0.90 and similarity_matrix[i, j] <= 0.95): clone_d.update({f'codeclone_{total_clones+1}':{f'function{i}':{'clone_fun_name':function_n[i],'clone1_path':files_info[i][0]},f'function{j}':{'clone_fun_name':function_n[j],'clone1_path':files_info[j][0]},'cloneType':'parametricClone'}}) report_json = json.dumps(clone_d, indent = 4) total_clones=total_clones+1 elif(similarity_matrix[i, j] > 0.95): clone_d.update({f'codeclone_{total_clones+1}':{f'function{i}':{'clone_fun_name':function_n[i],'clone_path':files_info[i][0]},f'function{j}':{'clone_fun_name':function_n[j],'clone_path':files_info[j][0] },'cloneType':'exactClone'}}) report_json = json.dumps(clone_d, indent = 4) final_report.append(clone_d) total_clones=total_clones+1 elif(similarity_matrix[i, j] > 0.80 and similarity_matrix[i, j] < 0.90): clone_d.update({f'codeclone_{total_clones+1}':{f'function{i}':{'clone_fun_name':function_n[i],'clone_path':files_info[i][0]},f'function{j}':{'clone_fun_name':function_n[j],'clone_path':files_info[j][0] },'cloneType':'NearMissClones'}}) report_json = json.dumps(clone_d, indent = 4) final_report.append(clone_d) total_clones=total_clones+1 else: ##add other conditionas in future pass ## To get clone type clone_type = [list(item.values())[2] for item in list(clone_d.values())] report_str = json.dumps(final_report) json_l=json.loads(report_str) json_keys = list(json_l[0].keys()) json_values = list(json_l[0].values()) end_time = time.time() total_time_taken = end_time - start_time # self.log.info("ccd_report: \n"+str(ccd_report)) f_df=pd.DataFrame(list(zip(json_keys, json_values,clone_type)), columns =['Clone', 'CloneDetails','CloneType']) codeclonereport_file = os.path.join(self.ccdreportpath,'clone_detection_report_sklearn.csv') f_df.to_csv(codeclonereport_file, index=False) ccd_report = f_df.to_markdown(tablefmt='psql') self.log.info("total_clones: \n"+str(total_clones)) exact_clone_count = f_df['CloneType'].str.count("exactClone").sum() parametric_clone_count = f_df['CloneType'].str.count("parametricClone").sum() nearmiss_clone_count = f_df['CloneType'].str.count("NearMissClones").sum() total_tokens = sum(total_tokens_used) # nearmiss_clone_count =0 self.log.info("exact_clone_count: \n"+str(exact_clone_count)) self.log.info("parametric_clone_count: \n"+str(parametric_clone_count)) self.log.info("nearmiss_clone_count: \n"+str(nearmiss_clone_count)) self.log.info("Total tokens used: \n"+str(total_tokens)) self.log.info("Total time taken to excute code clone detction: \t"+str(total_time_taken)) clone_info="1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces,\ 2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments and less similarity threshold (0.90-0.95), result in this clone,\ 3. Near-miss clone: Near-miss clone are clones detected with less similarity threshold." clone_count = {"Exact Clone":exact_clone_count,"Parametric Clone":parametric_clone_count,"Nearmiss Clone":nearmiss_clone_count} report_str = f"""Code_directory: {self.files_dir} Files: {total_files} Functions: {total_functions} Total_code_clones_detected: {total_clones} Tokens used: {total_tokens} Three_types_of_clone: 1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces. 2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments and less similarity threshold (0.90-0.95), result in this clone. 3. Near-miss clone: Near-miss clone are clones detected with less similarity threshold. Code_clones_count_by_clone_type: {clone_count} Clone_functions: {ccd_report} total_time_taken: {total_time_taken} """ codeclonereport_txt = os.path.join(self.ccdreportpath,'code_clone_report.txt') with open(codeclonereport_txt, "w") as f: f.write(report_str) report_dict = {"clone_info":clone_info,"total_clones":total_clones,'total_files':total_files,"exact_clone_count":exact_clone_count,'total_functions':total_functions,"total_tokens":total_tokens, "parametric_clone_count":parametric_clone_count,"nearmiss_clone_count":nearmiss_clone_count,"result_df":f_df } self.log.info("ccd_report: \n"+str(ccd_report)) # print("report_dict:\n\n",report_dict) # end_time = time.time() # total_time = (end_time - start_time) return report_dict except Exception as e: self.log.info("Clone detection function error. error msg: "+str(e)) # import traceback # print("traceback error: \n",traceback.print_exc()) if __name__ == "__main__": print("code clone detection started....") ##Use this for standalone fn debuging. ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import plotly.figure_factory as ff from plotly.subplots import make_subplots import plotly.graph_objects as go from wordcloud import WordCloud, STOPWORDS import pandas as pd import numpy as np from appbe import distribution import io import urllib import os import sys import base64 from appbe import help_Text as ht import math import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from natsort import natsorted from sklearn.cluster import KMeans import json from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator from appbe.aion_config import eda_setting from dython.nominal import associations def calculateNumberofCluster(featureData): Sum_of_squared_distances = [] K = range(1, 15) for k in K: km = KMeans(n_clusters=k) km = km.fit(featureData) Sum_of_squared_distances.append(km.inertia_) x1, y1 = 1, Sum_of_squared_distances[0] x2, y2 = 15, Sum_of_squared_distances[len(Sum_of_squared_distances) - 1] distances = [] for inertia in range(len(Sum_of_squared_distances)): x0 = inertia + 2 y0 = Sum_of_squared_distances[inertia] numerator = abs((y2 - y1) * x0 - (x2 - x1) * y0 + x2 * y1 - y2 * x1) denominator = math.sqrt((y2 - y1) ** 2 + (x2 - x1) ** 2) distances.append(numerator / denominator) n_clusters = distances.index(max(distances)) + 2 #print(n_clusters) return (n_clusters) def get_eda(request): hopkins_val = '' hopkins_tip = '' if request.session['datatype'] == 'Normal': from appbe.eda import ux_eda # EDA Subsampling changes # ---------------------------- edasampleSize = request.POST.get('SubsampleSize') edasampleSize = str(int(edasampleSize)/100) sampleFile = str(request.session['datalocation']) repText = sampleFile[sampleFile.find('sub_'):sampleFile.find('_sampled_') + 9] if len(repText) == 30: dataLocation = sampleFile.replace(repText,"") else: dataLocation = sampleFile eda_obj = ux_eda(dataLocation,request.session['delimiter'],request.session['textqualifier']) df0 = eda_obj.getdata() if os.path.isfile(dataLocation): if(len(edasampleSize) > 0): df0 = df0.sample(frac = float(edasampleSize)) #EDA Performance change # ---------------------------- dflength = len(df0) # sample_size = int(eda_setting()) # if dflength >= sample_size: # eda_obj.subsampleData(sample_size) # else: eda_obj.subsampleData(dflength) # ---------------------------- TrainSampleSelected = request.POST.get('TrainSampleSize') if(TrainSampleSelected == 'EDASize'): from pathlib import Path filePath = Path(dataLocation) import datetime timestamp = datetime.datetime.now().replace(microsecond=0).isoformat() timestamp = str(timestamp.replace(":","")) sub_sampledFile = filePath.parent/("sub_" + timestamp + "_sampled_"+filePath.name) # sub_sampledFile = filePath.parent/(usename + "_sub_sampled_"+filePath.name) df0.to_csv(sub_sampledFile,index=False,) request.session['datalocation'] = str(sub_sampledFile) records = df0.shape[0] request.session['NoOfRecords'] = records edaFeatures = request.POST.getlist('InputFeatures') request.session['edaFeatures'] = edaFeatures if(len(edaFeatures) > 0): eda_obj.subsetFeatures(edaFeatures) # ---------------------------- features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catfeatures = eda_obj.getFeatures() request.session['edanumericCatFeatures'] = numericCatFeatures request.session['edatextFeature'] = textFeature categoricalfeatures = catfeatures numericfeaturecount = eda_obj.getNumericFeatureCount() cluster_details = [] dataCharts = [] # correlated_features=[] pca_details = [] if numericfeaturecount > 1: try: cluster_details,hopkins_val = eda_obj.getClusterDetails() if hopkins_val!='': if float(hopkins_val) <0.3: hopkins_tip = ht.hopkins_tip[0] elif float(hopkins_val)>0.7: hopkins_tip = ht.hopkins_tip[2] else: hopkins_tip = ht.hopkins_tip[1] else: hopkins_tip = '' except Exception as e: print("========================"+str(e)) pass try: pca_map = eda_obj.getPCATop10Features() pca_details = pca_map yaxis_data = pca_map.tolist() xaxis_data = pca_map.index.values.tolist() import plotly.graph_objects as go cfig = go.Figure() cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name='Feature Importance')) cfig.update_layout(barmode='stack', xaxis_title='Features',yaxis_title='Explained Variance Ratio') bargraph = cfig.to_html(full_html=False, default_height=450, default_width=1000) dataCharts.append(bargraph) except: pass df = eda_obj.getdata() # try: # top5highcorr = eda_obj.getHighlyCorrelatedFeatures(5) # correlated_features = getHighlyCorrelatedFeatureCharts(df,top5highcorr) # except: # pass else: df = eda_obj.getdata() # # EDA Subsampling changes # # ---------------------------- # if os.path.isfile(dataLocation): # if dflength < 10000: # if(len(edasampleSize) > 0): # df = df.sample(frac = float(edasampleSize)) # ---------------------------- if len(textFeature) > 0: commonfeatures = eda_obj.getTopTextFeatures(10) # comment_words = eda_obj.word_token() del eda_obj wordcloudpic = '' showtextFeature = False if len(textFeature) > 0: showtextFeature = True # try: # stopwords = set(STOPWORDS) # wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords, # min_font_size=10).generate(comment_words) # try: # plt.clf() # except: # pass # plt.imshow(wordcloud, interpolation='bilinear') # plt.axis("off") # plt.tight_layout(pad=0) # image = io.BytesIO() # plt.savefig(image, format='png') # image.seek(0) # string = base64.b64encode(image.read()) # wordcloudpic = 'data:image/png;base64,' + urllib.parse.quote(string) # except: # pass xaxis_data = commonfeatures['most_common_words'].tolist() yaxis_data = commonfeatures['freq'].tolist() import plotly.graph_objects as go cfig = go.Figure() cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name='Feature Importance')) cfig.update_layout(barmode='stack', xaxis_title='Features',yaxis_title='Count') bargraph = cfig.to_html(full_html=False, default_height=450, default_width=1000) dataCharts.append(bargraph) df_top = df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) # if len(df) > 10000: # df1 = df.sample(n=10000, random_state=1) # else: # df1 = df df1 = df data_deep_json = df_top.to_json(orient='records') #df1.to_json(orient='records') try: gfsg = GenericFeatureStatisticsGenerator() proto = gfsg.ProtoFromDataFrames([{'name': 'train', 'table': df1}]) protostr = base64.b64encode(proto.SerializeToString()).decode("utf-8") except Exception as e: protostr='' print('protostr '+str(e)) try: correlationgraph = getCorrelationMatrix(df) except Exception as e: print(e) try: dataDrift = 'onRequest' #getDriftDistribution(numericCatFeatures, df[numericCatFeatures]) except Exception as e: dataDrift = '' print(e) selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] statusmsg = 'Successfully Done' DF_list = list() des1 = df.describe(include='all').T des1['missing count %'] = df.isnull().mean() * 100 des1['zero count %'] = df.isin([0]).mean() * 100 data = list(df.columns.values) des1.insert(0, 'Features', data) des1 = des1.to_json(orient="records") pca_df=pd.DataFrame() #print(pca_details) # if pca_details.empty: if len(pca_details) > 0: pca_df = pd.DataFrame({'Feature':pca_details.index, 'Explained Variance Ratio':pca_details.values}).round(4) pca_df = pca_df.to_json(orient="records") if len(df.columns) > 25: df3 = df[df.columns[0:24]] else: df3 = df.copy() #cor_mat = abs(df3.corr()) #cor_mat = cor_mat.round(2) try: if len(df3.columns) > 25: df3 = df3[df3.columns[0:24]] cor_mat= associations(df3,compute_only=True) cor_mat=cor_mat['corr'] #cor_mat = df3.corr() cor_mat = cor_mat.astype(float).round(2) except Exception as e: print("creating correlation mat issue: \n",e) pass data = list(cor_mat.index) cor_mat.insert(0, 'Features', data) cor_mat = cor_mat.to_json(orient="records") cluster_df = pd.DataFrame.from_dict(cluster_details) cluster_df = cluster_df.to_json(orient="records") #textFeature = json.dumps(textFeature) # 2.2 patch changes #------------------------------------------------- request.session['edaRecords'] = df.shape[0] print(textFeature) context = {'data_deep_json': data_deep_json, 'sampleFile':sampleFile,'protostr': protostr, 'data': df_json, 'oneda': True, 'dataCharts': dataCharts,'dataDrift': dataDrift, 'drift_tip': ht.drift_tip,'des1':des1,'cluster_df':cluster_df,'hopkins_val':hopkins_val, 'pca_df':pca_df,'cor_mat':cor_mat,'correlationgraph': correlationgraph, 'centroids':cluster_details, 'wordcloudpic': wordcloudpic, 'showtextFeature': showtextFeature, 'textFeature': textFeature, # 'featurepairgraph': correlated_features, 'data_overview_tip': ht.data_overview_tip,'timeseries_analysis_tip':ht.timeseries_analysis_tip, 'feature_importance_tip': ht.feature_importance_tip,'hopkins_tip':hopkins_tip, 'correlation_analysis_tip': ht.correlation_analysis_tip, 'exploratory_analysis_tip': ht.exploratory_analysis_tip, 'data_deep_drive_tip': ht.data_deep_drive_tip,'status_msg': statusmsg,'selected_use_case': selected_use_case, 'pair_graph_tip':ht.pair_graph_tip, 'fair_metrics_tip':ht.fair_metrics_tip, 'categoricalfeatures':categoricalfeatures, 'numericCatFeatures':numericCatFeatures, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning', 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'exploratory':True,'NumericFeatureList':numericFeature,'dateFeature':dateFeature,'targetFeature':targetFeature} return(context) # EDA Visualization changes # ---------------------------- def get_edaGraph(request): if request.session['datatype'] == 'Normal': from appbe.eda import ux_eda df_temp = dict(request.GET).get('features[]') graphType = request.GET.get('graphType') d3_url = request.GET.get('d3_url') mpld3_url = request.GET.get('mpld3_url') dataLocation = request.session['datalocation'] eda_obj = ux_eda(dataLocation) # 2.2 patch changes #------------------------------------------------- edaRecords = request.session['edaRecords'] #df = df.sample(n=int(edaRecords), random_state=1) eda_obj.subsampleData(edaRecords) eda_obj.subsetFeatures(df_temp) features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature, catfeatures = eda_obj.getFeatures() numericfeaturecount = eda_obj.getNumericFeatureCount() correlated_features=[] df = eda_obj.getdata() if numericfeaturecount > 1: try: if graphType == 'Default': top5highcorr = eda_obj.getHighlyCorrelatedFeatures(5) correlated_features = getHighlyCorrelatedFeatureCharts(df,top5highcorr) else: correlated_features = getFeatureCharts(df,graphType,d3_url,mpld3_url) except: pass return correlated_features # ---------------------------- # ---------------------- 12686:Data Distribution related Changes S T A R T ---------------------- def get_DataDistribution(request): selectedFeature = request.GET.get('selected_feature') _featureItem = [] _featureItem.append(selectedFeature) from appbe.eda import ux_eda dataLocation = request.session['datalocation'] eda_obj = ux_eda(dataLocation) df = eda_obj.getdata() numericCatFeatures = request.session['edanumericCatFeatures'] textFeature = request.session['edatextFeature'] # features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catfeatures = eda_obj.getFeatures() dataDrift = '' if selectedFeature in numericCatFeatures: dataDrift = getDriftDistribution(_featureItem, df[numericCatFeatures]) elif selectedFeature in textFeature: try: comment_words = eda_obj.word_token_for_feature(selectedFeature, df[_featureItem]) stopwords = set(STOPWORDS) wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords, min_font_size=10).generate(comment_words) try: plt.clf() except: pass plt.imshow(wordcloud, interpolation='bilinear') plt.axis("off") plt.tight_layout(pad=0) image = io.BytesIO() plt.savefig(image, format='png') image.seek(0) string = base64.b64encode(image.read()) # wordcloudpic = 'data:image/png;base64,' + urllib.parse.quote(string) dataDrift = urllib.parse.quote(string) except: dataDrift = '' del eda_obj return dataDrift # -------------------------------------------- E N D -------------------------------------------- def get_DeepDiveData(request): if request.session['datatype'] == 'Normal': from appbe.eda import ux_eda dataLocation = request.session['datalocation'] eda_obj = ux_eda(dataLocation) edaRecords = request.session['edaRecords'] edaFeatures = request.session['edaFeatures'] eda_obj.subsampleData(edaRecords) eda_obj.subsetFeatures(edaFeatures) df = eda_obj.getdata() data_deep_json = df.to_json(orient='records') return (data_deep_json) # Fairness Metrics changes # ---------------------------- def get_fairmetrics(request): import mpld3 if request.session['datatype'] == 'Normal': from appbe.eda import ux_eda df_temp = dict(request.GET).get('features[]') d3_url = request.GET.get('d3_url') mpld3_url = request.GET.get('mpld3_url') global metricvalue metricvalue = request.GET.get('metricvalue') dataLocation = request.session['datalocation'] # dataLocation = 'C:\\MyFolder\\AION\\AION Datasets\\AIF360\\database.csv' eda_obj = ux_eda(dataLocation, optimize=1) features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catfeatures = eda_obj.getFeatures() # data = eda_obj.getdata() data = pd.read_csv(dataLocation, na_values=['Unknown', ' ']) features_toEncode = features from sklearn.preprocessing import MinMaxScaler, LabelEncoder data_encoded = data.copy() categorical_names = {} encoders = {} # Use Label Encoder for categorical columns (including target column) for feature in features_toEncode: le = LabelEncoder() le.fit(data_encoded[feature]) data_encoded[feature] = le.transform(data_encoded[feature]) categorical_names[feature] = le.classes_ encoders[feature] = le data_perp = data_encoded protected_feature = df_temp[0] #'Victim Race' target_feature = df_temp[1] #'Perpetrator Sex' # ------Theil index----- Task->13843 from aif360.sklearn.metrics import generalized_entropy_index Ti_List = [] for items in categorical_names[protected_feature]: df = data[data[protected_feature]==items] le = LabelEncoder() le.fit(df[target_feature]) df[target_feature] = le.transform(df[target_feature]) tf = generalized_entropy_index(df[target_feature], alpha = 1) tf = round(tf, 4) Ti_List.append(tf) global Thi_idx Thi_idx = Ti_List #claas_size = categorical_names[protected_feature].size new_list = [item for item in categorical_names[protected_feature] if not(pd.isnull(item)) == True] claas_size = len(new_list) if claas_size > 10: return 'HeavyFeature' metrics = fair_metrics(categorical_names, data_perp, protected_feature, target_feature, claas_size) figure = plot_fair_metrics(metrics) html_graph = mpld3.fig_to_html(figure,d3_url=d3_url,mpld3_url=mpld3_url) return html_graph def fair_metrics(categorical_names, data_perp, protected_feature, target_feature, claas_size): import aif360 from aif360.datasets import StandardDataset from aif360.metrics import BinaryLabelDatasetMetric cols = [metricvalue] obj_fairness = [[0]] fair_metrics = pd.DataFrame(data=obj_fairness, index=['objective'], columns=cols) for indx in range(claas_size): priv_group = categorical_names[protected_feature][indx] privileged_class = np.where(categorical_names[protected_feature] == priv_group)[0] data_orig = StandardDataset(data_perp, label_name=target_feature, favorable_classes=[1], protected_attribute_names=[protected_feature], privileged_classes=[privileged_class]) dataset_pred = data_orig attr = dataset_pred.protected_attribute_names[0] idx = dataset_pred.protected_attribute_names.index(attr) privileged_groups = [{attr:dataset_pred.privileged_protected_attributes[idx][0]}] unprivileged_size = dataset_pred.unprivileged_protected_attributes[0].size unprivileged_groups = [] for idx2 in range(unprivileged_size): unprivileged_groups.extend([{attr:dataset_pred.unprivileged_protected_attributes[idx][idx2]}]) metric_pred = BinaryLabelDatasetMetric(dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) if metricvalue == "Theil Index": row = pd.DataFrame([Thi_idx[indx]], columns = cols , index = [priv_group]) elif metricvalue == "Disparate Impact": row = pd.DataFrame([[metric_pred.disparate_impact()]], columns = cols , index = [priv_group]) elif metricvalue == "Statistical Parity Difference": row = pd.DataFrame([[metric_pred.mean_difference()]], columns = cols , index = [priv_group]) #fair_metrics = fair_metrics.append(row) fair_metrics = pd.concat([fair_metrics,row]) return fair_metrics def plot_fair_metrics(fair_metrics): import matplotlib.patches as patches plt.style.use('default') import seaborn as sns fig, ax = plt.subplots(figsize=(10,4), ncols=1, nrows=1) plt.subplots_adjust( left = 0.125, bottom = 0.1, right = 0.9, top = 0.9, wspace = .5, hspace = 1.1 ) y_title_margin = 1.2 plt.suptitle("Fairness metrics", y = 1.09, fontsize=20) sns.set(style="dark") cols = fair_metrics.columns.values obj = fair_metrics.loc['objective'] if metricvalue == "Theil Index": size_rect = [0.5] rect = [-0.1] bottom = [-0.1] top = [2] bound = [[-0.1,0.1]] elif metricvalue == "Disparate Impact": size_rect = [0.4] rect = [0.8] bottom = [0] top = [2] bound = [[-0.1,0.1]] elif metricvalue == "Statistical Parity Difference": size_rect = [0.2] rect = [-0.1] bottom = [-1] top = [1] bound = [[-0.1,0.1]] #display(Markdown("### Check bias metrics :")) #display(Markdown("A model can be considered bias if just one of these five metrics show that this model is biased.")) for attr in fair_metrics.index[0:len(fair_metrics)].values: #display(Markdown("#### For the %s attribute :"%attr)) check = [bound[i][0] < fair_metrics.loc[attr][i] < bound[i][1] for i in range(0,1)] #display(Markdown("With default thresholds, bias against unprivileged group detected in **%d** out of 5 metrics"%(5 - sum(check)))) for i in range(0,1): plt.subplot(1, 1, i+1) xx = fair_metrics.index[1:len(fair_metrics)].values.tolist() yy = fair_metrics.iloc[1:len(fair_metrics)][cols[i]].values.tolist() palette = sns.color_palette('husl', len(xx)) ax = sns.pointplot(x=fair_metrics.index[1:len(fair_metrics)], y=yy, palette=palette, hue=xx) index = 0 for p in zip(ax.get_xticks(), yy): if (p[1] > 2.0): _color = palette.as_hex()[index] _val = 'Outlier(' + str(round(p[1],3)) + ')' ax.text(p[0]-0.5, 0.02, _val, color=_color) else: ax.text(p[0], p[1]+0.05, round(p[1],3), color='k') index = index + 1 plt.ylim(bottom[i], top[i]) plt.setp(ax.patches, linewidth=0) ax.get_xaxis().set_visible(False) ax.legend(loc='right', bbox_to_anchor=(1, 0.8), ncol=1) ax.add_patch(patches.Rectangle((-5,rect[i]), 10, size_rect[i], alpha=0.3, facecolor="green", linewidth=1, linestyle='solid')) # plt.axhline(obj[i], color='black', alpha=0.3) plt.title(cols[i], fontname="Times New Roman", size=20,fontweight="bold") ax.set_ylabel('') ax.set_xlabel('') return fig # ---------------------------- def getDriftDistribution(feature, dataframe, newdataframe=pd.DataFrame()): try: import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import scipy from scipy import stats from scipy.stats import norm import matplotlib.gridspec as gridspec import math import io, base64, urllib np.seterr(divide='ignore', invalid='ignore') from appbe.eda import ux_eda eda_obj = ux_eda() try: plt.clf() except: pass plt.rcParams.update({'figure.max_open_warning': 0}) sns.set(color_codes=True) pandasNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] if len(feature) > 4: numneroffeatures = len(feature) plt.figure(figsize=(10, numneroffeatures*2)) else: plt.figure(figsize=(10,5)) for i in enumerate(feature): dataType = dataframe[i[1]].dtypes if dataType not in pandasNumericDtypes: dataframe[i[1]] = pd.Categorical(dataframe[i[1]]) dataframe[i[1]] = dataframe[i[1]].cat.codes dataframe[i[1]] = dataframe[i[1]].astype(int) dataframe[i[1]] = dataframe[i[1]].fillna(dataframe[i[1]].mode()[0]) else: dataframe[i[1]] = dataframe[i[1]].fillna(dataframe[i[1]].mean()) plt.subplots_adjust(hspace=0.5, wspace=0.7, top=1) plt.subplot(math.ceil((len(feature) / 2)), 2, i[0] + 1) distname, sse = eda_obj.DistributionFinder(dataframe[i[1]]) try: ax = sns.distplot(dataframe[i[1]], label=distname) ax.legend(loc='best') if newdataframe.empty == False: dataType = newdataframe[i[1]].dtypes if dataType not in pandasNumericDtypes: newdataframe[i[1]] = pd.Categorical(newdataframe[i[1]]) newdataframe[i[1]] = newdataframe[i[1]].cat.codes newdataframe[i[1]] = newdataframe[i[1]].astype(int) newdataframe[i[1]] = newdataframe[i[1]].fillna(newdataframe[i[1]].mode()[0]) else: newdataframe[i[1]] = newdataframe[i[1]].fillna(newdataframe[i[1]].mean()) distname, sse = distribution.DistributionFinder(newdataframe[i[1]]) ax = sns.distplot(newdataframe[i[1]], label=distname) ax.legend(loc='best') except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) pass buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) string = base64.b64encode(buf.read()) uri = urllib.parse.quote(string) return uri except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def getCategoryWordCloud(df): labels = df.Label.unique() df_output = pd.DataFrame() tcolumns=['text'] for label in labels: df2 = df[df['Label'] == label] df2 = df2.reset_index() wordcloud,df_text = getWordCloud(df2,tcolumns) newrow = {'Label':label,'wordCloud':wordcloud} df_output = df_output.append(newrow,ignore_index=True) return(df_output) def getHighlyCorrelatedFeatureCharts(df, df_top): numOfRows = df.shape[0] cratio = 0.01 if (numOfRows < 1000): cratio = 0.2 elif (numOfRows < 10000): cratio = 0.1 elif (numOfRows < 100000): cratio = 0.01 barcolor = ["red", "green", "blue", "goldenrod", "magenta"] ffig = make_subplots(rows=2, cols=3) height = 800 rowno = 1 colno = 1 featureCharts = [] try: for index, row in df_top.iterrows(): feature1 = row['FEATURE_1'] feature2 = row['FEATURE_2'] df_temp = df[[feature1, feature2]] feature1data = df_temp[feature1] feature2data = df_temp[feature2] nUnique = len(feature1data.unique().tolist()) if nUnique / numOfRows >= cratio: feature1type = 'Continous' else: feature1type = 'Category' nUnique = len(feature2data.unique().tolist()) if nUnique / numOfRows >= cratio: feature2type = 'Continous' else: feature2type = 'Category' charttype = 0 if feature1type == 'Continous' and feature2type == 'Continous': df_temp[feature1] = pd.qcut(df_temp[feature1], q=8, duplicates='drop',precision=0) df_temp[feature1] = df_temp[feature1].astype(str).str.strip('()[]') feature1type = 'Category' xaxis = feature1 yaxis = feature2 charttype = 1 if feature1type == 'Category' and feature2type == 'Continous': xaxis = feature1 yaxis = feature2 charttype = 1 if feature1type == 'Continous' and feature2type == 'Category': xaxis = feature1 #xaxis = feature2 yaxis = feature2 #yaxis = feature1 charttype = 1 if feature1type == 'Category' and feature2type == 'Category': if (len(feature1data.unique().tolist()) < len(feature2data.unique().tolist())): xaxis = feature1 #xaxis = feature2 yaxis = feature2 #yaxis = feature1 else: xaxis = feature1 yaxis = feature2 if (len(df_temp[xaxis].unique().tolist()) > 5): df_temp[xaxis] = pd.qcut(df_temp[xaxis], q=5, duplicates='drop',precision=0) df_temp[xaxis] = df_temp[xaxis].astype(str).str.strip('()[]') if (len(df_temp[yaxis].unique().tolist()) > 5): df_temp[yaxis] = pd.qcut(df_temp[yaxis], q=3, duplicates='drop',precision=0) df_temp[yaxis] = df_temp[yaxis].astype(str).str.strip('()[]') charttype = 2 # if feature1type == 'Category' and feature2type == 'Category': if charttype == 2: uniqueclasses = df_temp[yaxis].unique().tolist() cfig = go.Figure() i = 1 for x in uniqueclasses: df_temp3 = df_temp.loc[df_temp[yaxis] == x] df_temp2 = df_temp3.groupby(xaxis, as_index=False)[yaxis].count() if df_temp2[xaxis].dtypes == "object": df_temp2 = df_temp2.set_index(xaxis).reindex( natsorted(df_temp2[xaxis].tolist(), key=lambda y: y.lower())).reset_index() xaxis_data = df_temp2[xaxis].tolist() yaxis_data = df_temp2[yaxis].tolist() cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name=x, marker_color=barcolor[i])) i = i + 1 if i == 5: break cfig.update_layout(barmode='stack', xaxis_title=xaxis, yaxis_title=yaxis) bargraph = cfig.to_html(full_html=False, default_height=450, default_width=400) featureCharts.append(bargraph) if charttype == 1: df_temp2 = df_temp.groupby(xaxis, as_index=False)[yaxis].mean() if df_temp2[xaxis].dtypes == "object": df_temp2 = df_temp2.set_index(xaxis).reindex( natsorted(df_temp2[xaxis].tolist(), key=lambda y: y.lower())).reset_index() xaxis_data = df_temp2[xaxis].tolist() yaxis_data = df_temp2[yaxis].tolist() cfig = go.Figure() cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name='Primary Product', marker_color='blue')) cfig.update_layout(xaxis_title=xaxis, yaxis_title=yaxis) bargraph = cfig.to_html(full_html=False, default_height=450, default_width=400) featureCharts.append(bargraph) colno += 1 if colno > 3: colno = 1 rowno += 1 except Exception as e: print(e) return (featureCharts) # EDA Visualization changes # ---------------------------- def getFeatureCharts(df, graphType, d3_url,mpld3_url): featureCharts = [] feature1 = df.columns[0] feature2 = df.columns[1] import seaborn as sns import mpld3 fig, ax = plt.subplots(figsize=[10,5]) if graphType == 'marker': df.plot(ax=ax, marker='o') # df[['age','education-num']].plot(ax=ax, marker='o') if graphType == 'area': df.plot(ax=ax, kind ="area") # df[['education-num','age']].plot(ax=ax, kind ="area") # UIprb if graphType == 'hexbin': df.plot.hexbin(ax=ax, x=feature1, y=feature2, gridsize=2) if graphType == 'boxplot': plt.boxplot(df) if graphType == 'scatter': ax.scatter(df[feature1], df[feature2]) if graphType == 'regplot': ax = sns.regplot(x= feature1, y=feature2, data= df, fit_reg = False, scatter_kws={"alpha": 0.5}) if graphType == 'lineplot': ax = sns.lineplot(x= feature1, y=feature2, data= df) if graphType == 'barplot': ax = sns.barplot(x= feature1, y=feature2, data= df) # ax = sns.barplot(x= 'age', y='fnlwgt', data= df) #Start_prb ax.legend() ax.set_xlabel(feature1) ax.set_ylabel(feature2) #print(d3_url) #print(mpld3_url) html_graph = mpld3.fig_to_html(fig,d3_url=d3_url,mpld3_url=mpld3_url) if graphType == 'kde': ax = sns.pairplot(df, kind="kde", height=4, x_vars=feature1,y_vars = feature2) # ax = sns.pairplot(df[['age','fnlwgt']], kind="kde") html_graph = mpld3.fig_to_html(ax.fig) if graphType == 'relplot': sns.set(style ="darkgrid") ax = sns.relplot(x =feature1, y =feature2, data = df) html_graph = mpld3.fig_to_html(ax.fig) featureCharts.append(html_graph) return (featureCharts) # ---------------------------- def MostCommonWords(stopwords, inputCorpus, num_of_words=10): try: from collections import Counter new = inputCorpus.str.split() new = new.values.tolist() corpus = [word for i in new for word in i if word not in stopwords] counter = Counter(corpus) most = counter.most_common() x, y = [], [] for word, count in most[: num_of_words + 1]: x.append(word) y.append(count) return pd.DataFrame([x, y], index=['most_common_words', 'freq']).T except: print("exception", sys.exc_info()) return False def removeFeature(df): featuresList = df.columns.values.tolist() modelFeatures = featuresList.copy() datetimeFeatures = [] sequenceFeatures = [] unimportantFeatures = [] featuresRatio = {} for i in featuresList: check = match_date_format(df[i]) if check == True: modelFeatures.remove(i) continue seq_check = check_seq_feature(df[i]) if seq_check == True: modelFeatures.remove(i) continue ratio = check_category(df[i]) if ratio != 0: featuresRatio[i] = ratio else: modelFeatures.remove(i) return featuresList, modelFeatures def check_category(data): total_record = len(data) nUnique = len(data.unique().tolist()) if nUnique == 1: return 0 ratio = nUnique / total_record return (ratio) def check_seq_feature(data): if data.dtypes in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']: total_record = data.count() count = (data - data.shift() == 1).sum() if ((total_record - count) == 1): return True return False def match_date_format(data): data = data.astype(str) beforecheckcount = (data.count()*80)/100 #####YYYY-MM-DD HH:MM:SS#### check1 = data[data.str.match( r'(^\d\d\d\d-(0?[1-9]|1[0-2])-(0?[1-9]|[12][0-9]|3[01]) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9]):([0-9]|[0-5][0-9])$)') == True] aftercheckcount = check1.count() if (beforecheckcount <= aftercheckcount): return True #####MM/DD/YYYY HH:MM#### check2 = data[data.str.match( r'(^(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])/(\d\d\d\d) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9])$)') == True] aftercheckcount = check2.count() if (beforecheckcount <= aftercheckcount): return True #####DD-MM-YYYY HH:MM#### check2 = data[data.str.match( r'(^(0?[1-9]|[12][0-9]|3[01])-(0?[1-9]|1[0-2])-(\d\d\d\d) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9])$)') == True] aftercheckcount = check2.count() if (beforecheckcount <= aftercheckcount): return True #####YYYY/MM/DD#### check2 = data[data.str.match(r'(^\d\d\d\d/(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])$)') == True] aftercheckcount = check2.count() if (beforecheckcount <= aftercheckcount): return True #####MM/DD/YYYY#### check2 = data[data.str.match(r'(^(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])/(\d\d\d\d)$)') == True] aftercheckcount = check2.count() if (beforecheckcount <= aftercheckcount): return True return False def check_text_features(df, modelFeatures): aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] textFeature = [] for i in enumerate(modelFeatures): dataType = df[i[1]].dtypes numOfRows = df.shape[0] if dataType not in aionNumericDtypes: if dataType != 'bool': nUnique = len(df[i[1]].unique().tolist()) textnumbericratio = 0.01 if (numOfRows < 1000): textnumbericratio = 0.2 elif (numOfRows < 10000): textnumbericratio = 0.1 elif (numOfRows < 100000): textnumbericratio = 0.01 if nUnique / numOfRows >= textnumbericratio: textFeature.append(i[1]) return (textFeature) def getWordCloud(df, text_columns): df_text = pd.DataFrame() stopwords = set(STOPWORDS) if (len(text_columns) > 1): df_text['combined'] = df[text_columns].apply(lambda row: ' '.join(row.values.astype(str)), axis=1) features = ['combined'] else: df_text[['combined']] = df[text_columns] features = ['combined'] df_text[features[0]] = df_text[features[0]].fillna("NA") textCorpus = df_text[features[0]] from text import TextProcessing tp = TextProcessing.TextProcessing() preprocessed_text = tp.transform(textCorpus) df_text['combined'] = preprocessed_text df_text_list = df_text.values.tolist() comment_words = "" for val in df_text_list: val = str(val) tokens = val.split() for i in range(len(tokens)): tokens[i] = tokens[i].lower() comment_words += " ".join(tokens) + " " wordcloud = WordCloud(stopwords=stopwords).generate(comment_words) try: plt.clf() except: pass try: plt.imshow(wordcloud, interpolation='bilinear') plt.axis("off") plt.tight_layout(pad=0) image = io.BytesIO() plt.savefig(image, format='png') image.seek(0) string = base64.b64encode(image.read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) except Exception as inst: print(inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) image_64='' return (image_64, df_text) def getTopTextFeatures(df_text): stopwords = set(STOPWORDS) commonfeatures = MostCommonWords(stopwords, df_text['combined']) xaxis_data = commonfeatures['most_common_words'].tolist() yaxis_data = commonfeatures['freq'].tolist() import plotly.graph_objects as go cfig = go.Figure() cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name='Feature Importance')) cfig.update_layout(barmode='stack', xaxis_title='Features') bargraph = cfig.to_html(full_html=False, default_height=450, default_width=1000) return (bargraph) def getPCATop10Features(df, modelFeatures): aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] categorial_features = [] for i in enumerate(modelFeatures): dataType = df[i[1]].dtypes if dataType not in aionNumericDtypes: categorial_features.append(i[1]) df[i[1]] = pd.Categorical(df[i[1]]) df[i[1]] = df[i[1]].cat.codes df[i[1]] = df[i[1]].astype(int) df[i[1]] = df[i[1]].fillna(df[i[1]].mode()[0]) else: df[i[1]] = df[i[1]].fillna(df[i[1]].mean()) from sklearn.decomposition import PCA pca = PCA(n_components=2).fit(df) map = pd.DataFrame(pca.components_, columns=modelFeatures) map = map.diff(axis=0).abs() map = map.iloc[1] map = map.sort_values(ascending=False).head(10) yaxis_data = map.tolist() xaxis_data = map.index.values.tolist() import plotly.graph_objects as go cfig = go.Figure() cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name='Feature Importance')) cfig.update_layout(barmode='stack', xaxis_title='Features') bargraph = cfig.to_html(full_html=False, default_height=450, default_width=1000) return (bargraph) def getCorrelationMatrix(df): try: #from dython.nominal import associations if len(df.columns) > 25: df3 = df[df.columns[0:24]] else: df3 = df.copy() cor_mat= associations(df3,compute_only=True) cor_mat=cor_mat['corr'] #cor_mat = df3.corr() cor_mat = cor_mat.astype(float).round(2) #print(cor_mat) z = cor_mat.values.tolist() fig = ff.create_annotated_heatmap(z, x=cor_mat.columns.tolist(), y=cor_mat.index.tolist(), annotation_text=z, colorscale='Blues') fig.layout.yaxis.automargin = True correlationgraph = fig.to_html(full_html=True, default_height=450, default_width=1000) except Exception as e: print(e) correlationgraph = '' return (correlationgraph) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from appbe.data_io import sqlite_db from os.path import expanduser import platform import pandas as pd import os from appbe.dataPath import DATA_DIR PUBLISH_PATH = os.path.join(DATA_DIR,'publish') DEPLOY_DATABASE_PATH = os.path.join(DATA_DIR,'sqlite') def chech_publish_info(usecasename): version = 0 status = 'Not Published' inputDriftStatus = 'No Drift' MODEL_DEPLOY_DATABASE_PATH = os.path.join(PUBLISH_PATH,usecasename,'database') sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db') if sqlite_dbObj.table_exists('publish'): data = sqlite_dbObj.read('publish',"usecase = '"+usecasename+"' and status = 'Published'") if data.shape[0] > 0: model_sqlite_dbObj = sqlite_db(MODEL_DEPLOY_DATABASE_PATH,'deploy.db') version = data['version'].iloc[0] status = 'Published' if model_sqlite_dbObj.table_exists('monitoring'): data = model_sqlite_dbObj.read('monitoring',"version = '"+str(version)+"'") if data.shape[0] > 0: msg = data['Msg'].iloc[-1] if 'Affected Columns' in msg: inputDriftStatus = 'Input Drift Found' return version,status,inputDriftStatus def check_input_data(usecasename): MODEL_DEPLOY_DATABASE_PATH = os.path.join(PUBLISH_PATH,usecasename,'database') sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db') data = pd.DataFrame() if sqlite_dbObj.table_exists('publish'): dataa = sqlite_dbObj.read('publish',"usecase = '"+usecasename+"' and status = 'Published'") if dataa.shape[0] > 0: modelsqlite_dbObj = sqlite_db(MODEL_DEPLOY_DATABASE_PATH,'deploy.db') if modelsqlite_dbObj.table_exists('prodData'): data = modelsqlite_dbObj.read('prodData') return data ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os from os.path import expanduser import platform import json import subprocess import re import sys import pandas as pd from django.http import HttpResponse from appbe.dataPath import DATA_DIR Usecaselocation = os.path.join(DATA_DIR,'Usecases') def mlstyles(request): try: from appbe.aion_config import settings usecasetab = settings() selectid = request.GET['usecaseid'] configFile = os.path.join(Usecaselocation, 'usecases.json') f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) #usecase = configSettingsJson['usecaselist'] desciption="" usecasename="" found = False for v_id in configSettingsJson['verticallist']: for p_id in v_id['usecaselist']: usecaseid = p_id.get('usecaseid') if str(usecaseid) == str(selectid) : usecasename = p_id.get('usecasename') desciption = p_id.get('desciption') usecaseid = p_id.get('usecaseid') iconname = p_id.get('iconname') prediction_input = p_id.get('prediction_input') outputtype = p_id.get('outputtype') smalldescription = p_id.get('smalldescription') trainingFeatures = p_id.get('trainingFeatures','None') if trainingFeatures != 'None': trainingFeatures = trainingFeatures.split(',') found = True break if found == True: break #print(usecaseid,selectid) context ={'usecasename':usecasename,'desciption':desciption,'prediction_input':prediction_input,'usecaseid':usecaseid,'trainingFeatures':trainingFeatures,'iconname':iconname,'smalldescription':smalldescription,'outputtype':outputtype,'usecasetab':usecasetab} return context except Exception as inst: print(inst) context = { 'error3':'error3','error1': "No UseCases to show"} return context def getusecasedetails(selectid): configFile = os.path.join(Usecaselocation, 'usecases.json') f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) #usecase = configSettingsJson['usecaselist'] desciption="" usecasename="" found = False for v_id in configSettingsJson['verticallist']: for p_id in v_id['usecaselist']: usecaseid = p_id.get('usecaseid') if str(usecaseid) == str(selectid) : usecasename = p_id.get('usecasename') desciption = p_id.get('desciption') usecaseid = p_id.get('usecaseid') modelConfig = p_id.get('modelConfig') folder = p_id.get('folder') prediction = p_id.get('prediction') prediction_input = p_id.get('prediction_input') ai_modeldata = p_id.get('modeldata') outputtype = p_id.get('outputtype') smalldescription = p_id.get('smalldescription') prediction_template = p_id.get('prediction_template') trainingFeatures = p_id.get('trainingFeatures','None') if trainingFeatures != 'None': trainingFeatures = trainingFeatures.split(',') found = True break if found == True: break #print(usecasename) return(usecasename,desciption,usecaseid,modelConfig,folder,prediction,prediction_input,ai_modeldata,outputtype,smalldescription,prediction_template,trainingFeatures) def mlpredict(request): selectid=request.POST.get('usecaseid') mlpredict =request.POST.get('mlpredict') usecasename,desciption,usecaseid,modelConfig,folder,prediction,prediction_input,ai_modeldata,outputtype,smalldescription,prediction_template,trainingFeatures = getusecasedetails(selectid) from appbe.aion_config import settings usecasetab = settings() usecasename = usecasename desciption = desciption input='' for x in prediction_input: if input != '': input += ',' input = request.POST.get(x['name']) if mlpredict in ['prediction','predictsingle']: if mlpredict == 'prediction': dataFile = request.POST.get('predictfilePath') if(os.path.isfile(dataFile) == False) or dataFile=="": context = {'usecaseid':selectid ,'dataFile':dataFile,'usecasename':usecasename,'desciption':desciption , 'error1': 'Please enter a valid csv filepath','usecasetab':usecasetab} return context, mlpredict else: inputFieldsDict = {} for feature in trainingFeatures: inputFieldsDict[feature] = request.POST.get(feature) dataFile = json.dumps(inputFieldsDict) try: predictionScriptPath= os.path.join(Usecaselocation,folder,'model',prediction) # predictionScriptPath = os.path.join(predictionscript, 'aion_prediction.py') outputStr = subprocess.check_output([sys.executable, predictionScriptPath, dataFile,input]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() predict_dict = json.loads(outputStr) #print(predict_dict) heading ='' timetaken='' print(predict_dict) if (predict_dict['status'] == 'SUCCESS'): predictionResults = predict_dict['data'] #print(predictionResults) if 'heading' in predict_dict: heading = predict_dict['heading'] if 'Time' in predict_dict: timetaken = round(predict_dict['Time'],2) if outputtype.lower() in ['similarityidentification','contextualsearch']: data = predictionResults[0] predictionResults= [] Results={} prediction = data['prediction'] i = 1 for x in prediction: te = '' for y in x: info = (str(x[y])[:100] + '...') if len(str(x[y])) > 100 else str(x[y]) te += y+': '+info+'\n\n' Results[i] = te i = i+1 predictionResults.append(Results) else: context = {'usecaseid':selectid ,'dataFile':dataFile,'prediction_input':prediction_input,'usecasename':usecasename,'desciption':desciption , 'error': 'Failed To perform prediction','usecasetab':usecasetab} return context, mlpredict print(heading) context = {'usecasename':usecasename,'desciption':desciption,'prediction_input':prediction_input,'usecaseid':selectid ,'dataFile':dataFile,'predictionResults': predictionResults,'outputtype':outputtype,'heading':heading,'timetaken':timetaken,'usecasetab':usecasetab,'trainingFeatures':trainingFeatures} return context, mlpredict except Exception as inst: print(inst) context = { 'usecaseid':selectid ,'dataFile':dataFile,'usecasename':usecasename,'desciption':desciption ,'errorp': 'Failed To perform prediction','usecasetab':usecasetab} return context, mlpredict if mlpredict == 'download_predict': # predictionResults = 'C:\\DataSets\\Classification\\bug_severity_class.csv' try: csvdata= os.path.join(Usecaselocation,folder,'Data',prediction_template) if os.path.isfile(csvdata) and os.path.exists(csvdata): df = pd.read_csv(csvdata,encoding='utf8',encoding_errors= 'replace') downloadFileName = usecasename.replace(" ", "_") + '_predict.csv' response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename='+downloadFileName df.to_csv(response, index=False) return response,mlpredict else: context = {'usecaseid':selectid ,'dataFile':dataFile,'usecasename':usecasename,'desciption':desciption, 'error': 'File not found','usecasetab':usecasetab} return context, mlpredict except Exception as inst: context = { 'usecaseid':selectid ,'usecasename':usecasename,'desciption':desciption, 'error3':'error3','error1': 'Failed To Download','usecasetab':usecasetab} return context, mltrain def process(data): cleaned_data = {"verticallist":[]} for vertical in data['verticallist']: updated_list = [] for usecase in vertical['usecaselist']: if usecase['prediction'] and usecase['prediction'] != "Not Implemented": updated_list.append(usecase) if updated_list: cleaned_data['verticallist'].append({'id':vertical['id'],'name':vertical['name'],'usecaselist':updated_list}) return cleaned_data def Aiusecases(request,selectedoption='Implemented'): try: from appbe.aion_config import settings usecasetab = settings() configFile = os.path.join(Usecaselocation, 'usecases.json') f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) if selectedoption == 'Implemented': configSettingsJson = process(configSettingsJson) usecasedetails = configSettingsJson['verticallist'] context ={'desciption1':usecasedetails,'selected':'AIusecases','usecasetab':usecasetab} return context except Exception as e: print(e) context ={'error':"No Usecases to Show",'selected':'AIusecases','usecasetab':usecasetab} return context def mltrain(request): from appbe.aion_config import settings usecasetab = settings() selectid =request.POST.get('usecaseid1') mltrain =request.POST.get('mltrain') usecasename,desciption,usecaseid,modelConfig,folder,prediction,prediction_input,ai_modeldata,outputtype,smalldescription,prediction_template,trainingFeatures = getusecasedetails(selectid) usecasename = usecasename desciption = desciption if mltrain == 'training': dataFile = request.POST.get('trainfilePath') if(os.path.isfile(dataFile) == False) or dataFile=="": context = {'usecaseid':selectid ,'datatrainFile':dataFile,'usecasename':usecasename,'desciption':desciption ,'error3':'error3','error1': 'Please enter a valid csv filepath'} return context, mltrain try: scriptPath = os.path.join(Usecaselocation,folder,'config','aion_train.py') print(scriptPath,dataFile) outputStr = subprocess.check_output([sys.executable, scriptPath, dataFile]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_learner_status:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() train = json.loads(outputStr) status = train['status'] DeployLocation = train['data']['deployLocation'] ModelType = train['data']['ModelType'] BestModel = train['data']['BestModel'] BestScore = train['data']['BestScore'] ScoreType = train['data']['ScoreType'] FeaturesUsed = train['data']['featuresused'] context={'result':train,'usecaseid':selectid ,'datatrainFile':dataFile,'usecasename':usecasename,'desciption':desciption,'status':status,'DeployLocation':DeployLocation,'ModelType':ModelType,'BestModel':BestModel,'BestScore':BestScore,'ScoreType':ScoreType,'FeaturesUsed':FeaturesUsed,'result':'result','usecasetab':usecasetab} return context,mltrain except Exception as inst: context = {'usecaseid':selectid ,'datatrainFile':dataFile,'usecasename':usecasename,'desciption':desciption, 'errort': 'Failed To perform Training','usecasetab':usecasetab} return context, mltrain if mltrain == 'download_train': try: csvdata= os.path.join(Usecaselocation,folder,'data',ai_modeldata) #print(csvdata) if os.path.isfile(csvdata) and os.path.exists(csvdata): df = pd.read_csv(csvdata,encoding='utf8',encoding_errors= 'replace') downloadFileName = usecasename.replace(" ", "_") + '_training.csv' response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename='+downloadFileName df.to_csv(response, index=False) return response,mltrain else: context = {'usecaseid':selectid ,'datatrainFile':dataFile,'usecasename':usecasename,'desciption':desciption, 'error': 'File not found','usecasetab':usecasetab} return context, mltrain except Exception as inst: context = { 'usecaseid':selectid ,'usecasename':usecasename,'desciption':desciption, 'error3':'error3','error1': 'Failed To Download','usecasetab':usecasetab} return context, mltrain ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import time from pathlib import Path import logging from datetime import datetime as dt class logg(): from appbe.dataPath import LOG_LOCATION def __init__(self, LOG_LOCATION): self.log_location = LOG_LOCATION def create_log(self,version): log_file_path = Path(self.log_location) log_file_path.mkdir(parents=True, exist_ok=True) time_stamp = dt.fromtimestamp(time.time()).strftime('%Y-%m-%d-%H-%M-%S') fileName='log_ux_'+time_stamp+'.log' filehandler = logging.FileHandler(log_file_path/fileName, 'a','utf-8') formatter = logging.Formatter('%(asctime)s %(message)s') filehandler.setFormatter(formatter) log = logging.getLogger('log_ux') log.propagate = False for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): log.removeHandler(hdlr) log.addHandler(filehandler) log.setLevel(logging.INFO) log.info('********** AION_'+str(version)+' **********') return log from typing import Union import numpy as np import pandas as pd from sklearn.neighbors import BallTree def hopkins(data_frame: Union[np.ndarray, pd.DataFrame], sampling_size: int) -> float: if type(data_frame) == np.ndarray: data_frame = pd.DataFrame(data_frame) data_frame_sample = sample_observation_from_dataset(data_frame, sampling_size) sample_distances_to_nearest_neighbours = get_distance_sample_to_nearest_neighbours( data_frame, data_frame_sample ) uniformly_selected_observations_df = simulate_df_with_same_variation( data_frame, sampling_size ) df_distances_to_nearest_neighbours = get_nearest_sample( data_frame, uniformly_selected_observations_df ) x = sum(sample_distances_to_nearest_neighbours) y = sum(df_distances_to_nearest_neighbours) if x + y == 0: raise Exception("The denominator of the hopkins statistics is null") return x / (x + y)[0] def get_nearest_sample(df: pd.DataFrame, uniformly_selected_observations: pd.DataFrame): tree = BallTree(df, leaf_size=2) dist, _ = tree.query(uniformly_selected_observations, k=1) uniformly_df_distances_to_nearest_neighbours = dist return uniformly_df_distances_to_nearest_neighbours def simulate_df_with_same_variation( df: pd.DataFrame, sampling_size: int ) -> pd.DataFrame: max_data_frame = df.max() min_data_frame = df.min() uniformly_selected_values_0 = np.random.uniform( min_data_frame[0], max_data_frame[0], sampling_size ) uniformly_selected_values_1 = np.random.uniform( min_data_frame[1], max_data_frame[1], sampling_size ) uniformly_selected_observations = np.column_stack( (uniformly_selected_values_0, uniformly_selected_values_1) ) if len(max_data_frame) >= 2: for i in range(2, len(max_data_frame)): uniformly_selected_values_i = np.random.uniform( min_data_frame[i], max_data_frame[i], sampling_size ) to_stack = (uniformly_selected_observations, uniformly_selected_values_i) uniformly_selected_observations = np.column_stack(to_stack) uniformly_selected_observations_df = pd.DataFrame(uniformly_selected_observations) return uniformly_selected_observations_df def get_distance_sample_to_nearest_neighbours(df: pd.DataFrame, data_frame_sample): tree = BallTree(df, leaf_size=2) dist, _ = tree.query(data_frame_sample, k=2) data_frame_sample_distances_to_nearest_neighbours = dist[:, 1] return data_frame_sample_distances_to_nearest_neighbours def sample_observation_from_dataset(df, sampling_size: int): if sampling_size > df.shape[0]: raise Exception("The number of sample of sample is bigger than the shape of D") data_frame_sample = df.sample(n=sampling_size) return data_frame_sample ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from appbe import exploratory_Analysis as ea import pandas as pd from appbe.checkConfiguration import start_check import json import os import ast import time import numpy as np from appfe.modelTraining.models import usecasedetails from appfe.modelTraining.models import Existusecases # from modelTraining.models import view from appbe.aion_config import kafka_setting from appbe.aion_config import running_setting from appbe.s3buckets import get_s3_bucket from appbe.gcsbuckets import get_gcs_bucket from appbe import help_Text as ht def is_value_na( value): if isinstance( value, str): return value.strip().lower() in ['','na','none'] return not value def set_ts_preprocessing(request,configSettingsJson): #Task 13052 Timeseries Preprocessing interpolationType = request.POST.get('interpolationType') ts_config = configSettingsJson['basic']['preprocessing']['timeSeriesForecasting'] for key in ts_config['interpolation']: configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['interpolation'][ key] = 'False' if interpolationType != 'na': configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['interpolation'][ interpolationType] = 'True' ts_config['rollingWindow'] = request.POST.get('rollingWindow') if ts_config['rollingWindow'] == 'True': ts_config['rollingWindowSize'] = request.POST.get('rollWindowsize') aggregation = request.POST.get('aaggregationType') for key in ts_config['aggregation']['type']: ts_config['aggregation']['type'][key]='False' if is_value_na(aggregation) == False: ts_config['aggregation']['type'][aggregation] = 'True' granularityType = request.POST.get('unitType') granularitySize = request.POST.get('garnularitysize') for key in ts_config['aggregation']['granularity']['unit']: ts_config['aggregation']['granularity']['unit'][key] = 'False' ts_config['aggregation']['granularity']['unit'][granularityType]='True' ts_config['aggregation']['granularity']['size'] = granularitySize configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']= ts_config return configSettingsJson def update_granularity(configSettingsJson,datapath=None): try: from AION.appbe.utils import set_true_option import pandas as pd from pathlib import Path MINUTES = 60 if not is_value_na(configSettingsJson['basic']['dateTimeFeature']): if not datapath: datapath = configSettingsJson['basic']['dataLocation'] if Path( datapath).exists(): df = pd.read_csv(datapath, nrows=2) if isinstance( configSettingsJson['basic']['dateTimeFeature'], list): datetime_feature = configSettingsJson['basic']['dateTimeFeature'][0] else: datetime_feature = configSettingsJson['basic']['dateTimeFeature'] datetime = pd.to_datetime(df[ datetime_feature]) if len(datetime) > 1: time_delta = (datetime[1] - datetime[0]).total_seconds() granularity_unit = configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['aggregation']['granularity']['unit'] if time_delta < (1 * MINUTES): set_true_option(granularity_unit, key='second') elif time_delta < (60 * MINUTES): set_true_option(granularity_unit, key='minute') elif time_delta < (24 * 60 * MINUTES): set_true_option(granularity_unit, key='hour') elif time_delta < (7 * 24 * 60 * MINUTES): set_true_option(granularity_unit, key='day') elif time_delta < (30 * 24 * 60 * MINUTES): set_true_option(granularity_unit, key='week') elif time_delta < (365 * 24 * 60 * MINUTES): set_true_option(granularity_unit, key='month') else: set_true_option(granularity_unit, key='year') return configSettingsJson except Exception as e: print(f'\nIgnoring error during granularity unit conversion\n:{str(e)}') return configSettingsJson def save(request): try: status = 'pass' msg = "" DEPLOY_LOCATION = request.session['deploylocation'] if request.method == 'POST': submittype = request.POST.get('BasicSubmit') if submittype != 'BasicDefault': filterjson = 'NA' timegroupingjson = 'NA' groupingjson = 'NA' if request.POST.get('filters') != '': filterjson = str(json.loads(request.POST.get('filters'))) if request.POST.get('timegroup') != '': timegroupingjson = str(json.loads(request.POST.get('timegroup'))) if request.POST.get('idgroup') != '': groupingjson = str(json.loads(request.POST.get('idgroup'))) configFile = request.session['config_json'] f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) temp = {} # Retraing settings changes # -------- S T A R T -------- prbType = request.POST.get('ProblemType') if prbType is None: prbType = request.POST.get('tempProblemType') # temp['ProblemType'] = request.POST.get('ProblemType') # request.session['Problem'] = request.POST.get('ProblemType') temp['ProblemType'] = prbType request.session['Problem'] = request.POST.get('ProblemType') # --------------------------- temp['ModelName'] = request.session['usecaseid'] temp['Version'] = str(request.session['ModelVersion']) temp['InputFeatures'] = request.POST.getlist('IncInputFeatures') temp['dataLocation'] = str(request.session['datalocation']) onlinelearning=request.POST.get('onlineLearning',None) if (onlinelearning is not None): if onlinelearning.lower() == 'onlinelearning': configSettingsJson['basic']['onlineLearning'] = 'True' if onlinelearning.lower() == 'distributedlearning': configSettingsJson['basic']['distributedLearning'] = 'True' temp['InputFeatures'] = request.POST.getlist('IncInputFeatures') temp['TargetFeatures'] = request.POST.getlist('TargetFeatures') temp['DateTimeFeatures'] = '' temp['IndexFeatures'] = '' for x in configSettingsJson['advance']['profiler']['normalization'].keys(): configSettingsJson['advance']['profiler']['normalization'][x] = 'False' configSettingsJson['advance']['profiler']['normalization']['standardScaler'] = 'True' for x in configSettingsJson['advance']['profiler']['numericalFillMethod'].keys(): configSettingsJson['advance']['profiler']['numericalFillMethod'][x] = 'False' configSettingsJson['advance']['profiler']['numericalFillMethod']['Mean'] = 'True' if onlinelearning.lower() == 'distributedlearning': for x in configSettingsJson['advance']['profiler']['categoricalFillMethod'].keys(): configSettingsJson['advance']['profiler']['categoricalFillMethod'][x] = 'False' configSettingsJson['advance']['profiler']['categoricalFillMethod']['MostFrequent'] = 'True' for x in configSettingsJson['advance']['profiler']['categoryEncoding'].keys(): configSettingsJson['advance']['profiler']['categoryEncoding'][x] = 'False' configSettingsJson['advance']['profiler']['categoryEncoding']['OneHotEncoding'] = 'True' configSettingsJson['advance']['profiler']['normalization']['standardScaler'] = 'False' for x in configSettingsJson['advance']['selector']['featureEngineering'].keys(): if x != 'numberofComponents': configSettingsJson['advance']['selector']['featureEngineering'][x] = 'False' elif prbType == 'llmFineTuning': if configSettingsJson['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'False': temp['InputFeatures'] = request.POST.getlist('IncInputFeatures') temp['TargetFeatures'] = request.POST.getlist('TargetFeatures') contextFeatures = request.POST.getlist('contextFeatures') configSettingsJson['basic']['contextFeature'] = ",".join([model for model in contextFeatures]) temp['DateTimeFeatures'] = '' temp['IndexFeatures'] = '' if request.POST.get('promptfriendlyname') != '': configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['prompt'] = request.POST.get('promptfriendlyname') else: configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['prompt'] = 'Instruction' if request.POST.get('responsefriendlyname') != '': configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['response'] = request.POST.get('responsefriendlyname') else: configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['response'] = '' else: if request.session['datatype'] == 'LLM_Document': for x in configSettingsJson['basic']['preprocessing']['llmFineTuning']['document'].keys(): configSettingsJson['basic']['preprocessing']['llmFineTuning']['document'][x] = 'False' configSettingsJson['basic']['preprocessing']['llmFineTuning']['document'][request.POST.get('dataPreprocessing')] = 'True' if request.session['datatype'] == 'LLM_Code': for x in configSettingsJson['basic']['preprocessing']['llmFineTuning']['objective'].keys(): configSettingsJson['basic']['preprocessing']['llmFineTuning']['objective'][x] = 'False' configSettingsJson['basic']['preprocessing']['llmFineTuning']['objective'][request.POST.get('llmObjective')] = 'True' for x in configSettingsJson['basic']['preprocessing']['llmFineTuning']['code'].keys(): configSettingsJson['basic']['preprocessing']['llmFineTuning']['code'][x] = 'False' configSettingsJson['basic']['preprocessing']['llmFineTuning']['code'][request.POST.get('dataPreprocessing')] = 'True' else: configSettingsJson['basic']['onlineLearning'] = 'False' configSettingsJson['basic']['distributedLearning'] = 'False' temp['InputFeatures'] = request.POST.getlist('InputFeatures') temp['TargetFeatures'] = request.POST.getlist('TargetFeatures') temp['DateTimeFeatures'] = request.POST.getlist('DateTimeFeatures') temp['IndexFeatures'] = request.POST.getlist('IndexFeatures') if (configSettingsJson['basic']['algorithms']['timeSeriesAnomalyDetection']['AutoEncoder'] == 'True'):#task 11997 if (request.POST.get('analysis') == 'MultiVariate'): configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['multiVariate'] = 'True' #task 11997 configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['uniVariate'] = 'False' #task 11997 else: #print(configSettingsJson) configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['uniVariate'] = 'True' configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['multiVariate'] = 'False' #task 11997 temp['UserID'] = '' temp['ItemID'] = '' temp['rating'] = '' temp['secondDocFeature'] = '' temp['firstDocFeature'] = '' temp['invoiceNoFeature'] = '' temp['itemFeature'] = '' model = '' if temp['ProblemType'].lower() == 'recommendersystem': model = request.POST.get('MachineLearningModels') if model == 'ItemRating': temp['ProblemType'] = 'RecommenderSystem' temp['MachineLearningModels'] = ['ItemRating'] temp['DeepLearningModels'] = '' temp['UserID'] = request.POST.get('UserID') temp['ItemID'] = request.POST.get('ItemID') temp['rating'] = request.POST.get('rating') temp['InputFeatures'] = [] temp['InputFeatures'].append(temp['UserID']) temp['InputFeatures'].append(temp['ItemID']) temp['InputFeatures'].append(temp['rating']) if model == 'TextSimilarity-Siamese': temp['ProblemType'] = 'recommenderSystem' temp['MachineLearningModels'] = ['TextSimilarity-Siamese'] temp['secondDocFeature'] = request.POST.get('secondDocFeature') temp['firstDocFeature'] = request.POST.get('firstDocFeature') temp['InputFeatures'] = [] temp['InputFeatures'].append(temp['secondDocFeature']) temp['InputFeatures'].append(temp['firstDocFeature']) if model == 'AssociationRules-Apriori': temp['ProblemType'] = 'recommenderSystem' temp['DeepLearningModels'] = '' temp['MachineLearningModels'] = ['AssociationRules-Apriori'] temp['invoiceNoFeature'] = request.POST.get('associationRuleInvoiceNo') temp['itemFeature'] = request.POST.get('associationRuleItem') temp['InputFeatures'] = [] temp['InputFeatures'].append(temp['invoiceNoFeature']) temp['InputFeatures'].append(temp['itemFeature']) temp['ScoringCriteria'] = request.POST.get('ScoringCriteria') if temp['ProblemType'].lower() not in ['recommendersystem','textsimilarity','associationrules','llmfinetuning']: temp['MachineLearningModels'] = request.POST.getlist('MachineLearningModels') temp['DeepLearningModels'] = request.POST.getlist('SelectDeepLearningModels') elif temp['ProblemType'].lower() == 'llmfinetuning': temp['MachineLearningModels'] = request.POST.getlist('MachineLearningModels') model = temp['MachineLearningModels'][0] supportedModelsSize = configSettingsJson['basic']['modelSize'][temp['ProblemType']][model] selectedModelSize = request.POST.get('modelSize') for x in supportedModelsSize.keys(): configSettingsJson['basic']['modelSize'][temp['ProblemType']][model][x] = 'False' configSettingsJson['basic']['modelSize'][temp['ProblemType']][model][selectedModelSize] = 'True' temp['noofforecasts'] = request.POST.get('noofforecasts') temp['inlierLabels'] = request.POST.get('inlierLabels') #temp['filterExpression'] = request.POST.get('filterExpression') if temp['ProblemType'].lower() in ['clustering','topicmodelling','similarityidentification','contextualsearch']: temp['TargetFeatures'] = '' configSettingsJson['basic']['modelName'] = temp['ModelName'] configSettingsJson['basic']['modelVersion'] = temp['Version'] configSettingsJson['basic']['dataLocation'] = str(temp['dataLocation']) configSettingsJson['basic']['deployLocation'] = DEPLOY_LOCATION if configSettingsJson['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'False': configSettingsJson['basic']['trainingFeatures'] = ",".join([model for model in temp['InputFeatures']]) configSettingsJson['basic']['dateTimeFeature'] = ",".join([model for model in temp['DateTimeFeatures']]) configSettingsJson['basic']['targetFeature'] = ",".join([model for model in temp['TargetFeatures']]) configSettingsJson['basic']['indexFeature'] = ",".join([model for model in temp['IndexFeatures']]) if filterjson == 'NA': configSettingsJson['basic']['filter'] = 'NA' else: configSettingsJson['basic']['filter'] = eval(filterjson) if timegroupingjson == 'NA': configSettingsJson['basic']['timegrouper'] = 'NA' else: configSettingsJson['basic']['timegrouper'] = eval(timegroupingjson) if groupingjson == 'NA': configSettingsJson['basic']['group'] = 'NA' else: configSettingsJson['basic']['group'] = eval(groupingjson) problemtyp = configSettingsJson['basic']['analysisType'] for i in list(problemtyp.keys()): configSettingsJson['basic']['analysisType'][i]='False' algorithm = configSettingsJson['basic']['algorithms'] for i in list(algorithm.keys()): for x in list(configSettingsJson['basic']['algorithms'][i].keys()): if x not in ['textSimilarityConfig','itemRatingConfig','associationRulesConfig','textSummarization']: configSettingsJson['basic']['algorithms'][i][x] = 'False' configSettingsJson['basic']['analysisType'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]] = 'True' # configSettingsJson['basic']['problem_type'] = temp['ProblemType'] scoring = configSettingsJson['basic']['scoringCriteria'] for i in list(scoring.keys()): for x in list(configSettingsJson['basic']['scoringCriteria'][i].keys()): configSettingsJson['basic']['scoringCriteria'][i][x] = 'False' if temp['ProblemType'].lower() in ["classification","regression","survivalanalysis","similarityidentification","timeseriesforecasting","contextualsearch"]: #task 11997 configSettingsJson['basic']['scoringCriteria'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]][temp['ScoringCriteria']] = 'True' # configSettingsJson['basic']['problem_type'] = temp['ProblemType'] # configSettingsJson['basic']['scoringCriteria'] = temp['ScoringCriteria'] configSettingsJson['basic']['noofforecasts'] = temp['noofforecasts'] configSettingsJson['basic']['inlierLabels'] = temp['inlierLabels'] #configSettingsJson['basic']['filterExpression'] = temp['filterExpression'] configSettingsJson['basic']['algorithms']['recommenderSystem']['itemRatingConfig']['userID'] = temp['UserID'] configSettingsJson['basic']['algorithms']['recommenderSystem']['itemRatingConfig']['itemID'] = temp['ItemID'] configSettingsJson['basic']['algorithms']['recommenderSystem']['itemRatingConfig']['rating'] = temp['rating'] configSettingsJson['basic']['algorithms']['recommenderSystem']['textSimilarityConfig']['baseFeature'] = temp['firstDocFeature'] configSettingsJson['basic']['algorithms']['recommenderSystem']['textSimilarityConfig']['comparisonFeature'] = temp['secondDocFeature'] configSettingsJson['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'] = temp['invoiceNoFeature'] configSettingsJson['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature'] = temp['itemFeature'] for x in temp['MachineLearningModels']: if temp['ProblemType'].lower() =='associationrules' or temp['ProblemType'].lower() == 'textsimilarity': temp['ProblemType'] = 'recommenderSystem' if request.POST.get('SearchType') != 'NAS' and request.POST.get('SearchType') != 'GoogleModelSearch'and request.POST.get('SearchType') != 'AutoGluon': configSettingsJson['basic']['algorithms'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]][x] = 'True' #for y in temp['DeepLearningModels']: # configSettingsJson['basic']['algorithms'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]][y] = 'True' configSettingsJson['basic']['output']['profilerStage'] = 'True' configSettingsJson['basic']['output']['selectorStage'] = 'True' for key in configSettingsJson['advance']['profiler']['textConversionMethod']: configSettingsJson['advance']['profiler']['textConversionMethod'][key] = 'False' if temp['ProblemType'].lower() != 'topicmodelling': configSettingsJson['advance']['profiler']['textConversionMethod']['TF_IDF'] ='True' else: configSettingsJson['advance']['profiler']['textConversionMethod']['CountVectors'] ='True' #print('============================') #print(temp['ProblemType'].lower()) #print('============================') if temp['ProblemType'].lower() == 'textsummarization': configSettingsJson['basic']['algorithms']['textSummarization']['Text Summarization'] = 'True' configSettingsJson['basic']['textSummarization']['KeyWords'] = str(request.POST.get('addKeywordsForSummarization')) configSettingsJson['basic']['textSummarization']['pathForKeywordFile'] = str(request.POST.get('DataFilePath')) if temp['ProblemType'].lower() not in ['recommendersystem','textsummarization','llmfinetuning']: if configSettingsJson['basic']['onlineLearning'] != 'True' and configSettingsJson['basic']['distributedLearning'] != 'True': jsonarr =request.POST.get('jsonarr') res = ast.literal_eval(jsonarr) for x in res: if x['type'].lower() == 'text': configSettingsJson['advance']['selector']['featureSelection']['allFeatures'] = 'False' configSettingsJson['advance']['selector']['featureSelection']['statisticalBased'] = 'True' configSettingsJson['advance']['selector']['featureSelection']['modelBased'] = 'False' if len(request.POST.get('traindfeatures').split(',')) > 30: configSettingsJson['advance']['selector']['featureSelection']['allFeatures'] = 'False' configSettingsJson['advance']['selector']['featureSelection']['statisticalBased'] = 'True' configSettingsJson['advance']['selector']['featureSelection']['modelBased'] = 'False' configSettingsJson['advance']['profiler']['featureDict'] = res configSettingsJson['basic']['indexFeature'] = request.POST.get('indexfeatures') configSettingsJson['basic']['trainingFeatures'] = request.POST.get('traindfeatures') configSettingsJson['basic']['dateTimeFeature'] = request.POST.get('datefeatures') if request.POST.get('SearchType') == 'GoogleModelSearch': configSettingsJson['basic']['algorithms'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]]['GoogleModelSearch_DNN'] = 'True' configSettingsJson['basic']['output']['profilerStage']= 'True' #---------- Time series Changes Task 13052 ----------------- if temp['ProblemType'].lower() == 'timeseriesforecasting': configSettingsJson = set_ts_preprocessing(request,configSettingsJson) status,msg= start_check(configSettingsJson) updatedConfigSettings = json.dumps(configSettingsJson) updatedConfigFile = request.session['config_json'] with open(updatedConfigFile, "w") as fpWrite: fpWrite.write(updatedConfigSettings) fpWrite.close() request.session['ModelStatus'] = 'Not Trained' selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] request.session['currentstate'] = 1 from appbe.telemetry import UpdateTelemetry UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'ProblemType',prbType) UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'Operation','Configured') context = {'tab': 'configure', 'temp': temp,'advconfig': configSettingsJson, 'basic_status_msg': 'Configuration Done', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'currentstate': request.session['currentstate'], 'selected': 'modeltraning','training':True,'basic_help':ht.basic_help} # return render(request, 'basicconfig.html', context) if submittype == 'BasicDefault': temp = {} temp['ModelName'] = request.session['UseCaseName'] temp['Version'] = request.session['ModelVersion'] dataLocation = str(request.session['datalocation']) df = pd.read_csv(dataLocation, encoding='latin1') featuresList = df.columns.values.tolist() datetimeFeatures = [] sequenceFeatures = [] unimportantFeatures = [] featuresRatio = {} for i in featuresList: check = ea.match_date_format(df[i]) if check == True: datetimeFeatures.append(i) unimportantFeatures.append(i) seq_check = ea.check_seq_feature(df[i]) if seq_check == True: sequenceFeatures.append(i) unimportantFeatures.append(i) ratio = ea.check_category(df[i]) if ratio != 0: featuresRatio[i] = ratio else: unimportantFeatures.append(i) targetFeature = min(featuresRatio, key=featuresRatio.get) unimportantFeatures.append(targetFeature) config = {} config['modelName'] = request.session['UseCaseName'] config['modelVersion'] = request.session['ModelVersion'] config['datetimeFeatures'] = datetimeFeatures config['sequenceFeatures'] = sequenceFeatures config['FeaturesList'] = featuresList config['unimportantFeatures'] = unimportantFeatures config['targetFeature'] = targetFeature request.session['currentstate'] = 1 context = {'tab': 'configure', 'temp': temp, 'config': config, 'currentstate': request.session['currentstate'], 'selected': 'modeltraning'} except Exception as e: print(e) import sys exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return status,msg,context def openbasicconf(request): # 10012:Decision Threshold related Changes data_is_under_RAM_threshold = True updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r+") configSettingsData = f.read() configSettingsJson = json.loads(configSettingsData) temp = {} # temp['ModelName'] = request.session['UseCaseName'] # temp['Version'] = request.session['ModelVersion'] if request.session['datatype'] == 'Video' or request.session['datatype'] == 'Image' or request.session['datatype'] == 'Document': folderLocation = str(request.session['datalocation']) dataFile = os.path.join(folderLocation, request.session['csvfullpath']) else: dataFile = str(request.session['datalocation']) # -------------------------------- 10012:Decision Threshold related Changes S T A R T ------------------------------- from appbe.dataIngestion import checkRAMThreshold data_is_under_RAM_threshold = checkRAMThreshold(request.session['datalocation']) # ------------------------------------------------------ E N D ------------------------------------------------------ # Retraing settings changes # -------- S T A R T -------- IsReTrainingCase = False if request.session['IsRetraining'] == 'Yes': IsReTrainingCase = True IsSameFeatures = True # --------------------------- featuresList = configSettingsJson['basic']['featureList'] unimportantFeatures = [] modelfeatures = configSettingsJson['basic']['trainingFeatures'] for x in featuresList: if x not in modelfeatures: unimportantFeatures.append(x) config = {} config['ModelName'] = request.session['usecaseid'] config['Version'] = request.session['ModelVersion'] config['datetimeFeatures'] = configSettingsJson['basic']['dateTimeFeature'] # .split(",") if configSettingsJson['basic']['indexFeature']: config['sequenceFeatures'] = configSettingsJson['basic']['indexFeature'] # .split(",") config['FeaturesList'] = featuresList config['unimportantFeatures'] = unimportantFeatures config['targetFeature'] = configSettingsJson['basic']['targetFeature'].split(",") problemtypes = configSettingsJson['basic']['analysisType'] onlineLearning = configSettingsJson['basic']['onlineLearning'] problem_type = "" for k in problemtypes.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break #print('123',problem_type) config['ProblemType'] = problem_type # config['ProblemType'] = configSettingsJson['basic']['problem_type'] scoring = configSettingsJson['basic']['scoringCriteria'] scoringCriteria = "" for k in scoring.keys(): if configSettingsJson['basic']['scoringCriteria'][k] == 'True': scoringCriteria = k break config['ScoringCriteria'] = scoringCriteria # config['ProblemType'] = configSettingsJson['basic']['problem_type'] # config['ScoringCriteria'] = configSettingsJson['basic']['scoringCriteria'] selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] if 'NoOfRecords' in request.session: records = request.session['NoOfRecords'] else: records = 'NA' if request.session['finalstate'] <= 1: request.session['finalstate'] = 1 request.session['currentstate'] = 1 # dataFile = str(request.session['datalocation']) # df = pd.read_csv(dataFile,encoding='utf8') if 'NoOfRecords' in request.session: noofforecast = 20 else: noofforecast = 20 config['noofforecasts'] = noofforecast if 'numericFeature' in request.session: numericFeature = request.session['numericFeature'] else: numericFeature = '' problemType = 'classification' for key in configSettingsJson['basic']['analysisType']: if configSettingsJson['basic']['analysisType'][key] == 'True': problemType = key break scoringCreteria = 'NA' if problemType in ['classification','regression','survivalAnalysis','timeSeriesForecasting']: #task 11997 for key in configSettingsJson['basic']['scoringCriteria'][problemType]: if configSettingsJson['basic']['scoringCriteria'][problemType][key] == 'True': scoringCreteria = key break selectAlgo = "" if problemType in ['classification','regression','timeSeriesForecasting', 'timeSeriesAnomalyDetection', 'recommenderSystem','clustering','anomalyDetection','topicModelling','survivalAnalysis','videoForecasting','imageClassification','objectDetection','stateTransition','llmFineTuning']: #task 11997 for key in configSettingsJson['basic']['algorithms'][problemType]: if configSettingsJson['basic']['algorithms'][problemType][key] == 'True': if selectAlgo != "": selectAlgo += ',' selectAlgo += key modelSize = '' if problemType == 'llmFineTuning': for key in configSettingsJson['basic']['modelSize']['llmFineTuning'][selectAlgo].keys(): if configSettingsJson['basic']['modelSize']['llmFineTuning'][selectAlgo][key] == 'True': modelSize = key break featuresdict = [feature['feature'] for feature in configSettingsJson['advance']['profiler']['featureDict']] context = {'tab': 'tabconfigure','modelSize':modelSize,'featuresdict':featuresdict, 'configsettings': configSettingsJson, 'temp': temp, 'config': config,'numericFeature':numericFeature,'onlineLearning':onlineLearning, 'noOfRecords': records, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'problemType':problemType,'scoringCreteria':scoringCreteria,'selectAlgo':selectAlgo, 'ModelVersion': ModelVersion, 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'], 'selected': 'modeltraning','IsSameFeatures':IsSameFeatures,'IsReTrainingCase':IsReTrainingCase,'basic_help':ht.basic_help # 10012:Decision Threshold related changes , 'DLCheckpoint':data_is_under_RAM_threshold} return context def gotoconf(request): selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] try: # 10012:Decision Threshold related Changes data_is_under_RAM_threshold = True ModelName = usecasedetails.objects.get(id=request.session['ModelName']) Version = request.session['ModelVersion'] import os if request.session['datatype'] in ['Video', 'Image','Document','Object']: folderLocation = str(request.session['datalocation']) dataFile = os.path.join(folderLocation, request.session['csvfullpath']) else: dataFile = str(request.session['datalocation']) # -------------------------------- 10012:Decision Threshold related Changes S T A R T ------------------------------- from appbe.dataIngestion import checkRAMThreshold data_is_under_RAM_threshold = checkRAMThreshold(request.session['datalocation']) # ------------------------------------------------------ E N D ------------------------------------------------------ if request.session['datatype'] not in ['LLM_Document','LLM_Code']: from appbe.eda import ux_eda if 'delimiter' not in request.session: request.session['delimiter'] = ',' if 'textqualifier' not in request.session: request.session['textqualifier'] = '"' eda_obj = ux_eda(dataFile,request.session['delimiter'],request.session['textqualifier'],optimize=1) featuresList,datetimeFeatures,sequenceFeatures,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catFeatures = eda_obj.getFeatures() else: featuresList = [] featuresList.append('Instruction') datetimeFeatures=[] sequenceFeatures=[] constantFeature=[] textFeature=[] targetFeature='Response' numericCatFeatures = [] numericFeature=[] catFeatures=[] featuresListJson = [] for x in featuresList: featureOperation={} featureOperation['feature'] = x if x in datetimeFeatures: featureOperation['type'] = 'date' featureOperation['fillMethod'] = 'na' featureOperation['categoryEncoding'] = 'na' elif x in textFeature: featureOperation['type'] = 'text' featureOperation['fillMethod'] = 'na' featureOperation['categoryEncoding'] = 'na' elif x in sequenceFeatures: featureOperation['type'] = 'index' featureOperation['fillMethod'] = 'median' featureOperation['categoryEncoding'] = 'na' elif (x in catFeatures) or (x in constantFeature): featureOperation['type'] = 'categorical' featureOperation['fillMethod'] = 'mode' featureOperation['categoryEncoding'] = 'targetEncoding' else: featureOperation['type'] = 'numerical' featureOperation['fillMethod'] = 'medium' featureOperation['categoryEncoding'] = 'na' featureOperation['outlierDetection'] = 'disable' featureOperation['outlierOperation'] = 'nochange' featureOperation['normalizer'] = 'none' featuresListJson.append(featureOperation) request.session['numericFeature'] = numericFeature records = 0 import os if os.path.isfile(dataFile): for chunk in pd.read_csv(dataFile, chunksize=20000,encoding="utf-8",encoding_errors= 'replace'): records = records+len(chunk) request.session['NoOfRecords'] = records filetimestamp = str(int(time.time())) CONFIG_FILE_PATH = request.session['configfilepath'] config_json_filename = os.path.join(CONFIG_FILE_PATH, 'AION_' + filetimestamp + '.json') outputfile = os.path.join(CONFIG_FILE_PATH, 'AION_OUTPUT_' + filetimestamp + '.json') request.session['outputfilepath'] = str(outputfile) modelname = request.session['usecaseid'] modelname = modelname.replace(" ", "_") DEPLOY_LOCATION = request.session['deploylocation'] request.session['logfilepath'] = os.path.join(DEPLOY_LOCATION, modelname,str(Version),'log','model_training_logs.log') request.session['config_json'] = config_json_filename #request.session['ModelVersion'] = Version request.session['ModelStatus'] = 'Not Trained' # p = Existusecases(DataFilePath=dataFile, DeployPath=DEPLOY_LOCATION, Status='Not Trained', # ConfigPath=config_json_filename, Version=Version, ModelName=ModelName, # TrainOuputLocation=outputfile) # p.save() # from AION_UX import telemetry # telemetry.telemetry_data('UseCaseCreated',modelname+'_'+str(Version),'UseCaseCreated') # request.session['modelid'] = p.id temp = {} temp['ModelName'] = request.session['usecaseid'] temp['Version'] = request.session['ModelVersion'] ''' featuresList = features #df.columns.values.tolist() datetimeFeatures = datetimeFeatures = [] sequenceFeatures = [] unimportantFeatures = [] featuresRatio = {} for i in featuresList: check = ea.match_date_format(df[i]) if check == True: datetimeFeatures.append(i) unimportantFeatures.append(i) seq_check = ea.check_seq_feature(df[i]) if seq_check == True: sequenceFeatures.append(i) unimportantFeatures.append(i) ratio = ea.check_category(df[i]) if ratio != 0: featuresRatio[i] = ratio else: unimportantFeatures.append(i) targetFeature = min(featuresRatio, key=featuresRatio.get) unimportantFeatures.append(targetFeature) ''' unimportantFeatures = list(datetimeFeatures) unimportantFeatures.extend(sequenceFeatures) #unimportantFeatures = list(set(unimportantFeatures) + set(sequenceFeatures)) unimportantFeatures.append(targetFeature) config = {} noofforecast = 20 config['ModelName'] = request.session['usecaseid'] config['Version'] = request.session['ModelVersion'] config['datetimeFeatures'] = datetimeFeatures config['sequenceFeatures'] = sequenceFeatures config['FeaturesList'] = featuresList config['unimportantFeatures'] = unimportantFeatures config['targetFeature'] = targetFeature config['noofforecasts'] = noofforecast DEFAULT_FILE_PATH = request.session['defaultfilepath'] # Retraing settings changes # -------- S T A R T -------- IsReTrainingCase = False if request.session['IsRetraining'] == 'Yes': id = request.session['ModelName'] p = usecasedetails.objects.get(id=id) model = Existusecases.objects.filter(ModelName=p) indexVal = model.count() - 1 configFile = str(model[indexVal].ConfigPath) # configFile = str(model[0].ConfigPath) # request.session['IsRetraining'] = 'No' IsReTrainingCase = True # --------------------------- else: configFile = os.path.join(DEFAULT_FILE_PATH, 'aion_config.json') f = open(configFile, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) # Retraing settings changes # -------- S T A R T -------- pickDefaultSettings = False IsSameFeatures = False if 'featureList' not in configSettingsJson['basic']: pickDefaultSettings = True IsSameFeatures = True else: if configSettingsJson['basic']['featureList'] == featuresList: pickDefaultSettings = False IsSameFeatures = True else: pickDefaultSettings = True if pickDefaultSettings: # --------------------------- configSettingsJson['basic']['featureList'] = featuresList configSettingsJson['basic']['dateTimeFeature'] = ",".join([feature for feature in datetimeFeatures]) configSettingsJson['basic']['indexFeature'] = sequenceFeatures trainingFeatures = list(set(featuresList) - set(unimportantFeatures)) configSettingsJson['basic']['trainingFeatures'] = ",".join([feature for feature in trainingFeatures]) configSettingsJson['basic']['targetFeature'] = targetFeature if request.session['datatype'].lower() in ['video','image','object','document','llm_document','llm_code']: for x in configSettingsJson['basic']['analysisType'].keys(): configSettingsJson['basic']['analysisType'][x] = 'False' configSettingsJson['basic']['folderSettings']['fileType'] = request.session['datatype'] configSettingsJson['basic']['folderSettings']['labelDataFile'] = request.session['csvfullpath'] configSettingsJson['basic']['folderSettings']['fileExtension'] = request.session['fileExtension'] if request.session['datatype'] in ['LLM_Document','LLM_Code']: configSettingsJson['basic']['analysisType']['llmFineTuning'] = 'True' configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['prompt']='Instruction' configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['response']='Response' configSettingsJson['basic']['preprocessing']['llmFineTuning']['unstructuredData'] = 'True' elif request.session['datatype'] == 'Video': configSettingsJson['basic']['analysisType']['videoForecasting'] = 'True' elif request.session['datatype'] == 'Image': configSettingsJson['basic']['analysisType']['imageClassification'] = 'True' elif request.session['datatype'] == 'Object': configSettingsJson['basic']['analysisType']['objectDetection'] = 'True' elif request.session['datatype'].lower() == 'document': df = pd.read_csv(dataFile, encoding='utf8',sep=request.session['delimiter'],quotechar=request.session['textqualifier'],nrows=100) noOfEmotyLevels = 0 shape = df.shape if shape[1] == 2: noOfEmotyLevels = df['Label'].isnull().sum() #print(noOfEmotyLevels) if noOfEmotyLevels == 100: configSettingsJson['basic']['analysisType']['topicModelling'] = 'True' else: configSettingsJson['basic']['analysisType']['classification'] = 'True' else: if 'uploadfiletype' in request.session: configSettingsJson['basic']['folderSettings']['fileType'] = request.session['uploadfiletype'] configSettingsJson['basic']['folderSettings']['labelDataFile'] = request.session['uploadLocation'] try: if isinstance(datetimeFeatures, list): if len(datetimeFeatures) != 0: configSettingsJson = update_granularity(configSettingsJson,datapath=dataFile) elif isinstance(datetimeFeatures, str): if datetimeFeatures != '': configSettingsJson = update_granularity(configSettingsJson,datapath=dataFile) except: pass # Retraing settings changes # -------- S T A R T -------- tot_count=len(numericCatFeatures) #task 11997 if (tot_count > 1): configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['multiVariate'] = 'True' configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['uniVariate'] = 'False' else: configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['uniVariate'] = 'True' configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['multiVariate'] = 'False' if 'delimiter' in request.session: configSettingsJson['basic']['fileSettings']['delimiters'] = request.session['delimiter'] else: configSettingsJson['basic']['fileSettings']['delimiters'] = ',' if 'textqualifier' in request.session: configSettingsJson['basic']['fileSettings']['textqualifier'] = request.session['textqualifier'] else: request.session['textqualifier'] = '"' configSettingsJson['advance']['profiler']['featureDict'] = featuresListJson configSettingsJson['basic']['onlineLearning'] = 'False' configSettingsJson['basic']['dataLocation'] = request.session['datalocation'] configSettingsJson['basic']['noOfRecords'] = request.session['NoOfRecords'] onlineLearning = configSettingsJson['basic']['onlineLearning'] updatedConfigSettings = json.dumps(configSettingsJson) with open(config_json_filename, "w") as fpWrite: fpWrite.write(updatedConfigSettings) fpWrite.close() ''' p = Existusecases(DataFilePath=dataFile, DeployPath=DEPLOY_LOCATION, Status='Not Trained', ConfigPath=config_json_filename, Version=Version, ModelName=ModelName, TrainOuputLocation=outputfile) p.save() ''' p = Existusecases.objects.get(ModelName=ModelName,Version=Version) p.DataFilePath = dataFile p.DeployPath = DEPLOY_LOCATION p.ConfigPath = config_json_filename p.TrainOuputLocation = outputfile p.save() #from appbe import telemetry #telemetry.telemetry_data('UseCaseCreated',modelname+'_'+str(Version),'UseCaseCreated') request.session['modelid'] = p.id # --------------------------- from appbe.compute import selectedInfratructure infra = selectedInfratructure() if infra.lower() in ['aws','gcp']: problemType = 'llmFineTuning' else: problemType = 'classification' #print(problemType) for key in configSettingsJson['basic']['analysisType']: if configSettingsJson['basic']['analysisType'][key] == 'True': problemType = key break scoringCreteria = 'NA' if problemType in ['classification','regression','survivalAnalysis','timeSeriesForecasting']: #task 11997 for key in configSettingsJson['basic']['scoringCriteria'][problemType]: if configSettingsJson['basic']['scoringCriteria'][problemType][key] == 'True': scoringCreteria = key break selectAlgo = "" if problemType in ['classification','regression','timeSeriesForecasting','timeSeriesAnomalyDetection', 'recommenderSystem','clustering','anomalyDetection','topicModelling','survivalAnalysis','videoForecasting','imageClassification','objectDetection','stateTransition','llmFineTuning']: #task 11997 for key in configSettingsJson['basic']['algorithms'][problemType]: if configSettingsJson['basic']['algorithms'][problemType][key] == 'True': if selectAlgo != "": selectAlgo += ',' selectAlgo += key modelSize = '' if problemType == 'llmFineTuning': for key in configSettingsJson['basic']['modelSize']['llmFineTuning'][selectAlgo].keys(): if configSettingsJson['basic']['modelSize']['llmFineTuning'][selectAlgo][key] == 'True': modelSize = key break movenext = True request.session['finalstate'] = 1 request.session['currentstate'] = 1 context = {'tab': 'tabconfigure','modelSize':modelSize,'tot_count':tot_count, 'temp': temp, 'configsettings': configSettingsJson, 'config': config,'numericFeature':numericFeature,'onlineLearning':onlineLearning, 'noOfRecords': records, 'selected_use_case': selected_use_case,'problemType':problemType,'scoringCreteria':scoringCreteria,'selectAlgo':selectAlgo,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'movenext': movenext, 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'], 'selected': 'modeltraning','advance':True,'basic_help':ht.basic_help # Retraing settings changes ,'IsSameFeatures':IsSameFeatures,'IsReTrainingCase':IsReTrainingCase # 10012:Decision Threshold related ,'DLCheckpoint':data_is_under_RAM_threshold} return context except UnicodeDecodeError as e: print(e) context = {'tab': 'tabconfigure','selected_use_case': selected_use_case,'ModelVersion': ModelVersion,'ModelStatus': ModelStatus,'selected': 'modeltraning','error': 'File Reading Error: '+str(e)} return context except Exception as e: print(e) import sys,os exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) context = {'tab': 'tabconfigure','selected_use_case': selected_use_case,'ModelVersion': ModelVersion,'ModelStatus': ModelStatus,'selected': 'modeltraning','error': 'Config Error: '+str(e)} return context ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import json import os,sys from appbe import help_Text as ht def save(request): from appbe.dataPath import DEFAULT_FILE_PATH if request.method == 'POST': submittype = request.POST.get('AdvanceSubmit') if submittype != 'AdvanceDefault': configFile = request.session['config_json'] f = open(configFile, "r+") configSettingsData = f.read() configSettings = json.loads(configSettingsData) try: if configSettings['basic']['analysisType']['llmFineTuning'].lower() == 'false': numericselectedmethod = request.POST.get('numericfillmethod') for x in list(configSettings['advance']['profiler']['numericalFillMethod'].keys()): configSettings['advance']['profiler']['numericalFillMethod'][x] = 'False' configSettings['advance']['profiler']['numericalFillMethod'][numericselectedmethod] = 'True' categoricalselectedmethod = request.POST.get('categorialfillmethod') for x in list(configSettings['advance']['profiler']['categoricalFillMethod'].keys()): configSettings['advance']['profiler']['categoricalFillMethod'][x] = 'False' configSettings['advance']['profiler']['categoricalFillMethod'][categoricalselectedmethod] = 'True' categoryEncodingMethod = request.POST.get('categoryencoding') for x in list(configSettings['advance']['profiler']['categoryEncoding'].keys()): configSettings['advance']['profiler']['categoryEncoding'][x] = 'False' configSettings['advance']['profiler']['categoryEncoding'][categoryEncodingMethod] = 'True' outlierDetection = request.POST.get('outlierDetection') for x in list(configSettings['advance']['profiler']['outlierDetection'].keys()): configSettings['advance']['profiler']['outlierDetection'][x] = 'False' if outlierDetection != 'Disable': configSettings['advance']['profiler']['outlierDetection'][outlierDetection] = 'True' #configSettings['advance']['profiler']['outlierDetectionStatus'] = request.POST.get('AnamolyDetectionStatus') #configSettings['advance']['profiler']['outlierDetectionMethod'] = request.POST.get('AnaTreatmentMethod') configSettings['advance']['profiler']['misValueRatio'] = request.POST.get('MisValueRatio') #configSettings['advance']['profiler']['categoricalToNumeric'] = request.POST.get('CategoricalToNumeric') configSettings['advance']['profiler']['numericFeatureRatio'] = request.POST.get('NumFeatureRatio') configSettings['advance']['profiler']['categoryMaxLabel'] = request.POST.get('CatMaxLabels') configSettings['advance']['selector']['categoryMaxLabel'] = request.POST.get('CatMaxLabels') normalizationtypes = configSettings['advance']['profiler']['normalization'] for k in normalizationtypes.keys(): configSettings['advance']['profiler']['normalization'][k] = 'False' if request.POST.get('NormalizationMethod').lower() != 'none': configSettings['advance']['profiler']['normalization'][request.POST.get('NormalizationMethod')] = 'True' #configSettings['advance']['profiler']['normalizationMethod'] = request.POST.get('NormalizationMethod') configSettings['advance']['profiler']['removeDuplicate'] = request.POST.get('removeDuplicate') # ---------------------------------------------- Debiasing Changes ---------------------------------------------- configSettings['advance']['profiler']['deBiasing']['FeatureName'] = request.POST.get('InputFeature') configSettings['advance']['profiler']['deBiasing']['ClassName'] = request.POST.get('InputClass') configSettings['advance']['profiler']['deBiasing']['Algorithm'] = request.POST.get('InputAlgorithm') configSettings['advance']['profiler']['deBiasing']['TargetFeature'] = configSettings['basic']['targetFeature'] # ---------------------------------------------- ---------------------------------------------- problemtypes = configSettings['basic']['analysisType'] problem_type = "" for k in problemtypes.keys(): if configSettings['basic']['analysisType'][k] == 'True': problem_type = k break if configSettings['basic']['analysisType']['llmFineTuning'].lower() == 'false' and configSettings['basic']['onlineLearning'].lower() == 'false' and configSettings['basic']['distributedLearning'].lower() == 'false': configSettings['advance']['profiler']['textCleaning']['removeNoise'] = request.POST.get('noiseStatus') # -------------------------------- 12301:Remove Noise Config related Changes S T A R T -------------------------------- if request.POST.get('noiseStatus') == 'True': configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['decodeHTML'] = request.POST.get('DecodeHTML') configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeHyperLinks'] = request.POST.get('removeHyperlinks') configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeMentions'] = request.POST.get('RemoveMentions') configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeHashtags'] = request.POST.get('removeHashtags') configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeEmoji'] = request.POST.get('removeEmoji') configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['unicodeToAscii'] = request.POST.get('unicodeToAscii') configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeNonAscii'] = request.POST.get('removeNonAscii') else: configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['decodeHTML'] = "False" configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeHyperLinks'] = "False" configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeMentions'] = "False" configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeHashtags'] = "False" configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeEmoji'] = "False" configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['unicodeToAscii'] = "False" configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeNonAscii'] = "False" # ---------------------------------------------------------------- E N D ---------------------------------------------------------------- configSettings['advance']['profiler']['textCleaning']['expandContractions'] = request.POST.get( 'expandContractions') configSettings['advance']['profiler']['textCleaning']['normalize'] = request.POST.get('normalize') if (request.POST.get('normalizeMethod') == 'Lemmatization'): configSettings['advance']['profiler']['textCleaning']['normalizeMethod']['lemmatization'] = "True" configSettings['advance']['profiler']['textCleaning']['normalizeMethod']['stemming'] = "False" else: configSettings['advance']['profiler']['textCleaning']['normalizeMethod']['stemming'] = "True" configSettings['advance']['profiler']['textCleaning']['normalizeMethod']['lemmatization'] = "False" configSettings['advance']['profiler']['textCleaning']['replaceAcronym'] = request.POST.get('replaceAcronym') if request.POST.get('acronymDict') != '' and request.POST.get('acronymDict') != 'None': configSettings['advance']['profiler']['textCleaning']['acronymConfig']['acronymDict'] = eval(request.POST.get( 'acronymDict')) configSettings['advance']['profiler']['textCleaning']['correctSpelling'] = request.POST.get( 'correctSpelling') configSettings['advance']['profiler']['textCleaning']['removeStopwords'] = request.POST.get( 'removeStopwords') if (request.POST.get('ExtendOrReplace') == 'NA'): configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['extend'] = "False" configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['replace'] = "False" elif (request.POST.get('ExtendOrReplace') == 'Extend'): configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['extend'] = "True" configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['replace'] = "False" else: configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['extend'] = "False" configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['replace'] = "True" configSettings['advance']['profiler']['textCleaning']['stopWordsConfig'][ 'stopwordsList'] = request.POST.get('stopwordsList') configSettings['advance']['profiler']['textCleaning']['removePunctuation'] = request.POST.get( 'removePunctuation') configSettings['advance']['profiler']['textCleaning']['removePunctuationConfig'][ 'removePuncWithinTokens'] = request.POST.get('removePuncWithinTokens') configSettings['advance']['profiler']['textCleaning']['removeNumericTokens'] = request.POST.get( 'removeNumericTokens') configSettings['advance']['profiler']['textCleaning']['removeNumericConfig'][ 'removeNumeric_IncludeSpecialCharacters'] = request.POST.get('removeNumeric_IncludeSpecialCharacters') if (request.POST.get('tokenizationLib') == 'nltk'): configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['nltk'] = "True" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][ 'textblob'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['spacy'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['keras'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][ 'gensim'] = "False" elif (request.POST.get('tokenizationLib') == 'textblob'): configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['nltk'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][ 'textblob'] = "True" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['spacy'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['keras'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][ 'gensim'] = "False" elif (request.POST.get('tokenizationLib') == 'spacy'): configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['nltk'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][ 'textblob'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['spacy'] = "True" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['keras'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][ 'gensim'] = "False" elif (request.POST.get('tokenizationLib') == 'keras'): configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['nltk'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][ 'textblob'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['spacy'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['keras'] = "True" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][ 'gensim'] = "False" else: configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['nltk'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][ 'textblob'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['spacy'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['keras'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['gensim'] = "True" if (request.POST.get('lemmatizationLib') == 'nltk'): configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib']['nltk'] = "True" configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib'][ 'textblob'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib'][ 'spacy'] = "False" elif (request.POST.get('lemmatizationLib') == 'textblob'): configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib']['nltk'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib'][ 'textblob'] = "True" configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib'][ 'spacy'] = "False" else: configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib']['nltk'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib'][ 'textblob'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib']['spacy'] = "True" if (request.POST.get('stopwordsRemovalLib') == 'nltk'): configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][ 'nltk'] = "True" configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][ 'gensim'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][ 'spacy'] = "False" elif (request.POST.get('stopwordsRemovalLib') == 'gensim'): configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][ 'nltk'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][ 'gensim'] = "True" configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][ 'spacy'] = "False" else: configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][ 'nltk'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][ 'gensim'] = "False" configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][ 'spacy'] = "True" configSettings['advance']['profiler']['textFeatureExtraction']['n_grams'] = request.POST.get('n_grams') configSettings['advance']['profiler']['textFeatureExtraction']['n_grams_config'][ 'min_n'] = int(request.POST.get('range_min_n')) configSettings['advance']['profiler']['textFeatureExtraction']['n_grams_config'][ 'max_n'] = int(request.POST.get('range_max_n')) configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags'] = request.POST.get('pos_tags') if (request.POST.get('pos_tags_lib') == 'nltk'): configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['nltk'] = "True" configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['textblob'] = "False" configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['spacy'] = "False" elif (request.POST.get('pos_tags_lib') == 'textblob'): configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['nltk'] = "False" configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['textblob'] = "True" configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['spacy'] = "False" else: configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['nltk'] = "False" configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['textblob'] = "False" configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['spacy'] = "True" textconvertionmethods = configSettings['advance']['profiler']['textConversionMethod'] for k in textconvertionmethods.keys(): configSettings['advance']['profiler']['textConversionMethod'][k] = 'False' if problem_type.lower() not in ['similarityidentification','contextualsearch']: configSettings['advance']['profiler']['textConversionMethod'][request.POST.get('textConvertionMethod')] = 'True' if 'embeddingSize' in configSettings['advance']['profiler']: glove = configSettings['advance']['profiler']['embeddingSize']['Glove'] for k in glove.keys(): configSettings['advance']['profiler']['embeddingSize']['Glove'][k] = 'False' configSettings['advance']['profiler']['embeddingSize']['Glove'][request.POST.get('txtglovedimensions')] = 'True' fastText = configSettings['advance']['profiler']['embeddingSize']['FastText'] for k in fastText.keys(): configSettings['advance']['profiler']['embeddingSize']['FastText'][k] = 'False' configSettings['advance']['profiler']['embeddingSize']['FastText'][request.POST.get('txtFastTextdimensions')] = 'True' if 'LatentSemanticAnalysis' in configSettings['advance']['profiler']['embeddingSize']: LatentSemanticAnalysis = configSettings['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'] for k in LatentSemanticAnalysis.keys(): configSettings['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'][k] = 'False' configSettings['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'][request.POST.get('txttfidfdimensions')] = 'True' if 'TF_IDF' in configSettings['advance']['profiler']['embeddingSize']: configSettings['advance']['profiler']['embeddingSize']['TF_IDF']['maxFeatures'] = request.POST.get('tfidfmaxfeatures') if 'CountVectors' in configSettings['advance']['profiler']['embeddingSize']: configSettings['advance']['profiler']['embeddingSize']['CountVectors']['maxFeatures'] = request.POST.get('cvmaxfeatures') if problem_type.lower() == 'imageclassification': configSettings['advance']['image_config']['img_width'] = int(request.POST.get('img_width')) configSettings['advance']['image_config']['img_height'] = int(request.POST.get('img_height')) configSettings['advance']['image_config']['img_channel'] = int(request.POST.get('img_channel')) configSettings['advance']['image_config']['lr'] = float(request.POST.get('lr')) configSettings['advance']['image_config']['epochs'] = int(request.POST.get('epochs')) configSettings['advance']['image_config']['test_split_ratio'] = float(request.POST.get('test_split_ratio')) if problem_type.lower() == "llmfinetuning": configSettings = llmadvancesettings(configSettings,request) if problem_type.lower() == 'objectdetection' or problem_type.lower() == 'imageclassification': configSettings['advance']['ImageAugmentation']['Enable'] = request.POST.get('advance_ImageAugmentation_Enable') configSettings['advance']['ImageAugmentation']['KeepAugmentedImages'] = request.POST.get('advance_ImageAugmentation_keepAugmentedImages') configSettings['advance']['ImageAugmentation']['Noise']['Blur'] = request.POST.get('advance_ImageAugmentation_Noise_Blur') configSettings['advance']['ImageAugmentation']['Noise']['Brightness'] = request.POST.get('advance_ImageAugmentation_Noise_Brightness') configSettings['advance']['ImageAugmentation']['Noise']['Contrast'] = request.POST.get('advance_ImageAugmentation_Noise_Contrast') configSettings['advance']['ImageAugmentation']['Transformation']['Flip'] = request.POST.get('advance_ImageAugmentation_Transformation_Flip') configSettings['advance']['ImageAugmentation']['Transformation']['Rotate'] = request.POST.get('advance_ImageAugmentation_Transformation_Rotate') configSettings['advance']['ImageAugmentation']['Transformation']['Shift'] = request.POST.get('advance_ImageAugmentation_Transformation_Shift') configSettings['advance']['ImageAugmentation']['Transformation']['Crop'] = request.POST.get('advance_ImageAugmentation_Transformation_Crop') configSettings['advance']['ImageAugmentation']['configuration']['Blur']['noOfImages'] = request.POST.get('noofblurimages') configSettings['advance']['ImageAugmentation']['configuration']['Blur']['limit'] = request.POST.get('limitblurimage') configSettings['advance']['ImageAugmentation']['configuration']['Brightness']['noOfImages'] = request.POST.get('noofbrightnessimages') configSettings['advance']['ImageAugmentation']['configuration']['Brightness']['limit'] = request.POST.get('limitbrightnessimage') configSettings['advance']['ImageAugmentation']['configuration']['Contrast']['noOfImages'] = request.POST.get('noofcontrastimages') configSettings['advance']['ImageAugmentation']['configuration']['Contrast']['limit'] = request.POST.get('limitcontrastimage') configSettings['advance']['ImageAugmentation']['configuration']['Flip']['noOfImages'] = request.POST.get('noofflipimages') configSettings['advance']['ImageAugmentation']['configuration']['Rotate']['noOfImages'] = request.POST.get('noofrotateimages') configSettings['advance']['ImageAugmentation']['configuration']['Shift']['noOfImages'] = request.POST.get('noofshiftimages') configSettings['advance']['ImageAugmentation']['configuration']['Crop']['noOfImages'] = request.POST.get('noofcropimages') configSettings['advance']['selector']['selectionMethod']['featureSelection'] = 'False' configSettings['advance']['selector']['selectionMethod']['featureEngineering'] = 'False' configSettings['advance']['selector']['featureSelection']['allFeatures'] = 'False' configSettings['advance']['selector']['featureSelection']['statisticalBased'] = 'False' configSettings['advance']['selector']['featureSelection']['modelBased'] = 'False' if(request.POST.get('selectionMethod') == 'FeatureSelection'): configSettings['advance']['selector']['selectionMethod']['featureSelection'] = 'True' else: configSettings['advance']['selector']['selectionMethod']['featureEngineering'] = 'True' if request.POST.get('allFeatures'): configSettings['advance']['selector']['featureSelection']['allFeatures'] = request.POST.get('allFeatures') if request.POST.get('statisticalBased'): configSettings['advance']['selector']['featureSelection']['statisticalBased'] = request.POST.get('statisticalBased') if request.POST.get('modelBased'): configSettings['advance']['selector']['featureSelection']['modelBased'] = request.POST.get('modelBased') dimentionalityreductionmethod = request.POST.get('dimentionalityreductionmethod') for x in list(configSettings['advance']['selector']['featureEngineering'].keys()): if x != 'numberofComponents': configSettings['advance']['selector']['featureEngineering'][x] = 'False' configSettings['advance']['selector']['featureEngineering'][dimentionalityreductionmethod] = 'True' configSettings['advance']['selector']['featureEngineering']['numberofComponents'] = request.POST.get('numberofComponents') #configSettings['advance']['selector']['categoricalFeatureRatio'] = request.POST.get('CatFeatureRatio') configSettings['advance']['selector']['statisticalConfig']['correlationThresholdFeatures'] = request.POST.get('correlationThresholdFeatures') configSettings['advance']['selector']['statisticalConfig']['correlationThresholdTarget'] = request.POST.get('correlationThresholdTarget') configSettings['advance']['selector']['statisticalConfig']['pValueThresholdFeatures'] = request.POST.get('pValueThresholdFeatures') configSettings['advance']['selector']['statisticalConfig']['pValueThresholdTarget'] = request.POST.get('pValueThresholdTarget') configSettings['advance']['selector']['statisticalConfig']['varianceThreshold'] = request.POST.get('VarianceThreshold') if problem_type.lower() == 'recommendersystem': configSettings['advance']['recommenderparam']['svd_params']= eval(request.POST.get('svd_params')) configSettings['advance']['associationrule']['modelParams']['apriori'] = eval(request.POST.get('apriori')) configSettings['advance']['textSimilarityConfig'] = eval(request.POST.get('textsimilarity')) if configSettings['basic']['distributedLearning'].lower() == 'true': configSettings['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Extreme Gradient Boosting (XGBoost)'] = eval(request.POST.get('classDistributedXGBoost')) configSettings['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Light Gradient Boosting (LightGBM)'] = eval(request.POST.get('classDistributedLightGBM')) configSettings['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Extreme Gradient Boosting (XGBoost)'] = eval(request.POST.get('DistributedXGBoostreg')) configSettings['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Light Gradient Boosting (LightGBM)'] = eval(request.POST.get('DistributedLightGBMreg')) if configSettings['basic']['onlineLearning'].lower() != 'true' and configSettings['basic']['distributedLearning'].lower() != 'true': if (problem_type.lower() == 'classification') or (problem_type.lower() == 'regression') or (problem_type.lower() == 'clustering') or (problem_type.lower() == 'topicmodelling'): if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Logistic Regression'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Logistic Regression'] = eval(request.POST.get('classification_LogisticRegression')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Naive Bayes'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Naive Bayes'] = eval(request.POST.get('classification_GaussianNB')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Support Vector Machine'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Support Vector Machine'] = eval(request.POST.get('classification_SVC')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['K Nearest Neighbors'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['K Nearest Neighbors'] = eval(request.POST.get('classification_KNeighborsClassifier')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Decision Tree'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Decision Tree'] = eval(request.POST.get('classification_DecisionTreeClassifier')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Random Forest'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Random Forest'] = eval(request.POST.get('classification_RandomForestClassifier')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Gradient Boosting'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Gradient Boosting'] = eval(request.POST.get('classification_GradientBoostingClassifier')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Extreme Gradient Boosting (XGBoost)'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Extreme Gradient Boosting (XGBoost)'] = eval(request.POST.get('classification_ExtremeGradientBoostingClassifier')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Light Gradient Boosting (LightGBM)'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Light Gradient Boosting (LightGBM)'] = eval(request.POST.get('classification_LightGradientBoostingClassifier')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Categorical Boosting (CatBoost)'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Categorical Boosting (CatBoost)'] = eval(request.POST.get('classification_CategoricalBoostingClassifier')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Linear Regression'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Linear Regression'] = eval(request.POST.get('regression_LinearRegression')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Lasso'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Lasso'] = eval(request.POST.get('regression_Lasso')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Ridge'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Ridge'] = eval(request.POST.get('regression_Ridge')) if problem_type.lower() == 'topicmodelling' and configSettings['basic']['algorithms']['topicModelling']['LDA'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['topicModellingParams']['LDA']= eval(request.POST.get('topicmodeling_lda')) if problem_type.lower() == 'clustering' and configSettings['basic']['algorithms']['clustering']['KMeans'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['clusteringModelParams']['KMeans']= eval(request.POST.get('cluster_kmeans')) if problem_type.lower() == 'clustering' and configSettings['basic']['algorithms']['clustering']['DBSCAN'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['clusteringModelParams']['DBSCAN']= eval(request.POST.get('cluster_DBSCAN')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Decision Tree'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Decision Tree'] = eval(request.POST.get('regression_DecisionTreeRegressor')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Random Forest'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Random Forest'] = eval(request.POST.get('regression_RandomForestRegressor')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Extreme Gradient Boosting (XGBoost)'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Extreme Gradient Boosting (XGBoost)'] = eval(request.POST.get('regression_XGBoostRegressor')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Light Gradient Boosting (LightGBM)'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Light Gradient Boosting (LightGBM)'] = eval(request.POST.get('regression_LightGBMRegressor')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Categorical Boosting (CatBoost)'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Categorical Boosting (CatBoost)'] = eval(request.POST.get('regression_CatBoostRegressor')) configSettings['advance']['mllearner_config']['modelparamsfile'] = request.POST.get('ModelParamFile') configSettings['advance']['mllearner_config']['optimizationMethod'] = request.POST.get('OptimizationMethod') configSettings['advance']['mllearner_config']['optimizationHyperParameter'][ 'iterations'] = request.POST.get('iterations') configSettings['advance']['mllearner_config']['optimizationHyperParameter'][ 'trainTestCVSplit'] = request.POST.get('trainTestCVSplit') configSettings['advance']['mllearner_config']['thresholdTunning'] = request.POST.get('thresholdTunning') configSettings['advance']['mllearner_config']['Stacking (Ensemble)'] = request.POST.get('EnsembleStacking') configSettings['advance']['mllearner_config']['Voting (Ensemble)'] = request.POST.get('EnsembleVoting') configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Logistic Regression']['enable'] = request.POST.get('ensemple_bagging_lr_enable') if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Logistic Regression']['enable'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Logistic Regression']['param'] = eval(request.POST.get('classi_ensemple_bagging_lr_param')) configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Naive Bayes']['enable'] = request.POST.get('ensemple_bagging_naivebayes_enable') if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Naive Bayes']['enable'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Naive Bayes']['param'] = eval(request.POST.get('classi_ensemple_bagging_naivebayes_param')) configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Support Vector Machine']['enable'] = request.POST.get('ensemple_bagging_svm_enable') if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Support Vector Machine']['enable'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Support Vector Machine']['param'] = eval(request.POST.get('classi_ensemple_bagging_svm_param')) configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['K Nearest Neighbors']['enable'] = request.POST.get('ensemple_bagging_knn_enable') if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['K Nearest Neighbors']['enable'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['K Nearest Neighbors']['param'] = eval(request.POST.get('classi_ensemple_bagging_knn_param')) configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Decision Tree']['enable'] = request.POST.get('ensemple_bagging_dt_enable') if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Decision Tree']['enable'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Decision Tree']['param'] = eval(request.POST.get('classi_ensemple_bagging_dt_param')) configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Random Forest']['enable'] = request.POST.get('ensemple_bagging_rf_enable') if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Random Forest']['enable'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Random Forest']['param'] = eval(request.POST.get('classi_ensemple_bagging_rf_param')) configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Linear Regression']['enable'] = request.POST.get('ensemple_bagging_lir_enable') if configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Linear Regression']['enable'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Linear Regression']['param'] = eval(request.POST.get('reg_ensemple_bagging_lir_param')) configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Decision Tree']['enable'] = request.POST.get('ensemple_bagging_dit_enable') if configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Decision Tree']['enable'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Decision Tree']['param'] = eval(request.POST.get('reg_ensemple_bagging_dit_param')) configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Ridge']['enable'] = request.POST.get('ensemple_bagging_ridge_enable') if configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Ridge']['enable'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Ridge']['param'] = eval(request.POST.get('reg_ensemple_bagging_ridge_param')) if problem_type.lower() == 'classification': if configSettings['advance']['mllearner_config']['Stacking (Ensemble)'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Stacking (Ensemble)'] = eval(request.POST.get('ensamblestackingClassifierparams')) if problem_type.lower() == 'regression': if configSettings['advance']['mllearner_config']['Stacking (Ensemble)'] == 'True': configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Stacking (Ensemble)'] = eval(request.POST.get('ensamblestackingRegressorparams')) configSettings['basic']['filterExpression'] = request.POST.get('filterExpression') #configSettings['advance']['mllearner_config']['trainPercentage'] = request.POST.get('trainPercentage') if (problem_type.lower() == 'classification') or (problem_type.lower() == 'regression'): configSettings['advance']['modelEvaluation']['smcStrategy'] = request.POST.get('smcStrategy') configSettings['advance']['modelEvaluation']['smcMaxDepth'] = request.POST.get('smcMaxDepth') configSettings['advance']['modelEvaluation']['smcCondition'] = request.POST.get('smcCondition') configSettings['advance']['modelEvaluation']['miCondition'] = request.POST.get('miCondition') if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Neural Network'] == 'True': configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Neural Network'] = eval( request.POST.get('dl_classification_SNN')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Recurrent Neural Network'] == 'True': configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network'] = eval( request.POST.get('dl_classification_RNN')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Recurrent Neural Network (GRU)'] == 'True': configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network (GRU)'] = eval( request.POST.get('dl_classification_GRURNN')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Recurrent Neural Network (LSTM)'] == 'True': configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network (LSTM)'] = eval( request.POST.get('dl_classification_LSTMRNN')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Convolutional Neural Network (1D)'] == 'True': configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Convolutional Neural Network (1D)'] = eval( request.POST.get('dl_classification_CNN')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification'].get('Neural Architecture Search') == 'True': configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Neural Architecture Search'] = eval( request.POST.get('dl_classification_NAS')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Neural Network'] == 'True': configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Neural Network'] = eval( request.POST.get('dl_regression_SNN')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Recurrent Neural Network'] == 'True': configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network'] = eval( request.POST.get('dl_regression_RNN')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Recurrent Neural Network (GRU)'] == 'True': configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (GRU)'] = eval( request.POST.get('dl_regression_GRURNN')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Recurrent Neural Network (LSTM)'] == 'True': configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (LSTM)'] = eval( request.POST.get('dl_regression_LSTMRNN')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Convolutional Neural Network (1D)'] == 'True': configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Convolutional Neural Network (1D)'] = eval( request.POST.get('dl_regression_CNN')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression'].get('Neural Architecture Search') == 'True': configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Neural Architecture Search'] = eval( request.POST.get('dl_regression_NAS')) #configSettings['advance']['dllearner_config']['optimizationMethod'] = request.POST.get('DLOptimizationMethod') else: if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Online Logistic Regression'] == 'True': configSettings['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Logistic Regression'] = eval(request.POST.get('OnlineLogisticRegression')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Online Decision Tree Classifier'] == 'True': configSettings['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Decision Tree Classifier'] = eval(request.POST.get('OnlineDecisionTreeClassifier')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Online Softmax Regression'] == 'True': configSettings['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Softmax Regression'] = eval(request.POST.get('OnlineSoftmaxRegression')) if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Online KNN Classifier'] == 'True': configSettings['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online KNN Classifier'] = eval(request.POST.get('OnlineKNNClassifier')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Online Linear Regression'] == 'True': configSettings['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online Linear Regression'] = eval(request.POST.get('OnlineLinearRegression')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Online Decision Tree Regressor'] == 'True': configSettings['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online Decision Tree Regressor'] = eval(request.POST.get('OnlineDecisionTreeRegressor')) if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Online KNN Regressor'] == 'True': configSettings['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online KNN Regressor'] = eval(request.POST.get('OnlineKNNRegressor')) configSettings['advance']['profiler']['targetEncodingParams'] = eval(request.POST.get('targetEncodingParams')) configSettings['advance']['profiler']['outlierDetectionParams'] = eval(request.POST.get('outlierDetectionParams')) if problem_type.lower() == 'objectdetection': configSettings['advance']['objectDetection']['pretrainedModel']= request.POST.get('objectdetectionpretrainedmodel') configSettings['advance']['objectDetection']['n_epoch'] = int(request.POST.get('objectDetection_n_epoch')) configSettings['advance']['objectDetection']['batch_size'] = int(request.POST.get('objectDetection_batch_size')) if problem_type.lower() == 'timeseriesforecasting': #task 11997 #task 13052 configSettings['advance']['timeSeriesForecasting']['fix_seasonality'] = request.POST.get('seasionality') # task 13052 configSettings['advance']['timeSeriesForecasting']['fix_stationarity'] =request.POST.get('stationarity') # task 13052 configSettings['advance']['timeSeriesForecasting']['modelParams']['ARIMA'] = eval(request.POST.get('ARIMA')) #task 11997 configSettings['advance']['timeSeriesForecasting']['modelParams']['FBPROPHET'] = eval(request.POST.get('FBPROPHET')) #task 11997 configSettings['advance']['timeSeriesForecasting']['modelParams']['LSTM'] = eval(request.POST.get('TSLSTM')) #task 11997 configSettings['advance']['timeSeriesForecasting']['modelParams']['Encoder_Decoder_LSTM_MVI_UVO'] = eval(request.POST.get('TSLSTMencoderdecoder')) configSettings['advance']['timeSeriesForecasting']['modelParams']['MLP'] = eval(request.POST.get('TSMLP')) #task 11997 if problem_type.lower() == 'timeseriesanomalydetection': configSettings['advance']['timeSeriesAnomalyDetection']['modelParams']['AutoEncoder'] = eval(request.POST.get('autoEncoderAD')) #task 11997 configSettings['advance']['timeSeriesAnomalyDetection']['modelParams']['DBScan'] = eval(request.POST.get('dbscanAD')) #task 13316 if problem_type.lower() == 'anomalydetection': configSettings['advance']['anomalyDetection']['modelParams']['IsolationForest'] = eval(request.POST.get('IsolationForest')) configSettings['advance']['anomalyDetection']['modelParams']['oneclassSVM'] = eval(request.POST.get('oneclassSVM')) configSettings['advance']['anomalyDetection']['modelParams']['DBScan'] = eval(request.POST.get('DBScanAD')) updatedConfigSettingsJson = json.dumps(configSettings) f.seek(0) f.write(updatedConfigSettingsJson) f.truncate() f.close() errormsg = 'NA' request.session['ModelStatus'] = 'Not Trained' except Exception as e: import sys exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) errormsg = 'Input value error' print(e) if 'NoOfRecords' in request.session: records = request.session['NoOfRecords'] else: records = 'NA' if request.session['datatype'] in ['Video', 'Image','Document']: folderLocation = str(request.session['datalocation']) dataFilePath = os.path.join(folderLocation, request.session['csvfullpath']) else: dataFilePath = str(request.session['datalocation']) # dataFilePath = configSettings['basic']['dataLocation'] #df = pd.read_csv(dataFilePath, encoding='latin1') featuresList = configSettings['basic']['featureList'] config = {} config['modelName'] = configSettings['basic']['modelName'] config['modelVersion'] = configSettings['basic']['modelVersion'] config['datetimeFeatures'] = configSettings['basic']['dateTimeFeature'] config['sequenceFeatures'] = configSettings['basic']['indexFeature'] config['FeaturesList'] = featuresList config['unimportantFeatures'] = list(set(featuresList) - set(configSettings['basic']['trainingFeatures'])) config['targetFeature'] = configSettings['basic']['targetFeature'] scoring = configSettings['basic']['scoringCriteria'] scoringCriteria = "" for k in scoring.keys(): if configSettings['basic']['scoringCriteria'][k] == 'True': scoringCriteria = k break config['scoringCriteria'] = scoringCriteria temp = {} temp['ModelName'] = configSettings['basic']['modelName'] temp['Version'] = configSettings['basic']['modelVersion'] selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] context = {'tab': 'advconfig', 'config': config, 'temp': temp, 'advconfig': configSettings, 'noOfRecords': records, 'advance_status_msg': 'Configuration Done', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'errormsg':errormsg, 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'], 'selected': 'modeltraining'} return context elif submittype == 'AdvanceDefault': try: MachineLearningModels = [] configFile = os.path.join(DEFAULT_FILE_PATH, 'aion_config.json') f = open(configFile, "r") configSettings = f.read() f.close() updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r+") configSettingsData = f.read() updateconfigSettingsJson = json.loads(configSettingsData) configSettingsJson = json.loads(configSettings) temp = {} temp['ModelName'] = request.session['UseCaseName'] temp['Version'] = request.session['ModelVersion'] config = {} config['modelName'] = request.session['UseCaseName'] config['modelVersion'] = request.session['ModelVersion'] config['datetimeFeatures'] = updateconfigSettingsJson['basic']['dateTimeFeature'] config['sequenceFeatures'] = updateconfigSettingsJson['basic']['indexFeature'] config['FeaturesList'] = updateconfigSettingsJson['basic']['trainingFeatures'] config['unimportantFeatures'] = '' config['targetFeature'] = updateconfigSettingsJson['basic']['targetFeature'] problemtypes = updateconfigSettingsJson['basic']['analysisType'] problem_type = "" for k in problemtypes.keys(): if updateconfigSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break selectAlgo = "" if problem_type in ['classification','regression','timeSeriesForecasting', 'timeSeriesAnomalyDetection', 'recommenderSystem','clustering','anomalyDetection','topicModelling','survivalAnalysis','videoForecasting','imageClassification','objectDetection','stateTransition']: #task 11997 for key in updateconfigSettingsJson['basic']['algorithms'][problem_type]: if updateconfigSettingsJson['basic']['algorithms'][problem_type][key] == 'True': if selectAlgo != "": selectAlgo += ',' selectAlgo += key if problem_type not in ['classification','regression']: break for key in updateconfigSettingsJson['basic']['algorithms'][problem_type]: if updateconfigSettingsJson['basic']['algorithms'][problem_type][key] == 'True': MachineLearningModels.append(key) if problem_type == 'objectDetection': from AION import pretrainedModels ptmObj = pretrainedModels() obModels = ptmObj.get_info(selectAlgo) else: obModels = {} problemType = problem_type selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] request.session['currentstate'] = 2 if request.session['finalstate'] <= 2: request.session['finalstate'] = 2 outlierDetection = 'False' updateconfigSettingsJson['advance'] = configSettingsJson['advance'] for x in list(updateconfigSettingsJson['advance']['profiler']['outlierDetection'].keys()): if updateconfigSettingsJson['advance']['profiler']['outlierDetection'][x] == 'True': outlierDetection = 'True' if outlierDetection == 'False': updateconfigSettingsJson['advance']['profiler']['outlierDetection']['Disable'] = 'True' else: updateconfigSettingsJson['advance']['profiler']['outlierDetection']['Disable'] = 'False' updateconfigSettingsJson = advanceConfigfields(updateconfigSettingsJson) #print(configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['ExtremeGradientBoostingClassifier']) updateconfigSettingsJson['advance']['profiler']['normalizationMethod'] = 'None' normalizationtypes = updateconfigSettingsJson['advance']['profiler']['normalization'] for k in normalizationtypes.keys(): if updateconfigSettingsJson['advance']['profiler']['normalization'][k] == 'True': updateconfigSettingsJson['advance']['profiler']['normalizationMethod'] = k break #---------------- default Hypermarameter changes--- ----------Usnish-------------- hyperparamFile = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','config', 'hyperparam_config.json')) with open(hyperparamFile) as json_file: hyperparamConfig = json.load(json_file) context = {'tab': 'advconfig','temp': temp,'advconfig': updateconfigSettingsJson, 'config': config, 'selected_use_case': selected_use_case,'MachineLearningModels':MachineLearningModels, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,"obModels":obModels,"problemType":problemType, 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'], 'selected': 'modeltraning','advance_help':ht.advance_help,'hyperparamConfig':hyperparamConfig} return context except Exception as e: print(e) def llmadvancesettings(configSettings,request): algo = '' for x in list(configSettings['basic']['algorithms']['llmFineTuning'].keys()): if configSettings['basic']['algorithms']['llmFineTuning'][x] == 'True': algo = x if algo == 'LLaMA-2': configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2']['fineTuningMethod'] = request.POST.get('llama2fullfinemethod') configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2']['epochs'] = request.POST.get('llama2epochs') configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2']['learning_rate'] = request.POST.get('llama2learningrate') if request.POST.get('llama2fullfinemethod') != 'Full Fine-Tuning': configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2']['lora_rank'] = request.POST.get('llama2lorarank') configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2']['lora_alpha'] = request.POST.get('llama2loraalpha') if algo == 'LLaMA-2-Chat': configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']['fineTuningMethod'] = request.POST.get('llama2chatfullfinemethod') configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']['epochs'] = request.POST.get('llmllama2chatepochs') configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']['learning_rate'] = request.POST.get('llama2chatlearningrate') if request.POST.get('llama2chatfullfinemethod') != 'Full Fine-Tuning': configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']['lora_rank'] = request.POST.get('llama2chatlorarank') configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']['lora_alpha'] = request.POST.get('llama2chatloraalpha') if algo == 'CodeLLaMA-2': configSettings['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']['fineTuningMethod'] = request.POST.get('CodeLLaMA2fullfinemethod') configSettings['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']['epochs'] = request.POST.get('CodeLLaMA2epochs') configSettings['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']['learning_rate'] = request.POST.get('CodeLLaMA2learningrate') if request.POST.get('CodeLLaMA2fullfinemethod') != 'Full Fine-Tuning': configSettings['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']['lora_rank'] = request.POST.get('CodeLLaMA2lorarank') configSettings['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']['lora_alpha'] = request.POST.get('CodeLLaMA2loraalpha') if algo == 'Falcon': configSettings['advance']['llmFineTuning']['modelParams']['Falcon']['fullFineTuning'] = request.POST.get('falconfullfinetuning') configSettings['advance']['llmFineTuning']['modelParams']['Falcon']['epochs'] = request.POST.get('falconepochs') configSettings['advance']['llmFineTuning']['modelParams']['Falcon']['learning_rate'] = request.POST.get('falconlearningrate') configSettings['advance']['llmFineTuning']['modelParams']['Falcon']['lora_rank'] = request.POST.get('falconlorarank') configSettings['advance']['llmFineTuning']['modelParams']['Falcon']['lora_alpha'] = request.POST.get('falconloraalpha') return configSettings def advanceConfigfields(configSettingsJson): try: configSettingsJson['advance']['mllearner_config']['EnsembleStacking'] = \ configSettingsJson['advance']['mllearner_config']['Stacking (Ensemble)'] configSettingsJson['advance']['mllearner_config']['EnsembleVoting'] = \ configSettingsJson['advance']['mllearner_config']['Voting (Ensemble)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'LogisticRegression'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Logistic Regression'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['GaussianNB'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Naive Bayes'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['SVC'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'Support Vector Machine'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'KNeighborsClassifier'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['K Nearest Neighbors'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'DecisionTreeClassifier'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Decision Tree'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'RandomForestClassifier'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Random Forest'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'GradientBoostingClassifier'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Gradient Boosting'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'ExtremeGradientBoostingClassifier'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'Extreme Gradient Boosting (XGBoost)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'LightGradientBoostingClassifier'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'Light Gradient Boosting (LightGBM)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'CategoricalBoostingClassifier'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][ 'Categorical Boosting (CatBoost)'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['SNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Neural Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['SimpleRNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'][ 'Recurrent Neural Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['GRURNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'][ 'Recurrent Neural Network (GRU)'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['LSTMRNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'][ 'Recurrent Neural Network (LSTM)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleStacking'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Stacking (Ensemble)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'LogisticRegression'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'Logistic Regression'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'NaiveBayes'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'Naive Bayes'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'SVM'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'Support Vector Machine'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'KNN'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'K Nearest Neighbors'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'DecisionTree'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'Decision Tree'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'RandomForest'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][ 'Random Forest'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SimpleRNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][ 'Recurrent Neural Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['GRURNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][ 'Recurrent Neural Network (GRU)'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['LSTMRNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][ 'Recurrent Neural Network (LSTM)'] configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['DQN'] = \ configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['Deep Q Network'] configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['DDQN'] = \ configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams'][ 'Dueling Deep Q Network'] configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['DQN'] = \ configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['Deep Q Network'] configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['DDQN'] = \ configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams'][ 'Dueling Deep Q Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['CNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'][ 'Convolutional Neural Network (1D)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['LinearRegression'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Linear Regression'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams'][ 'DecisionTreeRegressor'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Decision Tree'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams'][ 'RandomForestRegressor'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Random Forest'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['XGBoostRegressor'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams'][ 'Extreme Gradient Boosting (XGBoost)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['LightGBMRegressor'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams'][ 'Light Gradient Boosting (LightGBM)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['CatBoostRegressor'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams'][ 'Categorical Boosting (CatBoost)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleStacking'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Stacking (Ensemble)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging'][ 'LinearRegression'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging'][ 'Linear Regression'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging'][ 'DecisionTree'] = \ configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging'][ 'Decision Tree'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['NAS'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][ 'Neural Architecture Search'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['NAS'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'][ 'Neural Architecture Search'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Neural Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SimpleRNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][ 'Recurrent Neural Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['GRURNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][ 'Recurrent Neural Network (GRU)'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['LSTMRNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][ 'Recurrent Neural Network (LSTM)'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['CNN'] = \ configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][ 'Convolutional Neural Network (1D)'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][ 'OnlineLogisticRegression'] = \ configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][ 'Online Logistic Regression'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][ 'OnlineDecisionTreeClassifier'] = \ configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][ 'Online Decision Tree Classifier'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][ 'OnlineSoftmaxRegression'] = \ configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][ 'Online Softmax Regression'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][ 'OnlineKNNClassifier'] = \ configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][ 'Online KNN Classifier'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][ 'OnlineLinearRegression'] = \ configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][ 'Online Linear Regression'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][ 'OnlineDecisionTreeRegressor'] = \ configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][ 'Online Decision Tree Regressor'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][ 'OnlineKNNRegressor'] = \ configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][ 'Online KNN Regressor'] configSettingsJson['advance']['profiler']['textConversionMethod']['LatentSemanticAnalysis'] = \ configSettingsJson['advance']['profiler']['textConversionMethod']['LatentSemanticAnalysis'] configSettingsJson['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'] = \ configSettingsJson['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'] if 'llmFineTuning' in configSettingsJson['advance']: configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA2'] = \ configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA-2'] configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA2Chat'] = \ configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA-2-Chat'] configSettingsJson['basic']['algorithms']['llmFineTuning']['CodeLLaMA2'] = \ configSettingsJson['basic']['algorithms']['llmFineTuning']['CodeLLaMA-2'] configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA2'] = \ configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA-2'] configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA2Chat'] = \ configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat'] configSettingsJson['advance']['llmFineTuning']['modelParams']['CodeLLaMA2'] = \ configSettingsJson['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2'] configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA2'] = \ configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA-2'] configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA2Chat'] = \ configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA-2-Chat'] configSettingsJson['basic']['modelSize']['llmFineTuning']['CodeLLaMA2'] = \ configSettingsJson['basic']['modelSize']['llmFineTuning']['CodeLLaMA-2'] if 'distributedlearner_config' in configSettingsJson['advance']: configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams'][ 'DistributedXGBoost'] = \ configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams'][ 'Distributed Extreme Gradient Boosting (XGBoost)'] configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams'][ 'DistributedLightGBM'] = \ configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams'][ 'Distributed Light Gradient Boosting (LightGBM)'] configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams'][ 'DistributedXGBoost'] = \ configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams'][ 'Distributed Extreme Gradient Boosting (XGBoost)'] configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams'][ 'DistributedLightGBM'] = \ configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams'][ 'Distributed Light Gradient Boosting (LightGBM)'] problem_type = "" problemtypes = configSettingsJson['basic']['analysisType'] for k in problemtypes.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break deepLearning = 'False' machineLearning = 'False' reinforcementLearning = 'False' selectAlgo = "" if problem_type.lower() in ['classification','regression']: for key in configSettingsJson['basic']['algorithms'][problem_type]: if configSettingsJson['basic']['algorithms'][problem_type][key] == 'True': if key in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)','Neural Architecture Search']: deepLearning = 'True' if key in ['Logistic Regression','Naive Bayes','Decision Tree','Random Forest','Support Vector Machine','K Nearest Neighbors','Gradient Boosting','Extreme Gradient Boosting (XGBoost)','Light Gradient Boosting (LightGBM)','Categorical Boosting (CatBoost)','Linear Regression','Lasso','Ridge','Decision Tree','Random Forest','Bagging (Ensemble)']: machineLearning = 'True' if key in ['Deep Q Network','Dueling Deep Q Network']: reinforcementLearning = 'True' elif problem_type.lower() in ['clustering','topicmodelling']:#clustering(Bug 12611) machineLearning = 'True' configSettingsJson['basic']['deepLearning'] = deepLearning configSettingsJson['basic']['machineLearning'] = machineLearning configSettingsJson['basic']['reinforcementLearning'] = reinforcementLearning except Exception as e: print(e) return (configSettingsJson) def basicconfignex(request): #pemfilename = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','modelTraining','static','key','AION_GPU.pem')) try: updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r+") configSettingsData = f.read() configSettingsJson = json.loads(configSettingsData) #---------------- default Hypermarameter changes-------------Usnish-------------- hyperparamFile = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','config', 'hyperparam_config.json')) with open(hyperparamFile) as json_file: hyperparamConfig = json.load(json_file) #---------------- default Hypermarameter changes end-------------Usnish-------------- # ------------------ Debiasing Changes ------------------ categorical_features = [] class_list = [] MachineLearningModels = [] check_traget = configSettingsJson['basic']['targetFeature'] selectedDebiasingFeature = 'None' selectedDebiasingClass = 'None' selectedDebiasingAlgorithm = '' problemtypes = configSettingsJson['basic']['analysisType'] problem_type = "" for k in problemtypes.keys(): if configSettingsJson['basic']['analysisType'][k] == 'True': problem_type = k break if request.method == 'GET': for key in configSettingsJson['basic']['algorithms'][problem_type]: if configSettingsJson['basic']['algorithms'][problem_type][key] == 'True': MachineLearningModels.append(key) else: MachineLearningModels = request.POST.getlist('MachineLearningModels') if problem_type.lower() in ['classification','regression']: if check_traget != '': try: if 'deBiasing' in configSettingsJson['advance']['profiler']: deBiasing = configSettingsJson['advance']['profiler']['deBiasing'] selectedDebiasingFeature = deBiasing.get('FeatureName','None') selectedDebiasingClass = deBiasing.get('ClassName','None') selectedDebiasingAlgorithm = deBiasing.get('Algorithm','') if selectedDebiasingFeature != 'None': df = pd.read_csv(configSettingsJson['basic']['dataLocation'],encoding='utf8',encoding_errors= 'replace') classeslist = [] classeslist = df[selectedDebiasingFeature].unique().tolist() for item in classeslist: class_list.append(item) else: class_list.append('None') except: pass feature_dict = configSettingsJson['advance']['profiler']['featureDict'] for feature_config in feature_dict: if feature_config.get('type', '') == 'categorical' and feature_config['feature'] != check_traget: categorical_features.append(feature_config['feature']) # ------------------ ------------------ #print(categorical_features) temp = {} temp['ModelName'] = request.session['UseCaseName'] temp['Version'] = request.session['ModelVersion'] config = {} config['modelName'] = request.session['UseCaseName'] config['modelVersion'] = request.session['ModelVersion'] config['datetimeFeatures'] = configSettingsJson['basic']['dateTimeFeature'] config['sequenceFeatures'] = configSettingsJson['basic']['indexFeature'] config['FeaturesList'] = configSettingsJson['basic']['trainingFeatures'] config['unimportantFeatures'] = '' config['targetFeature'] = configSettingsJson['basic']['targetFeature'] deepLearning = 'False' machineLearning = 'False' reinforcementLearning = 'False' selectAlgo = "" print(problem_type) if problem_type.lower() in ['classification','regression']: for key in configSettingsJson['basic']['algorithms'][problem_type]: if configSettingsJson['basic']['algorithms'][problem_type][key] == 'True': if key in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)','Neural Architecture Search']: deepLearning = 'True' if key in ['Logistic Regression','Naive Bayes','Decision Tree','Random Forest','Support Vector Machine','K Nearest Neighbors','Gradient Boosting','Extreme Gradient Boosting (XGBoost)','Light Gradient Boosting (LightGBM)','Categorical Boosting (CatBoost)','Linear Regression','Lasso','Ridge','Decision Tree','Random Forest','Bagging (Ensemble)']: machineLearning = 'True' if key in ['Deep Q Network','Dueling Deep Q Network']: reinforcementLearning = 'True' elif problem_type.lower() in ['clustering','topicmodelling']:#clustering(Bug 12611) machineLearning = 'True' configSettingsJson['basic']['deepLearning'] = deepLearning configSettingsJson['basic']['machineLearning'] = machineLearning configSettingsJson['basic']['reinforcementLearning'] = reinforcementLearning if problem_type in ['classification','regression','timeSeriesForecasting', 'timeSeriesAnomalyDetection', 'recommenderSystem','clustering','anomalyDetection','topicModelling','survivalAnalysis','videoForecasting','imageClassification','objectDetection','stateTransition']: #task 11997 for key in configSettingsJson['basic']['algorithms'][problem_type]: if configSettingsJson['basic']['algorithms'][problem_type][key] == 'True': if selectAlgo != "": selectAlgo += ',' selectAlgo += key if problem_type not in ['classification','regression']: break if problem_type == 'objectDetection': from AION import pretrainedModels ptmObj = pretrainedModels() obModels = ptmObj.get_info(selectAlgo) else: obModels = {} problemType = problem_type selected_use_case = request.session['UseCaseName'] ModelVersion = request.session['ModelVersion'] ModelStatus = request.session['ModelStatus'] request.session['currentstate'] = 2 #configSettingsJson['advance']['remoteTraining']['ssh']['keyFilePath'] = pemfilename if request.session['finalstate'] <= 2: request.session['finalstate'] = 2 outlierDetection = 'False' for x in list(configSettingsJson['advance']['profiler']['outlierDetection'].keys()): if configSettingsJson['advance']['profiler']['outlierDetection'][x] == 'True': outlierDetection = 'True' if outlierDetection == 'False': configSettingsJson['advance']['profiler']['outlierDetection']['Disable'] = 'True' else: configSettingsJson['advance']['profiler']['outlierDetection']['Disable'] = 'False' if 'distributedLearning' not in configSettingsJson['basic']: configSettingsJson['basic']['distributedLearning'] = 'False' configSettingsJson['advance']['mllearner_config']['EnsembleStacking']=configSettingsJson['advance']['mllearner_config']['Stacking (Ensemble)'] configSettingsJson['advance']['mllearner_config']['EnsembleVoting']=configSettingsJson['advance']['mllearner_config']['Voting (Ensemble)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['LogisticRegression'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Logistic Regression'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['GaussianNB'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Naive Bayes'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['SVC'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Support Vector Machine'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['KNeighborsClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['K Nearest Neighbors'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['DecisionTreeClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Decision Tree'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['RandomForestClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Random Forest'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['GradientBoostingClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Gradient Boosting'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['ExtremeGradientBoostingClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Extreme Gradient Boosting (XGBoost)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['LightGradientBoostingClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Light Gradient Boosting (LightGBM)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['CategoricalBoostingClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Categorical Boosting (CatBoost)'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['SNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Neural Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['SimpleRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['GRURNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network (GRU)'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['LSTMRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network (LSTM)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']=configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleStacking']=configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Stacking (Ensemble)'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['LogisticRegression'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['Logistic Regression'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['NaiveBayes'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['Naive Bayes'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['SVM'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['Support Vector Machine'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['KNN'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['K Nearest Neighbors'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['DecisionTree'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['Decision Tree'] configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['RandomForest'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['Random Forest'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SimpleRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['GRURNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (GRU)'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['LSTMRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (LSTM)'] configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['DQN'] = configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['Deep Q Network'] configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['DDQN'] = configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['Dueling Deep Q Network'] configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['DQN'] = configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['Deep Q Network'] configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['DDQN'] = configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['Dueling Deep Q Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['CNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Convolutional Neural Network (1D)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['LinearRegression'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Linear Regression'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['DecisionTreeRegressor'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Decision Tree'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['RandomForestRegressor'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Random Forest'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['XGBoostRegressor'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Extreme Gradient Boosting (XGBoost)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['LightGBMRegressor'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Light Gradient Boosting (LightGBM)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['CatBoostRegressor'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Categorical Boosting (CatBoost)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleStacking']=configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Stacking (Ensemble)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging']=configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging']['LinearRegression'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging']['Linear Regression'] configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging']['DecisionTree'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging']['Decision Tree'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['NAS'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'].get('Neural Architecture Search') configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['NAS'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'].get('Neural Architecture Search') configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Neural Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SimpleRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['GRURNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (GRU)'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['LSTMRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (LSTM)'] configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['CNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Convolutional Neural Network (1D)'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['OnlineLogisticRegression'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Logistic Regression'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['OnlineDecisionTreeClassifier'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Decision Tree Classifier'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['OnlineSoftmaxRegression'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Softmax Regression'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['OnlineKNNClassifier'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online KNN Classifier'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['OnlineLinearRegression'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online Linear Regression'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['OnlineDecisionTreeRegressor'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online Decision Tree Regressor'] configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['OnlineKNNRegressor'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online KNN Regressor'] configSettingsJson['advance']['profiler']['textConversionMethod']['LatentSemanticAnalysis'] = configSettingsJson['advance']['profiler']['textConversionMethod']['LatentSemanticAnalysis'] configSettingsJson['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'] = configSettingsJson['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'] if 'llmFineTuning' in configSettingsJson['advance']: configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA2'] = configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA-2'] configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA2Chat'] = configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA-2-Chat'] configSettingsJson['basic']['algorithms']['llmFineTuning']['CodeLLaMA2'] = configSettingsJson['basic']['algorithms']['llmFineTuning']['CodeLLaMA-2'] configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA2'] = configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA-2'] configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA2Chat'] = configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat'] configSettingsJson['advance']['llmFineTuning']['modelParams']['CodeLLaMA2'] = configSettingsJson['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2'] configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA2'] = \ configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA-2'] configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA2Chat'] = \ configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA-2-Chat'] configSettingsJson['basic']['modelSize']['llmFineTuning']['CodeLLaMA2'] = \ configSettingsJson['basic']['modelSize']['llmFineTuning']['CodeLLaMA-2'] if 'distributedlearner_config' in configSettingsJson['advance']: configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['DistributedXGBoost'] = configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Extreme Gradient Boosting (XGBoost)'] configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['DistributedLightGBM'] = configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Light Gradient Boosting (LightGBM)'] configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams']['DistributedXGBoost'] = configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams']['Distributed Extreme Gradient Boosting (XGBoost)'] configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams']['DistributedLightGBM'] = configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams']['Distributed Light Gradient Boosting (LightGBM)'] configSettingsJson['advance']['profiler']['normalizationMethod'] = 'None' normalizationtypes = configSettingsJson['advance']['profiler']['normalization'] for k in normalizationtypes.keys(): if configSettingsJson['advance']['profiler']['normalization'][k] == 'True': configSettingsJson['advance']['profiler']['normalizationMethod'] = k break context = {'temp': temp, 'advconfig': configSettingsJson, 'MachineLearningModels':MachineLearningModels,'hyperparamConfig':hyperparamConfig,'config': config, 'selected_use_case': selected_use_case, 'categorical_features': categorical_features, 'selectedDebiasingFeature': selectedDebiasingFeature, 'selectedDebiasingAlgorithm': selectedDebiasingAlgorithm, 'Class_list': class_list, 'selectedDebiasingClass': selectedDebiasingClass, #Debiasing Changes 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,"obModels":obModels,"problemType":problemType, 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'], 'selected': 'modeltraning','advance_help':ht.advance_help} return context except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) context={'erroradvance':'Fail to load advance config Json file'} return context ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import kfp import kfp.dsl as dsl import json from pathlib import Path class aionpipelinets(): containerRegistry = str() containerLabel = str() containerSecret = str() pipelineName = 'AION MLOps Pipeline {0}' exeCmd = 'python' codeFile = 'aionCode.py' mntPoint = '/aion' inputArg = '-i' msIP = '0.0.0.0' port = '8094' cachingStrategy = 'P0D' deafultVolume = '2Gi' volName = 'aion-pvc' volMode = 'ReadWriteMany' fileExt = '.tar.gz' fileName = 'aion_mlops_pipeline_{0}' containerMM = 'modelmonitoring' containerDI = 'dataingestion' containerDT = 'datatransformation' containerFE = 'featureengineering' containerMR = 'modelregistry' containerMS = 'modelserving' containerImage = '{0}/{1}:{2}' models = {} nameSeprator = '-' modelsLiteral = 'models' modelNameLiteral = 'modelname' msTemplate = '{"apiVersion": "v1", "kind": "Pod", "metadata": {"name": "{{workflow.name}}-{0}"}, "spec": {"containers": [{"name": "{0}", "image": "{1}", "command": ["python"], "args": ["aionCode.py", "-ip", "{2}", "-pn", "{3}"],"volumeMounts": [{"name": "aion-pvc", "mountPath": "{4}"}], "ports": [{"name": "http", "containerPort": {3}, "protocol": "TCP"}]}], "imagePullSecrets": [{"name": "{5}"}], "volumes": [{"name": "aion-pvc", "persistentVolumeClaim": {"claimName": "{{workflow.name}}-{6}"}}]}}' def __init__(self, models, containerRegistry, containerLabel, containerSecret=str()): self.models = models self.containerRegistry = containerRegistry self.containerLabel = containerLabel self.containerSecret = containerSecret @dsl.pipeline( name=pipelineName.format(containerLabel), description=pipelineName.format(containerLabel), ) def aion_mlops(self, inputUri=str(), volSize=deafultVolume): vop = dsl.VolumeOp( name=self.volName + self.nameSeprator + self.containerLabel, resource_name=self.volName, modes=[self.volMode], size=volSize ) mm = dsl.ContainerOp( name=self.containerMM, image=self.containerImage.format(self.containerRegistry,self.containerMM,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, self.inputArg, inputUri, ], pvolumes={self.mntPoint: vop.volume} ) mm.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy di = dsl.ContainerOp( name=self.containerDI, image=self.containerImage.format(self.containerRegistry,self.containerDI,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, ], pvolumes={self.mntPoint: mm.pvolume} ) di.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy dt = dsl.ContainerOp( name=self.containerDT, image=self.containerImage.format(self.containerRegistry,self.containerDT,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, ], pvolumes={self.mntPoint: di.pvolume} ) dt.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy fe = dsl.ContainerOp( name=self.containerFE, image=self.containerImage.format(self.containerRegistry,self.containerFE,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, ], pvolumes={self.mntPoint: dt.pvolume} ) fe.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy dictMT = {} listMTOps = [] for model in self.models[self.modelsLiteral]: modelName = model[self.modelNameLiteral] mt=dsl.ContainerOp( name=modelName, image=self.containerImage.format(self.containerRegistry,modelName,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, ], pvolumes={self.mntPoint: fe.pvolume}) mt.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy listMTOps.append(mt) dictMT[self.mntPoint]=mt.pvolume mr = dsl.ContainerOp( name=self.containerMR, image=self.containerImage.format(self.containerRegistry,self.containerMR,self.containerLabel), command=self.exeCmd, arguments=[ self.codeFile, ], pvolumes=dictMT ).after(*tuple(listMTOps)) mr.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy msJson = self.msTemplate.replace(str({0}),self.containerMS).replace(str({1}),self.containerImage.format(self.containerRegistry,self.containerMS,self.containerLabel)).replace(str({2}),self.msIP).replace(str({3}),self.port).replace(str({4}),self.mntPoint).replace(str({5}),self.containerSecret).replace(str({6}),self.volName) ms = dsl.ResourceOp( name=self.containerMS + self.nameSeprator + self.containerLabel, k8s_resource=json.loads(msJson), ) ms.after(mr) def compilepl(self, targetPath=str()): filePath = self.fileName.format(self.containerLabel.lower()) + self.fileExt if targetPath != str(): filePath = Path(targetPath, filePath) kfp.compiler.Compiler().compile(self.aion_mlops, str(filePath)) def executepl(self, kfhost=str()): client = kfp.Client(kfhost) client.create_run_from_pipeline_func(self.aion_mlops,arguments={}) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import pandas as pd import requests import re import json import sys import time from appbe.aion_config import get_llm_data from appbe.dataPath import LOG_LOCATION from appbe.log_ut import logg import logging import openai import tiktoken openai.api_key = '' openai.api_base = '' openai.api_type = '' openai.api_version = '' deployment_name="Text-Datvinci-03" def generateLabelPerRecord(OrgData): OrgData['LabelFromGPT'] = OrgData['Head_Description'].apply(lambda x: \ generate_gpt3_response\ ("I am giving you the title and short description \ in the format [Title:Description], \ give me the related low level topics in one word in the \ format[Topic: your primary topic] along with top 5 important keywords in the \ format[Keywords: keywords]'{}' ".format(x))) #Cleaning the output as it is from ChatGPT OrgData['temp1'] = OrgData['LabelFromGPT'].apply(lambda x: (x.split('Topic:')[1]).replace(']','')) OrgData['LabelFromGPT'] = OrgData['temp1'].apply(lambda x: (x.split('Keywords:')[0]).replace(']','').rstrip()) OrgData['Keywords'] = OrgData['temp1'].apply(lambda x: (x.split('Keywords:')[1]).replace(']','')) OrgData = OrgData.drop(['temp1','Head_Description'], axis=1) return OrgData def generateLabelForChunkedRecords(OrgData): import io # OrgData = OrgData.head(120) Head_Description = {"Head_Description": [] } Head_Description2 = {"Head_Description": [] } Head_Description['Head_Description'] = OrgData['Head_Description'] strt_ind = 0 brk_ind = 0 # encoding = tiktoken.get_encoding('p50k_base') encoding = tiktoken.encoding_for_model("text-davinci-003") chunks = [] _cur_token_count = 0 _chunk_token_count = 0 for ind in Head_Description['Head_Description'].index: tokenized_text = encoding.encode(Head_Description['Head_Description'][ind]) _cur_token_count = len(tokenized_text) if _cur_token_count >= 600: OrgData['Head_Description'][ind] = OrgData['Head_Description'][ind][:1000] upto_ind = ind + 1 Head_Description2['Head_Description'] = OrgData['Head_Description'][brk_ind:ind] _chunk_token_count = encoding.encode(Head_Description2['Head_Description'].to_string()) if len(_chunk_token_count) >= 1200: brk_ind = ind # print(brk_ind) chunks.append(ind-1) _start_count = 0 if len(chunks) == 0: output = generate_gpt3_response("I am giving you datatable of text records \ for each record give me the related low level topics in one word as a data column called Topic\ and important top five keywords as a data column called Keywords. \ Provide me record number as Record and these two data columns as datatable for each record in the given datatable and number of records should be equivalent to the number of records in the given datatable of text records. '{}' ".format(Head_Description['Head_Description'])) out = io.StringIO(output[2:]) df = pd.read_csv(out, sep='\t') else: chunks.append(len(Head_Description['Head_Description'])) for ind_val in chunks: _cur_ind_val = ind_val _recordsSent = 0 Head_Description = {"Head_Description": [] } if _start_count == 0: Head_Description['Head_Description'] = OrgData['Head_Description'][strt_ind:_cur_ind_val].to_string() _recordsSent = len(OrgData['Head_Description'][strt_ind:_cur_ind_val]) else: Head_Description['Head_Description'] = OrgData['Head_Description'][_pre_ind_val:_cur_ind_val].to_string() _recordsSent = len(OrgData['Head_Description'][_pre_ind_val:_cur_ind_val]) _pre_ind_val = ind_val # if _start_count <= 5: output = generate_gpt3_response("I am giving you datatable of text records \ for each record give me the related low level topics in one word as a data column called Topic\ and important top five keywords as a data column called Keywords. \ Provide me record number as Record and these two data columns as datatable for each record in the given datatable and number of records should be equivalent to the number of records in the given datatable of text records. '{}' ".format(Head_Description['Head_Description'])) out = io.StringIO(output[2:]) if _start_count == 0: df = pd.read_csv(out, sep='\t') else: df_tmp = pd.read_csv(out, sep='\t') if len(df_tmp) > _recordsSent: df_tmp = df_tmp.head(_recordsSent) # df = df.append(df_tmp, ignore_index=True) df = pd.concat([df, df_tmp], ignore_index=True) _start_count += 1 OrgData['LabelFromGPT'] = df['Topic'] OrgData['Keywords'] = df['Keywords'] OrgData = OrgData.drop(['Head_Description'], axis=1) return OrgData # Text Data Labelling using LLM related changes # -------------------------------------------------------- def generateTextLabel(request, DATA_FILE_PATH): log = logging.getLogger('log_ux') key,url,api_type,api_version = get_llm_data() openai.api_key = key openai.api_base = url openai.api_type = api_type openai.api_version = api_version try: features = request.POST.getlist('InputFeatures') datapath = request.session['textdatapath'] OrgData = pd.read_csv(datapath) # OrgData = OrgData.head(2000) OrgData.fillna("", inplace = True) OrgData['Head_Description'] = OrgData[features[0]] if (len(features) > 1): for indx in range(len(features)): if (indx > 0): OrgData['Head_Description'] = OrgData['Head_Description'] + " "+ OrgData[features[indx]] # OrgData = generateLabelPerRecord(OrgData) OrgData = generateLabelForChunkedRecords(OrgData) df = OrgData filetimestamp = str(int(time.time())) datasetName = 'AION_TextLabelled' + filetimestamp+'.csv' dataFile = os.path.join(DATA_FILE_PATH,datasetName) df.to_csv(dataFile) request.session['texttopicdatapath'] = dataFile df_json = df.to_json(orient="records") df_json = json.loads(df_json) from appbe.dataPath import DATA_DIR from appbe.sqliteUtility import sqlite_db file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') newdata = {} newdata['datapath'] = [dataFile] newdata['datasetname'] = [datasetName] sqlite_obj.write_data(pd.DataFrame.from_dict(newdata), 'dataingest') ################################################ context = {'data_topic':df_json, 'selected':'DataOperations'} return context except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() errormsg = str(e) if 'Invalid URL' in errormsg or 'No connection adapters' in errormsg or 'invalid subscription key' in errormsg: errormsg = 'Access denied due to invalid subscription key or wrong API endpoint. Please go to settings and make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.' if 'Max retries exceeded with url' in errormsg: errormsg = 'Please make sure you have good internet connection and access to API endpoint for your resource.' fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) context = {'error': 'Failed to communicate LLM','LLM' : 'openAI', 'selected':'DataOperations', 'errormessage':errormsg} log.info('generateTextLabel -- Error : Failed to generate Text-Label.. '+str(e)) log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return context #function to return the queried response def generate_gpt3_response(user_text, print_output=False): """ Query OpenAI GPT-3 for the specific key and get back a response :type user_text: str the user's text to query for :type print_output: boolean whether or not to print the raw output JSON """ time.sleep(2) completions = openai.Completion.create( # engine='Text-Datvinci-03', # Determines the quality, speed, and cost. engine='text-davinci-003', engine=deployment_name, # Determines the quality, speed, and cost. engine='text-davinci-003', temperature=0, # Level of creativity in the response prompt=user_text, # What the user typed in max_tokens=2000, # Maximum tokens in the prompt AND response n=1, # The number of completions to generate stop=None, # An optional setting to control response generation ) # Displaying the output can be helpful if things go wrong if print_output: print(completions) # Return the first choice's text # print(completions.choices[0].text) return completions.choices[0].text # -------------------------------------------------------- ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json import os import rsa import boto3 #usnish import pandas as pd import time def add_new_GCSBucket(request): try: file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','gcsbuckets.conf')) with open(file_path, 'r') as f: data = json.load(f) f.close() if data == '': data = [] except: data = [] print(request.POST["aionreferencename"]) print(request.POST["serviceaccountkey"]) print(request.POST["bucketname"]) if request.POST["aionreferencename"] =='' or request.POST["serviceaccountkey"] == '' or request.POST["bucketname"] == '' : return 'error' newdata = {} newdata['Name'] = request.POST["aionreferencename"] newdata['GCSServiceAccountKey'] = request.POST["serviceaccountkey"] newdata['GCSbucketname'] = request.POST["bucketname"] data.append(newdata) with open(file_path, 'w') as f: json.dump(data, f) f.close() return 'success' def get_gcs_bucket(): try: file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','gcsbuckets.conf')) with open(file_path, 'r') as f: data = json.load(f) except: data = [] return data def read_gcs_bucket(name,filename,DATA_FILE_PATH): try: file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','gcsbuckets.conf')) with open(file_path, 'r') as f: data = json.load(f) except: data = [] found = False print(data) for x in data: if x['Name'] == name: GCSServiceAccountKey = x['GCSServiceAccountKey'] GCSbucketname = x['GCSbucketname'] found = True break print(found) print(name) try: if found: import io from google.cloud import storage storage_client = storage.Client.from_service_account_json(GCSServiceAccountKey) print(GCSServiceAccountKey) print(GCSbucketname) bucket = storage_client.get_bucket(GCSbucketname) blob = bucket.blob(filename) data = blob.download_as_string() df = pd.read_csv(io.BytesIO(data), encoding = 'utf-8', sep = ',',encoding_errors= 'replace') return 'Success',df except Exception as e: print(e) return 'Error', pd.DataFrame() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import shutil import subprocess import sys import glob from pathlib import Path import json from django.http import FileResponse from django.http import HttpResponse from importlib.metadata import version COMMON_PACKAGES = "'setuptools >=62.3.0','pandas==1.5.3','numpy==1.24.2','joblib==1.2.0','Cython==0.29.33','scipy==1.10.1',' scikit-learn==1.2.1','word2number==1.1','category_encoders==2.6.0'" DL_COMMON_PACKAGE = "'tensorflow==2.11.0'" TEXT_PACKAGES = "'spacy==3.5.0','nltk==3.8.1','textblob==0.15.3','demoji==1.1.0','bs4==0.0.1','text-unidecode==1.3','pyspellchecker==0.6.2','contractions==0.1.73','protobuf==3.19.6','lxml'" def createPackagePackage(request,id,version,usecasedetails,Existusecases): from appbe.pages import get_usecase_page #print('2') usecasedetail = usecasedetails.objects.get(id=id) models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS',Version=version) modelid = models[0].id p = Existusecases.objects.get(id=modelid) deploymentfolder = str(p.DeployPath) modelname = p.ModelName.usecaseid version = p.Version deployed_code = 'AION' dockerimage = os.path.join(deploymentfolder,'publish','docker_image') dockersetup = os.path.join(deploymentfolder,'publish','docker_setup') tempPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'temp_'+modelname+'_'+str(version)) try: shutil.rmtree(tempPath,ignore_errors=True) except: pass shutil.copytree(deploymentfolder,tempPath) shutil.rmtree(os.path.join(tempPath,'publish'), ignore_errors=True) try: Path(os.path.join(deploymentfolder,'publish')).mkdir(parents=True, exist_ok=True) os.mkdir(dockersetup) except: shutil.rmtree(dockersetup,ignore_errors=True) os.mkdir(dockersetup) try: os.mkdir(dockerimage) except: shutil.rmtree(dockerimage,ignore_errors=True) os.mkdir(dockerimage) shutil.copytree(tempPath, os.path.join(dockersetup,deployed_code)) shutil.rmtree(tempPath) docker_setup = os.path.join(dockersetup,'AION') try: os.mkdir(dockerimage) except: pass requirementfilename = os.path.join(dockersetup,'requirements.txt') installfilename = os.path.join(dockersetup,'install.py') dockerfile = os.path.join(dockersetup,'Dockerfile') dockerdata='FROM python:3.10-slim-buster' dockerdata+='\n' dockerdata+='WORKDIR /app' dockerdata+='\n' dockerdata+='COPY AION AION' dockerdata+='\n' dockerdata+='''RUN apt-get update \ && apt-get install -y build-essential manpages-dev \ && apt-get install -y libgomp1 \ && python -m pip install --no-cache-dir -r AION/requirements.txt ''' f = open(dockerfile, "w") f.write(str(dockerdata)) f.close() try: try: import docker client = docker.from_env() client.containers.list() except: status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['Status'] = 'Error' context['Msg'] = 'Docker should be installed and running on your machine. To build the docker image manually, the setup script is available at the following location: \\n'+dockersetup.replace('\\', '/') return context command = 'docker pull python:3.10-slim-buster' os.system(command); subprocess.check_call(["docker", "build", "-t",modelname.lower()+":"+str(version),"."], cwd=dockersetup) subprocess.check_call(["docker", "save", "-o",modelname.lower()+"_"+str(version)+".tar",modelname.lower()+":"+str(version)], cwd=dockersetup) dockerfilepath = os.path.join(dockersetup,modelname.lower()+"_"+str(version)+".tar") shutil.copyfile(dockerfilepath, os.path.join(dockerimage,modelname.lower()+"_"+str(version)+".tar")) shutil.rmtree(dockersetup) msg = 'Done' Status = 'SUCCESS' except Exception as e: msg = 'Error in docker images creation. To build manually docker image setup available in following location: '+dockersetup.replace('\\', '\\\\') Status = 'Fail' status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['Status'] = Status context['Msg'] = msg return context def downloadPackage(request,id,version,usecasedetails,Existusecases): try: if 'downloadstatus' in request.session: if request.session['downloadstatus'] == 'Downloading': return HttpResponse(json.dumps("Error Creating Package"), content_type="application/error") request.session['downloadstatus'] = 'Downloading' usecasedetail = usecasedetails.objects.get(id=id) models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS',Version=version) modelid = models[0].id p = Existusecases.objects.get(id=modelid) deployPath = str(p.DeployPath) if os.path.isdir(os.path.join(deployPath,'publish','package')): for f in os.listdir(os.path.join(deployPath,'publish','package')): if f.endswith('whl'): os.remove(os.path.join(deployPath,'publish','package',f)) usecasename = p.ModelName.usecaseid Version = p.Version deployed_code = usecasename targetname = usecasename+'_'+str(Version) whl_dir_name = 'WHEEL_'+usecasename+'_'+str(Version) deployLocation = os.path.join (deployPath,'..',whl_dir_name) try: os.makedirs(deployLocation) except OSError as e: shutil.rmtree(deployLocation) os.makedirs(deployLocation) shutil.copytree(deployPath,os.path.join(deployLocation,deployed_code)) initstring = 'import os' initstring += '\n' initstring += 'import sys' initstring += '\n' initstring += 'sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__))))' filename = os.path.join(deployLocation,deployed_code,'__init__.py') f = open(filename, "w") f.write(str(initstring)) f.close() textdata=0 learner_type = 'ml' requirementfile = os.path.join(deployPath,'requirements.txt') install_requires = '' if os.path.exists(requirementfile): fileobj = open(requirementfile, 'r') requirePackages = fileobj.readlines() fileobj.close() for package in requirePackages: if install_requires != '': install_requires = install_requires+',' install_requires = install_requires+'\''+package.strip()+'\'' setup_string = 'from setuptools import setup,find_packages' setup_string += '\n' setup_string += 'setup(name=\''+deployed_code+'\',' setup_string += '\n' setup_string += 'version=\'1\',' setup_string += '\n' setup_string += 'packages = find_packages(),' setup_string += '\n' setup_string += 'install_requires = ['+install_requires+'],' setup_string += '\n' setup_string += 'package_data={"'+deployed_code+'.pytransform":["*.*"],"'+deployed_code+'":["*.sav","*.json"],"":["*","*/*","*/*/*"]}' setup_string += '\n' setup_string += ')' filename = os.path.join(deployLocation,'setup.py') f = open(filename, "w") f.write(str(setup_string)) f.close() subprocess.check_call([sys.executable, "setup.py", "bdist_wheel"], cwd=deployLocation) shutil.copytree(os.path.join(deployLocation,'dist'),os.path.join(deployPath,'publish','package'),dirs_exist_ok=True) shutil.rmtree(deployLocation) if os.path.isdir(os.path.join(deployPath,'publish','package')): for f in os.listdir(os.path.join(deployPath,'publish','package')): if f.endswith('whl'): package = f zip_file = open(os.path.join(deployPath,'publish','package',package), 'rb') request.session['downloadstatus'] = 'Done' return FileResponse(zip_file) except Exception as e: print(e) request.session['downloadstatus'] = 'Done' return HttpResponse(json.dumps("Error Creating Package"), content_type="application/error") def installPackage(model,version,deployedPath): deployedPath = os.path.join(deployedPath,'publish','package') whlfilename='na' if os.path.isdir(deployedPath): for file in os.listdir(deployedPath): if file.endswith(".whl"): whlfilename = os.path.join(deployedPath,file) if whlfilename != 'na': subprocess.check_call([sys.executable, "-m", "pip", "uninstall","-y",model]) subprocess.check_call([sys.executable, "-m", "pip", "install","--no-dependencies",whlfilename]) status,pid,ip,port = checkModelServiceRunning(model) if status == 'Running': stopService(pid) startService(model,ip,port) return('Success') else: return('Installation Package not Found') def getMIDFromUseCaseVersion(id,version,usecasedetails,Existusecases): usecasedetail = usecasedetails.objects.get(id=id) models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS',Version=version) return(models[0].id) def stopService(pid): import psutil p = psutil.Process(int(pid)) p.terminate() def checkModelServiceRunning(package_name): from os.path import expanduser home = expanduser("~") if platform.system() == 'Windows': modelServices = os.path.join(home,'AppData','Local','HCLT','AION','services') else: modelServices = os.path.join(home,'HCLT','AION','target','services') filename = package_name+'_service.py' modelservicefile = os.path.join(modelServices,filename) status = 'Not Initialized' ip = '' port = '' pid = '' if os.path.exists(modelservicefile): status = 'Not Running' import psutil for proc in psutil.process_iter(): pinfo = proc.as_dict(attrs=['pid', 'name', 'cmdline','connections']) if 'python' in pinfo['name']: if filename in pinfo['cmdline'][1]: status = 'Running' pid = pinfo['pid'] for x in pinfo['connections']: ip = x.laddr.ip port = x.laddr.port return(status,pid,ip,port) def startService(package_name,ip,portNo): file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','bin','model_service.py')) from os.path import expanduser home = expanduser("~") if platform.system() == 'Windows': modelServices = os.path.join(home,'AppData','Local','HCLT','AION','services') else: modelServices = os.path.join(home,'HCLT','AION','target','services') if not os.path.isdir(modelServices): os.makedirs(modelServices) filename = package_name+'_service.py' modelservicefile = os.path.join(modelServices,filename) status = 'File Not Exist' if os.path.exists(modelservicefile): status = 'File Exist' r = ([line.split() for line in subprocess.check_output("tasklist").splitlines()]) for i in range(len(r)): if filename in r[i]: status = 'Running' if status == 'File Not Exist': shutil.copy(file_path,modelservicefile) with open(modelservicefile, 'r+') as file: content = file.read() file.seek(0, 0) line = 'from '+package_name+' import aion_performance' file.write(line+"\n") line = 'from '+package_name+' import aion_drift' file.write(line+ "\n") line = 'from '+package_name+' import featureslist' file.write(line+ "\n") line = 'from '+package_name+' import aion_prediction' file.write(line+ "\n") file.write(content) file.close() status = 'File Exist' if status == 'File Exist': command = "python "+modelservicefile+' '+str(portNo)+' '+str(ip) os.system('start cmd /c "'+command+'"') def checkInstalledPackge(package_name): import importlib.util spec = importlib.util.find_spec(package_name) if spec is None: return('Not Installed','','') else: if len(spec.submodule_search_locations) > 0: displaypath = os.path.join(spec.submodule_search_locations[0],'etc','display.json') with open(displaypath) as file: config = json.load(file) file.close() if 'usecasename' in config: modelName = config['usecasename'] else: modelName = 'NA' if 'version' in config: version = config['version'] else: version = 'NA' return('Installed',modelName,version) import pandas as pd import numpy as np def get_leaderboard(file_content): matched_lines = [line.replace('Model:-', '') for line in file_content.split('\n') if "Model:-" in line] df = pd.DataFrame(columns = ['Model', 'Iterations', 'Score (%)', 'Score Type', 'Best Score (%)']) import re try: for line in matched_lines: if 'Model Name::' in line: MODEL = line.split('::') model = MODEL[1] if 'ScoringType::' in line: S = line.split('::') #SC = ScorTyp[1] if 'make_scorer'in line: ST = line.split('make_scorer') ScorTyp = ST[1] df['Score Type'] = np.where(df['Model'] == model, ScorTyp,df['Score Type']) if 'Validation Score::' in line: BS = line.split('::') BestSc = round(float(BS[1]), 4)*100 BestSc = abs(BestSc) df['Best Score (%)'] = np.where(df['Model'] == model, BestSc, df['Best Score (%)']) if 'Iteration::' in line: l = line.split('::') word = re.findall(r'\[(.*?)\]', l[1]) if ';, score=' in line: sc = line.split('score=') SCR = sc[1].split(' ') Score = round(float(SCR[0]), 4)*100 Score = abs(Score) # df = df.concat({'Model': model, 'Iterations': word,'Score (%)': Scor,'Score Type': '', 'Best Score (%)': 0}, ignore_index=True) newdf = pd.DataFrame([{'Model': model, 'Iterations': word,'Score (%)': Score,'Score Type': '', 'Best Score (%)': 0}]) df = pd.concat([df,newdf],axis=0, ignore_index=True) LIST = [] for i in range(int(len(df['Score (%)'])/5)): l = (sum(df['Score (%)'][5*i:5*(i+1)])/5) #LIST.concat(l) LIST.append(l) for i in range(len(LIST)): df['Score (%)'][5*i:5*(i+1)]=LIST[i] CL = [line.replace('------->Type of Model :classification', 'Model :classification') for line in file_content.split('\n') if "------->Type of Model :classification" in line] for l in CL: if 'Model :classification' in l: df = df.sort_values(by = ['Best Score (%)'], ascending=False) RE = [line.replace('------->Type of Model :regression', 'Model :regression') for line in file_content.split('\n') if "------->Type of Model :regression" in line] for l in RE: if 'Model :regression' in l: df = df.sort_values(by = ['Best Score (%)']) except Exception as e: print(e) return df if __name__ == "__main__": file_path = r"C:\Users\richard.mochahari\AppData\Local\Programs\HCLTech\AION\data\target\AI0335\1\log\model_training_logs.log" my_file = open(file_path, 'r') file_content = my_file.read() my_file.close() print(get_leaderboard(file_content)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os.path import time import subprocess import sys from appbe.aion_config import kafka_setting from appbe.aion_config import running_setting from appbe import installPackage from appbe import compute from appbe.models import getusercasestatus import json import pandas as pd import ntpath import shutil import platform from pathlib import Path from appbe.dataPath import DATA_DIR LOG_FILE_PATH = os.path.join(DATA_DIR,'logs') def encrptpackage_command(request,Existusecases,usecasedetails): command = request.POST.get('encryptedsubmit') kafkaSetting = kafka_setting() ruuningSetting = running_setting() computeinfrastructure = compute.readComputeConfig() modelID = request.POST.get('modelID') p = Existusecases.objects.get(id=modelID) usecasename = p.ModelName.UsecaseName usecaseid = p.ModelName.usecaseid runningStatus,pid,ip,port = installPackage.checkModelServiceRunning(usecasename) installationStatus,modelName,modelVersion=installPackage.checkInstalledPackge(usecasename) try: tacking_url =request.get_host() except Exception as e: tacking_url = '127.0.0.1' usecasedetail = usecasedetails.objects.get(id=p.ModelName.id) usecase = usecasedetails.objects.all() models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS') for model in models: model.scoringCreteria = 'NA' model.score = 'NA' model.deploymodel = 'NA' if os.path.isdir(str(model.DeployPath)): modelPath = os.path.join(str(model.DeployPath),'etc','output.json') try: with open(modelPath) as file: outputconfig = json.load(file) file.close() if outputconfig['status'] == 'SUCCESS': model.scoringCreteria = outputconfig['data']['ScoreType'] model.score = outputconfig['data']['BestScore'] model.deploymodel = outputconfig['data']['BestModel'] model.modelType = outputconfig['data']['ModelType'] model.maacsupport = 'True' model.flserversupport = 'False' supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"] if model.deploymodel in supportedmodels: model.flserversupport = 'True' else: model.flserversupport = 'False' supportedmodels = ["Logistic Regression", "Naive Bayes","Decision Tree","Support Vector Machine","K Nearest Neighbors","Gradient Boosting","Random Forest","Linear Regression","Lasso","Ridge","Extreme Gradient Boosting (XGBoost)","Light Gradient Boosting (LightGBM)","Categorical Boosting (CatBoost)"] if model.deploymodel in supportedmodels: model.maacsupport = 'True' else: model.maacsupport = 'False' supportedmodels = ["Extreme Gradient Boosting (XGBoost)"] if model.deploymodel in supportedmodels: model.encryptionsupport = 'True' else: model.encryptionsupport = 'False' except Exception as e: pass if command.lower() == 'secureclient': try: encryptedclient = os.path.join(str(p.DeployPath),'publish','SecureClient') shutil.rmtree(encryptedclient, ignore_errors=True) logPath = os.path.join(encryptedclient,'logs') scriptPath = os.path.join(encryptedclient,'script') modelPath = os.path.join(encryptedclient,'model') Path(modelPath).mkdir(parents=True, exist_ok=True) Path(encryptedclient).mkdir(parents=True, exist_ok=True) Path(logPath).mkdir(parents=True, exist_ok=True) Path(scriptPath).mkdir(parents=True, exist_ok=True) encryptedclientOrg = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','encryptedPackage')) modelProfiler = os.path.normpath(os.path.join(str(p.DeployPath),'script','inputprofiler.py')) modelselector = os.path.normpath(os.path.join(str(p.DeployPath),'aion_predict.py')) preprocessmodel = os.path.normpath(os.path.join(str(p.DeployPath),'model','preprocess_pipe.pkl')) # shutil.copy2(modelProfiler,scriptPath) # shutil.copy2(modelselector,scriptPath) ## For bug 15975 if os.path.exists(modelProfiler): shutil.copy2(modelProfiler,scriptPath) if os.path.exists(modelselector): shutil.copy2(modelselector,scriptPath) if os.path.exists(preprocessmodel): shutil.copy2(preprocessmodel,modelPath) if model.modelType.lower() == 'classification': try: opfile = os.path.normpath(os.path.join(encryptedclientOrg,'Readme.txt')) shutil.copy2(opfile,encryptedclient) opfile = os.path.normpath(os.path.join(encryptedclientOrg,'requirements.txt')) shutil.copy2(opfile,encryptedclient) except: #failed to copy readme,requirements.txt files pass opfile = os.path.normpath(os.path.join(encryptedclientOrg,'client','heMulticlass.py')) shutil.copy2(opfile,scriptPath) opfile = os.path.normpath(os.path.join(encryptedclientOrg,'client','aion_hemulticlient.py')) shutil.copy2(opfile,encryptedclient) os.rename(os.path.join(encryptedclient,'aion_hemulticlient.py'),os.path.join(encryptedclient,'aion_sclient.py')) elif model.modelType.lower() == 'regression': try: opfile = os.path.normpath(os.path.join(encryptedclientOrg,'Readme.txt')) shutil.copy2(opfile,encryptedclient) opfile = os.path.normpath(os.path.join(encryptedclientOrg,'requirements.txt')) shutil.copy2(opfile,encryptedclient) except Exception as e: print(e) #failed to copy readme,requirements.txt files pass opfile = os.path.normpath(os.path.join(encryptedclientOrg,'client','heRegression.py')) shutil.copy2(opfile,scriptPath) opfile = os.path.normpath(os.path.join(encryptedclientOrg,'client','aion_heregressionclient.py')) shutil.copy2(opfile,encryptedclient) os.rename(os.path.join(encryptedclient,'aion_hemulticlient.py'),os.path.join(encryptedclient,'aion_sclient.py')) except Exception as e: Status = 'Error' Msg = 'Secure client error: Check log file for more details' Status = 'SUCCESS' Msg = 'Secure Client Code Generated at '+encryptedclient path= encryptedclient #Task 9981 elif command.lower() == 'secureserver': try: configPath = os.path.join(str(p.DeployPath),'etc','secure_config.json') modelpath = usecasename+'_'+str(p.Version)+'.sav' config = {'model_name':modelpath} with open(configPath, "w") as outfile: json.dump(config, outfile) encryptedclientOrg = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','encryptedPackage')) if model.modelType.lower() == 'classification': opfile = os.path.normpath(os.path.join(encryptedclientOrg,'server','heMulticlass.py')) shutil.copy2(opfile,str(p.DeployPath)) try: os.remove(os.path.join(str(p.DeployPath),'aion_spredict.py')) except OSError: pass os.rename(os.path.join(str(p.DeployPath),'heMulticlass.py'),os.path.join(str(p.DeployPath),'aion_spredict.py')) Status = 'SUCCESS' Msg = 'Secure rest end point enabled http://'+str(tacking_url)+'/api/spredict?usecaseid='+usecaseid+'&version='+str(p.Version) except Exception as e: Status = 'Error' Msg = 'Secure rest end point error: Check log file for more details' nouc = 0 from appbe.pages import get_usecase_page status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['Status'] = Status context['Msg'] = Msg if command.lower() == 'secureclient': #Task 9981 context['path'] = path ''' selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) context = {'tab': 'upload','nouc':nouc,'usecasedetail': usecase, 'models': models, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'installationStatus':installationStatus,'modelName':modelName,'modelVersion':modelVersion,'usecasename':usecasename,'pid':pid,'ip':ip,'port':port,'usecaseid':p.ModelName.id,'Status':Status,'Msg':Msg} ''' return(context) def download_sclient(request,context): #Task 9981 import os from django.http import HttpResponse, Http404 try: file_name = 'SecureClient_'+request.POST.get('modelsignature') path = context['path'] file_path = shutil.make_archive(file_name, 'zip', path) if os.path.exists(file_path): with open(file_path, 'rb') as fh: response = HttpResponse(fh.read(),content_type='application/x-zip-compressed') response['Content-Disposition'] = 'inline; filename=' + os.path.basename(file_path) os.remove(file_path) return response except: raise Http404 ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os from pathlib import Path def label_filename(request): filename = 'LabeledData.csv' labelPath = os.path.join(request.session['datalocation'],'AION','Labels') Path(labelPath).mkdir(parents=True, exist_ok=True) filePath = os.path.join(labelPath,filename) return filePath ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json import pandas as pd def get_true_option(d, default_value=None): if isinstance(d, dict): for k, v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def get_true_options(d): options = [] if isinstance(d, dict): for k, v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): options.append(k) return options def check_datetime(config): dateTime = config['basic']['dateTimeFeature'] if dateTime == '' or dateTime.lower()=='na': return False return True def check_dtype(d): flag= 1 for item in d: if item["type"].lower() != "text" and item["type"].lower() != "index": flag = 0 break return flag def check_text(d): #task 12627 flag= 0 for item in d: if item["type"].lower() == "text": flag = 1 break return flag def check_labelencoding(ftr_dict_list, target_ftr): for ftr_dict in ftr_dict_list: if ftr_dict['feature']!=target_ftr and ftr_dict['type'].lower()=='categorical' and ftr_dict['categoryEncoding'].lower()!='labelencoding': return False return True class timeseries(): def __init__(self,config): self.config=config if self.config['basic']['analysisType']['timeSeriesForecasting'].lower()=='true': #task 11997 self.problemType = 'timeSeriesForecasting' elif self.config['basic']['analysisType']['timeSeriesAnomalyDetection'].lower()=='true': self.problemType = 'timeSeriesAnomalyDetection' #task 11997 def validate_basic_config(self,status='pass',msg=None): #task 12627 date_time_status = check_datetime(self.config) text_status = check_text(self.config['advance']['profiler']['featureDict']) if not date_time_status and text_status: msg = 'For time series problem,\\n* One feature should be in datetime format\\n* Text feature not supported ' return 'error', msg elif not date_time_status: msg = 'For time series problem, one feature should be in datetime format' return 'error', msg elif text_status: msg = 'For time series problem, text feature not supported ' return 'error', msg selected_algos = get_true_options(self.config['basic']['algorithms'][self.problemType]) #task 11997 if isinstance(self.config['basic']['targetFeature'],str): targetFeature = list(self.config['basic']['targetFeature'].split(',')) if self.problemType=='timeSeriesForecasting': #task 11997 if len(targetFeature) > 1: if 'ARIMA' in selected_algos: status = 'error' msg = "ARIMA is not supported for multilabel (target) feature" return status, msg if "FBPROPHET" in selected_algos: status = 'error' msg = "FBPROPHET is not supported for multiLabel (target) feature" return status, msg if 'MLP' in selected_algos: status = 'error' msg = "MLP is not supported for multiLabel (target) feature" return status, msg if len(targetFeature) == 1 and 'VAR' in selected_algos: status = 'error' msg = "VAR is not supported for singleLabel (target) feature" return status, msg elif self.problemType=='timeSeriesAnomalyDetection': #task 11997 anomChecker = anomaly(self.config) status, msg = anomChecker.validate_basic_config() return status, msg class anomaly(): def __init__(self,config): self.config = config if self.config['basic']['analysisType']['anomalyDetection'].lower()=='true': #task 11997 self.problemType = 'anomalyDetection' elif self.config['basic']['analysisType']['timeSeriesAnomalyDetection'].lower()=='true': #task 11997 self.problemType = 'timeSeriesAnomalyDetection' def validate_basic_config(self,status='pass',msg=None): #task 12627 date_time_status = check_datetime(self.config) targetFeature = self.config['basic']['targetFeature'] if self.problemType=='anomalyDetection' and date_time_status: status = 'error' msg = 'Date feature detected. For anomaly detection on time series change problem type to Time Series Anomaly Detection or drop Date feature' return status, msg if targetFeature.lower()!= 'na' and targetFeature!= "" and self.config['basic']['inlierLabels'] == '': status = 'error' msg = 'Please provide inlier label in case of supervised anomaly detection' return status, msg class survival(): def __init__(self,config): self.config = config self.problemType= 'survivalAnalysis' def validate_basic_config(self): dateTimeStatus = check_datetime(self.config) labelencoding_status = check_labelencoding(self.config['advance']['profiler']['featureDict'], self.config['basic']['targetFeature']) if not dateTimeStatus and not labelencoding_status: msg = 'For survival analysis problem,\\n* One feature should be in datetime format\\n* Encoding of categorical features should be of label encoding ' return 'error', msg elif not dateTimeStatus: msg = 'One feature should be in datetime format for survival analysis problem. Please select it from model feature' return 'error', msg elif not labelencoding_status: msg = 'Categorical features are expected to be label encoded for survival analysis problem. Please select it from feature encoding' return 'error', msg else: return 'pass', " " class associationrule(): def __init__(self,config): self.config=config def validate_basic_config(self,status='pass', msg=None): if self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'].lower() == '' or self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'].lower() == 'na' or self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature'].lower() == '' or self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature'].lower() == 'na': return "error","Make sure to configure invoice feature and item feature" elif self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'] == self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature']: return "error","Make sure to invoice feature and item feature is configure correctly" else: return "pass", " " class itemrating(): #task 6081 def __init__(self,config): self.config = config def validate_basic_config(self): data_loc = self.config['basic']['dataLocation'] data_length = len(pd.read_csv(data_loc)) if data_length >= 1000000: return 'error', "Recommender System can handle data up to 1 million records. Please try with a smaller dataset." else: return "pass"," " class documentsimilarity(): def __init__(self,config): self.config=config def validate_basic_config(self,status='pass', msg=None): flag = check_dtype(self.config['advance']['profiler']['featureDict']) if flag == 1: return "pass", " " else: msg="Make sure to change the feature type from Categorical to Text and drop Numerical features for document similarity" return "error", msg def validate(config): try: problem_type = get_true_option(config['basic']['analysisType']) status = 'pass' msg = '' if 'timeseries' in problem_type.lower(): #task 11997 obj = timeseries(config) elif problem_type.lower() == 'survivalanalysis': obj = survival(config) elif problem_type.lower() == 'anomalydetection': obj = anomaly(config) elif problem_type.lower() in ['similarityidentification','contextualsearch']: obj = documentsimilarity(config) elif problem_type.lower() == 'recommendersystem': if config['basic']['algorithms']['recommenderSystem']['AssociationRules-Apriori'].lower() == 'true': obj = associationrule(config) elif config['basic']['algorithms']['recommenderSystem']['ItemRating'].lower() == 'true': #task 6081 obj = itemrating(config) else: return 'pass',"" else: return 'pass',"" status,msg= obj.validate_basic_config() print(status, msg, 'io') return(status,msg) except Exception as e: print(e) def start_check(config): return validate(config) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os,sys def read_service_url_params(request): hosturl =request.get_host() url='http://'+hosturl+'/api/' return url def read_monitoring_service_url_params(request): hosturl =request.get_host() file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','aion.config')) file = open(file_path, "r") data = file.read() file.close() service_url = '127.0.0.1' service_port='60050' for line in data.splitlines(): if 'aion_service_url=' in line: service_url= line.split('=',1)[1] if 'aion_service_port=' in line: service_port= line.split('=',1)[1] url='http://'+hosturl+'/api/' return url def read_performance_service_url_params(request): hosturl =request.get_host() file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','aion.config')) file = open(file_path, "r") data = file.read() file.close() service_url = '127.0.0.1' service_port='60050' for line in data.splitlines(): if 'aion_service_url=' in line: service_url= line.split('=',1)[1] if 'aion_service_port=' in line: service_port= line.split('=',1)[1] url='http://'+hosturl+'/api/' return url def read_pattern_anomaly_url_params(request): hosturl =request.get_host() file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','aion.config')) file = open(file_path, "r") data = file.read() file.close() service_url = '127.0.0.1' service_port='60050' for line in data.splitlines(): if 'aion_service_url=' in line: service_url= line.split('=',1)[1] if 'aion_service_port=' in line: service_port= line.split('=',1)[1] url='http://'+hosturl+'/api/pattern_anomaly_predict/' return url def read_pattern_anomaly_setting_url_params(request): hosturl =request.get_host() file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','aion.config')) file = open(file_path, "r") data = file.read() file.close() service_url = '127.0.0.1' service_port='60050' for line in data.splitlines(): if 'aion_service_url=' in line: service_url= line.split('=',1)[1] if 'aion_service_port=' in line: service_port= line.split('=',1)[1] url='http://'+hosturl+'/api/pattern_anomaly_settings/' return url ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' #Standard Library modules import sqlite3 import pandas as pd from pathlib import Path class sqlite_writer(): def __init__(self, target_path): self.target_path = Path(target_path) database_file = self.target_path.stem + '.db' self.db = sqlite_db(self.target_path, database_file) def file_exists(self, file): if file: return self.db.table_exists(file) else: return False def read(self, file): return self.db.read(file) def write(self, data, file): self.db.write(data, file) def close(self): self.db.close() class sqlite_db(): def __init__(self, location, database_file=None): if not isinstance(location, Path): location = Path(location) if database_file: self.database_name = database_file else: self.database_name = location.stem + '.db' db_file = str(location/self.database_name) self.conn = sqlite3.connect(db_file) self.cursor = self.conn.cursor() self.tables = [] def table_exists(self, name): if name in self.tables: return True elif name: query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';" listOfTables = self.cursor.execute(query).fetchall() if len(listOfTables) > 0 : self.tables.append(name) return True return False def read(self, table_name,condition=''): if condition == '': return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn) else: return pd.read_sql_query(f"SELECT * FROM {table_name} WHERE {condition}", self.conn) def create_table(self,name, columns, dtypes): query = f'CREATE TABLE IF NOT EXISTS {name} (' for column, data_type in zip(columns, dtypes): query += f"'{column}' TEXT," query = query[:-1] query += ');' self.conn.execute(query) return True def update(self,table_name,updates,condition): update_query = f'UPDATE {table_name} SET {updates} WHERE {condition}' self.cursor.execute(update_query) self.conn.commit() return True def write(self,data, table_name): if not self.table_exists(table_name): self.create_table(table_name, data.columns, data.dtypes) tuple_data = list(data.itertuples(index=False, name=None)) insert_query = f'INSERT INTO {table_name} VALUES(' for i in range(len(data.columns)): insert_query += '?,' insert_query = insert_query[:-1] + ')' self.cursor.executemany(insert_query, tuple_data) self.conn.commit() return True def delete(self, name): pass def close(self): self.conn.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' # def exploratorory_help(): # # # # return (data_overview_tip, feature_importance_tip, correlation_analysis_tip, exploratory_analysis_tip, data_deep_drive_tip, drift_tip) drift_tip = 'A data distribution represents a list of all of the possible values of each of the variables as provided in the data. Based on how the data values are distributed, it can be mapped to some well-known distribution curves so that the nature of the distribution can be shown.' data_overview_tip = 'Data Overview give users a quick understanding of the distribution of values across the features and provides summary statistics of the features. It helps to uncover several uncommon and common issues such as unexpected feature values, missing feature values and data skew.' timeseries_analysis_tip = "Time Series Analysis provides information about the stationarity and seasonality of each of the features in the ingested data." feature_importance_tip = 'Feature Importance provides a features and grades the features on a scale of relative importance' correlation_analysis_tip = 'Correlation Analysis provides the strength of relationships among various features. Values range from 0 (least correlation) to 1 (highest correlation). A high correlation means that two or more variables have a strong relationship with each other, while a weak correlation means that the variables are hardly related.' exploratory_analysis_tip = 'This provides an unsupervised clustering view of the data and provides insights on how the data is distributed. It helps profile the attributes of different clusters and gives insight into underlying patterns of different clusters and find similarities in the data points.' data_deep_drive_tip = 'Data Deep Dive provides an interactive interface for exploring the relationship between data points across all the different features of a dataset. Each individual item in the visualization represents a data point. Data can be grouped and binned in multiple dimensions based on their feature values.' pair_graph_tip = 'It is used to present the correlations between two selected features.' fair_metrics_tip = 'It provides interface to detect the bias in data associated with a sensitive or protected attribute and used for training.' hopkins_tip =['Since the value is in between (0.0, 0.3), it indicates that the data has a high tendency to cluster.','Since the value is around 0.5, it indicates that the data distriution is random.','Since the value is in between (0.7, 0.99), it indicates that the data is regularly spaced.'] basic_help={'RowFiltering':'You can easily filter rows based on whether the column match a condition or not'} advance_help = {'NumericFillMethod':'This is used to handle the null values present in the numerical dataset.','NumericFillMethod_Median':'Replace with middle value of the data set. Efficient and not affected by outliers.','NumericFillMethod_Mean':'Replace with average value of the columns. Affected by outliers.','NumericFillMethod_Max':'Replace all nulls with maximum value in the column.','NumericFillMethod_KNN':'This implements KNN algorithm to replace the null','NumericFillMethod_Zero':'Replace the null with 0 value','NumericFillMethod_Drop':'To remove all the null values in the dataset','NumericFillMethod_Min':'Replace all null with minimum value present in the column','CategoricalFillMethod':'This is used to handle the null values present in the categorical dataset.','CategoricalFillMethod_Mode':'Replace with most common values in the dataset. Suggested for categorical columns.','CategoricalFillMethod_Zero':'Replace the null with 0 value.','CategoricalFillMethod_KNN':'This implements KNN algorithm to replace the null','CategoricalFillMethod_Drop':'To remove all the null values in the dataset.','OutlierDetection':'An unusual data point that differs significantly from other data points.','OutlierDetection_IQR':'Identifying the outliers with interquatile range by dividing the data into quartiles.','OutlierDetection_Zscore':'If the z score of a data point is more than 3, it indicates that the data point is an outlier.','OutlierDetection_Isolation':'Randomly sub-sampled data is processed in a tree structure based on randomly selected features.','MissValueRatio':'Permitted Missing Value Ratio i.e., Number of missing values by total number of obervation. If the number of missing value in a columns is more than ratio than the columns will be assumped as empty column','NumericFeatureRatio':'In case column is mix of number and text value. If the number of numeric columns to number of rows ratio is greator than the value mentioned it is assumed as numeric columns and remaining rows which have text values will be removed','NormalStandard':'Standardize features by removing the mean and scaling to unit variance.','NormalMinMax':'This scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.','NormalLogNormal':'When a feature does not follow a linear distributio, that helps minimize skewness and map any distribution to a normal one as close as possible.','RemoveNoise':'Used to remove the noise present in the text data. Noise like special characters, unicode, emojis, hyperlinks,hashtags, html parameters etc.','ExpandContractions':'Contractions are words or combinations of words that are shortened by dropping letters and replacing them by an apostrophe.','Normalize':'Normalization is the process of converting a token into its base form. In the normalization process, the inflectional form of a word is removed so that the base form can be obtained.','Lemmatization':'It is a more effective option than stemming because it converts the word into its root word, rather than just stripping the suffices.','Stemming':'It refers to the removal of suffices, like ing,ly,s etc. by a simple rule-based approach.','NGrams':'The combination of multiple words used together.','PosTags':'The process of classifying words into their parts of speech and labeling them accordingly is known as part-of-speech tagging, or simply POS-tagging.','FeatureSelection':'Feature selection is for filtering irrelevant or redundant features from your dataset. The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones.','FeatureEngineering':'Feature extraction is for creating a new, smaller set of features that stills captures most of the useful information. Again, feature selection keeps a subset of the original features while feature extraction creates new ones.','PCA':'Principle Component Analysis (PCA) is a common feature extraction method in data science. Technically, PCA finds the eigenvectors of a covariance matrix with the highest eigenvalues and then uses those to project the data into a new subspace of equal or less dimensions.','StatisticalBased':'Features are selected on the basis of statistics measures. This method does not depend on the learning algorithm and chooses the features as a pre-processing step. The filter method filters out the irrelevant feature and redundant columns from the model by using different metrics through ranking.','ModelBased':'Different tree-based methods of feature selection help us with feature importance to provide a way of selecting features. Here, feature importance specifies which feature has more importance in model building or has a great impact on the target variable.','CorrelationThreshold':'Correlation Threshold for Statistican Based Feature Selection. Correlation relation analysis done on input features vs target feature and features having correlation value grather then threshold picks for training','PValue':'P Value again for Statistical Based Feature Selection','Variance':'For Feature Selection, features should have higher variance from threshold.','Normalization':'The goal of normalization is to change the values of numeric columns in the dataset to use a common scale , without distoring differences in the ranges of values or losing information.','SVD':'The singular value decomposition (SVD) provides another way to factorize a matrix, into singular vectors and singular values. The SVD allows us to discover some of the same kind of information as the eigendecomposition.','ReplaceAcro':'Replace any abrivations into its full form Eg:{"DM":"DirectMessage"}', 'Factoranalysis':' This algorithm creates factors from the observed variables to represent the common variance i.e. variance due to correlation among the observed variables.','ICA':'ICA stands for Independent Components Analysis and it is a linear dimension reduction method, which transforms the dataset into columns of independent components.','optimizationmethod':'Optimization is the process where we train the model iteratively that results in a maximum and minimum function evaluation.','Random':'Random search is a method in which random combinations of hyperparameters are selected and used to train a model. The best random hyperparameter combinations are used. Random search bears some similarity to grid search.','Grid':'Grid search is essentially an optimization algorithm which lets to select the best parameters for your optimization problemfrom a list of parameter options that provided, hence automating the trial-and-error method.','Bays':'Bayesian optimisation in turn takes into account past evaluations when choosing the hyperparameter set to evaluate next. This approach typically requires less iterations to get to the optimal set of hyperparameter values.','Stopwords':'Stop words are commonly eliminated which are commonly used that they carry very little useful information. They are passed in a list ["Stopword1","Stopword2"]','Tokenization':'It is essentially splitting a phrase, sentence, paragraph, or an entire text document into smaller units, such as individual words or terms. Choose the library for tokenization','Lemma':'In lemmatization, the transformation uses a dictionary to map different variants of a word back to its root format.','Stopwords1':'Stop words are commonly eliminated which are commonly used that they carry very little useful information.Select from the below library to remove them', 'Genetic':'The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population evolves toward an optimal solution.','CV':'Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into.','Ensemble':'Ensemble learning is a general meta approach to machine learning that seeks better predictive performance by combining the predictions from multiple models.','EnsembleStatus':'Enable or disable according to the preference','TargetEncoding':'Target encoding is the process of replacing a categorical value with the mean of the target variable','OneHotEndoding':'Encode categorical features as a one-hot numeric array.','LabelEncoding':'Encode target labels with value between 0 and n_classes-1.','SMCStrategy':'A most_frequent model - The default. In regression the prediction is equal to the mean value, in classification the prediction is equal to the most common value.\n A uniform model - In regression, selects a random value from the y range. In classification, selects one of the labels by random.\n A stratified model - Draws the prediction from the distribution of the labels in the train.\n A tree model - Trains a simple decision tree with a given depth. The depth can be customized using the max_depth parameter.','SMCGain':'The gain is calculated as:\ngain = (model score - simple score)/(perfect score - simple score)','SMCTreeDepth':'the max depth of the tree (used only if simple model type is tree).','MIcondition':'Measure model average inference time (in seconds) per sample'} ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import random import string from sklearn import datasets import pandas as pd import names # pip install names import time import numpy as np import argparse import json import os import platform import time import sys from appbe.dataPath import CONFIG_FILE_PATH def randStr(chars = 'XYZABCDE', N=2): return ''.join(random.choice(chars) for _ in range(N)) def load_json_config(file): with open(file, 'r') as openfile: json_object = json.load(openfile) for key, value in json_object.items(): print(key, value) return json_object def gen_data_classification(number_samples=10000, number_numerical_features=25, file_name='file_class.csv', number_categorical_features=2, number_text_features=2, missing_proportion=0.1, number_informative=20, number_class=2, weights=[0.5,0.5], shift=0.0, value_range_dict={0:(1, 2)}): # TO-DO: need to add min max vlinear/non-linear try: features, output = datasets.make_classification( n_samples=number_samples, n_features=number_numerical_features, n_informative=number_informative, n_classes=number_class, weights = weights, # 20% of the targets will be 0, 80% will be 1. default is 50/50 shift=shift, ) columns = [] # Numerical Features for i in range(number_numerical_features): columns.append('Feature_' + str(i)) features = pd.DataFrame(features, columns=columns) # Setting min max value for features for col_name in features.columns: for key, value in value_range_dict.items(): if (str(features.columns.get_loc(col_name)) == key): for item in features[col_name].values: if item < value[0]: features.loc[features[col_name] == item, col_name] = random.uniform(value[0],value[1]) if item > value[1]: features.loc[features[col_name] == item, col_name] = random.uniform(value[0],value[1]) df_list = [] df_list.append(features) # Add Categorical Features for j in range(number_categorical_features): categorical_feature1_list = [] number_categories_per_feature = random.randint(2, 5) for i in range(number_categories_per_feature): categorical_feature1_list.append(randStr(N=3)) print("Categories of Categorical Feature " + str(j) + ": ", categorical_feature1_list) categorical_feature1 = [] for k in range(number_samples): categorical_feature1.append(random.choice(categorical_feature1_list)) categorical_feature1 = pd.DataFrame(categorical_feature1, columns=['Categorical'+str(j)]) df_list.append(categorical_feature1) # Add Text Features for l in range(number_text_features): text_feature = [] for k in range(number_samples): text_feature.append(names.get_full_name()) # text_feature.append(r.get_random_word()) text_feature = pd.DataFrame(text_feature, columns=['Name'+str(l)]) # text_feature = pd.DataFrame(text_feature, columns=['Word' + str(l)]) df_list.append(text_feature) output = pd.DataFrame(output, columns=['Target']) df_list.append(output) df_final = pd.concat(df_list, axis=1) for col in df_final.columns: # df_final.loc[df_final.sample(frac=0.1).index, col] = np.NaN df_final.loc[df_final[col].sample(frac=missing_proportion).index, col] = np.NaN # Check to see proportion of NaN values: # df.isnull().sum() / len(df) df_final.to_csv(file_name) return True except Exception as e: print(e) return False def gen_data_regression( number_samples=10000, number_numerical_features=25, file_name='file_regress.csv', number_categorical_features=2, number_text_features=2, missing_proportion=0.1, number_informative=10, number_target=1, bias=0.0, noise=0.0, value_range_dict={1:(5, 10)} ): try: features, output = datasets.make_regression( n_samples=number_samples, n_features=number_numerical_features, n_informative=number_informative, n_targets=number_target, bias=bias, noise=noise, ) columns = [] for i in range(number_numerical_features): columns.append('Feature_' + str(i)) features = pd.DataFrame(features, columns=columns) for col_name in features.columns: for key, value in value_range_dict.items(): if (str(features.columns.get_loc(col_name)) == key): for item in features[col_name].values: if item < value[0]: features.loc[features[col_name] == item, col_name] = random.uniform(value[0],value[1]) if item > value[1]: features.loc[features[col_name] == item, col_name] = random.uniform(value[0],value[1]) df_list = [] df_list.append(features) for j in range(number_categorical_features): categorical_feature1_list = [] number_categories_per_feature = random.randint(2, 5) for i in range(number_categories_per_feature): categorical_feature1_list.append(randStr(N=3)) print("Categories of Categorical Feature " + str(j) + ": ", categorical_feature1_list) categorical_feature1 = [] for k in range(number_samples): categorical_feature1.append(random.choice(categorical_feature1_list)) categorical_feature1 = pd.DataFrame(categorical_feature1, columns=['Categorical' + str(j)]) df_list.append(categorical_feature1) for l in range(number_text_features): text_feature = [] for k in range(number_samples): text_feature.append(names.get_full_name()) text_feature = pd.DataFrame(text_feature, columns=['Name'+str(l)]) df_list.append(text_feature) output = pd.DataFrame(output, columns=['Target']) df_list.append(output) df_final = pd.concat(df_list, axis=1) for col in df_final.columns: # df_final.loc[df_final.sample(frac=0.1).index, col] = np.NaN df_final.loc[df_final[col].sample(frac=missing_proportion).index, col] = np.NaN # Check to see proportion of NaN values: # df.isnull().sum() / len(df) df_final.to_csv(file_name) return True except Exception as e: print(e) return False def gen_data_series(univariate="True", start_time='2000-01-01 00:00', end_time='2022-12-31 00:00', number_samples=10000, number_numerical_features=25, file_name='file_regress.csv', number_categorical_features=2, # number_text_features=2, missing_proportion=0.1, number_informative=10, number_target=1, bias=0.0, noise=0.0, value_range_dict={1:(5, 10)} ): try: if univariate == "True": number_numerical_features = 1 number_categorical_features = 0 features, output = datasets.make_regression( n_samples=number_samples, n_features=number_numerical_features, n_informative=number_informative, n_targets=number_target, bias=bias, noise=noise, ) columns = [] # Numerical Features for i in range(number_numerical_features): columns.append('Feature_' + str(i)) features = pd.DataFrame(features, columns=columns) # Setting min max value for features for col_name in features.columns: for key, value in value_range_dict.items(): if (str(features.columns.get_loc(col_name)) == key): for item in features[col_name].values: if item < value[0]: features.loc[features[col_name] == item, col_name] = random.uniform(value[0],value[1]) if item > value[1]: features.loc[features[col_name] == item, col_name] = random.uniform(value[0],value[1]) df_list = [] df_list.append(features) # Add Categorical Features for j in range(number_categorical_features): categorical_feature1_list = [] number_categories_per_feature = random.randint(2, 5) for i in range(number_categories_per_feature): categorical_feature1_list.append(randStr(N=3)) print("Categories of Categorical Feature " + str(j) + ": ", categorical_feature1_list) categorical_feature1 = [] for k in range(number_samples): categorical_feature1.append(random.choice(categorical_feature1_list)) categorical_feature1 = pd.DataFrame(categorical_feature1, columns=['Categorical'+str(j)]) df_list.append(categorical_feature1) # df2['date'] = pd.date_range(start='1890-01-01', freq="sec",periods=len(df2)) time_feature = pd.date_range(start=start_time, end=end_time, periods=number_samples) #freq="1sec" time_feature = pd.DataFrame(time_feature, columns=['Date']) # df_list.append(time_feature) df_list.insert(0, time_feature) output = pd.DataFrame(output, columns=['Feature_' + str(number_numerical_features)]) if univariate != "True": df_list.append(output) df_final = pd.concat(df_list, axis=1) for col in df_final.columns: # df_final.loc[df_final.sample(frac=0.1).index, col] = np.NaN df_final.loc[df_final[col].sample(frac=missing_proportion).index, col] = np.NaN # Check to see proportion of NaN values: # df.isnull().sum() / len(df) df_final.to_csv(file_name) return True except Exception as e: print(e) return False def data_generated_csv(): datajson = os.path.join(CONFIG_FILE_PATH, 'data_generated.json') with open(datajson, 'r+') as f: dictionary = json.load(f) # f.close() if dictionary.get('problemType') == 'classification': number_samples = dictionary.get("number_samples") number_numerical_features = dictionary.get("number_numerical_features") number_categorical_features = dictionary.get("number_categorical_features") number_text_features = dictionary.get("number_text_features") missing_proportion = dictionary.get("missing_proportion") number_informative = dictionary.get("number_informative") number_class = dictionary.get("number_class") weights = dictionary.get("weights") shift = dictionary.get("shift") data_path = dictionary.get("data_path") value_range_dict = dictionary.get("value_range_dict") gen_data_classification(number_samples=number_samples, number_numerical_features=number_numerical_features, file_name=data_path, number_categorical_features=number_categorical_features, number_text_features=number_text_features, missing_proportion=missing_proportion, number_informative=number_informative, number_class=number_class, weights=weights, shift=shift, value_range_dict=value_range_dict) elif dictionary.get('problemType') == 'regression': number_samples = dictionary.get("number_samples") number_numerical_features = dictionary.get("number_numerical_features") number_categorical_features = dictionary.get("number_categorical_features") number_text_features = dictionary.get("number_text_features") missing_proportion = dictionary.get("missing_proportion") number_informative = dictionary.get("number_informative") number_target = dictionary.get("number_target") bias = dictionary.get("bias") noise = dictionary.get("noise") data_path = dictionary.get("data_path") value_range_dict = dictionary.get("value_range_dict") gen_data_regression(number_samples=number_samples, number_numerical_features=number_numerical_features, file_name=data_path, number_categorical_features=number_categorical_features, number_text_features=number_text_features, missing_proportion=missing_proportion, number_informative=number_informative, number_target=number_target, bias=bias, noise=noise, value_range_dict=value_range_dict) elif dictionary.get('problemType') == 'timeseriesforecasting': #task 11997 data_path = dictionary.get("data_path") is_univariate = dictionary.get("univariate") number_samples = dictionary.get("number_samples") number_numerical_features = dictionary.get("number_numerical_features") number_categorical_features = dictionary.get("number_categorical_features") missing_proportion = dictionary.get("missing_proportion") number_informative = dictionary.get("number_informative") number_target = dictionary.get("number_target") bias = dictionary.get("bias") noise = dictionary.get("noise") value_range_dict = dictionary.get("value_range_dict") gen_data_series(univariate=is_univariate, number_samples=number_samples, number_numerical_features=number_numerical_features, file_name=data_path, number_categorical_features=number_categorical_features, # number_text_features=2, missing_proportion=missing_proportion, number_informative=number_informative, number_target=number_target, bias=bias, noise=noise, value_range_dict=value_range_dict) if __name__ == "__main__": data_generated_csv() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import sqlite3 from pathlib import Path import json import os import rsa import boto3 #usnish import pandas as pd import time import sqlite3 class sqlite_db(): def __init__(self, location, database_file=None): if not isinstance(location, Path): location = Path(location) if database_file: self.database_name = database_file else: self.database_name = location.stem db_file = str(location/self.database_name) self.conn = sqlite3.connect(db_file) self.cursor = self.conn.cursor() def table_exists(self, name): query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';" listOfTables = self.cursor.execute(query).fetchall() return len(listOfTables) > 0 def read_data(self, table_name): query = f"SELECT * FROM {table_name}" row = self.cursor.execute(query).fetchall() return list(row) #return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn) def create_table(self,name, columns, dtypes): query = f'CREATE TABLE IF NOT EXISTS {name} (' for column, data_type in zip(columns, dtypes): query += f"'{column}' TEXT," query = query[:-1] query += ');' self.conn.execute(query) return True def delete_record(self,table_name,col_name, col_value): try: query = f"DELETE FROM {table_name} WHERE {col_name}='{col_value}'" self.conn.execute(query) self.conn.commit() return 'success' except Exception as e : print(str(e)) print("Deletion Failed") return 'error' def get_data(self,table_name,col_name,col_value): query = f"SELECT * FROM {table_name} WHERE {col_name}='{col_value}'" row = self.cursor.execute(query).fetchone() if(row == None): return [] return list(row) def write_data(self,data, table_name): if not self.table_exists(table_name): self.create_table(table_name, data.columns, data.dtypes) tuple_data = list(data.itertuples(index=False, name=None)) insert_query = f'INSERT INTO {table_name} VALUES(' for i in range(len(data.columns)): insert_query += '?,' insert_query = insert_query[:-1] + ')' self.cursor.executemany(insert_query, tuple_data) self.conn.commit() return True def close(self): self.conn.close() def add_new_GCSBucket(request): try: from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') print(request.POST["aionreferencename"]) print(request.POST["serviceaccountkey"]) print(request.POST["bucketname"]) if request.POST["aionreferencename"] =='' or request.POST["serviceaccountkey"] == '' or request.POST["bucketname"] == '' : return 'error' newdata = {} newdata['Name'] = [request.POST["aionreferencename"]] newdata['GCSServiceAccountKey'] = [request.POST["serviceaccountkey"]] newdata['GCSbucketname'] = [request.POST["bucketname"]] name = request.POST["aionreferencename"] if sqlite_obj.table_exists("gcsbucket"): if(len(sqlite_obj.get_data("gcsbucket",'Name',name))>0): return 'error1' sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'gcsbucket') except: return 'error' def get_gcs_bucket(): try: from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') temp_data = sqlite_obj.read_data('gcsbucket') data = [] for x in temp_data: data_dict = {} data_dict['Name'] = x[0] data_dict['GCSServiceAccountKey'] = x[1] data_dict['GCSbucketname'] = x[2] data.append(data_dict) except Exception as e: print(e) data = [] return data def read_gcs_bucket(name,filename,DATA_FILE_PATH): try: from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') data = sqlite_obj.get_data("gcsbucket",'Name',name) except: data = [] found = False if len(data)!=0: GCSServiceAccountKey = data[1] GCSbucketname = data[2] found = True #print(found) #print(name) try: if found: import io from google.cloud import storage #print(GCSServiceAccountKey) #print(GCSbucketname) try: storage_client = storage.Client.from_service_account_json(GCSServiceAccountKey) bucket = storage_client.get_bucket(GCSbucketname) blob = bucket.blob(filename) data = blob.download_as_string() df = pd.read_csv(io.BytesIO(data), encoding = 'utf-8', sep = ',',encoding_errors= 'replace') except Exception as e: return "Error",str(e), pd.DataFrame() return 'Success',"",df except Exception as e: print(e) return 'Error',"Please check bucket configuration",pd.DataFrame() def remove_gcs_bucket(name): from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') return sqlite_obj.delete_record('gcsbucket','Name',name) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import pandas as pd import requests from io import StringIO import json import time import shutil import sys from appbe import compute from appbe.aion_config import kafka_setting from appbe.aion_config import running_setting from appbe.s3bucketsDB import get_s3_bucket from appbe.gcsbucketsDB import get_gcs_bucket from appbe.azureStorageDB import get_azureStorage from appbe.aion_config import eda_setting from appbe.s3bucketsDB import read_s3_bucket from appbe.gcsbucketsDB import read_gcs_bucket from appbe.azureStorageDB import read_azureStorage from appbe.validatecsv import csv_validator import time from appbe.dataPath import LOG_LOCATION from appbe.dataPath import DATA_FILE_PATH from appbe.log_ut import logg import logging def langchain_splittext(filename): try: from langchain.document_loaders import PyPDFLoader from langchain.text_splitter import RecursiveCharacterTextSplitter loader = PyPDFLoader(filename) pages = loader.load() text_splitter = RecursiveCharacterTextSplitter(chunk_size=500,chunk_overlap=50) texts = text_splitter.split_documents(pages) return(texts) except Exception as e: print(e) def pd_lanfchain_textsplitter(datalocation,data): try: document=[] for i in range(len(data)): filename = os.path.join(datalocation,data.loc[i,"File"]) out = langchain_splittext(filename) for doc in out: print(doc.page_content) document.append(doc.page_content) my_data = pd.DataFrame({'instruction': document}) n = 1 my_data["response"] = my_data["instruction"].tolist()[n:] + my_data["instruction"].tolist()[:n] filetimestamp = str(int(time.time())) filename = os.path.join(DATA_FILE_PATH, 'LLMTuning_' + filetimestamp+'.csv') my_data.to_csv(filename,index=False) return(filename) except Exception as e: print(e) def getimpfeatures(dataFile, numberoffeatures,delimiter,textqualifier): imp_features = [] if numberoffeatures > 20: try: from appbe.eda import ux_eda eda_obj = ux_eda(dataFile,delimiter,textqualifier,optimize=1) if eda_obj.getNumericFeatureCount() >= 2: pca_map = eda_obj.getPCATop10Features() imp_features = pca_map.index.values.tolist() except Exception as e: print(e) pass return imp_features def pdf2text(inpFileName): try: from pypdf import PdfReader reader = PdfReader(inpFileName) number_of_pages = len(reader.pages) text="" OrgTextOutputForFile="" for i in range(number_of_pages) : page = reader.pages[i] text1 = page.extract_text() text=text+text1 import nltk tokens = nltk.sent_tokenize(text) for sentence in tokens: sentence=sentence.replace("\n", " ") if len(sentence.split()) < 4 : continue if len(str(sentence.split(',')).split()) < 8 : continue if any(chr.isdigit() for chr in sentence) : continue OrgTextOutputForFile= OrgTextOutputForFile+str(sentence.strip()) #print("\n\n\n\nOrgTextOutputForFile------------->\n\n\n",OrgTextOutputForFile) return (OrgTextOutputForFile) except Exception as e: print("Encountered exception. {0}".format(e)) def getcommonfields(): computeinfrastructure = compute.readComputeConfig() from appbe.aion_config import settings usecasetab = settings() kafkaSetting = kafka_setting() ruuningSetting = running_setting() context = {'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting,'usecasetab':usecasetab,'azurestorage':get_azureStorage()} return context def getusercasestatus(request): if 'UseCaseName' in request.session: selected_use_case = request.session['UseCaseName'] else: selected_use_case = 'Not Defined' if 'ModelVersion' in request.session: ModelVersion = request.session['ModelVersion'] else: ModelVersion = 0 if 'ModelStatus' in request.session: ModelStatus = request.session['ModelStatus'] else: ModelStatus = 'Not Trained' return selected_use_case,ModelVersion,ModelStatus def delimitedsetting(delimiter='',textqualifier='',other=''): if delimiter != '': if delimiter.lower() == 'tab' or delimiter.lower() == '\t': delimiter = '\t' elif delimiter.lower() == 'semicolon' or delimiter.lower() == ';': delimiter = ';' elif delimiter.lower() == 'comma' or delimiter.lower() == ',': delimiter = ',' elif delimiter.lower() == 'space' or delimiter.lower() == ' ': delimiter = ' ' elif delimiter.lower() == 'other' or other.lower() != '': if other != '': delimiter = other else: delimiter = ',' elif delimiter != '': delimiter = delimiter else: delimiter = ',' else: delimiter = ',' if textqualifier == '': textqualifier = '"' return delimiter,textqualifier def multipleZipExtraction(data,DATA_FILE_PATH): from zipfile import ZipFile try: import glob filetimestamp = str(int(time.time())) extracted_data = os.path.join(DATA_FILE_PATH, 'extracted_' + filetimestamp) os.mkdir(extracted_data) with ZipFile(data, 'r') as zObject: zObject.extractall(extracted_data) csv_files = glob.glob(r'{}\*.{}'.format(extracted_data,'csv')) df_csv_append = pd.DataFrame() for file in csv_files: df = pd.read_csv(file) df_csv_append = df_csv_append.append(df, ignore_index=True) for f in os.listdir(extracted_data): os.remove(os.path.join(extracted_data, f)) #os.mkdir(extracted_data) combined_data = os.path.join(extracted_data,filetimestamp+'.csv') df_csv_append.to_csv(combined_data) return combined_data except Exception as e: if os.path.exists(extracted_data): shutil.rmtree(extracted_data) #print (e) return '' def tarFileExtraction(data,DATA_FILE_PATH): try: import tarfile filetimestamp = str(int(time.time())) extracted_data = os.path.join(DATA_FILE_PATH, 'extracted_' + filetimestamp) os.mkdir(extracted_data) if data.endswith('tar'): file = tarfile.open(data) file.extractall(extracted_data) file.close() for f in os.listdir(extracted_data): if f.endswith('csv') or f.endswith('tsv'): dataFile = os.path.join(extracted_data,f) return dataFile except Exception as e: if os.path.exists(extracted_data): shutil.rmtree(extracted_data) print (e) return '' # ------ changes for the bug 10379 starts---------------- By Usnish ------ def checkRamAfterLoading(dataPath): import psutil availableRam = psutil.virtual_memory()[1]/1e9 filesize = os.path.getsize(dataPath)/1e9 return availableRam < 2*filesize def checkRamBeforeLoading(dataPath): import psutil filesize = os.path.getsize(dataPath)/1e9 totalRam = psutil.virtual_memory()[0] / 1e9 if( filesize > 0.8 * totalRam): return "File size is larger than the 80% of Total RAM." return "" # ------ changes for the bug 10379 ends---------------- By Usnish ------ # ---------- 10012:Decision Threshold related Changes S T A R T ---------- # This method is used to check If -> # 80% of available RAM size is greater than ingested data (or not). def checkRAMThreshold(dataPath): import psutil availableRam = psutil.virtual_memory()[1]/1e9 filesize = os.path.getsize(dataPath)/1e9 return (0.8 * availableRam) > filesize # ---------------------- E N D ---------------------- # Text Data Labelling using LLM related changes # -------------------------------------------------------- def ingestTextData(request, DATA_FILE_PATH): log = logging.getLogger('log_ux') try: Datapath = request.FILES['DataFilePath'] from appbe.eda import ux_eda ext = str(Datapath).split('.')[-1] request.session['uploadfiletype'] = 'Local' request.session['datatype'] = 'Normal' filetimestamp = str(int(time.time())) if ext.lower() in ['csv','tsv','tar','zip','avro','parquet']: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext) else: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp) with open(dataFile, 'wb+') as destination: for chunk in Datapath.chunks(): destination.write(chunk) destination.close() dataPath = dataFile request.session['textdatapath'] = dataPath # import pdb # pdb.set_trace() # check_df = pd.read_csv(dataPath) eda_obj = ux_eda(dataPath) check_df = eda_obj.getdata() df_top = check_df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) # featuresList = check_df.columns.tolist() features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catfeatures = eda_obj.getFeatures() noTextFeature = False if len(textFeature) == 0: noTextFeature = True context = {'raw_data':df_json, 'featuresList':textFeature, 'selected':'DataOperations', 'noTextFeature':noTextFeature} return context except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) context = {'error': 'Failed to read data','emptycsv' : 'emptycsv'} log.info('Text Data Ingestion -- Error : Failed to read data, '+str(e)) log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return context # ---------------------- E N D --------------------------- def ingestDataFromFile(request,DATA_FILE_PATH): log = logging.getLogger('log_ux') delimiter,textqualifier = delimitedsetting(request.POST.get('delimiters'),request.POST.get('qualifier'),request.POST.get('delimiters_custom_value')) request.session['delimiter'] = delimiter request.session['textqualifier'] = textqualifier context = getcommonfields() selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request) context.update({'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,}) try: t1 = time.time() request.session['uploadfiletype'] = '' request.session['uploadLocation'] = '' data_is_large = False check_df = pd.DataFrame() if request.method == 'POST': if 'ModelVersion' in request.session: ModelVersion = request.session['ModelVersion'] else: ModelVersion = 0 if 'ModelName' not in request.session: movenext = False request.session['currentstate'] = 0 context.update({'tab': 'tabconfigure', 'error': 'Please Create/Select the Use Case First', 'movenext': movenext,'currentstate': request.session['currentstate']}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please Create/Select the Use Case First') return context else: type = request.POST.get("optradio") if type == "s3Bucket": try: request.session['uploadfiletype'] = 'S3Bucket' bucketname = request.POST.get('s3bucketname') fileName = request.POST.get('s3file') if fileName != '': status,msg,check_df = read_s3_bucket(bucketname,fileName,DATA_FILE_PATH) if status == 'Success': filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') check_df.to_csv(dataFile, index=False) request.session['datalocation'] = dataFile else : request.session['currentstate'] = 0 #usnish context.update({'error': str(msg),'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0' + 'sec' + ' : ' + 'Error : ' + str(msg)) return context else: #usnish request.session['currentstate'] = 0 context.update({'error': 'Please provide a file name','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please provide a file name') return context except Exception as e: request.session['currentstate'] = 0 context.update({'error': str(e),'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+ str(e)) return context '''request.session['datalocation'] = "S3"''' # -------------------------------- Graviton-Integration Changes S T A R T -------------------------------- elif type == "graviton": try: dataServiceId = request.POST.get('dataservice') metadataId = request.POST.get('metadata') data = [] from appbe.aion_config import get_graviton_data graviton_url,graviton_userid = get_graviton_data() gravitonURL = graviton_url gravitonUserId = graviton_userid # url = 'https://xenius.azurewebsites.net/api/getdata?userid=1&dataserviceid='+str(dataserviceId) +'&metadataid=' +str(metadataId) url = gravitonURL + 'getdata?userid=' + gravitonUserId +'&dataserviceid='+str(dataServiceId) +'&metadataid=' +str(metadataId) print(url) response = requests.get(url) statuscode = response.status_code if statuscode == 200: json_dictionary = json.loads(response.content) data = json_dictionary['result'] firstElement = next(iter(data[0].keys())) check_df = pd.DataFrame.from_dict(data[0][firstElement]) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') check_df.to_csv(dataFile, index=False) request.session['uploadfiletype'] = 'Graviton' request.session['datalocation'] = str(dataFile) except Exception as e: print(e) request.session['currentstate'] = 0 context.update({'error':'Check log file for more details','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error :'+str(e)) return context # ------------------------------------------------ E N D ------------------------------------------------- elif type == "azurestorage": try: request.session['uploadfiletype'] = 'AzureStorage' azurename = request.POST.get('azurename') directoryname = request.POST.get('azuredirectory') if directoryname != '': status,msg,check_df = read_azureStorage(azurename,directoryname,DATA_FILE_PATH) if status == 'Success': filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') check_df.to_csv(dataFile, index=False) '''request.session['datalocation'] = "S3"''' request.session['datalocation'] = dataFile else : request.session['currentstate'] = 0 #usnish context.update({'error': str(msg),'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : ' +str(msg)) return context else: #usnish request.session['currentstate'] = 0 context.update({'error': 'Please provide a file name','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please provide a file name') return context except Exception as e: print(e) request.session['currentstate'] = 0 context.update({'error': 'File does not exist','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : File does not exist, '+str(e)) return context elif type == "googleBucket": try: request.session['uploadfiletype'] = 'GCPBucket' bucketname = request.POST.get('gcpbucketname') fileName = request.POST.get('file1') if fileName != '': status,msg,check_df = read_gcs_bucket(bucketname,fileName,DATA_FILE_PATH) if status == 'Success': filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') check_df.to_csv(dataFile, index=False) '''request.session['datalocation'] = "S3"''' request.session['datalocation'] = dataFile else : request.session['currentstate'] = 0 #usnish context.update({'error': str(msg),'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+str(msg)) return context else: #usnish request.session['currentstate'] = 0 context.update({'error': 'Please provide a file name','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please provide a file name') return context except Exception as e: request.session['currentstate'] = 0 context.update({'error': 'File does not exist','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : File does not exist, ' + str(e)) return context elif type == "url": try: request.session['uploadfiletype'] = 'URL' url_text = request.POST.get('urlpathinput') log.info('Data ingesttion from URL..') request.session['uploadLocation'] = url_text url = url_text check_df = pd.read_csv(url) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') check_df.to_csv(dataFile,index=False) request.session['datalocation'] = dataFile except Exception as e: request.session['currentstate'] = 0 e = str(e) print(e) if e.find("tokenizing")!=-1: error = "This is not an open source URL to access data" context.update({'error': error, 'ModelVersion': ModelVersion, 'emptycsv': 'emptycsv'}) elif e.find("connection")!=-1: error = "Can not access the URL through HCL network, please try with other network" context.update({'error': error, 'ModelVersion': ModelVersion, 'emptycsv': 'emptycsv'}) else: error = 'Please provide a correct URL' context.update({'error': error,'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error : '+error + ', '+str(e)) return context elif type == "nifi": try: request.session['uploadfiletype'] = 'Nifi' log.info('Data ingesttion from Nifi..') url_text = request.POST.get('nifiurlpathinput') request.session['uploadLocation'] = url_text response = requests.get(url_text) csv_str = response.content.decode('utf-8') check_df = pd.read_csv(StringIO(csv_str)) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') check_df.to_csv(dataFile,index=False) request.session['datalocation'] = dataFile except requests.exceptions.ConnectionError: request.session['currentstate'] = 0 context.update({'error': 'Connection Error','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error:Connection Error') return context except Exception as e: print(e) request.session['currentstate'] = 0 e = str(e) context.update({'error': e,'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error : '+str(e)) return context elif type == "tblaiondata": try: dataset = request.POST.get('datasetname') print('dataset',dataset) from appbe.dataPath import DATA_DIR from appbe.sqliteUtility import sqlite_db file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') temp_data = sqlite_obj.read_data('dataingest') dataFile = '' for x in temp_data: if x[1] == dataset: dataFile = x[0] check_df = pd.read_csv(dataFile) request.session['datalocation'] = dataFile except Exception as e: request.session['currentstate'] = 0 context.update({'error': 'Failed to read data','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : File does not exist, ' + str(e)) return context else: if request.FILES: Datapath = request.FILES['DataFilePath'] if Datapath.size > 31457280: context.update({'tab': 'tabconfigure','error': 'Upload limit is 30 MB only, use local file option for larger file','currentstate': request.session['currentstate'], 'ModelVersion': ModelVersion}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error : Upload limit is 30 MB only, use local file option for larger file') return context ext = str(Datapath).split('.')[-1] request.session['uploadfiletype'] = 'Local' request.session['datatype'] = 'Normal' filetimestamp = str(int(time.time())) if ext.lower() in ['csv','tsv','tar','zip','avro','parquet']: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext) else: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp) with open(dataFile, 'wb+') as destination: for chunk in Datapath.chunks(): destination.write(chunk) destination.close() dataPath = dataFile else: dataPath = request.POST.get('localfilePath') #print(os.path.getsize(dataPath)) # 10012:Decision Threshold related Changes - S T A R T #removed few lines related to the check to not allow data to be ingested # E N D if request.POST.get('optfiletype') == 'avro': try: import pandavro as pdx if os.path.isdir(dataPath): for f in os.listdir(dataPath): if f.endswith('avro'): processed_df = pdx.read_avro(f) if not df.empty: df = df.append(processed_df, ignore_index=True) else: df = pd.DataFrame(processed_df) elif os.path.isfile(dataPath): import pandavro as pdx df = pdx.read_avro(dataPath) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') df.to_csv(dataFile, index=False) request.session['datalocation'] = str(dataFile) except Exception as e: print(e) elif request.POST.get('optfiletype') == 'parquet': if os.path.isdir(dataPath): for f in os.listdir(dataPath): if f.endswith('parquet'): processed_df = pd.read_parquet(f, engine='pyarrow') if not df.empty: df = df.append(processed_df, ignore_index=True) else: df = pd.DataFrame(processed_df) elif os.path.isfile(dataPath): df = pd.read_parquet(dataPath, engine='pyarrow') filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') df.to_csv(dataFile, index=False) request.session['datalocation'] = str(dataFile) elif request.POST.get('optfiletype') == 'dilimeted': if os.path.isdir(dataPath): for f in os.listdir(dataPath): if f.endswith('csv') or f.endswith('tsv'): processed_df = pd.read_csv(dataFile, encoding='utf8',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,encoding_errors= 'replace') if not df.empty: df = df.append(processed_df, ignore_index=True) else: df = pd.DataFrame(processed_df) filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') df.to_csv(dataFile, index=False,sep=delimiter,quotechar=textqualifier) request.session['datalocation'] = str(dataFile) else: dataFile = dataPath request.session['uploadfiletype'] = 'Local' request.session['datatype'] = 'Normal' FileReadingstatus = True request.session['currentstate'] = 0 if dataPath.endswith('tar'): dataFile = tarFileExtraction(dataPath,DATA_FILE_PATH) if dataPath.endswith('zip'): dataFile = multipleZipExtraction(dataPath,DATA_FILE_PATH) if dataFile == '': FileReadingstatus = False msg = 'Please provide a file name' elif dataFile.endswith(".xls") or dataFile.endswith(".xlsx"): FileReadingstatus = False msg = 'Please provide a dilimited file' elif not os.path.isfile(dataFile): FileReadingstatus = False msg = 'File does not exist' else: check_df = pd.DataFrame(); try: try: cvobj = csv_validator() valid_header, validrows, rownumbers = cvobj.validate_header(dataFile,delimiter,textqualifier) request.session['datalocation'] = str(dataFile) if not validrows: FileReadingstatus = False msg = 'Data Format issue' else: if valid_header: check_df = pd.read_csv(dataFile, encoding='utf8',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,nrows=100,encoding_errors= 'replace') request.session['datalocation'] = str(dataFile) else: check_df = pd.read_csv(dataFile, header=None, encoding='utf8', prefix='X',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,encoding_errors= 'replace') filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv') check_df.to_csv(dataFile, index=False) request.session['datalocation'] = str(dataFile) except Exception as e: print(e) check_df = pd.read_csv(dataFile, encoding='utf8',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,nrows=100) request.session['datalocation'] = str(dataFile) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+str(e)) except UnicodeDecodeError: FileReadingstatus = False msg = 'Only utf8 file encoding supported' log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error:'+msg) except pd.errors.EmptyDataError: FileReadingstatus = False msg = 'File is empty' log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error:'+msg) except pd.errors.ParserError: FileReadingstatus = False msg = 'File Parsng Error' log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+msg) except FileNotFoundError: FileReadingstatus = False msg = 'File does not exist' request.session['currentstate'] = 0 log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+msg) except Exception as e: msg = 'File Read Error' FileReadingstatus = False print(e) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str( ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : ' + msg+', '+str(e)) if check_df.empty and FileReadingstatus: FileReadingstatus = False msg = 'Date file is empty' if not FileReadingstatus: context.update({'tab': 'tabconfigure','error': msg,'currentstate': request.session['currentstate'], 'ModelVersion': ModelVersion}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error : '+msg) return context # -------------------------------- 10012:Decision Threshold related Changes S T A R T ------------------------------- data_is_under_RAM_threshold = checkRAMThreshold(request.session['datalocation']) msg = "" if data_is_under_RAM_threshold == False: msg = "AION will not be able to train on data set provided as it is bigger than available RAM, Please choose distributed learning for further processing." # ------------------------------------------------------ E N D ------------------------------------------------------ check_df.rename(columns=lambda x: x.strip(), inplace=True) featuresList = check_df.columns.tolist() numberoffeatures = len(featuresList) imp_features = getimpfeatures(dataFile,numberoffeatures,delimiter,textqualifier) samplePercentage = 100 samplePercentval = 0 showRecommended = False sample_size = int(eda_setting()) dflength = len(check_df) if dflength > sample_size: samplePercentage = round(float((sample_size/dflength) * 100),2) samplePercentval = samplePercentage / 100 showRecommended = True df_top = check_df.head(10) df_json = df_top.to_json(orient="records") df_json = json.loads(df_json) statusmsg = 'Data File Uploaded Successfully ' request.session['currentstate'] = 0 request.session['finalstate'] = 0 request.session['datatype'] = 'Normal' records = check_df.shape[0] request.session['NoOfRecords'] = records statusmsg = 'Data File Uploaded Successfully' t2 = time.time() log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + str( round(t2 - t1)) + ' sec' + ' : ' + 'Success') # EDA Subsampling changes context.update({'range':range(1,101),'samplePercentage':samplePercentage, 'samplePercentval':samplePercentval, 'showRecommended':showRecommended,'featuresList': featuresList,'tab': 'tabconfigure', 'data': df_json, 'status_msg': statusmsg, 'selected': 'modeltraning','imp_features':imp_features,'numberoffeatures':numberoffeatures, 'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'], 'exploratory': False}) if msg!="": context.update({'data_size_alert': msg}) return context except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) request.session['currentstate'] = 0 context.update({'error': 'Failed to read data','emptycsv' : 'emptycsv'}) log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error : Failed to read data, '+str(e)) log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return context import os import re import json import time import sys import tiktoken import openai import requests from appbe.aion_config import get_llm_data import logging import pdfplumber from docx import Document openai.api_key = '' openai.api_base = '' openai.api_type = '' openai.api_version = '' deployment_name="GPT-35-Turbo" model_name='gpt-3.5-turbo' set_tokens_limit = 500 set_tokens_limit_offline = 400 set_prompt="You are an expert user generating questions and answers. You will be passed a page extracted from a documentation. Generate a numbered list of questions as Q. and equivelant answer as A. for every question based *solely* on the given text." # QnA Generator using LLM related changes # -------------------------------------------------------- def ingestDataForQA(request, DATA_FILE_PATH): log = logging.getLogger('log_ux') try: Datapath = request.FILES['DataFileQnA'] from appbe.eda import ux_eda ext = str(Datapath).split('.')[-1] request.session['uploadfiletype'] = 'Local' request.session['datatype'] = 'Normal' filetimestamp = str(int(time.time())) if ext.lower() in ['txt','pdf','docx']: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext) else: dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp) with open(dataFile, 'wb+') as destination: for chunk in Datapath.chunks(): destination.write(chunk) destination.close() dataPath = dataFile request.session['textdatapathQA'] = dataPath llm_choice = request.POST.get("llm_choice") _result = '' # if llm_choice == 'Haystack': # _result = generateQA_Haystack(request, DATA_FILE_PATH) if llm_choice == 'Offline': _result = generateQA_Offline(request, DATA_FILE_PATH) else: _result = generateQA_OpenAI(request, DATA_FILE_PATH) return _result except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) context = {'error': 'Failed to read data','emptytxt' : 'emptytxt'} log.info('Text Data Ingestion -- Error : Failed to read data, '+str(e)) log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return context # ---------------------- E N D --------------------------- def generateQA_OpenAI(request, DATA_FILE_PATH): log = logging.getLogger('log_ux') try: file_path = request.session['textdatapathQA'] # Read the file content if file_path.endswith('.pdf'): pdf_file=pdfplumber.open(file_path) file_content = " ".join([x.extract_text() for x in pdf_file.pages]) elif file_path.endswith('.docx'): doc_file=Document(file_path) file_content = " \n".join([x.text for x in doc_file.paragraphs]) else: with open(file_path, "r", encoding="utf-8",errors = "ignore") as file: file_content = file.read() text = file_content.strip() #text = text.strip() extracted_QnA = [] chunk_counter = 0 num_tokens_text = count_tokens_text(text) if num_tokens_text > set_tokens_limit: for sub_text in split_text(text): chunk_counter = chunk_counter + 1 _result = extract_questions_from_splittedtext(sub_text) print(f"Currently executed chunk no is - {chunk_counter}.") extracted_QnA.extend(_result) else: _prompt = set_prompt msg = [ {"role": "system", "content": _prompt}, {"role": "user", "content": text} ] extracted_QnA = run_model(msg) quesCount = len(extracted_QnA) context = {'extracted_QnA':extracted_QnA, 'quesCount':quesCount} filetimestamp = str(int(time.time())) output_filepath = os.path.join(DATA_FILE_PATH,'AION_QnA' + filetimestamp+'.txt') # Save the extracted questions as a JSON file with open(output_filepath, 'w') as output_file: json.dump(extracted_QnA, output_file, indent=4) print(f"QnAs have been saved to {output_filepath}.") request.session['QnAfilepath'] = output_filepath return context except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() errormsg = str(e) if 'Invalid URL' in errormsg or 'No connection adapters' in errormsg or 'invalid subscription key' in errormsg: errormsg = 'Access denied due to invalid subscription key or wrong API endpoint. Please go to settings and make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.' if 'The API type provided in invalid' in errormsg: errormsg = "The API type provided is invalid. Please select one of the supported API types:'azure', 'azure_ad' or 'open_ai'" if 'Max retries exceeded with url' in errormsg: errormsg = 'Please make sure you have good internet connection and access to API endpoint for your resource.' fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) context = {'error': 'Failed to generate QnA List using openAI','LLM' : 'openAI', 'selected':'DataOperations', 'errormessage':errormsg} log.info('generateQA_OpenAI -- Error : Failed to generate QnA List using openAI.. '+str(e)) log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return context def run_model(msg): key,url,api_type,api_version = get_llm_data() openai.api_key = key openai.api_base = url openai.api_type = api_type openai.api_version = api_version completions = openai.ChatCompletion.create(engine=deployment_name, temperature=0.0, max_tokens=2000, n=1, stop=None, messages=msg) # return completions.choices[0].message.content _questionList = completions.choices[0].message.content question_pattern = re.compile(r"^Q\s*\d+\.\s*(.+)$", re.MULTILINE) questions = question_pattern.findall(_questionList) answer_pattern = re.compile(r"^A\s*\d+\.\s*(.+)$", re.MULTILINE) answers = answer_pattern.findall(_questionList) if (len(questions) > 0) and (not re.search(r"[.!?)]$", questions[-1].strip())): print(f"WARNING: Popping incomplete question: '{questions[-1]}'") questions.pop() extracted_QnA = [] for question, answer in zip(questions, answers): extracted_QnA.append({'question': question, 'answer': answer}) return extracted_QnA def count_tokens_text(text): import tiktoken model_type = model_name encoding = tiktoken.encoding_for_model(model_type) encoded_text = encoding.encode(text) return len(encoded_text) def extract_questions_from_splittedtext(text): _prompt = set_prompt msg = [ {"role": "system", "content": _prompt}, {"role": "user", "content": text} ] _ques_ans_List = run_model(msg) return _ques_ans_List def split_text(text): lines = text.split('\n') current_section = '' sections = [] _lastsection = 0 for line in lines: num_tokens_text = count_tokens_text(''.join([current_section,line])) if num_tokens_text < set_tokens_limit: current_section = ''.join([current_section,line]) else: sections.append(current_section) current_section = line _lastsection = 1 if _lastsection == 1: sections.append(current_section) return sections # --------------------------------------------------------------------------------- # def generateQA_Haystack(request, DATA_FILE_PATH): file_path = request.session['textdatapathQA'] # Read the file content with open(file_path, "r", encoding="utf-8") as file: file_content = file.read() text = file_content.strip() text = text.strip() docs = [] num_tokens_text = count_tokens_text(text) if num_tokens_text > set_tokens_limit: for sub_text in split_text(text): docs.append({"content": sub_text}) else: docs = [{"content": text}] from pprint import pprint from tqdm.auto import tqdm from haystack.nodes import QuestionGenerator, BM25Retriever, FARMReader # from haystack.document_stores import ElasticsearchDocumentStore from haystack.document_stores import InMemoryDocumentStore # from haystack.document_stores import PineconeDocumentStore from haystack.pipelines import ( QuestionGenerationPipeline, RetrieverQuestionGenerationPipeline, QuestionAnswerGenerationPipeline, ) from haystack.utils import print_questions document_store = InMemoryDocumentStore(use_bm25=True) document_store.write_documents(docs) question_generator = QuestionGenerator() # reader = FARMReader("deepset/roberta-base-squad2") # reader.save("my_local_roberta_model") reader_local = FARMReader(model_name_or_path="my_local_roberta_model_1") qag_pipeline = QuestionAnswerGenerationPipeline(question_generator, reader_local) extracted_QnA = [] for idx, document in enumerate(tqdm(document_store)): print(f"\n * Generating questions and answers for document {idx}: {document.content[:100]}...\n") result = qag_pipeline.run(documents=[document]) print_questions(result) answers = [] questions = result['queries'] answerList = result["answers"] for _answers in answerList: for answer in _answers: ans = answer.answer answers.append(ans) for question, answer in zip(questions, answers): extracted_QnA.append({'question': question, 'answer': answer}) quesCount = len(extracted_QnA) context = {'extracted_QnA':extracted_QnA, 'quesCount':quesCount} filetimestamp = str(int(time.time())) output_filepath = os.path.join(DATA_FILE_PATH,'AION_QnA' + filetimestamp+'.txt') # Save the extracted questions as a JSON file with open(output_filepath, 'w') as output_file: json.dump(extracted_QnA, output_file, indent=4) print(f"QnAs have been saved to {output_filepath}.") request.session['QnAfilepath'] = output_filepath return context # --------------------------------------------------------------------------------- # def generateQA_Offline(request, DATA_FILE_PATH): log = logging.getLogger('log_ux') try: file_path = request.session['textdatapathQA'] if file_path.endswith('.pdf'): pdf_file=pdfplumber.open(file_path) file_content = " ".join([x.extract_text() for x in pdf_file.pages]) elif file_path.endswith('.docx'): doc_file=Document(file_path) file_content = " \n".join([x.text for x in doc_file.paragraphs]) else: with open(file_path, "r", encoding="utf-8",errors = "ignore") as file: file_content = file.read() # # Read the file content # with open(file_path, "r", encoding="utf-8") as file: # file_content = file.read() text = file_content.strip() # text = text.strip() docs = [] # num_tokens_text = count_tokens_text(text) # if num_tokens_text > set_tokens_limit: # for sub_text in split_text(text): # docs.append(sub_text) # else: # docs.append(text) model_name = "valhalla/t5-base-qg-hl" num_tokens_text = count_tokens_text_offline(text, model_name) if num_tokens_text > set_tokens_limit_offline: for sub_text in split_text_for_Offline(text, model_name): docs.append(sub_text) else: docs.append(text) from question_generation.pipelines import pipeline extracted_QnA = [] extracted_QnAList = [] nlp = pipeline("question-generation", model = model_name) # nlp = pipeline("question-generation", model="valhalla/t5-base-e2e-qg") # nlp = pipeline("e2e-qg", model="valhalla/t5-base-qg-hl") # nlp = pipeline("multitask-qa-qg", model="valhalla/t5-base-qa-qg-hl") for _text in docs: res = nlp(_text) print(res) extracted_QnAList.extend(res) for _record in extracted_QnAList: extracted_QnA.append({'question': _record['question'], 'answer': _record['answer'].replace('<pad>', '')}) quesCount = len(extracted_QnA) context = {'extracted_QnA':extracted_QnA, 'quesCount':quesCount} filetimestamp = str(int(time.time())) output_filepath = os.path.join(DATA_FILE_PATH,'AION_QnA' + filetimestamp+'.txt') # Save the extracted questions as a JSON file with open(output_filepath, 'w') as output_file: json.dump(extracted_QnA, output_file, indent=4) print(f"T5 based QnAs have been saved to {output_filepath}.") request.session['QnAfilepath'] = output_filepath return context except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() errormsg = str(e) fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) context = {'error': 'Failed to generate QnA List using T5','LLM' : 'T5', 'selected':'DataOperations', 'errormessage':errormsg} log.info('generateQA_Offline -- Error : Failed to generate QnA List using T5.. '+str(e)) log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) return context def split_text_for_Offline(text, model_name): lines = text.split('\n') current_section = '' sections = [] _lastsection = 0 for line in lines: num_tokens = count_tokens_text_offline(''.join([current_section,line]), model_name) if num_tokens < set_tokens_limit_offline: current_section = ''.join([current_section,line]) else: sections.append(current_section) current_section = line _lastsection = 1 if _lastsection == 1: sections.append(current_section) return sections def count_tokens_text_offline(text, model_name): from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) inputs = tokenizer(text, return_tensors="pt") input_ids = inputs["input_ids"] _token_count = len(input_ids[0]) return _token_count ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import shutil import subprocess import sys import time import glob import re from appbe.pages import get_usecase_page import json from django.http import FileResponse def startIncrementallearning(request,usecasedetails,Existusecases,DATA_FILE_PATH): try: modelid = request.POST.get('modelid') #incfilepath = request.POST.get('incfilepath') Datapath = request.FILES['incfilepath'] filetimestamp = str(int(time.time())) dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.csv') with open(dataFile, 'wb+') as destination: for chunk in Datapath.chunks(): destination.write(chunk) # destination.close()#bugfix 11656 incfilepath = dataFile p = Existusecases.objects.get(id=modelid) deployPath = str(p.DeployPath) scriptPath = os.path.abspath(os.path.join(deployPath,'aion_inclearning.py')) request.session['IsRetraining'] = 'No' if not os.path.exists(scriptPath): status,context,action = get_usecase_page(request,usecasedetails,Existusecases) context['Msg'] = 'Incremental/Online learning not supported for this model.For online training select Online Training in basic configuration page and provide with training' else: outputStr = subprocess.check_output([sys.executable, scriptPath, incfilepath]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'aion_learner_status:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() decoded_data = json.loads(outputStr) status,context,action = get_usecase_page(request,usecasedetails,Existusecases) if decoded_data['status'] == 'SUCCESS': msg = decoded_data['Msg'] context['Status'] = 'SUCCESS' context['Msg'] = msg else: msg = decoded_data['Msg'] context['Status'] = 'SUCCESS' context['Msg'] = msg except Exception as e: print(e) try: status,context,action = get_usecase_page(request,usecasedetails,Existusecases) except Exception as msg: context['errorMsg'] = msg return action,context import os import openai from langchain.llms import AzureOpenAI from sentence_transformers.SentenceTransformer import SentenceTransformer import time import datetime import pandas as pd import sys import subprocess import importlib from appbe.aion_config import get_llm_data from appbe.dataPath import DATA_FILE_PATH remote_data_dir = "/home/aion/data/storage/llm_testing_data" remote_data_processeddata_dir = '/home/aion/data/storage/processed_data' remote_config_dir = '/home/aion/data/config' sh_file_path = '/home/aion/llm/sbin/llm_testing.sh' prompt_command = '/home/aion/llm/sbin/llm_testing.sh' PRE_CONTEXT = "Answer the following question in a concise manner.\n" DEFAULT_PARAMS = { 'OPENAI_API_TYPE' : "azure", 'OPENAI_API_BASE' : "", 'OPENAI_API_KEY' : "", 'OPENAI_API_VERSION' : "2023-03-15-preview" } faq="" def getAMIDetails(config,selectedAMI): y = {} for x in config: print(x) if x['id'] == selectedAMI: return x return y class test_LLM(): def __init__(self, deployment_name='Text-Datvinci-03', params=DEFAULT_PARAMS, transformer=None, sentence_txfr_model='sentence-transformers/paraphrase-mpnet-base-v2'): self.deployment_name=deployment_name self.set_params( params) self.transformer = transformer self.sentence_txfr_model = sentence_txfr_model def fiddlerAuditorCheck(self): status = importlib.util.find_spec('auditor') if not status: subprocess.check_call([sys.executable, "-m", "pip","uninstall", "-q","-y","notebook"]) subprocess.check_call([sys.executable, "-m", "pip", "install","-q", "notebook==6.4.5" ]) subprocess.check_call([sys.executable, "-m", "pip", "install","-q","fiddler-auditor==0.0.2"]) subprocess.check_call([sys.executable, "-m", "pip", "install","-q","notebook==7.0.2"]) status = importlib.util.find_spec('auditor') return status def set_params(self, params={}): valid_params = ['OPENAI_API_TYPE','OPENAI_API_KEY','OPENAI_API_BASE','OPENAI_API_VERSION'] for key, value in params.items(): if 'OPENAI_API_TYPE' == key: openai.api_type = value os.environ['OPENAI_API_TYPE'] = openai.api_type elif 'OPENAI_API_KEY' == key: openai.api_key = value os.environ['OPENAI_API_KEY'] = openai.api_key elif 'OPENAI_API_BASE' == key: openai.api_base = value os.environ['OPENAI_API_BASE'] = openai.api_base elif key in valid_params: os.environ[key] = value def run(self,modelName, temperature, similarity_threshold, perturbations_per_sample, prompts, reference_generation,pre_context=PRE_CONTEXT): if not self.fiddlerAuditorCheck(): raise ValueError('Fiddler-auditor is not instlled "python -m pip install fiddler-auditor==0.0.2"') openai_llm = AzureOpenAI(deployment_name=self.deployment_name, temperature=temperature, openai_api_key=openai.api_key) from auditor.perturbations import Paraphrase from auditor.evaluation.expected_behavior import SimilarGeneration from auditor.evaluation.evaluate import LLMEval # For Azure OpenAI, it might be the case the api_version for chat completion # is different from the base model so we need to set that parameter as well. if self.transformer: azure_perturber = self.transformer else: azure_perturber = Paraphrase( model="GPT-35-Turbo", api_version="2023-03-15-preview", num_perturbations=perturbations_per_sample, ) sent_xfmer = SentenceTransformer(self.sentence_txfr_model) similar_generation = SimilarGeneration( similarity_model=sent_xfmer, similarity_threshold=similarity_threshold,) llm_eval = LLMEval( llm=openai_llm, expected_behavior=similar_generation, transformation=azure_perturber,) test_result = llm_eval.evaluate_prompt_correctness( prompt=prompts, pre_context=pre_context, reference_generation=reference_generation, perturbations_per_sample=perturbations_per_sample ) return test_result def runmultiple(self,modelName, temperature, similarity_threshold, perturbations_per_sample, prompts, reference_generation,pre_context=PRE_CONTEXT,faq=faq): if not self.fiddlerAuditorCheck(): raise ValueError('Fiddler-auditor is not instlled "python -m pip install fiddler-auditor==0.0.2"') from auditor.evaluation.expected_behavior import SimilarGeneration from auditor.evaluation.evaluate import LLMEval openai_llm = AzureOpenAI(deployment_name=self.deployment_name, temperature=temperature, openai_api_key=openai.api_key) from auditor.perturbations import Paraphrase # For Azure OpenAI, it might be the case the api_version for chat completion # is different from the base model so we need to set that parameter as well. if self.transformer: azure_perturber = self.transformer else: azure_perturber = Paraphrase( model="GPT-35-Turbo", api_version="2023-03-15-preview", num_perturbations=perturbations_per_sample, ) sent_xfmer = SentenceTransformer(self.sentence_txfr_model) similar_generation = SimilarGeneration( similarity_model=sent_xfmer, similarity_threshold=similarity_threshold,) llm_eval = LLMEval( llm=openai_llm, expected_behavior=similar_generation, transformation=azure_perturber,) rows = faq.shape[0] prompts = list(faq['Question']) listofDf = [] for i in range(rows): test_result = llm_eval.evaluate_prompt_robustness( prompt=prompts[i], pre_context=pre_context, ) try: now = datetime.datetime.now().strftime("%H%M%S") name = str(i)+str(now)+'.html' test_result.save(name) df_iter=pd.read_html(name) df_actual = df_iter[0] listofDf.append(df_actual) except: pass perturbatedDF = pd.concat(listofDf) return perturbatedDF def run_offline_model(self, usecasename,modelName, temperature, similarity_threshold, perturbations_per_sample, reference_generation, prompts,isfinetuned): from appbe.compute import readComputeConfig from appbe.prediction import get_instance cloud_infra = readComputeConfig() dataFile = os.path.join(DATA_FILE_PATH, 'prompt.csv') remoteFile = os.path.join(remote_data_dir, 'prompt.csv') if not reference_generation: reference_generation = '' prompt = pd.DataFrame([{'prompts':prompts, 'reference_generation':reference_generation}]) prompt.to_csv(dataFile, index=False) hypervisor, instanceid, region, image = get_instance(usecasename) key, url, api_type, api_version = get_llm_data() if hypervisor == 'AWS': aws_access_key_id = cloud_infra['awsCredentials']['accessKey'] aws_secret_key = cloud_infra['awsCredentials']['secretAccessKey'] currentDirectory = os.path.dirname(os.path.abspath(__file__)) LLM_DIR = os.path.normpath(os.path.join(currentDirectory, '..', 'llm')) if image != '' and image != 'NA': amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['amis'], image) else: amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], instanceid) if region == '' or region == 'NA': region = amiDetails['regionName'] from llm.aws_instance_api import start_instance # print(aws_access_key_id, aws_secret_key, instanceid, region) status, msg, ip = start_instance(aws_access_key_id, aws_secret_key, instanceid, region) if status.lower() == 'success': pem_file = os.path.join(LLM_DIR, amiDetails['ssh']['keyFilePath']) username = amiDetails['ssh']['userName'] # cope file to server for sinfle prompt from AION.llm.ssh_command import copy_files_to_server copy_files_to_server(ip,pem_file,dataFile,'',username,'',remote_data_dir,remote_config_dir) if isfinetuned: command = prompt_command + ' ' + usecasename + ' ' + str(modelName) \ + ' ' + str(temperature) + ' ' + str(similarity_threshold) + ' ' \ + str(perturbations_per_sample) + \ ' '+ str(key) + \ ' '+ str(url) + \ ' '+ str(api_type) + \ ' '+ str(api_version)+ \ ' '+ str("single") else: command = prompt_command + ' ' + 'BaseModel' + ' ' + str(modelName) \ + ' ' + str(temperature) + ' ' + str(similarity_threshold) + ' ' \ + str(perturbations_per_sample) + \ ' '+ str(key) + \ ' '+ str(url) + \ ' '+ str(api_type) + \ ' '+ str(api_version)+ \ ' '+ str("single") from llm.ssh_command import run_ssh_cmd buf = run_ssh_cmd(ip, pem_file, username, '', '', command) print(buf) return buf def run_multiple_offline_model(self, usecasename,modelName, temperature, similarity_threshold, perturbations_per_sample, faq,isfinetuned): dataFile = os.path.join(DATA_FILE_PATH, 'prompt.csv') remoteFile = os.path.join(remote_data_dir, 'prompt.csv') faq.to_csv(dataFile, index=False) print("This is done") from appbe.compute import readComputeConfig from appbe.prediction import get_instance cloud_infra = readComputeConfig() hypervisor, instanceid, region, image = get_instance(usecasename) key, url, api_type, api_version = get_llm_data() if hypervisor == 'AWS': aws_access_key_id = cloud_infra['awsCredentials']['accessKey'] aws_secret_key = cloud_infra['awsCredentials']['secretAccessKey'] currentDirectory = os.path.dirname(os.path.abspath(__file__)) LLM_DIR = os.path.normpath(os.path.join(currentDirectory, '..', 'llm')) if image != '' and image != 'NA': amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['amis'], image) else: amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], instanceid) if region == '' or region == 'NA': region = amiDetails['regionName'] from llm.aws_instance_api import start_instance # print(aws_access_key_id, aws_secret_key, instanceid, region) status, msg, ip = start_instance(aws_access_key_id, aws_secret_key, instanceid, region) if status.lower() == 'success': pem_file = os.path.join(LLM_DIR, amiDetails['ssh']['keyFilePath']) username = amiDetails['ssh']['userName'] #print(ip,pem_file,promptfile,'',username,'',remote_data_dir,remote_config_dir) from AION.llm.ssh_command import copy_files_to_server copy_files_to_server(ip,pem_file,dataFile,'',username,'',remote_data_dir,remote_config_dir) if isfinetuned: command = prompt_command + ' ' + usecasename + ' ' + str(modelName) \ + ' ' + str(temperature) + ' ' + str(similarity_threshold) + ' ' \ + str(perturbations_per_sample) + \ ' '+ str(key) + \ ' '+ str(url) + \ ' '+ str(api_type) + \ ' '+ str(api_version)+ \ ' '+ str("multiple") else: command = prompt_command + ' ' + 'BaseModel' + ' ' + str(modelName) \ + ' ' + str(temperature) + ' ' + str(similarity_threshold) + ' ' \ + str(perturbations_per_sample) + \ ' '+ str(key) + \ ' '+ str(url) + \ ' '+ str(api_type) + \ ' '+ str(api_version)+ \ ' '+ str("multiple") from llm.ssh_command import run_ssh_cmd buf = run_ssh_cmd(ip, pem_file, username, '', '', command) print(buf) return buf ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import time import subprocess import sys import json import pandas as pd def getDataSetRecordsCount(datalocation): try: records = 0 if os.path.isfile(datalocation): for chunk in pd.read_csv(datalocation, chunksize=20000): records = records+len(chunk) if records == 0: records = 'NA' except Exception as e: print(e) records = 'NA' return records def get_train_model_details(deploy_location,request): updatedConfigFile = request.session['config_json'] f = open(updatedConfigFile, "r") configSettings = f.read() f.close() usename = request.session['usecaseid'].replace(" ", "_") outputfile = os.path.join(deploy_location,usename,str(request.session['ModelVersion']),'etc','output.json') if os.path.isfile(outputfile): f1 = open(outputfile, "r+", encoding="utf-8") outputStr = f1.read() f1.close() resultJsonObj = json.loads(outputStr) trainingStatus = resultJsonObj['status'] if trainingStatus.lower() == 'success': details = resultJsonObj['data'] modelType = details['ModelType'] bestModel = details['BestModel'] return trainingStatus,modelType,bestModel else: return trainingStatus,'NA','NA' else: return 'Not Trained','NA','NA' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json import os import platform import time import sys from os.path import expanduser from pathlib import Path import ast import pandas as pd from appbe.dataPath import CONFIG_FILE_PATH def generate_json_config(request): from appbe.create_dummy_dataset import gen_data_classification from appbe.create_dummy_dataset import gen_data_regression from appbe.create_dummy_dataset import gen_data_series try: problem_type = request.POST.get('ProblemType') datadict1 = request.POST.get('rangedict') datadict = eval(datadict1) if request.POST.get('univariate') == "True": features = request.POST.get('features') features = '1' catfeatures = request.POST.get('catfeatures') catfeatures = '0' informative = request.POST.get('informative') informative = '1' elif request.POST.get('univariate') == "False": features = request.POST.get('features') catfeatures = request.POST.get('catfeatures') informative = request.POST.get('informative') data_path = request.POST.get('dataypath') number_informative = int(request.POST.get('informative')) number_numerical_features = int(request.POST.get('features')) if os.path.isdir(data_path): raise Exception('Incorrect path. Please include filename. Eg: C:/AION/data.csv') if os.path.isfile(data_path): raise ValueError('The file ({}) exists.'.format(os.path.basename(data_path))) if number_informative > number_numerical_features: raise ValueError('The No. numerical features ({}) must larger than No. informative features ({}).'.format(number_numerical_features, number_informative)) if problem_type == 'classification': status = gen_data_classification(int(request.POST.get('samples')),int(request.POST.get('features')),request.POST.get('dataypath'),int(request.POST.get('catfeatures')),int(request.POST.get('txtfeatures')),float(request.POST.get('proportion')),int(request.POST.get('informative')),int(request.POST.get('class')),[float(val) for val in request.POST.get('weights').split(",")],float(request.POST.get('shift')),datadict) elif problem_type == 'regression': status = gen_data_regression(int(request.POST.get('samples')),int(request.POST.get('features')),request.POST.get('dataypath'),int(request.POST.get('catfeatures')),int(request.POST.get('txtfeatures')),float(request.POST.get('proportion')),int(request.POST.get('informative')),int(request.POST.get('target')),float(request.POST.get('bias')),float(request.POST.get('noise')),datadict) elif problem_type == 'timeseriesforecasting': #task 11997 status = gen_data_series(request.POST.get('univariate'),request.POST.get('starttime'),request.POST.get('endtime'),int(request.POST.get('samples')),int(features),request.POST.get('dataypath'),int(catfeatures),float(request.POST.get('proportion')),int(informative),int(request.POST.get('target')),float(request.POST.get('bias')),float(request.POST.get('noise')),datadict) else: raise Exception("Unsupperted Problem Type.") if status: from appbe.dataPath import DATA_DIR from appbe.sqliteUtility import sqlite_db file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') if request.POST["dataypath"] =='' or request.POST["dataset"] == '': return 'error' newdata = {} newdata['datapath'] = [request.POST.get('dataypath')] newdata['datasetname'] = [request.POST.get('dataset')] sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'dataingest') else: raise Exception("Data Genration failed.") except Exception as e: print(e) raise Exception(str(e)) if __name__ == "__main__": generate_json_config('classification') generate_json_config('regression') generate_json_config('timeseriesforecasting') #task 11997 import json import os import sys import re import numpy as np def check_unsupported_col(config): #bugId14444 unsupported_chars = '[]<>#{}@&' try: featureList = config['basic']['featureList'] return any([x in y for x in unsupported_chars for y in featureList]) except Exception as e: print(str(e)) return False def check_granularity(configSettingsJson,datapath=None): try: from AION.appbe.utils import get_true_option import pandas as pd from pathlib import Path seconds_per_unit = {'second':1,'minute':60,'hour':60 * 60,'day':24 * 60 * 60,'week':7 * 24 * 60 * 60,'month':30 * 24 * 60 * 60,'year':365 * 24 * 60 * 60} if not get_true_option(configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['aggregation']['type']): return '' if isinstance( configSettingsJson['basic']['dateTimeFeature'], list): datetime_feature = configSettingsJson['basic']['dateTimeFeature'][0] else: datetime_feature = configSettingsJson['basic']['dateTimeFeature'] if get_true_option(configSettingsJson['basic']['analysisType']) == 'timeSeriesForecasting' and datetime_feature: if not datapath: datapath = configSettingsJson['basic']['dataLocation'] if Path( datapath).exists(): df = pd.read_csv(datapath, nrows=2) datetime = pd.to_datetime(df[ datetime_feature]) if len(datetime) > 1: source_time_delta = (datetime[1] - datetime[0]).total_seconds() granularity_unit = get_true_option(configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['aggregation']['granularity']['unit']) size = int(configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['aggregation']['granularity']['size']) target_time_delta = size * seconds_per_unit[granularity_unit] amplify = int(source_time_delta / target_time_delta) if amplify > 20: return f'Current Granularity setting will amplify the data approx {amplify} times. Depending on your system configuration, this may cause Memory error' return '' except Exception as e: return '' def getStatusCount(matched_lines,total_steps): stepsdone = 0 leaner = True #print(matched_lines) for line in matched_lines: if 'AION feature transformation completed' in line: stepsdone = stepsdone + 1 elif 'AION feature engineering completed' in line: stepsdone = stepsdone + 1 elif 'AION Association Rule completed' in line: stepsdone = stepsdone + 1 elif 'AION Image Classification completed' in line: stepsdone = stepsdone + 1 elif 'AION Association Rule completed' in line: stepsdone = stepsdone + 1 elif 'AION State Transition completed' in line: stepsdone = stepsdone + 1 elif 'AION SurvivalAnalysis completed' in line: stepsdone = stepsdone + 1 elif 'AION Recommender completed' in line: stepsdone = stepsdone + 1 elif 'AION Gluon Stop' in line: stepsdone = stepsdone + 1 elif 'AION Evaluation Stop' in line: stepsdone = stepsdone + 1 elif 'AION Object Detection completed' in line: stepsdone = stepsdone + 1 elif ('training completed' in line) and leaner: stepsdone = stepsdone + 1 leaner = False elif 'Prediction Service completed' in line: stepsdone = stepsdone + 1 elif 'AION TimeSeries Forecasting started' in line: #task 11997 stepsdone = stepsdone + 1 elif 'Distributed Learning Completed' in line: stepsdone = stepsdone + 4 elif 'AION Batch Deployment completed' in line: stepsdone = stepsdone + 2 match_lines = [] for line in matched_lines: count = len(line)-len(line.lstrip()) uline = line.split('...') uline = uline[1] if count == 0: uline = '|... <span style="border: 1px solid black; line-height:2; padding: 2px">'+uline+'</span>' elif count == 8 or count == 1: uline = ' |... <span style="border: 1px dashed darkblue; line-height:2; padding: 2px">'+uline+'</span>' elif count == 16 or count == 2: uline = ' |... <span style="border: 1px dotted darkgray; line-height:2; padding: 2px">'+uline+'</span>' elif count == 32 or count == 3: uline = ' |... <span style="border: 1px dotted lightgray ; line-height:2; padding: 2px">'+uline+'</span>' else: uline = line match_lines.append(uline) stepline = '<b>Stage: ' + str(stepsdone) + '/' + str(total_steps) + ' Complete</b>' match_lines.insert(0, stepline) #print(match_lines) output = "\n".join([status_text for status_text in match_lines]) output = "<pre>{}</pre>".format(output) #print(output) return(output) def calculate_total_interations(config): try: noOfIterations = 0 problemtypes = config['basic']['analysisType'] problem_type = "" for key in problemtypes: if config['basic']['analysisType'][key] == 'True': problem_type = key break if problem_type.lower() in ['classification','regression']: algorithms = config['basic']['algorithms'][problem_type] for key in algorithms: if config['basic']['algorithms'][problem_type][key] == 'True': if key not in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)','Deep Q Network','Dueling Deep Q Network']: if problem_type.lower() == 'classification': configparam = config['advance']['mllearner_config']['modelParams']['classifierModelParams'][key] else: configparam = config['advance']['mllearner_config']['modelParams']['regressorModelParams'][key] param = paramDefine(configparam,config['advance']['mllearner_config']['optimizationMethod']) interationsum = 1 for x in param.values(): interationsum = interationsum*len(x) if config['advance']['mllearner_config']['optimizationMethod'].lower() == 'random': if interationsum > int(config['advance']['mllearner_config']['optimizationHyperParameter']['iterations']): interationsum = int(config['advance']['mllearner_config']['optimizationHyperParameter']['iterations']) noOfIterations = noOfIterations+interationsum else: if key in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)']: if problem_type.lower() == 'classification': configparam = config['advance']['dllearner_config']['modelParams']['classifierModelParams'][key] else: configparam = config['advance']['dllearner_config']['modelParams']['regressorModelParams'][key] interationsum = 1 for j in list(configparam.keys()): if isinstance(configparam[j],(list,dict,tuple,str)): x = configparam[j].split(',') interationsum = interationsum*len(x) noOfIterations = noOfIterations+interationsum elif key in ['Deep Q Network','Dueling Deep Q Network']: if problem_type.lower() == 'classification': configparam = config['advance']['rllearner_config']['modelParams']['classifierModelParams'][key] interationsum = 1 for j in list(configparam.keys()): if isinstance(configparam[j],(list,dict,tuple,str)): x = configparam[j].split(',') interationsum = interationsum*len(x) noOfIterations = noOfIterations+interationsum elif problem_type.lower() in ['llmfinetuning']: algorithms = config['basic']['algorithms'][problem_type] for key in algorithms: if config['basic']['algorithms'][problem_type][key] == 'True': noOfIterations = configparam = config['advance']['llmFineTuning']['modelParams'][key]['epochs'] break else: noOfIterations= 'NA' except Exception as e: print(e) noOfIterations = 'NA' pass return(noOfIterations) def paramDefine(paramSpace, method): paramDict = {} for j in list(paramSpace.keys()): inp = paramSpace[j] try: isLog = False isLin = False isRan = False isList = False isString = False try: # check if functions are given as input and reassign paramspace v = paramSpace[j] if 'logspace' in paramSpace[j]: paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "") isLog = True elif 'linspace' in paramSpace[j]: paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "") isLin = True elif 'range' in paramSpace[j]: paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "") isRan = True elif 'list' in paramSpace[j]: paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "") isList = True elif '[' and ']' in paramSpace[j]: paramSpace[j] = v.split('[')[1].split(']')[0].replace(" ", "") isList = True x = paramSpace[j].split(',') except: x = paramSpace[j] str_arg = paramSpace[j] # check if arguments are string try: test = eval(x[0]) except: isString = True if isString: paramDict.update({j: hp.choice(j, x)} if method == 'bayesopt' else {j: x}) else: res = eval(str_arg) if isLin: y = eval('np.linspace' + str(res)) paramDict.update({j: hp.uniform(j, eval(x[0]), eval(x[1]))} if method == 'bayesopt' else {j: y}) elif isLog: y = eval('np.logspace' + str(res)) paramDict.update( {j: hp.uniform(j, 10 ** eval(x[0]), 10 ** eval(x[1]))} if method == 'bayesopt' else {j: y}) elif isRan: y = eval('np.arange' + str(res)) paramDict.update({j: hp.choice(j, y)} if method == 'bayesopt' else {j: y}) # check datatype of argument elif isinstance(eval(x[0]), bool): y = list(map(lambda i: eval(i), x)) paramDict.update({j: hp.choice(j, eval(str(y)))} if method == 'bayesopt' else {j: y}) elif isinstance(eval(x[0]), float): res = eval(str_arg) if len(str_arg.split(',')) == 3 and not isList: y = eval('np.linspace' + str(res)) #print(y) paramDict.update({j: hp.uniform(j, eval(x[0]), eval(x[1]))} if method == 'bayesopt' else {j: y}) else: y = list(res) if isinstance(res, tuple) else [res] paramDict.update({j: hp.choice(j, y)} if method == 'bayesopt' else {j: y}) else: res = eval(str_arg) if len(str_arg.split(',')) == 3 and not isList: y = eval('np.linspace' +str(res)) if eval(x[2]) >= eval(x[1]) else eval('np.arange'+str(res)) else: y = list(res) if isinstance(res, tuple) else [res] paramDict.update({j: hp.choice(j, y)} if method == 'bayesopt' else {j: y}) except Exception as inst: print(inst) return paramDict def calculate_total_activities(config): req_step = 0 problemtypes = config['basic']['analysisType'] problem_type = "" for key in problemtypes: if config['basic']['analysisType'][key] == 'True': problem_type = key break Modelproblem = problem_type if Modelproblem.lower() in ['classification','regression','clustering','anomalydetection','topicmodelling']: req_step = req_step+4 if Modelproblem.lower() in ['timeseriesforecasting','imageclassification','objectdetection','multilabelprediction','similarityidentification','contextualsearch']: #task 11997 req_step = req_step+2 if Modelproblem.lower() in ['survivalanalysis']: req_step = req_step+3 if Modelproblem.lower() in ['recommendersystem']: if config['basic']['algorithms']['recommenderSystem']['ItemRating'] == 'True': req_step = req_step+3 if config['basic']['algorithms']['recommenderSystem']['AssociationRules-Apriori'] == 'True': req_step = req_step+1 if Modelproblem.lower() in ['statetransition']: req_step = req_step+1 return (req_step) def getModelStatus(Existusecases,modelid): model = Existusecases.objects.get(id=modelid) return(model.Status) def changeModelStatus(Existusecases,modelid,status,problemType,deployPath): model = Existusecases.objects.get(id=modelid) model.Status = status model.ProblemType = problemType model.DeployPath = deployPath model.save() def checkversionrunningstatus(modelid,usecasedetails,Existusecases): modelx = Existusecases.objects.get(id=modelid) ConfigPath = str(modelx.ConfigPath) status = 'Running' try: if os.path.exists(ConfigPath): with open(ConfigPath, 'r') as json_file: data = json.load(json_file) json_file.close() deployPath = str(data['basic']['deployLocation']) modelName = data['basic']['modelName'] modelVersion = data['basic']['modelVersion'] modelName = modelName.replace(" ", "_") logfile = os.path.join(deployPath,modelName,str(modelVersion),'log','model_training_logs.log') print(logfile) if os.path.exists(logfile): with open(logfile) as f: contents = f.read() f.close() contents = re.search(r'aion_learner_status:(.*)', str(contents), re.IGNORECASE).group(1) contents = contents.strip() print(contents) if contents != '': resultJsonObj = json.loads(contents) odataFile = str(modelx.TrainOuputLocation) with open(odataFile, 'w') as json_file: json.dump(resultJsonObj, json_file) json_file.close() modelx.Status = resultJsonObj['status'] status = modelx.Status if resultJsonObj['status'] == 'SUCCESS': modelx.DeployPath = str(resultJsonObj['data']['deployLocation']) if resultJsonObj['data']['ModelType'] in ['clustering','anomalydetection']: modelx.ProblemType = 'unsupervised' else: modelx.ProblemType = 'supervised' modelx.save() except Exception as e: pass return status def updateLLM_Model_training_logs(deployPath,modelName,modelVersion,model,configPath): from appbe.prediction import get_instance hypervisor,instanceid,region,image = get_instance(modelName+'_'+str(modelVersion)) from llm.llm_tuning import llm_logs cloudconfig = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','config','compute_conf.json')) llm_logs(configPath,cloudconfig,instanceid,hypervisor,model) def checkModelUnderTraining(request,usecasedetails,Existusecases): try: models = Existusecases.objects.filter(Status='Running') for model in models: ConfigPath = str(model.ConfigPath) try: if os.path.exists(ConfigPath): with open(ConfigPath, 'r') as json_file: data = json.load(json_file) json_file.close() deployPath = str(data['basic']['deployLocation']) modelName = data['basic']['modelName'] modelVersion = data['basic']['modelVersion'] modelName = modelName.replace(" ", "_") if data['basic']['analysisType']['llmFineTuning'] == 'True': mlmodels ='' algorihtms = data['basic']['algorithms']['llmFineTuning'] for k in algorihtms.keys(): if data['basic']['algorithms']['llmFineTuning'][k] == 'True': if mlmodels != '': mlmodels += ', ' mlmodels += k updateLLM_Model_training_logs(deployPath,modelName,modelVersion,mlmodels,ConfigPath) logfile = os.path.join(deployPath,modelName,str(modelVersion),'log','model_training_logs.log') if os.path.exists(logfile): with open(logfile,encoding="utf-8") as f: contents = f.read() f.close() contents = re.search(r'aion_learner_status:(.*)', str(contents), re.IGNORECASE).group(1) contents = contents.strip() if contents != '': resultJsonObj = json.loads(contents) odataFile = str(model.TrainOuputLocation) with open(odataFile, 'w') as json_file: json.dump(resultJsonObj, json_file) json_file.close() modelx = Existusecases.objects.get(id=model.id) modelx.Status = resultJsonObj['status'] if resultJsonObj['status'] == 'SUCCESS': modelx.DeployPath = str(resultJsonObj['data']['deployLocation']) if resultJsonObj['data']['ModelType'] in ['clustering','anomalydetection']: modelx.ProblemType = 'unsupervised' else: modelx.ProblemType = 'supervised' modelx.save() except Exception as e: print(ConfigPath) print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) pass except Exception as e: print(e) # -*- coding: utf-8 -*- import os import glob, os import pandas as pd from openai.embeddings_utils import cosine_similarity import numpy as np from openai.embeddings_utils import get_embedding import tiktoken import openai import importlib.util from sklearn.preprocessing import StandardScaler import numpy as np from sklearn.cluster import DBSCAN from sklearn import metrics import time from tqdm import tqdm import concurrent.futures from openai.error import RateLimitError, Timeout try: import chromadb from chromadb.api.types import Documents, Embeddings except: #Looks no chromadb installed,just proceed to use csv embedd pass from openai.embeddings_utils import get_embedding import json from openai.embeddings_utils import cosine_similarity from langchain.schema import Document from langchain.vectorstores import Chroma import warnings import logging warnings.simplefilter(action='ignore', category=FutureWarning) """Code clone detection parent class, based on user input data,the class will detect similar code snippets in the python file """ class CodeCloneDetection: #Constructor for base inputs def __init__(self,rootdir,openai_baseurl, openai_key,openai_api_type,openai_api_version,embedd_storage_path,generativeai_embedding_engine,generativeai_embedding_model,generativeai_chat_model,generativeai_deploymentId): self.rootdir=rootdir self.embedd_storage_path=embedd_storage_path self.openai_baseurl=openai_baseurl self.openai_key=openai_key self.openai_api_type=openai_api_type self.openai_api_version=openai_api_version self.ccdreportpath = os.path.join(self.embedd_storage_path, "codeCloneReport") self.generativeai_chat_model=generativeai_chat_model self.generativeai_embedding_engine = generativeai_embedding_engine self.generativeai_embedding_model = generativeai_embedding_model self.generativeai_deploymentId = generativeai_deploymentId try: os.makedirs(self.ccdreportpath, exist_ok = True) except OSError as error: print("Directory 'codeclonedetection' can not be created",self.ccdreportpath) try: self.logpath = os.path.join(self.ccdreportpath,'codeclonelog.log') logging.basicConfig(level=logging.INFO,filename=self.logpath,filemode='w',format='%(message)s') self.log = logging.getLogger() except Exception as e: print("code clone log object creation error.",e) def get_function_name(self,code): """ Extract function name from a line beginning with "def " """ assert code.startswith("def ") return code[len("def "): code.index("(")] def get_until_no_space(self,all_lines, i) -> str: """ Get all lines until a line outside the function definition is found. """ ret = [all_lines[i]] for j in range(i + 1, i + 10000): if j < len(all_lines): if len(all_lines[j]) == 0 or all_lines[j][0] in [" ", "\t", ")"]: ret.append(all_lines[j]) else: break return "\n".join(ret) def chunk_functions(self,function_code, chunk_size): """ To chunk input for gpt models because max token per model is 4090 """ try: # chunk_size = 1900 chunks = [function_code[i:i + chunk_size] for i in range(0, len(function_code), chunk_size)] except Exception as e: self.log.info('Error in chunking input prompt data.') return chunks def get_functions(self,filepath): """ Get all functions in a Python file. """ try: whole_code = open(filepath).read().replace("\r", "\n") all_lines = whole_code.split("\n") for i, l in enumerate(all_lines): if l.startswith("def "): code = self.get_until_no_space(all_lines, i) function_name = self.get_function_name(code) yield {"code": code, "function_name": function_name, "filepath": filepath} except Exception as e: self.log.info("Error in getting function from file. Error message: \n"+str(e)) def get_clone_function_details(self): """ To get available functions from python files """ try: code_root=self.rootdir from glob import glob code_files = [y for x in os.walk(code_root) for y in glob(os.path.join(x[0], '*.py'))] if code_files: all_funcs = [] total_locs = 0 for code_file in code_files: with open(code_file) as f: total_locs += len(f.readlines()) funcs = list(self.get_functions(code_file)) for func in funcs: all_funcs.append(func) return all_funcs,code_root,code_files,total_locs else: self.log.info("no python files available in the dir:"+str(code_root)) return {"pythondiles_error":"No python files are found."} except Exception as e: print("Error in reading the functions from the given directory. Error message: \n",e) self.log.info("Error in reading the functions from the given directory. Error message: \n"+str(e)) def getOpenAICredentials(self): """ To set openai credential using user input """ #Currently only support openai try: package_name = 'openai' lib_name = importlib.util.find_spec(package_name) if lib_name is None: return "openai_pkg_check_failed" else: embedding_model_lib ='openai' # if isinstance(self.openai_baseurl,str) and isinstance(self.openai_key,str) and isinstance(self.openai_api_type,str): os.environ['OPENAI_API_TYPE'] = self.openai_api_type os.environ['OPENAI_API_BASE'] = self.openai_baseurl # os.environ['OPENAI_API_VERSION'] = '2023-05-15' # os.environ['OPENAI_API_VERSION'] = "2022-12-01" os.environ['OPENAI_API_VERSION'] = self.openai_api_version os.environ['OPENAI_API_KEY'] = self.openai_key if (embedding_model_lib.lower()=='openai'): try: openai.api_type=os.getenv('OPENAI_API_TYPE') openai.api_base = os.getenv('OPENAI_API_BASE') openai.api_key = os.getenv('OPENAI_API_KEY') openai.api_version = os.getenv('OPENAI_API_VERSION') except Exception as e: self.log.info("Unable to get openai credentials,please provide proper credentials."+str(e)) return {"error_msg":"openai_environment_error"} except Exception as e: print("Openai credential set and get function error. Error message: \n",e) return openai.api_type,openai.api_base,openai.api_key,openai.api_version def get_embedding_local(self,model: str, text: str) -> list[float]: """ To get embedding data for single user given prompt text""" try: response = openai.Embedding.create( input=text, engine=self.generativeai_embedding_engine) except Exception as e: self.log.info("openai embedding creation error."+str(e)) return result['data'][0]['embedding'] def get_embeddings_pyfiles(self,all_funcs): """ To get embedding for python functions """ try: import tiktoken openai_api_type,openai_api_base,openai_api_key,openai_api_version = self.getOpenAICredentials() encoding = tiktoken.encoding_for_model("text-embedding-ada-002") df = pd.DataFrame(all_funcs) df["tokens"] = df["code"].apply(lambda c: len(encoding.encode(c))) embedding_cost = df["tokens"].sum() * (0.0004/1000) EMBEDDING_FILEPATH=self.ccdreportpath+'\code_embeddings.csv' self.log.info("embedding storage location: "+str(EMBEDDING_FILEPATH)) vdb_status = self.get_vdb_status('chromadb') ##Currently chromadb not integrated vdb_status = False if not vdb_status: df['code_embedding'] = df['code'].apply(lambda x: get_embedding(x, engine=self.generativeai_embedding_engine)) df['filepath'] = df['filepath'].apply(lambda x: x.replace(self.rootdir, "")) df.to_csv(EMBEDDING_FILEPATH, index=False) else: df = self.chromadb_embedding(df) """ Please uncomment below, currently assumption is each run we create embedd based on python files dir """ import numpy as np df = pd.read_csv(EMBEDDING_FILEPATH) df["code_embedding"] = df["code_embedding"].apply(eval).apply(np.array) except Exception as e: self.log.info("Error in get_embeddings_pyfiles for embedding conversion process. Error Message: "+str(e)) raise Exception("Error in get_embeddings_pyfiles for embedding conversion process.") return df,embedding_cost def search_functions_vectordb(df,db, code_query, n=3, pprint=True, n_lines=7): """ Search function for user query (prompt content), used for vector database embedding query option. """ try: docs = db.similarity_search_with_score(code_query )[:n] docs = [{"similarities":score, "code": d.page_content, **d.metadata} for d,score in docs] res = pd.DataFrame(docs).drop("_additional", axis=1) ##Uncomment for debug # if pprint: # for r in res.iterrows(): # print(r[1].filepath+" : "+r[1].function_name + " score=" + str(round(r[1].similarities, 3))) # print("\n".join(r[1].code.split("\n")[:n_lines])) # print('-'*70) except Exception as e: self.log.info("Error in search_functions_vectordb to get similarity information based on user query. Error Message: "+str(e)) raise Exception("Error in search_functions_csv to get similarity information based on user query.") return res def search_functions_csv(self,df, code_query, n=3, pprint=True, n_lines=7): """ Search function for user query (prompt content), used for csv embedding query option. """ try: embedding = get_embedding(code_query, engine=self.generativeai_embedding_engine) df['similarities'] = df.code_embedding.apply(lambda x: cosine_similarity(x, embedding)) res = df.sort_values('similarities', ascending=False) ## uncomment for debug purpose # if pprint: # for r in res.iterrows(): # print(r[1].filepath+" : "+r[1].function_name + " score=" + str(round(r[1].similarities, 3))) # print("\n".join(r[1].code.split("\n")[:n_lines])) # print('-'*70) except Exception as e: self.log.info("Error in search_functions_functions_csv to get similarity information based on user query. Error Message: "+str(e)) raise Exception("Error in search_functions_csv to get similarity information based on user query.") return res def get_prediction(self,prompt_data): """ To get prediction for given user data """ try: all_funcs,code_root,code_files,total_locs=self.get_clone_function_details() if not isinstance(all_funcs,type(None)): df,embedding_cost=self.get_embeddings_pyfiles(all_funcs) res = self.search_functions_csv(df, prompt_data, n=3) return res else: return dict({"error":"Empty_root_directory"}) except Exception as e: self.log.info("Error in get prediction for user prompt information. Error Message: "+str(e)) raise Exception("Error in get prediction for user prompt information. .") def get_vdb_status(self,vdb_name): """ To check chromadb python package installed or not""" try: vdb_name = 'chromadb' vdb_status=False lib_name = importlib.util.find_spec(vdb_name) if lib_name is None: vdb_status=False else: vdb_status=True ## Processing the files and create a embedding and save it using csv. except Exception as e: self.log.info("Error in checking chromadb installed or not. Error Message: "+str(e)) raise Exception("Error in checking chromadb installed or not. .") ## Currently vector db (chromadb) not implemented, so vdb_status is set as False vdb_status = False return vdb_status def create_chroma_db(self,documents, name): """ Craete chromadb instance (persistant) """ #get openai status openai_api_type,openai_api_base,openai_api_key,openai_api_version = self.getOpenAICredentials() # openai_api_type,openai_api_base,openai_api_key = self.getOpenAICredentials() try: from langchain.embeddings.openai import OpenAIEmbeddings embed_function = OpenAIEmbeddings(deployment=self.generativeai_embedding_engine, chunk_size=1) except: from chromadb.utils import embedding_functions embed_function = embedding_functions.OpenAIEmbeddingFunction( api_key=openai.api_key, api_base=openai.api_base, api_type = openai.api_type, model_name=self.generativeai_embedding_model ) try: # chroma_client = chromadb.Client() persist_directory = self.embedd_storage_path chroma_client = chromadb.Client( Settings( persist_directory=persist_directory, chroma_db_impl="duckdb+parquet", ) ) # Start from scratch chroma_client.reset() chroma_client.persist() try: embed_function = OpenAIEmbeddings(deployment=self.generativeai_embedding_engine, chunk_size=1) except: embed_function = OpenAIEmbeddings() db = Chroma.from_documents(documents, embed_function, persist_directory=persist_directory) db.persist() except Exception as e: self.log.info("Error in chromadb based embeding creation. Error Message: "+str(e)) raise Exception("Error in chromadb based embeding creation.") return db,chroma_client def chromadb_embedding(self,df): """ Base chromadb embedding creation and storage function, it calls above create_chroma_db() to create db. """ try: documents = df.apply(lambda x: Document(page_content= x["code"], metadata= {"function_name": x["function_name"], "filepath": x["filepath"]}), axis=1) #setup the chromadb db,chroma_client = self.create_chroma_db(documents,collection_name) try: chromadb_df=pd.DataFrame(db) except: db_json = db.get(include=['embeddings', 'documents', 'metadatas']) chromadb_df = pd.read_json(db_json) self.log.info("chromadb_df records (top ~5 records): "+str(chromadb_df.head(5))) except Exception as e: self.log.info("chromadb embedding error. Error message: "+str(e)) return chromadb_df def num_tokens_from_string(self, string: str) -> int: """ Get number of tokens of text using tiktokens lib.""" encoding = tiktoken.encoding_for_model("text-embedding-ada-002") num_tokens = len(encoding.encode(string)) return num_tokens def validate_code_clone_with_explanation(self,code1, code2, verbose=False): """ Validate clone detection code snippet and get explanation from openai chat model (gpt-3.5-turbo) """ ## Openai using 4 chars as 1 token, same method here followed. Here,we dont need to call tiktoken lib to save cost. if (len(code1)/4 >1900): chunk = self.chunk_functions(code1, 1900) code1 = chunk[0] print("In side , len of code1\n",len(code1)) if (len(code2)/4 >1900): chunk = self.chunk_functions(code2, 1900) code2 = chunk[0] print("In side , len of code2\n",len(code2)) try: SYS_ROLE = "You are a Senior Code Reviewer, who helps in Code review and integration using code clone detection approach." openai_api_type,openai_api_base,openai_api_key,openai_api_version = self.getOpenAICredentials() # openai_api_type,openai_api_base,openai_api_key = self.getOpenAICredentials() prompt = f"""Given two code snippets, find if they are clones or not with suitable explaination. Four types of clone: 1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces. 2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone. 3. Near-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones. 4. Semantic clone: The code snippets have different syntax but with alike functionality results in this clone. Use JSON object format with following keys: IsClone: (True, False) wheather two code snippets are clone or not. CloneType: (Exact clone, Parameterized clone, Never-miss clone, Semantic clone) Choose appropriate clone type or "None". Explanation: A short explanation for the above answer. ### Code Snippets: ## Code 1: {code1} ## Code 2: {code2} ### Answer(Valid JSON object): """ response = openai.ChatCompletion.create(deployment_id=self.generativeai_deploymentId, messages=[{"role": "system", "content": SYS_ROLE}, {"role": "user", "content": prompt},], temperature = 0,max_tokens = 3900,request_timeout=90) text = response['choices'][0]['message']['content'] if verbose: self.log.info("validate_code_clone_with_explanation, text: "+str(text)) except Exception as e: print(" validate_code_clone_with_explanation: \n",e) response = "OpenAI Model Connection" if e.code == "invalid_request" and "token limit" in e.message.lower(): # Implement your logic to reduce the length of messages or split them into smaller parts # Modify messages or take appropriate action self.log.info("Given function is too large and exceeds openai chat model token limit,please review the source file function length. "+str(e)) return response def validate_code_clone_with_explanation_davinci(self,code1, code2, verbose=False): """ Validate clone detection code snippet and get explanation from openai chat model (davinci) """ if (len(code1)/4 >1900): chunk = self.chunk_functions(code1, 1900) code1 = chunk[0] if (len(code2)/4 >1900): chunk = self.chunk_functions(code2, 1900) code2 = chunk[0] try: SYS_ROLE = "You are a Senior Code Reviewer, who helps in Code review and integration. Detecting code clone in the repository." openai_api_type,openai_api_base,openai_api_key,openai_api_version = self.getOpenAICredentials() # openai_api_type,openai_api_base,openai_api_key = self.getOpenAICredentials() prompt = f"""Given two code snippets, find if they are clones or not with suitable explaination. Four types of clone: 1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces. 2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone. 3. Near-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones. 4. Semantic clone: The code snippets have different syntax but with alike functionality results in this clone. Use JSON object format with following keys: IsClone: (True, False) wheather two code snippets are clone or not. CloneType: (Exact clone, Parameterized clone, Never-miss clone, Semantic clone) Choose appropriate clone type or "None". Explanation: A short explanation for the above answer. ### Code Snippets: ## Code 1: {code1} ## Code 2: {code2} ### Answer(Valid JSON object): """ # response = openai.Completion.create(engine='Text-Datvinci-03', prompt=prompt, temperature=0, max_tokens=1166) response = openai.Completion.create(engine=self.generativeai_chat_model, prompt=prompt, temperature=0, max_tokens=3900) text = response.choices[0]["text"] if verbose: self.log.info("validate_code_clone_with_explanation, text (chatmodel response) "+str(text)) except Exception as e: response = "OpenAI Model Connection Error" if e.code == "invalid_request" and "token limit" in e.message.lower(): # Implement your logic to reduce the length of messages or split them into smaller parts # Modify messages or take appropriate action self.log.info("Given function is too large and exceeds openai chat model token limit,please review the source file function length. Error msg: "+str(e)) return response ## For dbscan based clone detction from python files, we use CodeCloneDetection parent class. (Using inheritance) class CodeCloneDetectionFiles(CodeCloneDetection): """For dbscan based clone detction from python files, we use CodeCloneDetection parent class. (Using inheritance) """ def __init__(self,root_dir,openai_baseurl, openai_key,openai_api_type,openai_api_version,embedd_storage_path,generativeai_embedding_engine,generativeai_embedding_model,generativeai_chat_model,generativeai_deploymentId): super().__init__(root_dir,openai_baseurl, openai_key,openai_api_type,openai_api_version,embedd_storage_path,generativeai_embedding_engine,generativeai_embedding_model,generativeai_chat_model,generativeai_deploymentId) def get_embedd_fns(self): """ To get embedd vector, using parent class methods""" try: ## Processing the files and create a embedding and save it using csv. vdb_status = super().get_vdb_status('chromadb') self.log.info("<------- AION Code Clone Detection started ... ------>\n ") if not vdb_status: openai_api_type,openai_api_base,openai_api_key,openai_api_version = super().getOpenAICredentials() # openai_api_type,openai_api_base,openai_api_key = self.getOpenAICredentials() all_funcs,code_root,code_files,total_locs = super().get_clone_function_details() if (openai.api_key or openai_api_key): if not isinstance(all_funcs,type(None)): embedded_df,embedding_cost=super().get_embeddings_pyfiles(all_funcs) else: return status except Exception as e: # print("Error in getting embedding vector using openai. Error message: ",e) self.log.info("Error in getting embedding vector using openai. Error message: "+str(e)) raise Exception("Error in getting embedding vector using openai.") return embedded_df,embedding_cost def dbscan_clone_detection(self,df): """ DBScan based code clone similarity detection (for functions in given dir """ try: vdb_status = super().get_vdb_status('chromadb') if not vdb_status: X = np.array(list(df.code_embedding.values)) else: X = np.array(list(df.embeddings.values)) #X = StandardScaler().fit_transform(X) db = DBSCAN(eps=0.2, min_samples=2).fit(X) labels = db.labels_ # Number of clusters in labels, ignoring noise if present. n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0) n_noise_ = list(labels).count(-1) df["cluster"] = labels cluster_result = [] for i in range(n_clusters_): cluster_df = df.loc[df['cluster'] == i] # with open("{}/cluster_{}.txt".format(self.ccdreportpath,i), "w") as f: for index, row in cluster_df.iterrows(): cluster_result.append({"cluster_id": i,"filepath": row.filepath,"function_name": row.function_name,"code": row.code }) # f.write(f"Source File: {row.filepath}, Function Name: {row.function_name}") #f.write(f"\n{row.code}\n\n{'-'*80}\n\n") cluster_result_df = pd.DataFrame(cluster_result) codeclonereport_df = os.path.join(self.ccdreportpath,'cluster_result.csv') cluster_result_df.to_csv(codeclonereport_df, index=False) return cluster_result_df except Exception as e: self.log.info("Error in dbscan based similar code clone clustering. Error Message: "+str(e)) raise Exception("Error in dbscan based similar code clone clustering.") def make_pairs(self,data_list:list): try: if len(data_list) <=1: return [] return [(data_list[0], d) for d in data_list[1:]] + self.make_pairs(data_list[1:]) except Exception as e: self.log.info("Error in make pairs function, error message: "+str(e)) raise Exception("Error in clone code mapping.") def code_clone_check_with_retry(self,code1,code2, retry_interval=1): """ Call chat models for code clone detection with retry mechanism. """ try: # res = super().validate_code_clone_with_explanation(code1,code2) ##sj if (self.generativeai_embedding_model.lower() =='text-embedding-ada-002' and self.generativeai_chat_model.lower() == 'text-datvinci-03'): res = super().validate_code_clone_with_explanation_davinci(code1,code2) return res elif (self.generativeai_embedding_model.lower() =='text-embedding-ada-002' and self.generativeai_chat_model.lower() == 'gpt-3.5-turbo'): res = super().validate_code_clone_with_explanation(code1,code2) return res except (RateLimitError, Timeout) as e: self.log.info("Calling chat model issue in code clone check function, error message: "+str(e)) time.sleep(retry_interval) return self.code_clone_check_with_retry(code1, code2) def res_formater(self,inp): """ Function to format gpt-3.5 or text-davinci-003 response body. """ try: line = inp.replace('{','') line = line.replace('}','') line = line.replace('"','') end=line.split(',') d1={} l2=[] for l in end: l=l.split(',') for i in l: l1=i.split(":") l2.append(l1) import pandas as pd df=pd.DataFrame(l2) df=df.T df.columns = df.iloc[0] df = df[1:] df.columns = df.columns.str.replace('[#,@,&,\']', '') # df.to_csv('test1.csv', index=False) response=df.iloc[0] fl=response.to_list() clone_status=fl[0] clone_type=fl[1] result=fl[2] except Exception as e: self.log.info("chat model response formatter error. Error message: "+str(e)) return clone_status,clone_type,result def getcloneresult_modelspecific(self,code_clone_check_tasks,embedding_cost): """ get the clone type and associated information from chat model response data. """ try: max_workers = min(len(code_clone_check_tasks), 100) all_funcs,code_root,code_files,total_locs = super().get_clone_function_details() if (self.generativeai_chat_model.lower() == 'text-datvinci-03'): self.log.info("<--- Text-Datvinci-03 chat model based code clone detection. --->") code_clone_result = [] for task in code_clone_check_tasks: response=self.code_clone_check_with_retry(task[0]["code"], task[1]["code"]) with concurrent.futures.ThreadPoolExecutor(max_workers= max_workers) as executor: llm_requests = { executor.submit(self.code_clone_check_with_retry, task[0]["code"], task[1]["code"]): task for task in code_clone_check_tasks } with tqdm(total= len(llm_requests)) as progress: for future in concurrent.futures.as_completed(llm_requests): task = llm_requests[future] try: res = future.result() try: my_openai_obj1 = res["choices"][0]["text"] clone_status,clone_type,result = self.res_formater(my_openai_obj1) model_value=res['model'] total_tokens_value=res['usage']['total_tokens'] code_clone_result.append({"task": task, "result":result, "IsClone": clone_status, "CloneType": clone_type, "model":model_value, "total_tokens":total_tokens_value}) except Exception as e: self.log.info("getCloneReport, code_clone_result.append error: "+str(e)) except Exception as exc: self.log.info("getCloneReport error (text davinci chat model): "+str(exc)) progress.update() ## Please uncomment below part if you need to check chat model response body. #codeclonecheckresult_json = os.path.join(self.ccdreportpath,'code_clone_chatmodel_responsebody.json') #with open(codeclonecheckresult_json, "w+") as fp: #json.dump(code_clone_result, fp, indent=2) code_clone_result_json=json.dumps(code_clone_result) clone_report=pd.read_json(code_clone_result_json) cr_totaltokens = clone_report['total_tokens'] total_amt = (cr_totaltokens).sum() * (0.002/1000) clone_report["function1"] = clone_report["task"].apply(lambda x: x[0]["filepath"] + " -> " + x[0]["function_name"]) clone_report["function2"] = clone_report["task"].apply(lambda x: x[1]["filepath"] + " -> " + x[1]["function_name"]) # clone_report["clone_type"] = clone_report["result"].apply(lambda x: x["CloneType"]) clone_report["clone_type"] = clone_report["CloneType"] code_dir = code_root total_files = len(code_files) total_locs = total_locs total_functions = len(all_funcs) total_tokens = clone_report['total_tokens'].sum() total_cost= embedding_cost + clone_report['total_tokens'].sum() * (0.002/1000) total_clones = len(clone_report[clone_report.clone_type != "None"]) code_clone_count_by_df = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count')).to_markdown(tablefmt='psql') clone_functions = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1").to_markdown(tablefmt='psql', index=False) code_clone_count_dict = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count')) clone_function_dict = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1") ##Final report on code clone detection report_str = f"""Code_directory: {code_dir} Files: {total_files} LOCs: {total_locs} Functions: {total_functions} Total_code_clones_detected: {total_clones} Tokens used: {total_tokens} Total cost(embedding + clone check): {total_cost} Four_types_of_clone: 1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces. 2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone. 3. Near-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones. 4. Semantic clone: The code snippets have different syntax but with alike functionality results in this clone. Code_clones_count_by_clone_type: {code_clone_count_by_df} Clone_functions: {clone_functions} """ codeclonereport_txt = os.path.join(self.ccdreportpath,'code_clone_report.txt') with open(codeclonereport_txt, "w") as f: f.write(report_str) report_dict=dict({"Code_directory":code_dir,"total_files":total_files, "total_locs":total_locs,"total_functions":total_functions,"total_clones":total_clones, "total_tokens":total_tokens,"total_cost":total_cost, "Code_clones_count_by_clone_type":code_clone_count_dict,"clone_functions":clone_function_dict}) ## report for chat model is gpt 3.5 turbo elif (self.generativeai_chat_model.lower() == 'gpt-3.5-turbo'): try: self.log.info("<--- gpt-3.5-turbo chat model based code clone detection. --->") code_clone_result = [] for task in code_clone_check_tasks: response=self.code_clone_check_with_retry(task[0]["code"], task[1]["code"]) with concurrent.futures.ThreadPoolExecutor(max_workers= max_workers) as executor: llm_requests = { executor.submit(self.code_clone_check_with_retry, task[0]["code"], task[1]["code"]): task for task in code_clone_check_tasks } with tqdm(total= len(llm_requests)) as progress: for future in concurrent.futures.as_completed(llm_requests): task = llm_requests[future] try: res = future.result() my_openai_obj1 = res["choices"][0]["message"]['content'] clone_status,clone_type,result = self.res_formater(my_openai_obj1) # result = json.loads(res['choices'][0]['message']['content']) total_tokens = res["usage"]["total_tokens"] code_clone_result.append({"task": task, "result":result , "CloneType": clone_type, "total_tokens": total_tokens}) except Exception as exc: self.log.info("gpt 3.5 chat model error: "+str(exc)) progress.update() except Exception as e: print("In gpt3.5,getcloneresult_modelspecific fn exception : \n",e) import traceback print("traceback, In gpt3.5,getcloneresult_modelspecific fn exception \n",traceback.print_exc()) ## Please uncomment below part if you need to check chat model response body. #codeclonecheckresult_json = os.path.join(self.ccdreportpath,'code_clone_chatmodel_responsebody.json') #with open(codeclonecheckresult_json, "w+") as fp: #json.dump(code_clone_result, fp, indent=2) try: code_clone_result_json=json.dumps(code_clone_result) clone_report = pd.read_json(code_clone_result_json) codeclone_total_amt = clone_report["total_tokens"].sum() * (0.002/1000) clone_report["function1"] = clone_report["task"].apply(lambda x: x[0]["filepath"] + " -> " + x[0]["function_name"]) clone_report["function2"] = clone_report["task"].apply(lambda x: x[1]["filepath"] + " -> " + x[1]["function_name"]) # clone_report["clone_type"] = clone_report["result"].apply(lambda x: x["CloneType"]) clone_report["clone_type"] = clone_report["CloneType"] code_dir = code_root total_files = len(code_files) total_locs = total_locs total_functions = len(all_funcs) total_tokens = clone_report["total_tokens"].sum() except Exception as e: self.log.info("Error in getting clone report: "+str(e)) total_cost= embedding_cost + clone_report["total_tokens"].sum() * (0.002/1000) total_clones = len(clone_report[clone_report.clone_type != "None"]) code_clone_count_by_df = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count')).to_markdown(tablefmt='psql') clone_functions = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1").to_markdown(tablefmt='psql', index=False) code_clone_count_dict = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count')) clone_function_dict = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1") report_str = f"""Code_directory: {code_dir} Files: {total_files} LOCs: {total_locs} Functions: {total_functions} Total code clones detected: {total_clones} Tokens used: {total_tokens} Total cost(embedding + clone check): {total_cost} Four types of clone: 1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces. 2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone. 3. Near-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones. 4. Semantic clone: The code snippets have different syntax but with alike functionality results in this clone. 5. None: Not a clone, discard this one. Code_clones_count_by_clone_type: {code_clone_count_by_df} Clone_functions: {clone_functions} """ codeclonereport_txt = os.path.join(self.ccdreportpath,'code_clone_report.txt') with open(codeclonereport_txt, "w") as f: f.write(report_str) report_dict=dict({"Code_directory":code_dir,"total_files":total_files, "total_locs":total_locs,"total_functions":total_functions,"total_clones":total_clones, "total_tokens":total_tokens,"total_cost":total_cost, "Code_clones_count_by_clone_type":code_clone_count_dict,"clone_functions":clone_function_dict}) except Exception as e: self.log.info("Error in clone type and information retrival process .Error message: "+str(e)) return code_clone_result,report_str,report_dict def getCloneReport(self): """ To get the clone report from the given python directory """ try: self.log.info("To get clone report, we are calling embedding and chat model.") import time vdb_status = super().get_vdb_status('chromadb') start_time = time.time() # self.log.info("code clone detection start time."+str(start_time)) if not vdb_status: embedded_df,embedding_cost = self.get_embedd_fns() cluster_df = self.dbscan_clone_detection(embedded_df) cluster_df_group = cluster_df.groupby("cluster_id") len_cluster_df_group = len(cluster_df_group) code_clone_check_tasks = [] for name, group in cluster_df_group: res = self.make_pairs(group.to_dict(orient="records")) code_clone_check_tasks += res #For text-embedding-ada-002 and gpt 3.5 chat model code_clone_result,report_str,report_dict = self.getcloneresult_modelspecific(code_clone_check_tasks,embedding_cost) end_time = time.time() total_time_taken = end_time - start_time self.log.info("Total time taken for code clone detction: "+str(total_time_taken)) self.log.info("<------------- Final code clone report: -------------------> \n"+str(report_str)) report_df = pd.DataFrame.from_dict(report_dict, orient="index").reset_index() report_df.columns = ['ccd_properties', 'Values'] report_df=report_df.T codecloneresult_df = os.path.join(self.ccdreportpath,'code_clone_report_df.csv') report_df.to_csv(codecloneresult_df) return report_str,report_dict,report_df,json.dumps(report_str) else: #Below code indended for vector db. all_funcs,code_root,code_files,total_locs = super().get_clone_function_details() df = pd.DataFrame(all_funcs) df['filepath'] = df['filepath'].apply(lambda x: x.replace(code_root, "")) chromadb_df=super().chromadb_embedding(df) df = self.dbscan_clone_detection(chromadb_df) cluster_df_group = cluster_df.groupby("cluster_id") len_cluster_df_group = len(cluster_df_group) code_clone_check_tasks = [] for name, group in cluster_df_group: res = self.make_pairs(group.to_dict(orient="records")) code_clone_check_tasks += res code_clone_result = [] max_workers = min(len(code_clone_check_tasks), 100) with concurrent.futures.ThreadPoolExecutor(max_workers= max_workers) as executor: llm_requests = { executor.submit(self.code_clone_check_with_retry, task[0]["code"], task[1]["code"]): task for task in code_clone_check_tasks } with tqdm(total= len(llm_requests)) as progress: for future in concurrent.futures.as_completed(llm_requests): task = llm_requests[future] try: res = future.result() code_clone_result.append({"task": task, "result": json.loads(res['choices'][0]['message']['content']), "total_tokens": res["usage"]["total_tokens"]}) except Exception as exc: print('%r generated an exception: %s' % (task, exc)) progress.update() with open("code_clone_check_result.json", "w+") as fp: json.dump(code_clone_result, fp, indent=2) code_clone_result_json=json.dumps(code_clone_result) clone_report=pd.read_json(code_clone_result_json) total_amt = clone_report["total_tokens"].sum() * (0.002/1000) clone_report["function1"] = clone_report["task"].apply(lambda x: x[0]["filepath"] + " -> " + x[0]["function_name"]) clone_report["function2"] = clone_report["task"].apply(lambda x: x[1]["filepath"] + " -> " + x[1]["function_name"]) clone_report["clone_type"] = clone_report["result"].apply(lambda x: x["CloneType"]) all_funcs,code_root,code_files,total_locs = super().get_clone_function_details() code_dir = code_root total_files = len(code_files) total_locs = total_locs total_functions = len(all_funcs) total_tokens = clone_report["total_tokens"].sum() # total_cost= embedding_cost + clone_report["total_tokens"].sum() * (0.002/1000) total_clones = len(clone_report[clone_report.clone_type != "None"]) code_clone_count_by_df = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count')).to_markdown(tablefmt='psql') clone_functions = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1").to_markdown(tablefmt='psql', index=False) code_clone_count_dict = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count')) clone_function_dict = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1") ##Final report on code clone detection report_str = f"""Code_directory: {code_dir} Files: {total_files} LOCs: {total_locs} Functions: {total_functions} Total code clones detected: {total_clones} Tokens used: {total_tokens} Four types of clone: 1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces. 2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone. 3. Near-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones. 4. Semantic clone: The code snippets have different syntax but with alike functionality results in this clone. Code_clones_count_by_clone_type: {code_clone_count_by_df} Clone_functions: {clone_functions} """ with open("code_clone_report.txt", "w") as f: f.write(report_str) # print(report_str) self.log.info("<------------- Final code clone report: -------------------> \n"+str(report_str)) self.log.info("<------------- clone_functions code clone report: -------------------> \n"+str(clone_functions)) report_dict=dict({"Code_directory":code_dir,"total_files":total_files, "total_locs":total_locs,"total_functions":total_functions,"total_clones":total_clones, "total_tokens":total_tokens, "Code_clones_count_by_clone_type":code_clone_count_dict,"clone_functions": clone_function_dict}) report_df= pd.DataFrame([report_dict.keys(), report_dict.values()]).T report_df.columns = ["Code_directory", "total_files","total_locs","total_functions","total_clones","total_tokens","Code_clones_count_by_clone_type","clone_functions"] report_df.to_csv("code_clone_report_df.csv") return report_str,report_dict,report_df,json.dumps(report_str) except Exception as e: self.log.info("Error in clone detection function call. Error Message: \n"+str(e)) raise Exception("Error in clone detection function.") #For testing and code instance privacy if __name__=='__main__': ## For testing purpose.Uncomment n use. root_directory = r"C:\AION_Works\Anomaly_Detection\anomalydetectionpackage\code_clone_testing_pyfiles\code_clone_testing_pyfiles_large" embedd_storage_path = r"C:\AION_Works\ccddir" generativeai_credentials={'openai_baseurl':"", 'openai_key':"", 'openai_api_type':"", 'openai_api_version':"", 'generativeai_embedding_engine':"", 'generativeai_embedding_model':"", 'generativeai_chat_model':"", 'generativeai_deploymentId':""} openai_baseurl = generativeai_credentials['openai_baseurl'] openai_key = generativeai_credentials['openai_key'] openai_api_type = generativeai_credentials['openai_api_type'] openai_api_version = generativeai_credentials['openai_api_version'] generativeai_embedding_engine = generativeai_credentials['generativeai_embedding_engine'] generativeai_embedding_model = generativeai_credentials['generativeai_embedding_model'] generativeai_chat_model = generativeai_credentials['generativeai_chat_model'] generativeai_deploymentId = generativeai_credentials['generativeai_deploymentId'] codeclonedetection_obj = CodeCloneDetectionFiles(root_directory,openai_baseurl, openai_key,openai_api_type,openai_api_version,embedd_storage_path,generativeai_embedding_engine,generativeai_embedding_model,generativeai_chat_model,generativeai_deploymentId) report_str,report_dict,report_json = codeclonedetection_obj.getCloneReport() print("End of code clone detection....\n") ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import sqlite3 from pathlib import Path import json import os import rsa import boto3 #usnish import pandas as pd import time import sqlite3 class sqlite_db(): def __init__(self, location, database_file=None): if not isinstance(location, Path): location = Path(location) if database_file: self.database_name = database_file else: self.database_name = location.stem db_file = str(location/self.database_name) self.conn = sqlite3.connect(db_file) self.cursor = self.conn.cursor() def table_exists(self, name): query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';" listOfTables = self.cursor.execute(query).fetchall() return len(listOfTables) > 0 def read_data(self, table_name): query = f"SELECT * FROM {table_name}" row = self.cursor.execute(query).fetchall() return list(row) #return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn) def create_table(self,name, columns, dtypes): query = f'CREATE TABLE IF NOT EXISTS {name} (' for column, data_type in zip(columns, dtypes): query += f"'{column}' TEXT," query = query[:-1] query += ');' self.conn.execute(query) return True def delete_record(self,table_name,col_name, col_value): try: query = f"DELETE FROM {table_name} WHERE {col_name}='{col_value}'" self.conn.execute(query) self.conn.commit() return 'success' except Exception as e : print(str(e)) print("Deletion Failed") return 'error' def get_data(self,table_name,col_name,col_value): query = f"SELECT * FROM {table_name} WHERE {col_name}='{col_value}'" row = self.cursor.execute(query).fetchone() if(row == None): return [] return list(row) def write_data(self,data, table_name): if not self.table_exists(table_name): self.create_table(table_name, data.columns, data.dtypes) tuple_data = list(data.itertuples(index=False, name=None)) insert_query = f'INSERT INTO {table_name} VALUES(' for i in range(len(data.columns)): insert_query += '?,' insert_query = insert_query[:-1] + ')' self.cursor.executemany(insert_query, tuple_data) self.conn.commit() return True def close(self): self.conn.close() def add_new_azureStorage(request): try: from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') if request.POST["azurename"] =='' or request.POST["azureaccountkey"] == '' or request.POST["containername"] == '' : return 'error' newdata = {} newdata['azurename'] = [request.POST["azurename"]] newdata['azureaccountkey'] = [request.POST["azureaccountkey"]] newdata['containername'] = [request.POST["containername"]] name = request.POST["azurename"] if sqlite_obj.table_exists("azurebucket"): if(len(sqlite_obj.get_data('azurebucket','azurename',name))>0): return 'error1' sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'azurebucket') except: return 'error' def get_azureStorage(): try: from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') temp_data = sqlite_obj.read_data('azurebucket') data = [] for x in temp_data: data_dict = {} data_dict['azurename'] = x[0] data_dict['azureaccountkey'] = x[1] data_dict['containername'] = x[2] data.append(data_dict) except Exception as e: print(e) data = [] return data def read_azureStorage(name,directoryname,DATA_FILE_PATH): try: from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') data = sqlite_obj.get_data('azurebucket','azurename',name) except: data = [] found = False if len(data)!=0: storage_account_name = str(data[0]) storage_account_key = str(data[1]) azure_container_name = data[2] found = True try: if found: root_dir = str(directoryname) from azure.storage.filedatalake import DataLakeServiceClient import io import pandavro as pdx from detect_delimiter import detect try: service_client = DataLakeServiceClient(account_url="{}://{}.dfs.core.windows.net".format("https", storage_account_name), credential=storage_account_key) print(azure_container_name) file_system_client = service_client.get_file_system_client(azure_container_name) print(root_dir) file_paths = file_system_client.get_paths(path=root_dir) main_df = pd.DataFrame() for path in file_paths: if not path.is_directory: file_client = file_system_client.get_file_client(path.name) file_ext = os.path.basename(path.name).split('.', 1)[1] if file_ext in ["csv", "tsv"]: with open(csv_local, "wb") as my_file: download = file_client.download_file() download.readinto(my_file) with open(csv_local, 'r') as file: data = file.read() row_delimiter = detect(text=data, default=None, whitelist=[',', ';', ':', '|', '\t']) processed_df = pd.read_csv(csv_local, sep=row_delimiter) if file_ext == "parquet": download = file_client.download_file() stream = io.BytesIO() download.readinto(stream) processed_df = pd.read_parquet(stream, engine='pyarrow') if file_ext == "avro": with open(avro_local, "wb") as my_file: download = file_client.download_file() download.readinto(my_file) processed_df = pdx.read_avro(avro_local) if not main_df.empty: main_df = main_df.append(processed_df, ignore_index=True) else: main_df = pd.DataFrame(processed_df) except Exception as e: msg = str(e).split(".")[0] print(msg) return 'Error',str(msg), pd.DataFrame() return "Success","",main_df except: return 'Error',"Please check bucket configuration", pd.DataFrame() def remove_azure_bucket(name): from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') return sqlite_obj.delete_record('azurebucket','azurename',name) import json import os import random import time from avro.datafile import DataFileReader from avro.io import DatumReader from pyarrow.parquet import ParquetFile from snorkel.labeling.model import LabelModel from snorkel.labeling import PandasLFApplier, LFAnalysis import pandas as pd import pandavro as pdx import pyarrow as pa import numpy as np import platform from os.path import expanduser home = expanduser("~") if platform.system() == 'Windows': DATA_FILE_PATH = os.path.join(home,'AppData','Local','Programs','HCLTech','AION','data','storage') else: DATA_FILE_PATH = os.path.join(home,'HCLT','AION','data') def get_join(condition): if condition["join"] == 'and': return "&" elif condition["join"] == 'or': return "|" else: return "" def create_labelling_function(rule_list, label_list): lfs_main_func = 'def lfs_list_create():\n' lfs_main_func += '\tfrom snorkel.labeling import labeling_function\n' lfs_main_func += '\timport numpy as np\n' lfs_main_func += '\timport json\n' lfs_main_func += '\tABSTAIN = -1\n' lfs_main_func += '\tlabels = json.loads(json.dumps(' + json.dumps(label_list) + '))\n' lfs_list = '\tlfs_list=[' for rule in rule_list: lfs_list += 'lf_' + rule["rule_name"] + ',' lfs = '\t@labeling_function()\n' lfs += '\tdef lf_' + rule["rule_name"] + '(data):\n' lfs += '\t\treturn np.where(' for condition in rule["conditions"]: if "string" in condition["sel_datatype"]: if condition["sel_condition"] in ["==", "!="]: cond_statement = '(data["' + condition["sel_column"] + '"]' + condition[ "sel_condition"] + '("' + str(condition["input_value"]) + '"))' + get_join(condition) else: cond_statement = '(data["' + condition["sel_column"] + '"].' + condition[ "sel_condition"] + '("' + str(condition["input_value"]) + '"))' + get_join(condition) else: cond_statement = '(data["' + condition["sel_column"] + '"]' + condition["sel_condition"] + \ str(condition["input_value"]) + ')' + get_join(condition) lfs += cond_statement lfs += ', labels.index("' + rule["label"] + '"), ABSTAIN)\n' lfs_main_func += lfs if lfs_list.endswith(","): lfs_list = lfs_list.rstrip(lfs_list[-1]) lfs_list += ']\n' else: lfs_list += ']\n' lfs_main_func += lfs_list lfs_main_func += '\treturn lfs_list\n' lfs_main_func += 'lfs_list_create()' f = open(os.path.join(DATA_FILE_PATH, 'lfs_list.txt'), 'w') f.write(lfs_main_func) f.close() return lfs_main_func def label_dataset(rule_list, file_ext, label_list, not_satisfy_label): file_path = os.path.join(DATA_FILE_PATH, "uploaded_file." + file_ext) if file_ext in ["csv", "tsv"]: df = pd.read_csv(file_path) elif file_ext == "json": df = pd.json_normalize(pd.read_json(file_path).to_dict("records")) elif file_ext == "avro": reader = DataFileReader(open(file_path, "rb"), DatumReader()) schema = json.loads(reader.meta.get('avro.schema').decode('utf-8')) df = pdx.read_avro(file_path, schema=schema, na_dtypes=True) elif file_ext == "parquet": df = pd.read_parquet(file_path, engine="pyarrow") labelling_functions = create_labelling_function(rule_list, label_list) exec(labelling_functions) lfs = eval('lfs_list_create()') applier = PandasLFApplier(lfs) l_data = applier.apply(df) label_model = LabelModel(cardinality=len(label_list) + 1, verbose=True) label_model.fit(l_data, n_epochs=500, log_freq=50, seed=123) df["label"] = label_model.predict(L=l_data, tie_break_policy="abstain") df.loc[df["label"] == -1, "label"] = not_satisfy_label for item in label_list: df.loc[df["label"] == label_list.index(item), "label"] = item if file_ext in ["csv", "tsv"]: df.to_csv(os.path.join(DATA_FILE_PATH, "result_file." + file_ext), index=False) elif file_ext == "parquet": df.to_parquet(os.path.join(DATA_FILE_PATH, "result_file." + file_ext), engine="pyarrow", index=False) elif file_ext == "avro": pdx.to_avro(os.path.join(DATA_FILE_PATH, "result_file." + file_ext), df) else: raise ValueError("Invalid file format") num_records = len(df.index) size_take = 100 if num_records <= size_take: size_take = num_records display_df = df.sample(n=size_take) return display_df.to_html(classes='table table-striped text-left', justify='left', index=False) def create_sample_function(rule, label_list, not_satisfy_label): lfs_main_func = 'def lf_rule_apply(data):\n' lfs_main_func += '\timport numpy as np\n' lfs_main_func += '\tABSTAIN = -1\n' lfs_main_func += '\tlabels = json.loads(json.dumps(' + json.dumps(label_list) + '))\n' lfs = '\treturn np.where(' for condition in rule["conditions"]: if "string" in condition["sel_datatype"]: if condition["sel_condition"] in ["==", "!="]: cond_statement = '(data["' + condition["sel_column"] + '"]' + condition["sel_condition"] + '("' + str( condition["input_value"]) + '"))' + get_join(condition) else: cond_statement = '(data["' + condition["sel_column"] + '"].str.' + condition[ "sel_condition"] + '("' + str(condition["input_value"]) + '"))' + get_join(condition) print(cond_statement) else: cond_statement = '(data["' + condition["sel_column"] + '"]' + condition["sel_condition"] + \ str(condition["input_value"]) + ')' + get_join(condition) lfs += cond_statement lfs += ', "' + rule["label"] + '", "' + not_satisfy_label + '")\n' lfs_main_func += lfs return lfs_main_func def get_sample_result_of_individual_rule(rule_json, file_ext, label_list, not_satisfy_label): file_path = os.path.join(DATA_FILE_PATH, "uploaded_file." + file_ext) size_take = 100 if file_ext in ["csv", "tsv"]: num_records = sum(1 for line in open(file_path)) - 1 if num_records > size_take: skip = sorted(random.sample(range(1, num_records + 1), num_records - size_take)) else: skip = 0 df = pd.read_csv(file_path, skiprows=skip) elif file_path.endswith(".json"): df = pd.read_json(file_path) df = pd.json_normalize(df.to_dict("records")) elif file_path.endswith(".avro"): reader = DataFileReader(open(file_path, "rb"), DatumReader()) schema = json.loads(reader.meta.get('avro.schema').decode('utf-8')) df = pdx.read_avro(file_path, schema=schema, na_dtypes=True) elif file_path.endswith(".parquet"): pf = ParquetFile(file_path) take_rows = next(pf.iter_batches(batch_size=size_take)) df = pa.Table.from_batches([take_rows]).to_pandas() # file_content = pd.read_parquet(file_path, engine="pyarrow") else: raise ValueError("Invalid file format") rule_applier_func = create_sample_function(rule_json, label_list, not_satisfy_label) exec(rule_applier_func) df[rule_json["rule_name"]] = eval('lf_rule_apply')(df) return df.to_html(classes='table table-striped text-left', justify='left', index=False) def create_sample_function_ver2(rule_json, label_list, not_satisfy_label): lfs_main_func = 'def lf_rule_apply(data):\n' lfs_main_func += '\timport numpy as np\n' lfs_main_func += '\tABSTAIN = -1\n' lfs_main_func += '\tlabels = json.loads(json.dumps(' + json.dumps(label_list) + '))\n' counter = 0 for condition in rule_json["conditions"]: lfs_return = condition["sel_label"] if counter > 0: lfs_return_condition = '\telif' else: lfs_return_condition = '\tif' for label_condition in condition["label_condition"]: if label_condition["sel_datatype"] == "string": if label_condition["sel_condition"] == "contains": lfs_return_condition += '((' + str(label_condition["input_value"]) + ') in data["' + \ label_condition["sel_column"] + '"])' + get_join(label_condition) elif label_condition["sel_condition"] in ["==", "!="]: lfs_return_condition += '(data["' + label_condition["sel_column"] + '"]' + label_condition[ "sel_condition"] + '("' + str( label_condition["input_value"]) + '"))' + get_join(label_condition) else: lfs_return_condition += '(data["' + label_condition["sel_column"] + '"].' + label_condition[ "sel_condition"] + '("' + str(label_condition["input_value"]) + '"))' + get_join( label_condition) else: lfs_return_condition += '(data["' + label_condition["sel_column"] + '"]' + label_condition[ "sel_condition"] + str(label_condition["input_value"]) + ')' + get_join(label_condition) if get_join(label_condition) == "": lfs_return_condition += ":\n" lfs_return_condition += '\t\treturn "' + lfs_return + '"\n' lfs_main_func += lfs_return_condition counter += 1 lfs_return_condition = '\n\telse:\n' lfs_return_condition += '\t\treturn "' + not_satisfy_label + '"' lfs_main_func += lfs_return_condition return lfs_main_func def get_sample_result_of_individual_rule_ver2(rule_json, file_ext, label_list, not_satisfy_label): file_path = os.path.join(DATA_FILE_PATH, "uploaded_file." + file_ext) size_take = 100 if file_ext in ["csv", "tsv"]: num_records = sum(1 for line in open(file_path)) - 1 if num_records > size_take: skip = sorted(random.sample(range(1, num_records + 1), num_records - size_take)) else: skip = 0 df = pd.read_csv(file_path, skiprows=skip) elif file_path.endswith(".json"): df = pd.read_json(file_path) df = pd.json_normalize(df.to_dict("records")) elif file_path.endswith(".avro"): reader = DataFileReader(open(file_path, "rb"), DatumReader()) schema = json.loads(reader.meta.get('avro.schema').decode('utf-8')) df = pdx.read_avro(file_path, schema=schema, na_dtypes=True) elif file_path.endswith(".parquet"): pf = ParquetFile(file_path) take_rows = next(pf.iter_batches(batch_size=size_take)) df = pa.Table.from_batches([take_rows]).to_pandas() # file_content = pd.read_parquet(file_path, engine="pyarrow") else: raise ValueError("Invalid file format") rule_applier_func = create_sample_function_ver2(rule_json, label_list, not_satisfy_label) exec(rule_applier_func) df[rule_json["rule_name"]] = df.apply(eval('lf_rule_apply'), axis=1) return df.to_html(classes='table table-striped text-left', justify='left', index=False) def create_labelling_function_ver2(rule_list, label_list): lfs_main_func = 'def lfs_list_create():\n' lfs_main_func += '\tfrom snorkel.labeling import labeling_function\n' lfs_main_func += '\timport numpy as np\n' lfs_main_func += '\timport json\n' lfs_main_func += '\tABSTAIN = -1\n' lfs_main_func += '\tlabels = json.loads(json.dumps(' + json.dumps(label_list) + '))\n' lfs_list = '\tlfs_list=[' for rule in rule_list: lfs_list += 'lf_' + rule["rule_name"] + ',' lfs = '\t@labeling_function()\n' lfs += '\tdef lf_' + rule["rule_name"] + '(data):\n' counter = 0 for condition in rule["conditions"]: lfs_return = 'labels.index("' + condition["sel_label"] + '")' if counter > 0: lfs_return_condition = '\t\telif' else: lfs_return_condition = '\t\tif' for label_condition in condition["label_condition"]: if label_condition["sel_datatype"] == "string": if label_condition["sel_condition"] == "contains": lfs_return_condition += '((' + str(label_condition["input_value"]) + ') in data["' + \ label_condition["sel_column"] + '"])' + get_join(label_condition) elif label_condition["sel_condition"] in ["==", "!="]: lfs_return_condition += '(data["' + label_condition["sel_column"] + '"]' + label_condition[ "sel_condition"] + '("' + str( label_condition["input_value"]) + '"))' + get_join(label_condition) else: lfs_return_condition += '(data["' + label_condition["sel_column"] + '"].' + label_condition[ "sel_condition"] + '("' + str(label_condition["input_value"]) + '"))' + get_join( label_condition) else: lfs_return_condition += '(data["' + label_condition["sel_column"] + '"]' + label_condition[ "sel_condition"] + str(label_condition["input_value"]) + ')' + get_join(label_condition) if get_join(label_condition) == "": lfs_return_condition += ":\n" lfs_return_condition += '\t\t\treturn ' + lfs_return + '\n' lfs += lfs_return_condition counter += 1 lfs_return_condition = '\n\t\telse:\n' lfs_return_condition += '\t\t\treturn ABSTAIN\n' lfs += lfs_return_condition lfs_main_func += lfs if lfs_list.endswith(","): lfs_list = lfs_list.rstrip(lfs_list[-1]) lfs_list += ']\n' else: lfs_list += ']\n' lfs_main_func += lfs_list lfs_main_func += '\treturn lfs_list\n' lfs_main_func += 'lfs_list_create()' # f = open(os.path.join(DATA_FILE_PATH, 'lfs_list.txt'), 'w') # f.write(lfs_main_func) # f.close() return lfs_main_func def get_rule_name_list(rule_list): rule_name_list = [] for rule in rule_list: rule_name_list.append(rule["rule_name"]) return rule_name_list def label_dataset_ver2(request,rule_list, file_ext, label_list, not_satisfy_label, label_weightage, include_proba): file_path = os.path.join(DATA_FILE_PATH, "uploaded_file." + file_ext) if file_ext in ["csv", "tsv"]: df = pd.read_csv(file_path) elif file_ext == "json": df = pd.json_normalize(pd.read_json(file_path).to_dict("records")) elif file_ext == "avro": reader = DataFileReader(open(file_path, "rb"), DatumReader()) schema = json.loads(reader.meta.get('avro.schema').decode('utf-8')) df = pdx.read_avro(file_path, schema=schema, na_dtypes=True) elif file_ext == "parquet": df = pd.read_parquet(file_path, engine="pyarrow") labelling_functions = create_labelling_function_ver2(rule_list, label_list) exec(labelling_functions) lfs = eval('lfs_list_create()') applier = PandasLFApplier(lfs) l_data = applier.apply(df) label_model = LabelModel(cardinality=len(label_list), verbose=True) label_model.fit(l_data, n_epochs=500, log_freq=50, seed=123, class_balance=label_weightage) df["label"] = label_model.predict(L=l_data, tie_break_policy="abstain") if include_proba: prediction_of_prob = label_model.predict_proba(L=l_data) for label in label_list: df[label + "_prob"] = np.around(prediction_of_prob[:, label_list.index(label)], 2) * 100 df.loc[df["label"] == -1, "label"] = not_satisfy_label filetimestamp = str(int(time.time())) datasetName = "AION_labelled_"+filetimestamp + '.' + file_ext request.session['AION_labelled_Dataset'] = datasetName for item in label_list: df.loc[df["label"] == label_list.index(item), "label"] = item if file_ext in ["csv", "tsv"]: df.to_csv(os.path.join(DATA_FILE_PATH, datasetName), index=False) elif file_ext == "parquet": df.to_parquet(os.path.join(DATA_FILE_PATH, datasetName), engine="pyarrow", index=False) elif file_ext == "avro": pdx.to_avro(os.path.join(DATA_FILE_PATH, datasetName), df) else: raise ValueError("Invalid file format") #### saving file to database from appbe.dataPath import DATA_DIR from appbe.sqliteUtility import sqlite_db file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') newdata = {} newdata['datapath'] = [os.path.join(DATA_FILE_PATH, datasetName)] newdata['datasetname'] = [datasetName] sqlite_obj.write_data(pd.DataFrame.from_dict(newdata), 'dataingest') num_records = len(df.index) size_take = 100 if num_records <= size_take: size_take = num_records display_df = df.sample(n=size_take) weightage = np.around(label_model.get_weights(), 2) rule_name_list = get_rule_name_list(rule_list) analysis_df = LFAnalysis(l_data, lfs).lf_summary() analysis_df["Rule"] = analysis_df.index analysis_df["Rule"] = analysis_df["Rule"].str.replace("lf_", "") analysis_df = analysis_df[["Rule", "Polarity", "Coverage", "Overlaps", "Conflicts"]] weightage_dict = dict(zip(rule_name_list, weightage)) analysis_json = analysis_df.to_dict(orient="records") for item in analysis_json: item["Weightage"] = weightage_dict[item["Rule"]] analysis_df = pd.json_normalize(analysis_json) # rules_weightage = [] # for key in weightage_dict: # rules_weightage.append({ # "label": key, # "y": weightage_dict[key], # "legendText": key # }) response = { # "rule_name_list": rule_name_list, # "weightage_list": list(weightage), "analysis_df": analysis_df.to_html(classes='table table-striped text-left', justify='left', index=False), "result_html": display_df.to_html(classes='table table-striped text-left', justify='left', index=False) } return response def get_label_and_weightage(test_file_ext, marked_label_column,file_delim_test, custom_test_delim ): file_path = os.path.join(DATA_FILE_PATH, "test_data_file." + test_file_ext) if test_file_ext in ["csv", "tsv"]: df = pd.read_csv(file_path) elif test_file_ext == "json": df = pd.json_normalize(pd.read_json(file_path).to_dict("records")) elif test_file_ext == "avro": reader = DataFileReader(open(file_path, "rb"), DatumReader()) schema = json.loads(reader.meta.get('avro.schema').decode('utf-8')) df = pdx.read_avro(file_path, schema=schema, na_dtypes=True) elif test_file_ext == "parquet": df = pd.read_parquet(file_path, engine="pyarrow") json_df = pd.DataFrame(df[marked_label_column].value_counts(normalize=True) * 100) json_dict = json.loads(json_df.to_json()) label_with_weightage = [] for k in json_dict[marked_label_column]: label_with_weightage.append( {"label_name": k, "label_weightage": np.around(json_dict[marked_label_column][k], 2)}) return label_with_weightage ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json import os import rsa import boto3 #usnish import pandas as pd import time def add_new_bucket(request): try: file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','s3bucket.conf')) with open(file_path, 'r') as f: data = json.load(f) except: data = [] if request.POST["aionreferencename"] =='' or request.POST["s3bucketname"] == '' or request.POST["awsaccesskey"] == '' : return 'error' pkeydata='''-----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAxIHM1FphEMMwViUrG0b2Bqf8tOxbhUWlnmjgFt5A25qbY1AfnrMv fVx8+7iCcZ/3TY9Jv2I584SOc1tvsgESCke/t6+o/u2esPBsnDFzV62l3Zvw0m4e wQeKlFC8EoOblyIXRbZdelSJinzlr9lOiKuid/xPvXHou6jxF1A2W7a89A2PM4Re n0W9YkjB7dRGW1sSrpruHdVJvgHhGZFZ7sCTue0jVOnc5sT3Tq5saLfEDqHyKxlq i/mcThmcTfisRIYFH5pyt/Ysr4VVP924QlcoqPOyg3RMCS3G0VjstSoVwNhxWrs/ lujDuCnpxvWzNpq21OWmF66GXxwiq+6W0wIDAQAB -----END RSA PUBLIC KEY-----''' pubkey = rsa.PublicKey.load_pkcs1(pkeydata) awssecretaccesskey = rsa.encrypt(request.POST["awssecretaccesskey"].encode(), pubkey) print(awssecretaccesskey) newdata = {} newdata['Name'] = request.POST["aionreferencename"] newdata['AWSAccessKeyID'] = request.POST["awsaccesskey"] newdata['AWSSecretAccessKey'] = str(awssecretaccesskey) newdata['S3BucketName'] = request.POST["s3bucketname"] data.append(newdata) with open(file_path, 'w') as f: json.dump(data, f) f.close() def get_s3_bucket(): try: file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','s3bucket.conf')) with open(file_path, 'r') as f: data = json.load(f) except: data = [] return data def read_s3_bucket(name,filename,DATA_FILE_PATH): privkey = '''-----BEGIN RSA PRIVATE KEY----- MIIEqQIBAAKCAQEAxIHM1FphEMMwViUrG0b2Bqf8tOxbhUWlnmjgFt5A25qbY1Af nrMvfVx8+7iCcZ/3TY9Jv2I584SOc1tvsgESCke/t6+o/u2esPBsnDFzV62l3Zvw 0m4ewQeKlFC8EoOblyIXRbZdelSJinzlr9lOiKuid/xPvXHou6jxF1A2W7a89A2P M4Ren0W9YkjB7dRGW1sSrpruHdVJvgHhGZFZ7sCTue0jVOnc5sT3Tq5saLfEDqHy Kxlqi/mcThmcTfisRIYFH5pyt/Ysr4VVP924QlcoqPOyg3RMCS3G0VjstSoVwNhx Wrs/lujDuCnpxvWzNpq21OWmF66GXxwiq+6W0wIDAQABAoIBAC/VbNfQPEqJSO3f VFPqfR73q2MbGdgiMQOTgeDvLxiF1QdizJ+j/I5mgiIAMviXuOpPU+NbdMHbZZWd D15kNlD8UCXVg6yyiOuHStjmjK4uHe8I86E1nxTb0hbyZCWZlbk/WizlDHInu+dT KdIZcq2AIidU6tAxtwA0ingHaRSoXDlSGwOTEigNqmWOKnDTVg0SMscoHOD7siXF DHm1/lkvD3uvcZk6c7fGxC8SgNX2dj6n/Nbuy0Em+bJ0Ya5wq4HFdLJn3EHZYORF ODUDYoGaSxeXqYsGg/KHJBc8J7xW9FdN9fGbHfw1YplrmiGL3daATtArjMmAh0EQ H8Sj7+ECgYkA3oWMCHi+4t8txRPkg1Fwt8dcqYhGtqpAus3NESVurAdi0ZPqEJcQ 4cUbflwQPhX0TOaBlkgzdP8DMdcW/4RalxHsAh5N8ezx/97PQMb3Bht0WsQUBeYJ xLV7T2astjTRWactGCG7dwTaUYRtU3FqL6//3CysmA12B5EMX0udNBOTKwmaYKww AwJ5AOISS7f12Q0fgTEVY0H8Zu5hHXNOA7DN92BUzf99iPx+H+codLet4Ut4Eh0C cFmjA3TC78oirp5mOOQmYxwaFaxlZ7Rs60dlPFrhz0rsHYPK1yUOWRr3RcXWSR13 r+kn+f+8k7nItfGi7shdcQW+adm/EqPfwTHM8QKBiQCIPEMrvKFBzVn8Wt2A+I+G NOyqbuC8XSgcNnvij4RelncN0P1xAsw3LbJTfpIDMPXNTyLvm2zFqIuQLBvMfH/q FfLkqSEXiPXwrb0975K1joGCQKHxqpE4edPxHO+I7nVt6khVifF4QORZHDbC66ET aTHA3ykcPsGQiGGGxoiMpZ9orgxyO3l5Anh92jmU26RNjfBZ5tIu9dhHdID0o8Wi M8c3NX7IcJZGGeCgywDPEFmPrfRHeggZnopaAfuDx/L182pQeJ5MEqlmI72rz8bb JByJa5P+3ZtAtzc2RdqNDIMnM7fYU7z2S279U3nZv0aqkk3j9UDqNaqvsZMq73GZ y8ECgYgoeJDi+YyVtqgzXyDTLv6MNWKna9LQZlbkRLcpg6ELRnb5F/dL/eB/D0Sx QpUFi8ZqBWL+A/TvgrCrTSIrfk71CKv6h1CGAS02dXorYro86KBLbJ0yp1T/WJUj rHrGHczglvoB+5stY/EpquNpyca03GcutgIi9P2IsTIuFdnUgjc7t96WEQwL -----END RSA PRIVATE KEY-----''' try: file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','s3bucket.conf')) with open(file_path, 'r') as f: data = json.load(f) except: data = [] awssecretaccesskey = '' found = False for x in data: if x['Name'] == name: awssecretaccesskey = x['AWSSecretAccessKey'] aws_access_key_id = x['AWSAccessKeyID'] bucketName = x['S3BucketName'] found = True break if found: privkey = rsa.PrivateKey.load_pkcs1(privkey,'PEM') awssecretaccesskey = eval(awssecretaccesskey) awssecretaccesskey = rsa.decrypt(awssecretaccesskey, privkey) awssecretaccesskey = awssecretaccesskey.decode('utf-8') #awssecretaccesskey = 'SGcyJavYEQPwTbOg1ikqThT+Op/ZNsk7UkRCpt9g'#rsa.decrypt(awssecretaccesskey, privkey) client_s3 = boto3.client('s3', aws_access_key_id=aws_access_key_id, aws_secret_access_key=str(awssecretaccesskey)) #print(bucketName,filename) try: response = client_s3.get_object(Bucket=bucketName, Key=filename) df = pd.read_csv(response['Body']) except Exception as e: print(e)#usnish return 'Error', pd.DataFrame() #return 'Error', pd.DataFrame() return 'Success',df return 'Error', pd.DataFrame() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pyodbc as pyodbc import pandas as pd import json import sqlalchemy as db import pandas as pd import urllib def get_connection(request): dbType = request.session['dbType'] connection_string = "" if dbType.lower()=="sqlite": filepath = request.session['filepath'] #table = request.session["tablenamesql"] connection_string = "sqlite:///"+str(filepath) elif dbType.lower() in ["postgresql","mysql","mssql"]: db_name = request.session['dbname'] password = request.session['password'] user = request.session['username'] port = request.session['port'] host = request.session['host'] password=urllib.parse.quote_plus(password) if dbType.lower()=="postgresql": connection_string = "postgresql+psycopg2://" + user + ":" + password + "@" + host + ":" + port + "/" + db_name if dbType.lower()=="mysql": connection_string = "mysql+pyodbc://" + user + ":" + password + "@" + host + ":" + port + "/" + db_name if dbType.lower()=="mssql": driver=request.session['driver'] params = urllib.parse.quote_plus( 'Driver=%s;' % driver + 'Server=tcp:%s,' % host + '%s;' % port + 'Database=%s;' % db_name + 'Uid=%s;' % user + 'Pwd={%s};' % password + 'Encrypt=yes;' + 'TrustServerCertificate=no;' + 'Connection Timeout=30;') connection_string = 'mssql+pyodbc:///?odbc_connect=' + params return connection_string def list_tables(request): connection_string = get_connection(request) engine = db.create_engine(connection_string) connection = engine.connect() metadata = db.MetaData() metadata.reflect(engine) dt_list = [] try: dt_list= list(metadata.tables.keys()) print(dt_list) return dt_list except: print("Something went wrong") return dt_list def list_tables_fields(request,table_list): connection_string = get_connection(request) engine = db.create_engine(connection_string) connection = engine.connect() metadata = db.MetaData() metadata.reflect(engine) table_field_obj = {} table_field_obj['data'] = [] try: # filepath = request.session['filepath'] #table = request.session["tablenamesql"] table_list = json.loads(table_list) for table in table_list: tf_obj = {} tf_obj['TableName'] = str(table).strip() tf_obj['Fields']= [] table = db.Table(table, metadata, autoload=True, autoload_with=engine) col = table.columns.keys() tempdata = [] for x in col: my_list = {"column_name": x,"is_select":"false"} tempdata.append(my_list) tf_obj['Fields'] = tempdata table_field_obj['data'].append(tf_obj) return json.dumps(table_field_obj) except Exception as e: print("Something went wrong "+str(e)) return table_field_obj def get_data(connection_string,table): engine = db.create_engine(connection_string) connection = engine.connect() metadata = db.MetaData() metadata.reflect(engine) table = db.Table(table,metadata, autoload=True, autoload_with=engine) query = db.select([table]) ResultProxy = connection.execute(query) ResultSet = ResultProxy.fetchall() col = table.columns.keys() return pd.DataFrame(ResultSet, columns=col) def getDataFromSingleTable(request): dbType = request.session['dbType'] if dbType.lower() == "sqlite": table = request.session["tablenamesql"] else: table = request.session["tablename"] connection_string = get_connection(request) df = get_data(connection_string,table) return df def validatequery(request,table_details,join_details,where_details): resultdata = [] try: table_details = json.loads(table_details) join_details = json.loads(join_details) where_details = json.loads(where_details) connection_string = get_connection(request) engine = db.create_engine(connection_string) connection = engine.connect() metadata = db.MetaData() metadata.reflect(engine) sel_col = [] for item in table_details: table = item["TableName"] table = db.Table(table, metadata, autoload=True, autoload_with=engine) for ele in item["Fields"]: if str(ele["is_select"]).lower() == 'true': sel_col.append(table.columns[ele["column_name"]]) join_condition = [] where_clause = "" for item in join_details: table1 = item["Table1Name"] table1 = db.Table(table1, metadata, autoload=True, autoload_with=engine) left_join = table1.columns[item["Table1Field"]] table2 = item["Table2Name"] table2 = db.Table(table2, metadata, autoload=True, autoload_with=engine) right_join = table2.columns[item["Table2Field"]] join_condition = "{left_join} {Condition}= {right_join}".format(left_join=left_join, Condition=item["Condition"],right_join= right_join) '''dbType = request.session['dbType'] if dbType.lower()=="sqlite": for item in where_details: where_clause = "{table}.'{column}'{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"]) if dbType.lower()=="postgresql": for item in where_details: where_clause = "{table}.{column}{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"]) ''' if len(join_details)!=0: try: for item in where_details: where_clause = "{table}.'{column}'{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"]) query =db.select(sel_col).\ select_from(table1.join(table2,db.text(join_condition))). \ where(db.and_(db.text(where_clause))) ResultProxy = connection.execute(query) ResultSet = ResultProxy.fetchall() except: for item in where_details: where_clause = "{table}.{column}{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"]) query =db.select(sel_col).\ select_from(table1.join(table2,db.text(join_condition))). \ where(db.and_(db.text(where_clause))) ResultProxy = connection.execute(query) ResultSet = ResultProxy.fetchall() else: table = table_details[0]["TableName"] table = db.Table(table, metadata, autoload=True, autoload_with=engine) try: for item in where_details: where_clause = "{table}.'{column}'{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"]) query = db.select(sel_col). \ select_from(table). \ where(db.and_(db.text(where_clause))) ResultProxy = connection.execute(query) ResultSet = ResultProxy.fetchall() except: for item in where_details: where_clause = "{table}.{column}{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"]) query = db.select(sel_col). \ select_from(table). \ where(db.and_(db.text(where_clause))) ResultProxy = connection.execute(query) ResultSet = ResultProxy.fetchall() if len(ResultSet) > 0: data = pd.DataFrame(ResultSet) data.columns = ResultSet[0].keys() print(data) return data,"query exectuted successfully" else: return pd.DataFrame(),"No rows returned" # conn = get_connection(server_url,username_actian,password_actian,database_actian) # sql_text = query # cur = conn.cursor() # resultdata = simple_select(cur, query) # cur.close() #df = pd.DataFrame(resultdata) #print(df) except Exception as e: print(e) return pd.DataFrame(), str(e) import json import os import pandas as pd import urllib, base64 def check_deepCheckPlots(deployedLocation): deepCheck = 'False' boostOverfit = 'False' boostOverfitCond = 'False' mi='False' miCond='False' smc = 'False' smsCond = 'False' boostOverfitFile= os.path.join(deployedLocation,'log','boosting_overfit.html') boostOverfitCondFile= os.path.join(deployedLocation,'log','boosting_overfit_condition.html') smcFile= os.path.join(deployedLocation,'log','smc.html') smcCondFile= os.path.join(deployedLocation,'log','smc_condition.html') miFile= os.path.join(deployedLocation,'log','mi.html') miConFile= os.path.join(deployedLocation,'log','mi_con.html') file_exists = os.path.exists(boostOverfitFile) if file_exists: deepCheck = 'True' boostOverfit = 'True' file_exists = os.path.exists(boostOverfitCondFile) if file_exists: deepCheck = 'True' boostOverfitCond = 'True' file_exists = os.path.exists(miFile) if file_exists: deepCheck = 'True' mi = 'True' file_exists = os.path.exists(miConFile) if file_exists: deepCheck = 'True' miCond = 'True' file_exists = os.path.exists(smcFile) if file_exists: deepCheck = 'True' smc = 'True' file_exists = os.path.exists(smcCondFile) if file_exists: deepCheck = 'True' smsCond = 'True' output = {'deepCheck':deepCheck,'boostOverfit':boostOverfit,'boostOverfitCond':boostOverfitCond,'mi':mi,'miCond':miCond,'smc':smc,'smsCond':smsCond} return output def FeaturesUsedForTraining(output_json): resultJsonObj = json.loads(output_json) result = {} result['Status'] = resultJsonObj['status'] result['ModelType'] = resultJsonObj['data']['ModelType'] result['ScoreType'] = resultJsonObj['data']['ScoreType'] result['FeaturesUsed'] = resultJsonObj['data']['featuresused'] result['BestModel'] = resultJsonObj['data']['BestModel'] return result def ParseResults(output_json): msg1 = 'Results...' resultJsonObj = json.loads(output_json) result = {} survical_images = [] result['Status'] = resultJsonObj['status'] result['ModelType'] = resultJsonObj['data']['ModelType'] if 'vmDetails' in resultJsonObj['data']: result['DeployLocation'] = resultJsonObj['data']['vmDetails'] else: result['DeployLocation'] = resultJsonObj['data']['deployLocation'] result['BestModel'] = resultJsonObj['data']['BestModel'] if str(resultJsonObj['data']['BestScore']) == "NA": result['BestScore'] = 'NA' else: result['BestScore'] = round(float(resultJsonObj['data']['BestScore']), 2) result['ScoreType'] = resultJsonObj['data']['ScoreType'] result['FeaturesUsed'] = resultJsonObj['data']['featuresused'] ##### Training Confusion Matrix result['problem_type'] = result['ModelType'] if result['ModelType'].lower() == 'timeseriesanomalydetection': result['problem_type'] = 'TimeSeriesAnomalydetection' if result['ModelType'] == 'classification' or result['ModelType'].lower() == 'distributed classification' or (result['ModelType'] == 'anomalydetection' and (result['BestScore']) != 0) or result['ModelType'] == 'ImageClassification': bestmodel = resultJsonObj['data']['BestModel'] if bestmodel.lower() == 'nas': modelSummary= os.path.join(result['DeployLocation'],'summary.txt') f = open(modelSummary, 'r') file_content = f.read() f.close() #print(file_content) result['modelSummary'] = file_content #task 11997 if result['ModelType'].lower() == 'classification': result['problem_type'] = 'Classification' elif result['ModelType'].lower() == 'anomalydetection': result['problem_type'] = 'AnomalyDetection' elif result['ModelType'].lower() == 'imageclassification': result['problem_type'] = 'ImageClassification' elif result['ModelType'].lower() == 'distributed classification': result['problem_type'] = 'Distributed Classification' try: result['deepCheck'] = check_deepCheckPlots(result['DeployLocation']) except Exception as e: print(e) if 'ConfusionMatrix' in resultJsonObj['data']['trainmatrix']: TrainConfusionMatrix = resultJsonObj['data']['trainmatrix']['ConfusionMatrix'] numLabels = len(TrainConfusionMatrix) TrainConfusionMatrixList = [] for act_key, value in TrainConfusionMatrix.items(): temp = {} temp['Label'] = act_key for pred_key, pred_value in value.items(): temp[pred_key] = pred_value TrainConfusionMatrixList.append(temp) result['TrainConfusionMatrix'] = TrainConfusionMatrixList TrainClassificationReport = resultJsonObj['data']['trainmatrix']['ClassificationReport'] numRows = len(TrainClassificationReport) TrainClassificationReportList = [] metrics_keys_list = [] for key, value in TrainClassificationReport.items(): temp = {} temp['Label'] = key if isinstance( value, dict): for metricsKey, metricsValue in value.items(): temp[metricsKey] = round(metricsValue, 4) if metricsKey not in metrics_keys_list: metrics_keys_list.append( metricsKey) else: if metrics_keys_list: for key in metrics_keys_list: temp[key] = round(value, 4) TrainClassificationReportList.append(temp) result['TrainClassificationReport'] = TrainClassificationReportList result['Train_ROC_AUC_SCORE'] = round(float(resultJsonObj['data']['trainmatrix']['ROC_AUC_SCORE']), 4) else: result['TrainClassificationReport'] = '' result['Train_ROC_AUC_SCORE']='' ##### Testing Confusion Matix if 'ConfusionMatrix' in resultJsonObj['data']['matrix']: ConfusionMatrix = resultJsonObj['data']['matrix']['ConfusionMatrix'] numLabels = len(ConfusionMatrix) ConfusionMatrixList = [] for act_key, value in ConfusionMatrix.items(): temp = {} temp['Label'] = act_key for pred_key, pred_value in value.items(): temp[pred_key] = pred_value ConfusionMatrixList.append(temp) result['ConfusionMatrix'] = ConfusionMatrixList ClassificationReport = resultJsonObj['data']['matrix']['ClassificationReport'] numRows = len(ClassificationReport) ClassificationReportList = [] metrics_keys_list = [] for key, value in ClassificationReport.items(): temp = {} temp['Label'] = key if isinstance( value, dict): for metricsKey, metricsValue in value.items(): temp[metricsKey] = round(metricsValue, 4) if metricsKey not in metrics_keys_list: metrics_keys_list.append( metricsKey) else: if metrics_keys_list: for key in metrics_keys_list: temp[key] = round(value, 4) ClassificationReportList.append(temp) result['ClassificationReport'] = ClassificationReportList result['ROC_AUC_SCORE'] = round(float(resultJsonObj['data']['matrix']['ROC_AUC_SCORE']), 4) elif result['ModelType'] == 'similarityIdentification': result['problem_type'] = 'similarityIdentification' elif result['ModelType'] == 'contextualSearch': result['problem_type'] = 'contextualSearch' elif result['ModelType'] == 'MultiLabelPrediction': result['problem_type'] = 'MultiLabelPrediction' matrix = resultJsonObj['data']['matrix'] training_matrix = [] for x in matrix: fmatrix = {} fmatrix['feature'] = x performance = {} for y in matrix[x]: performance[y] = matrix[x][y] fmatrix['performance'] = performance training_matrix.append(fmatrix) testmatrix = resultJsonObj['data']['testmatrix'] testing_matrix = [] for x in testmatrix: fmatrix = {} fmatrix['feature'] = x performance = {} for y in testmatrix[x]: performance[y] = testmatrix[x][y] fmatrix['performance'] = performance testing_matrix.append(fmatrix) result['testing_matrix'] = testing_matrix result['training_matrix'] = training_matrix elif result['ModelType'] == 'regression' or result['ModelType'].lower() == 'distributed regression': try: result['deepCheck'] = check_deepCheckPlots(result['DeployLocation']) except Exception as e: print(e) try: result['problem_type'] = 'Regression' testing_matrix = {} if 'MAE' in resultJsonObj['data']['matrix']: testing_matrix['MAE'] = float(resultJsonObj['data']['matrix'].get('MAE','0')) testing_matrix['R2Score'] = float(resultJsonObj['data']['matrix'].get('R2Score','0')) testing_matrix['MSE'] = float(resultJsonObj['data']['matrix'].get('MSE','0')) testing_matrix['MAPE'] = float(resultJsonObj['data']['matrix'].get('MAPE','0')) testing_matrix['RMSE'] = float(resultJsonObj['data']['matrix'].get('RMSE','0')) testing_matrix['NormalisedRMSEPercentage'] = float(resultJsonObj['data']['matrix'].get('Normalised RMSE(%)','0')) result['testing_matrix'] = testing_matrix training_matrix = {} training_matrix['MAE'] = float(resultJsonObj['data']['trainmatrix'].get('MAE','0')) training_matrix['R2Score'] = float(resultJsonObj['data']['trainmatrix'].get('R2Score','0')) training_matrix['MSE'] = float(resultJsonObj['data']['trainmatrix'].get('MSE','0')) training_matrix['MAPE'] = float(resultJsonObj['data']['trainmatrix'].get('MAPE','0')) training_matrix['RMSE'] = float(resultJsonObj['data']['trainmatrix'].get('RMSE','0')) training_matrix['NormalisedRMSEPercentage'] = float(resultJsonObj['data']['trainmatrix'].get('Normalised RMSE(%)','0')) result['training_matrix'] = training_matrix except Exception as e: print(e) elif result['ModelType'] == 'Text Similarity': result['problem_type'] = 'textsimilarity' testing_matrix = {} testing_matrix['Accuracy'] = float(resultJsonObj['data']['matrix']['Accuracy']) testing_matrix['ROC_AUC'] = float(resultJsonObj['data']['matrix']['ROC AUC']) result['testing_matrix'] = testing_matrix training_matrix = {} training_matrix['Accuracy'] = float(resultJsonObj['data']['trainmatrix']['Accuracy']) training_matrix['ROC_AUC'] = float(resultJsonObj['data']['trainmatrix']['ROC AUC']) result['training_matrix'] = training_matrix elif result['ModelType'] == 'RecommenderSystem': #taskid 11190 result['problem_type'] = 'Recommender' testing_matrix = {} testing_matrix['RMSE'] = 'NA' result['testing_matrix'] = testing_matrix training_matrix = {} training_matrix['RMSE'] = 'NA' result['training_matrix'] = training_matrix elif result['ModelType'] == 'SurvivalAnalysis': result['problem_type'] = 'SurvivalAnalysis' survivalProbabilityjson = resultJsonObj['data']['survivalProbability'] performanceimages = resultJsonObj['data']['imageLocation'] start = '[' end = ']' performanceimages = performanceimages[performanceimages.find(start) + len(start):performanceimages.rfind(end)] performanceimages = performanceimages.split(',') for imagefile in performanceimages: imagefile = imagefile.replace("'", "") string = base64.b64encode(open(imagefile, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) survical_images.append(image_64) result['survivalProbability'] = survivalProbabilityjson elif result['ModelType'] == 'StateTransition': result['problem_type'] = 'StateTransition' stateprobabilityfile = os.path.join(result['DeployLocation'],'stateTransitionProbability.csv') clusterfile = os.path.join(result['DeployLocation'],'stateClustering.csv') if(os.path.isfile(stateprobabilityfile)): df_prob = pd.read_csv(stateprobabilityfile) df_prob = df_prob[['State','NextState','Probability']] result['probability'] = df_prob if(os.path.isfile(clusterfile)): df_clus = pd.read_csv(clusterfile) df_clus = df_clus[['clusterid','clusterlist']] result['cluster'] = df_clus elif result['ModelType'].lower() == 'timeseriesforecasting': #task 11997 result['problem_type'] = 'TimeSeriesForecasting' if result['BestModel'] == 'FBPROPHET': imagefile = os.path.join(result['DeployLocation'],'log','img','prophet_fig.png') string = base64.b64encode(open(imagefile, "rb").read()) image_64 = 'data:image/png;base64,' + urllib.parse.quote(string) survical_images.append(image_64) testing_matrix = {} testing_matrix['MAE'] = float(resultJsonObj['data']['matrix']['MAE']) testing_matrix['MSE'] = float(resultJsonObj['data']['matrix']['MSE']) testing_matrix['R2'] = float(resultJsonObj['data']['matrix']['R2']) testing_matrix['RMSE'] = float(resultJsonObj['data']['matrix']['RMSE']) result['testing_matrix'] = testing_matrix forecastjson = resultJsonObj['data']['forecasts'] result['forecast'] = forecastjson if result['BestModel'] == 'VAR': ''' FeaturesMatrix = resultJsonObj['data']['matrix']['FeaturesMatrix'] mae = '' mse = '' mape = '' rmse = '' for x in FeaturesMatrix: if mae != '': mae += ',' if mse != '': mse += ',' if R2 != '': R2 += ',' if rmse != '': rmse += ',' featurename = x['Features'] mae = mae + featurename + '=' + x['MAE'] mse = mse + featurename + '=' + x['MSE'] R2 = R2 + featurename + '=' + x['R2'] rmse = rmse + featurename + '=' + x['RMSE'] testing_matrix = {} testing_matrix['MAE'] = mae testing_matrix['MSE'] = mse testing_matrix['R2'] = R2 testing_matrix['RMSE'] = rmse result['testing_matrix'] = testing_matrix forecastjson = resultJsonObj['data']['forecasts'] result['forecast'] = forecastjson ''' testing_matrix = {} testing_matrix['MAE'] = float(resultJsonObj['data']['matrix']['MAE']) testing_matrix['MSE'] = float(resultJsonObj['data']['matrix']['MSE']) testing_matrix['R2'] = float(resultJsonObj['data']['matrix']['R2']) testing_matrix['RMSE'] = float(resultJsonObj['data']['matrix']['RMSE']) result['testing_matrix'] = testing_matrix forecastjson = resultJsonObj['data']['forecasts'] result['forecast'] = forecastjson elif result['BestModel'] == 'LSTM' or result['BestModel'] == 'MLP': testing_matrix = {} testing_matrix['MSE'] = float(resultJsonObj['data']['matrix']['MSE']) testing_matrix['RMSE'] = float(resultJsonObj['data']['matrix']['RMSE']) result['testing_matrix'] = testing_matrix forecastjson = resultJsonObj['data']['forecasts'] result['forecast'] = forecastjson else: testing_matrix = {} testing_matrix['MAE'] = float(resultJsonObj['data']['matrix']['MAE']) testing_matrix['MSE'] = float(resultJsonObj['data']['matrix']['MSE']) testing_matrix['R2'] = float(resultJsonObj['data']['matrix']['R2']) testing_matrix['RMSE'] = float(resultJsonObj['data']['matrix']['RMSE']) result['testing_matrix'] = testing_matrix forecastjson = resultJsonObj['data']['forecasts'] result['forecast'] = forecastjson elif result['ModelType'] == 'topicmodelling': result['problem_type'] = 'TopicModelling' topics = resultJsonObj['topics'] df_topic = [] dataDict = {} for x in topics: dataDict = {} words = topics[x] print(words) word = '' for key in words: print(key) if word != '': word = word+', ' word = word+key+'('+str(round(words[key],2))+')' dataDict["ID"] = x dataDict["Words"] = word df_topic.append(dataDict) result['topics'] = df_topic elif result['ModelType'].lower() == 'association rule': result['problem_type'] = 'AssociationRules' deploy_location = result['DeployLocation'] freq_item_file = os.path.join(result['DeployLocation'],'frequentItems.csv') if(os.path.isfile(freq_item_file)): rules_file = os.path.join(result['DeployLocation'],'associationRules.csv') if(os.path.isfile(rules_file)): df_rules = pd.read_csv(rules_file) df_rules = df_rules[['antecedents','consequents','support','confidence','lift']] #df_rules['antecedents'] = df_rules['antecedents'] result['rules'] = df_rules else: result['error'] = 'There are no association found in frequent items above that threshold (minThreshold)' else: result['error'] = 'There are no frequent items above that threshold (minSupport), try by reducing the minSupport value' elif result['ModelType'] == 'clustering': result['problem_type'] = 'Clustering' testing_matrix = {} if 'SilHouette_Avg' in resultJsonObj['data']['matrix']: testing_matrix['SilHouette_Avg'] = round(float(resultJsonObj['data']['matrix']['SilHouette_Avg']),2) else: testing_matrix['SilHouette_Avg'] = 'NA' if 'DaviesBouldinScore' in resultJsonObj['data']['matrix']: testing_matrix['DaviesBouldinScore'] = round(float(resultJsonObj['data']['matrix']['DaviesBouldinScore']),2) else: testing_matrix['DaviesBouldinScore'] = 'NA' if 'CalinskiHarabazScore' in resultJsonObj['data']['matrix']: testing_matrix['CalinskiHarabazScore'] = round(float(resultJsonObj['data']['matrix']['CalinskiHarabazScore']),2) else: testing_matrix['CalinskiHarabazScore'] = 'NA' centroidpath = os.path.join(result['DeployLocation'],'centers.csv') if(os.path.isfile(centroidpath)): df_center = pd.read_csv(centroidpath) df_center = df_center.rename(columns={"Unnamed: 0": "Cluster"}) result['centerpoints'] = round(df_center,2) result['testing_matrix'] = testing_matrix training_matrix = {} if 'SilHouette_Avg' in resultJsonObj['data']['matrix']: training_matrix['SilHouette_Avg'] = round(float(resultJsonObj['data']['matrix']['SilHouette_Avg']),2) training_matrix['DaviesBouldinScore'] = round(float(resultJsonObj['data']['matrix']['DaviesBouldinScore']),2) training_matrix['CalinskiHarabazScore'] = round(float(resultJsonObj['data']['matrix']['CalinskiHarabazScore']),2) else: training_matrix['SilHouette_Avg'] = 'NA' training_matrix['DaviesBouldinScore'] = 'NA' training_matrix['CalinskiHarabazScore'] = 'NA' result['training_matrix'] = training_matrix #print(result) evaluatedModelsList = resultJsonObj['data']['EvaluatedModels'] #print(evaluatedModelsList) for index in range(len(evaluatedModelsList)): if evaluatedModelsList[index]['Score'] == 'NA': evaluatedModelsList[index]['Score'] = 'NA' else: evaluatedModelsList[index]['Score'] = round(float(evaluatedModelsList[index]['Score']), 4) if result['ModelType'] == 'classification': evaluatedModelsList = sorted(evaluatedModelsList, key=lambda k: k['Score'],reverse=True) else: evaluatedModelsList = sorted(evaluatedModelsList, key=lambda k: k['Score']) result['EvaluatedModels'] = evaluatedModelsList result['LogFile'] = resultJsonObj['data']['LogFile'] return result, survical_images ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os,sys import json def getInstanceonGCP(image,instances): try: from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') if sqlite_obj.table_exists('LLMTuning'): data = sqlite_obj.read_data('LLMTuning','image="'+image['id']+'"') for values in data: instance = {} instance['type'] = 'instance' instance['id'] = values[2] instance['workLoad'] = image['workLoad'] instance['machineImageProjectID'] = image['machineImageProjectID'] instance['ssh'] = image['ssh'] instance['machineConfiguration'] = image['machineConfiguration'] instance['instanceType'] = image['instanceType'] instances.append(instance) except Exception as e: print(e) return instances def getInstanceonAWS(amiid,instances): try: from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') if sqlite_obj.table_exists('LLMTuning'): data = sqlite_obj.read_data('LLMTuning','image="'+amiid['id']+'"') for values in data: instance = {} instance['type'] = 'instance' instance['id'] = values[2] instance['workLoad'] = amiid['workLoad'] instance['regionName'] = amiid['regionName'] instance['ssh'] = amiid['ssh'] instance['machineConfiguration'] = amiid['machineConfiguration'] instance['instanceType'] = amiid['instanceType'] instances.append(instance) except Exception as e: print(e) return instances def updatelocalsetings(request): from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR import pandas as pd file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') if sqlite_obj.table_exists('computeInfrastructure'): updated_data = 'selectedInfrastructure="Local"' sqlite_obj.update_data(updated_data,'','computeInfrastructure') def updateToComputeSettings(infratructure): from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR import pandas as pd file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') if sqlite_obj.table_exists('computeInfrastructure'): updated_data = 'selectedInfrastructure="'+infratructure+'"' sqlite_obj.update_data(updated_data,'','computeInfrastructure') def updateGCPConfig(request): try: credentialsJson = request.POST.get('credentialsJson') projectID = request.POST.get('gcpProjectid') machineType = request.POST.get('gcpmachineType') selectedID = request.POST.get('gcpInstance') gcpZone = request.POST.get('gcpZone') workload = request.POST.get('gcpworkload') noOfInstance = request.POST.get('GCPnoofinstance') #print(credentialsJson,projectID,machineType,selectedID,gcpZone,workload,noOfInstance) if credentialsJson != '' and projectID != '': from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR import pandas as pd file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') if sqlite_obj.table_exists('gcpCredentials'): updated_data = 'credentialsJson="'+credentialsJson+'",projectID="'+projectID+'",machineType="'+machineType+'",selectedID="'+selectedID+'",regionName="'+gcpZone+'",noOfInstance="'+str(noOfInstance)+'",workload="'+workload+'"' sqlite_obj.update_data(updated_data,'','gcpCredentials') else: newdata = {} newdata.update({'id':['1'],'credentialsJson': [credentialsJson],'projectID': [projectID],'machineType':[machineType],'selectedID':[selectedID],'regionName':[gcpZone],'noOfInstance':[noOfInstance],'workload':[workload]}) sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'gcpCredentials') return('success') else: return('error') except Exception as e: print(e) return('error') def updateComputeConfig(request): try: AWSAccessKeyID = request.POST.get('AWSAccessKeyID') AWSSecretAccessKey = request.POST.get('AWSSecretAccessKey') workload = request.POST.get('workload') machineType = request.POST.get('machineType') selectedID = request.POST.get('amiInstance') regionName = request.POST.get('regionName') noOfInstance = request.POST.get('NoOfInstance') securitygroupid = request.POST.get('AWSSecuritygroupID') if AWSAccessKeyID != '' and AWSSecretAccessKey != '': from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR import pandas as pd file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') if sqlite_obj.table_exists('awsCredentials'): column_names = sqlite_obj.column_names('awsCredentials') if 'securitygroupid' not in column_names: query = 'Alter Table awsCredentials ADD securitygroupid TEXT' sqlite_obj.execute_query(query) updated_data = 'AWSAccessKeyID="'+AWSAccessKeyID+'",AWSSecretAccessKey="'+AWSSecretAccessKey+'",machineType="'+machineType+'",selectedID="'+selectedID+'",regionName="'+regionName+'",noOfInstance="'+noOfInstance+'",workload="'+workload+'",securitygroupid="'+securitygroupid+'"' sqlite_obj.update_data(updated_data,'','awsCredentials') else: newdata = {} newdata.update({'id':['1'],'AWSAccessKeyID': [AWSAccessKeyID],'AWSSecretAccessKey': [AWSSecretAccessKey],'machineType':[machineType],'selectedID':[selectedID],'regionName':[regionName],'noOfInstance':[noOfInstance],'workload':[workload],'securitygroupid':[securitygroupid]}) sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'awsCredentials') return('success') else: return('error') except Exception as e: print(e) return('error') def selectedInfratructure(): from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') selcInfra = 'Local' if sqlite_obj.table_exists('computeInfrastructure'): data = sqlite_obj.read_data('computeInfrastructure') for values in data: selcInfra = values[1] return selcInfra def readComputeConfig(): try: file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','compute_conf.json')) f = open(file_path, "r") configSettings = f.read() f.close() configSettingsJson = json.loads(configSettings) from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR import pandas as pd file_path = os.path.join(DATA_DIR, 'sqlite') sqlite_obj = sqlite_db(file_path, 'config.db') selcInfra = 'Local' if sqlite_obj.table_exists('computeInfrastructure'): data = sqlite_obj.read_data('computeInfrastructure') for values in data: selcInfra = values[1] else: data = {} data.update({'id':['1'],'selectedInfrastructure': ['Local']}) sqlite_obj.write_data(pd.DataFrame.from_dict(data),'computeInfrastructure') configSettingsJson['computeInfrastructure'] = selcInfra for ami in configSettingsJson['AWS_EC2']['amis']: configSettingsJson['AWS_EC2']['instances'] = getInstanceonAWS(ami,configSettingsJson['AWS_EC2']['instances']) for image in configSettingsJson['GCP']['machineImage']: configSettingsJson['GCP']['instances'] = getInstanceonGCP(image,configSettingsJson['GCP']['instances']) AWSAccessKeyID = '' AWSSecretAccessKey = '' securitygroupid = '' machineType = 'AMI' selectedID = '' regionName = '' noofInfra = 1 workLoad = 'LLM' if sqlite_obj.table_exists('awsCredentials'): column_names = sqlite_obj.column_names('awsCredentials') #print(column_names) if 'workload' not in column_names: query = 'Alter Table awsCredentials ADD workload TEXT' sqlite_obj.execute_query(query) if 'securitygroupid' not in column_names: query = 'Alter Table awsCredentials ADD securitygroupid TEXT' sqlite_obj.execute_query(query) data = sqlite_obj.read_data('awsCredentials') for values in data: AWSAccessKeyID = values[1] AWSSecretAccessKey = values[2] machineType = values[3] selectedID = values[4] regionName = values[5] noofInfra = values[6] workLoad = values[7] securitygroupid = values[8] selectedAWS = {} selectedAWS['accessKey'] = AWSAccessKeyID selectedAWS['secretAccessKey'] = AWSSecretAccessKey selectedAWS['machineType']=machineType selectedAWS['selectedID'] = selectedID selectedAWS['regionName'] = regionName selectedAWS['noOfInstance']=noofInfra selectedAWS['workLoad'] = workLoad selectedAWS['securitygroupid'] = securitygroupid configSettingsJson['awsCredentials'] = selectedAWS gcpCredentials='' projectID = '' selectedID = '' machineType = '' regionName = '' noOfInstance = 1 workLoad = 'LLM' if sqlite_obj.table_exists('gcpCredentials'): column_names = sqlite_obj.column_names('gcpCredentials') if 'workload' not in column_names: query = 'Alter Table gcpCredentials ADD workload TEXT' sqlite_obj.execute_query(query) data = sqlite_obj.read_data('gcpCredentials') for values in data: gcpCredentials = values[1] projectID = values[2] machineType = values[3] selectedID = values[4] regionName = values[5] noOfInstance = values[6] workLoad = values[7] selectedGCP = {} selectedGCP['gcpCredentials'] = gcpCredentials selectedGCP['selectedID'] = selectedID selectedGCP['projectID'] = projectID selectedGCP['machineType'] = machineType selectedGCP['regionName'] = regionName selectedGCP['noOfInstance'] = noOfInstance selectedAWS['workLoad'] = workLoad configSettingsJson['gcpCredentials'] = selectedGCP #print(configSettingsJson) return(configSettingsJson) except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import csv import logging import pandas as pd class csv_validator: def __init__(self): self.log = logging.getLogger('eion') def __text_header(self, filename, threshold=0.75): df = pd.read_csv(filename, header=None,nrows=1000) numeric_columns = df.dtypes[df.dtypes != object] if not len(numeric_columns): first_row_len = df.iloc[0].str.len() index = 0 for c in df: if (df[c].map(len).mean() * threshold <= first_row_len[index]): return False index += 1 return True return False def validate_header(self, filename,delimiter,textqualifier,threshold=0.75): with open(filename, 'rt',encoding='utf-8') as csvfile: has_header = csv.Sniffer().has_header(csvfile.read(8192)) csvfile.seek(0) if not has_header: has_header = self.__text_header(filename, threshold) reader = csv.reader(csvfile, delimiter=delimiter,quotechar=textqualifier) good_csv = True col_len = len(next(reader)) bad_lines = [] offset = 2 # +1 for first read and +1 for python index start at 0 for index, row in enumerate(reader): if len(row) != col_len: good_csv = False if(index == 1 and has_header): offset += 1 bad_lines.append(index + offset) return has_header, good_csv, bad_lines if __name__ == '__main__': import sys val = csv_validator() print(val.validate_header(sys.argv[1])) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import math import sys,os import pandas as pd from sklearn.cluster import KMeans from sklearn.decomposition import PCA import numpy as np import scipy.stats as st from sklearn.preprocessing import StandardScaler from dython.nominal import associations class ux_eda (): def __init__(self, dataPath=pd.DataFrame(),delimiter=',',textqualifier='"',optimize=None,): aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] self.dataFrame = pd.DataFrame() if isinstance(dataPath, pd.DataFrame): self.dataFrame = dataPath if optimize == 1: self.dataFrame = self.dataFrame.sample(n=1000, random_state=1) else: if optimize == 1: self.dataFrame = pd.read_csv(dataPath,nrows=1000,encoding='utf-8',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,na_values=['-','?'],encoding_errors= 'replace') else: self.dataFrame = pd.read_csv(dataPath, encoding='utf-8',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,na_values=['-','?'],encoding_errors= 'replace') self.dataFrame.rename(columns=lambda x: x.strip(), inplace=True) self.features = self.dataFrame.columns.tolist() self.indexFeature = [] self.dateFeature = [] self.categoricalFeature = [] self.constantFeature = [] self.textFeature = [] self.numericFeature = [] self.numericAndCatFeature = [] for feature, featureType in zip(self.features, self.dataFrame.dtypes): if self.__check_seq_feature(self.dataFrame[feature]): self.indexFeature.append(feature) elif self.__match_date_format(self.dataFrame[feature]): self.dateFeature.append(feature) elif self.__check_constant_features(self.dataFrame[feature]): self.constantFeature.append(feature) elif self.__check_category_features(self.dataFrame[feature]): self.categoricalFeature.append(feature) elif featureType == 'object': ''' numOfRows = self.dataFrame.shape[0] distinctCount = len(self.dataFrame[feature].unique()) tempDff = self.dataFrame[feature] self.dataFrame[feature]=self.dataFrame[feature].apply(lambda x: self.testNum(x)) tempDf = self.dataFrame[feature] tempDf = tempDf.dropna() numberOfNonNullVals = tempDf.count() numericRatio = 0.8 if(numberOfNonNullVals > int(numOfRows * numericRatio)): self.numericFeature.append(feature) else: self.dataFrame[feature] = tempDff ''' self.textFeature.append(feature) elif featureType in aionNumericDtypes: self.numericFeature.append(feature) # self.dataFrame[self.categoricalFeature] = self.dataFrame[self.categoricalFeature].apply(lambda x: x.cat.codes) self.numericAndCatFeature = self.numericFeature + self.categoricalFeature # EDA Performance change # ---------------------------- def subsampleData(self, subsampleData): self.dataFrame = self.dataFrame.sample(n=subsampleData, random_state=1) def get_features_datatype(self,v,num_list,cat_list,text_list): """ To get exact datatype of the feature in Data Overview.""" if v in cat_list: return 'Categorical' elif v in num_list: return 'Numerical' elif v in text_list: return 'Text' def getCorrelationMatrix(self): try: if len(self.dataFrame.columns) > 25: df3 = df[self.dataFrame.columns[0:24]] else: df3 = self.dataFrame.copy() cor_mat= associations(self.dataFrame,compute_only=True) cor_mat=cor_mat['corr'] cor_mat = cor_mat.astype(float).round(2) cor_mat.replace(np.nan, 0, inplace=True) cor_mat.fillna('None',inplace=True) return cor_mat except Exception as e: print(e) correlationgraph = pd.DataFrame() return (correlationgraph) def dataDistribution(self): df_eda_actual = self.dataFrame.copy() des1 = df_eda_actual.describe(include='all').T des1['missing count %'] = df_eda_actual.isnull().mean() * 100 des1['zero count %'] = df_eda_actual.isin([0]).mean() * 100 dataColumns = list(self.dataFrame.columns.values) des1.insert(0, 'Features', dataColumns) actual_df_numerical_features = df_eda_actual.select_dtypes(exclude='object') actual_df_categorical_features = df_eda_actual.select_dtypes(include='object') #For text features textFeature_df = df_eda_actual.filter(self.textFeature) actual_df_categorical_features = actual_df_categorical_features.drop(self.textFeature, axis=1) for i in des1['Features']: num_cols = actual_df_numerical_features.columns.to_list() cat_cols = actual_df_categorical_features.columns.to_list() text_cols = self.textFeature des1['Features Type'] = des1['Features'].apply(lambda x: self.get_features_datatype(x, num_cols,cat_cols,text_cols)) curr_columns = des1.columns.to_list() curr_columns.remove('Features Type') insert_i = curr_columns.index('Features')+1 curr_columns.insert(insert_i,'Features Type') des1 = des1[curr_columns] return des1 # ---------------------------- def subsetFeatures(self, edaFeatures): print(self.dataFrame.columns) self.dataFrame = self.dataFrame[edaFeatures] self.features = edaFeatures self.indexFeature = [] self.dateFeature = [] self.categoricalFeature = [] self.constantFeature = [] self.textFeature = [] self.numericFeature = [] self.numericAndCatFeature = [] print('abc') aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] for feature, featureType in zip(self.features, self.dataFrame.dtypes): if self.__check_seq_feature(self.dataFrame[feature]): self.indexFeature.append(feature) elif self.__match_date_format(self.dataFrame[feature]): self.dateFeature.append(feature) elif self.__check_constant_features(self.dataFrame[feature]): self.constantFeature.append(feature) elif self.__check_category_features(self.dataFrame[feature]): self.categoricalFeature.append(feature) elif featureType == 'object': ''' numOfRows = self.dataFrame.shape[0] distinctCount = len(self.dataFrame[feature].unique()) tempDff = self.dataFrame[feature] self.dataFrame[feature]=self.dataFrame[feature].apply(lambda x: self.testNum(x)) tempDf = self.dataFrame[feature] tempDf = tempDf.dropna() numberOfNonNullVals = tempDf.count() numericRatio = 0.8 if(numberOfNonNullVals > int(numOfRows * numericRatio)): self.numericFeature.append(feature) else: self.dataFrame[feature] = tempDff ''' self.textFeature.append(feature) elif featureType in aionNumericDtypes: self.numericFeature.append(feature) print('def') self.numericAndCatFeature = self.numericFeature + self.categoricalFeature # ---------------------------- def testNum(self,value): try: x=eval(value) return x except: return np.nan def getFeatures(self): leastRatioFeature = self.__LeastfeatureRatio() return (self.features, self.dateFeature, self.indexFeature, self.constantFeature, self.textFeature, leastRatioFeature,self.numericAndCatFeature,self.numericFeature,self.categoricalFeature) def getNumericFeatureCount(self): return(len(self.numericAndCatFeature)) def calculateNumberofCluster(self): df = self.dataFrame[self.numericFeature] return self.__NumberofCluster(df) def getTopTextFeatures(self,topn): df_text = pd.DataFrame() if (len(self.textFeature) > 1): df_text['combined'] = self.dataFrame[self.textFeature].apply(lambda row: ' '.join(row.values.astype(str)), axis=1) features = ['combined'] else: df_text[['combined']] = self.dataFrame[self.textFeature] features = ['combined'] df_text[features[0]] = df_text[features[0]].fillna("NA") textCorpus = df_text[features[0]] from text import eda texteda_obj = eda.ExploreTextData() df = texteda_obj.MostCommonWords(textCorpus,topn) return df def __NumberofCluster(self, featureData): Sum_of_squared_distances = [] K = range(1, 15) for k in K: km = KMeans(n_clusters=k) km = km.fit(featureData) Sum_of_squared_distances.append(km.inertia_) x1, y1 = 1, Sum_of_squared_distances[0] x2, y2 = 15, Sum_of_squared_distances[len(Sum_of_squared_distances) - 1] distances = [] for inertia in range(len(Sum_of_squared_distances)): x0 = inertia + 2 y0 = Sum_of_squared_distances[inertia] numerator = abs((y2 - y1) * x0 - (x2 - x1) * y0 + x2 * y1 - y2 * x1) denominator = math.sqrt((y2 - y1) ** 2 + (x2 - x1) ** 2) distances.append(numerator / denominator) n_clusters = distances.index(max(distances)) + 2 return (n_clusters) #13841 : TrustedAI: hopkins stat def getHopkinsVal(self,df): try: from appbe.hopkinsStat import hopkins from sklearn.preprocessing import StandardScaler,OneHotEncoder from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer numeric_transformer = Pipeline( steps=[("imputer", SimpleImputer(missing_values=np.nan,strategy="mean")), ("standard_scaler", StandardScaler())] ) categorical_transformer = Pipeline( steps=[ ("imputer", SimpleImputer(missing_values=np.nan,strategy="most_frequent")), ("encoder", OneHotEncoder(handle_unknown="ignore")) ] ) preprocessor = ColumnTransformer( transformers=[ ("num", numeric_transformer, self.numericFeature), ("cat", categorical_transformer, self.categoricalFeature) ] ) pipe = Pipeline([('scaler',preprocessor)]) scaled_df = pipe.fit_transform(df) if type(scaled_df) != np.ndarray: scaled_df = scaled_df.toarray() score = round(hopkins(scaled_df,scaled_df.shape[0]),2) return str(score) except Exception as e: print(e) return '' def getClusterDetails(self): aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] df_clus = pd.get_dummies(self.dataFrame[self.numericAndCatFeature], prefix_sep='####') for i in df_clus.columns: dataType = df_clus[i].dtypes if dataType not in aionNumericDtypes: df_clus[i] = df_clus[i].fillna(df_clus[i].mode()[0]) else: df_clus[i] = df_clus[i].fillna(df_clus[i].mean()) n = self.__NumberofCluster(df_clus) n = n - 1 kmeans = KMeans(n_clusters=n, init='k-means++', max_iter=10, n_init=10, random_state=0) # Fit and predict y_means = kmeans.fit_predict(df_clus) centroids = kmeans.cluster_centers_.squeeze() labels = kmeans.labels_ features = df_clus.columns cluster_details = [] for j in range(len(features)): cluster = {} feature = features[j] perflag = 0 if '####' in feature: x = features[j].split('####') feature = x[0] + ' ' + x[1] + '(%)' perflag = 1 else: feature = feature + '(AVG)' cluster['label'] = feature total_sum = 0 if perflag == 1: for i in range(n): centroid = centroids[i] value = round(centroid[j], 2) total_sum = total_sum + value for i in range(n): centroid = centroids[i] value = round(centroid[j], 2) if perflag == 1: value = (value / total_sum) * 100 value = round(value, 2) cluster['Cluster ' + str(i + 1)] = value cluster_details.append(cluster) hopkins_val = self.getHopkinsVal(self.dataFrame,) return cluster_details,hopkins_val def getHighlyCorrelatedFeatures(self,noOfTop): df_corr = abs(self.dataFrame[self.numericAndCatFeature].corr()).stack().reset_index() df_corr.columns = ['FEATURE_1', 'FEATURE_2', 'CORRELATION'] mask_dups = (df_corr[['FEATURE_1', 'FEATURE_2']].apply(frozenset, axis=1).duplicated()) | ( df_corr['FEATURE_1'] == df_corr['FEATURE_2']) df_corr = df_corr[~mask_dups] df_corr = df_corr.sort_values(by='CORRELATION', ascending=False) df_top = df_corr.head(n=noOfTop) return(df_top) # ---------------------- 12686:Data Distribution related Changes S T A R T ---------------------- def word_token_for_feature(self, selectedFeature, dataframe): comment_words = "" try: df_text = pd.DataFrame() df_text[[selectedFeature]] = dataframe features = [selectedFeature] df_text[features[0]] = df_text[features[0]].fillna("NA") textCorpus = df_text[features[0]] from text import TextProcessing tp = TextProcessing.TextProcessing() preprocessed_text = tp.transform(textCorpus) df_text[selectedFeature] = preprocessed_text df_text_list = df_text.values.tolist() for val in df_text_list: val = str(val) tokens = val.split() for i in range(len(tokens)): tokens[i] = tokens[i].lower() comment_words += " ".join(tokens) + " " except: comment_words = "" return comment_words # -------------------------------------------- E N D -------------------------------------------- def word_token(self): df_text = pd.DataFrame() if (len(self.textFeature) > 1): df_text['combined'] = self.dataFrame[self.textFeature].apply(lambda row: ' '.join(row.values.astype(str)), axis=1) features = ['combined'] else: df_text[['combined']] = self.dataFrame[self.textFeature] features = ['combined'] df_text[features[0]] = df_text[features[0]].fillna("NA") textCorpus = df_text[features[0]] from text import TextProcessing tp = TextProcessing.TextProcessing() preprocessed_text = tp.transform(textCorpus) df_text['combined'] = preprocessed_text df_text_list = df_text.values.tolist() comment_words = "" for val in df_text_list: val = str(val) tokens = val.split() for i in range(len(tokens)): tokens[i] = tokens[i].lower() comment_words += " ".join(tokens) + " " if comment_words == "": comment_words = 'Not found any token' return comment_words def getdata(self): return self.dataFrame def getPCATop10Features(self): aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] df = self.dataFrame[self.numericAndCatFeature] for feature in self.numericAndCatFeature: if feature in self.categoricalFeature: df[feature] = pd.Categorical(df[feature]) df[feature] = df[feature].cat.codes df[feature] = df[feature].fillna(df[feature].mode()[0]) else: df[feature] = df[feature].fillna(df[feature].mean()) pca = PCA(n_components=2).fit(StandardScaler().fit_transform(df)) mapping = pd.DataFrame(pca.components_, columns=self.numericAndCatFeature) mapping = mapping.diff(axis=0).abs() mapping = mapping.iloc[1] mapping = mapping.sort_values(ascending=False).head(10) return mapping def getTopRows(self, rows=5): return self.dataFrame.head(rows) def __check_seq_feature(self, data): if data.dtypes in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']: total_record = data.count() count = (data - data.shift() == 1).sum() if ((total_record - count) == 1): return True return False def __match_date_format(self, data): try: ## Using regex lib, we are check if any col contains datetime format like yyyy-mm-dd or yyyy/mm/dd format. if it finds return true. import re u_data = data.to_string() date_find = (re.findall(r"[0-9]{1,4}[\_|\-|\/|\|][0-9]{1,2}[\_|\-|\/|\|][0-9]{1,4}", u_data) or re.findall(r'\d{,2}\-[A-Za-z]{,9}\-\d{,4}', u_data) or re.findall(r"[0-9]{1,4}[\_|\-|\/|\|][0-9]{1,2}[\_|\-|\/|\|][0-9]{1,4}.\d" , u_data) or re.findall(r"[0-9]{1,4}[\_|\-|\/|\|][A-Za-z]{,9}[\_|\-|\/|\|][0-9]{1,4}", u_data)) if (date_find): try: data = pd.to_datetime(data, utc=True) return True except Exception as e: ##If not a datetime col, just pass to return false statement. pass except Exception as e: data = data.astype(str) beforecheckcount = data.count() #####YYYY-MM-DD HH:MM:SS#### check1 = data[data.str.match( r'(^\d\d\d\d-(0?[1-9]|1[0-2])-(0?[1-9]|[12][0-9]|3[01]) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9]):([0-9]|[0-5][0-9])$)') == True] aftercheckcount = check1.count() if (beforecheckcount == aftercheckcount): return True #####MM/DD/YYYY HH:MM#### check2 = data[data.str.match( r'(^(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])/(\d\d\d\d) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9])$)') == True] aftercheckcount = check2.count() if (beforecheckcount == aftercheckcount): return True #####DD-MM-YYYY HH:MM#### check2 = data[data.str.match( r'(^(0?[1-9]|[12][0-9]|3[01])-(0?[1-9]|1[0-2])-(\d\d\d\d) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9])$)') == True] aftercheckcount = check2.count() if (beforecheckcount == aftercheckcount): return True #####YYYY/MM/DD#### check2 = data[data.str.match(r'(^\d\d\d\d/(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])$)') == True] aftercheckcount = check2.count() if (beforecheckcount == aftercheckcount): return True #####MM/DD/YYYY#### check2 = data[data.str.match(r'(^(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])/(\d\d\d\d)$)') == True] aftercheckcount = check2.count() if (beforecheckcount == aftercheckcount): return True #####YYYY-MM-DD HH:MM:SS.fff#### check11 = data[data.str.match( r'(^\d\d\d\d-(0?[1-9]|1[0-2])-(0?[1-9]|[12][0-9]|3[01]) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9]):([0-9]|[0-5][0-9])\.(\d{3})$)') == True] aftercheckcount = check11.count() if (beforecheckcount == aftercheckcount): return True return False def __check_category_features(self, modelFeatures): aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] dataType = modelFeatures.dtypes numOfRows = len(modelFeatures) if dataType not in aionNumericDtypes: if dataType != 'bool': nUnique = len(modelFeatures.unique().tolist()) if nUnique <= 30: return True return False def __check_constant_features(self, modelFeatures): return len(modelFeatures.unique().tolist()) == 1 def __featureRatio(self, modelFeatures): if len(modelFeatures): return len(modelFeatures.unique().tolist()) / len(modelFeatures) return 0 def __LeastfeatureRatio(self): ratio = 1 feat = "" for feature in (self.numericAndCatFeature + self.textFeature): r = self.__featureRatio(self.dataFrame[feature]) if r < ratio: ratio = r feat = feature return feat def getDistribution(self): aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] df = self.dataFrame[self.numericAndCatFeature] dist={} for feature in self.numericAndCatFeature: if feature in self.categoricalFeature: df[feature] = pd.Categorical(df[feature]) df[feature] = df[feature].cat.codes df[feature] = df[feature].fillna(df[feature].mode()[0]) else: df[feature] = df[feature].fillna(df[feature].mean()) distributionname,sse = self.DistributionFinder(df[feature]) if distributionname == '': dist[feature] = 'Unknown' else: dist[feature] = distributionname return dist def DistributionFinder(self,data): try: distributionName = "" sse = 0.0 KStestStatic = 0.0 dataType = "" if (data.dtype == "float64"): dataType = "Continuous" elif (data.dtype == "int"): dataType = "Discrete" elif (data.dtype == "int64"): dataType = "Discrete" if (dataType == "Discrete"): distributions = [st.bernoulli, st.binom, st.geom, st.nbinom, st.poisson] index, counts = np.unique(data.astype(int), return_counts=True) if (len(index) >= 2): best_sse = np.inf y1 = [] total = sum(counts) mean = float(sum(index * counts)) / total variance = float((sum(index ** 2 * counts) - total * mean ** 2)) / (total - 1) dispersion = mean / float(variance) theta = 1 / float(dispersion) r = mean * (float(theta) / 1 - theta) datamin = data.min() datamax = data.max() for j in counts: y1.append(float(j) / total) pmf1 = st.bernoulli.pmf(index, mean) pmf2 = st.binom.pmf(index, len(index), p=mean / len(index)) pmf3 = st.geom.pmf(index, 1 / float(1 + mean)) pmf4 = st.nbinom.pmf(index, mean, r) pmf5 = st.poisson.pmf(index, mean) sse1 = np.sum(np.power(y1 - pmf1, 2.0)) sse2 = np.sum(np.power(y1 - pmf2, 2.0)) sse3 = np.sum(np.power(y1 - pmf3, 2.0)) sse4 = np.sum(np.power(y1 - pmf4, 2.0)) sse5 = np.sum(np.power(y1 - pmf5, 2.0)) sselist = [sse1, sse2, sse3, sse4, sse5] best_distribution = 'NA' for i in range(0, len(sselist)): if best_sse > sselist[i] > 0: best_distribution = distributions[i].name best_sse = sselist[i] elif (len(index) == 1): best_distribution = "Constant Data-No Distribution" best_sse = 0.0 distributionName = best_distribution sse = best_sse elif (dataType == "Continuous"): distributions = [st.uniform, st.expon, st.weibull_max, st.weibull_min, st.chi, st.norm, st.lognorm, st.t, st.gamma, st.beta] best_distribution = st.norm.name best_sse = np.inf datamin = data.min() datamax = data.max() nrange = datamax - datamin y, x = np.histogram(data.astype(float), bins='auto', density=True) x = (x + np.roll(x, -1))[:-1] / 2.0 for distribution in distributions: params = distribution.fit(data.astype(float)) arg = params[:-2] loc = params[-2] scale = params[-1] pdf = distribution.pdf(x, loc=loc, scale=scale, *arg) sse = np.sum(np.power(y - pdf, 2.0)) if (best_sse > sse > 0): best_distribution = distribution.name best_sse = sse distributionName = best_distribution sse = best_sse except: response = str(sys.exc_info()[0]) message = 'Job has Failed' + response exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) return distributionName, sse import pandas as pd import numpy as np from statsmodels.tsa.stattools import adfuller from statsmodels.tsa.stattools import kpss from statsmodels.tsa.seasonal import seasonal_decompose import logging import os import warnings warnings.filterwarnings('ignore') ## Main class to find out seassonality and stationary in timeseries data. class StationarySeasonalityTest: def __init__(self,df,featurename,datetimefeature): self.df=df self.targetFeature=featurename self.datetimefeature=datetimefeature ## to get the timeseries data stationary information def stationary_model(self,df,target_feature,stationary_check_method): stationary_status=None if (stationary_check_method.lower()=='adfuller'): stats_model=adfuller(df[target_feature]) statistic, p_value, n_lags, num_bservations,critical_values,info_criterion_best=stats_model[0],stats_model[1],stats_model[2],stats_model[3],stats_model[4],stats_model[5] if (p_value>0.05): stationary_status=str("Non-Stationary") elif(p_value<0.05): stationary_status=str("Stationary") ##kpss is opposite to ADF in considering null hypothesis. In KPSS, if null hypothesis,then it is stationary as oppose to ADF. elif (stationary_check_method.lower()=='kpss'): from statsmodels.tsa.stattools import kpss stats_model = kpss(df[target_feature]) statistic, p_value, n_lags, critical_values=stats_model[0],stats_model[1],stats_model[2],stats_model[3] ##In kpss, the stationary condition is opposite to Adafuller. if (p_value>0.05): stationary_status=str("Stationary") else: stationary_status=str("Non-Stationary") return stats_model,n_lags,p_value,stationary_status ## Get stationary details def stationary_check(self,target_feature,time_col,method): df=self.df df[time_col]=pd.to_datetime(df[time_col]) df=df.set_index(time_col) try: stationary_check_method=method except: stationary_check_method='adfuller' if (len(target_feature) == 1): try: if isinstance(target_feature,list): target_feature=''.join(target_feature) elif isinstance(target_feature,int): target_feature=str(target_feature) elif isinstance(target_feature,str): pass except Exception as e: pass stationary_result={} stats_model,n_lags,p_value,stationary_status=self.stationary_model(df,target_feature,stationary_check_method) # stationary_result[target_feature]=stationary_status stationary_result[target_feature]=stationary_status elif(len(target_feature) > 1): stationary_result={} for col in df.columns: stats_model,n_lags,p_value,stationary_status=self.stationary_model(df,col,stationary_check_method) stationary_result[col]=stationary_status else: pass stationary_val=None for v in stationary_result.values(): stationary_val=v stationary_combined_res=dict() c_dict=[k for k,v in stationary_result.items() if 'non-stationary' in v] if (len(c_dict)>=1): stationary_combined_res['dataframe_stationarity']='Non-Stationary' else: stationary_combined_res['dataframe_stationarity']='Stationary' return stats_model,n_lags,p_value,stationary_val,stationary_combined_res #Get seasonality by using seasonal_decompose lib. def seasonality_model(self,target_feature,df): seasonality_status=None try: try: stats_model = kpss(df[target_feature]) statistic, p_value, n_lags, critical_values=stats_model[0],stats_model[1],stats_model[2],stats_model[3] except: n_lags=1 pass try: df_target=self.df[target_feature] decompose_result_mult = seasonal_decompose(df_target,model='additive', extrapolate_trend='freq', period=n_lags) except Exception as e: ##If additive model (type of seasonal component) failed, use multiplicative decompose_result_mult = seasonal_decompose(df_target,model='multiplicative', extrapolate_trend='freq', period=1) trend = decompose_result_mult.trend observed=decompose_result_mult.observed seasonal = decompose_result_mult.seasonal residual = decompose_result_mult.resid try: if isinstance(df_target, pd.Series): auto_correlation = df_target.autocorr(lag=n_lags) elif isinstance(df_target, pd.DataFrame): df_target = df_target.squeeze() auto_correlation = df_target.autocorr(lag=n_lags) except: pass if (seasonal.sum()==0): seasonality_status="Non-Seasonal" else: seasonality_status="Seasonal" # #Please use the below plot for GUI show (seasonality components) # decompose_result_mult.plot().savefig('seasonality_plot.png') df['observed'] = decompose_result_mult.observed df['residual'] = decompose_result_mult.resid df['seasonal'] = decompose_result_mult.seasonal df['trend'] = decompose_result_mult.trend except Exception as e: print("Seasonality function exception: \t",e) return df,decompose_result_mult,seasonality_status ##Main function to check seasonlity in data def seasonal_check(self,target_feature,time_col,seasonal_model): df=self.df try: df[time_col]=pd.to_datetime(df[time_col]) except Exception as e: pass df=df.set_index(time_col) if (len(target_feature)==1): try: if isinstance(target_feature,list): target_feature=''.join(target_feature) elif isinstance(target_feature,int): target_feature=str(target_feature) elif isinstance(target_feature,str): pass except Exception as e: ## Because of EDA, all log messages removed. (self.log.info ) pass ## Seasonal component for individual feature based. seasonality_result=dict() df,decompose_result_mult,seasonality_status = self.seasonality_model(target_feature,df) # seasonality_result[target_feature]=seasonality_status seasonality_result['Feature: '+str(target_feature)]=seasonality_status elif(len(target_feature) > 1): seasonality_result=dict() for col in df.columns: df,decompose_result_mult,seasonality_status = self.seasonality_model(col,df) seasonality_result[col]=seasonality_status else: pass # ## Seasonal component for whole dataset seasonality_val=None for v in seasonality_result.values(): seasonality_val=v seasonality_combined_res=dict() c_dict=[k for k,v in seasonality_result.items() if 'non-seasonality' in v] if (len(c_dict)>=1): seasonality_combined_res['dataframe_seasonality']='No Seasonal elements' else: seasonality_combined_res['dataframe_seasonality']='contains seasonal elements.' return df,decompose_result_mult,seasonality_val,seasonality_combined_res #Main user defined caller for stationary and seasonality (SS) def analysis(self,seasonality_status,stationarity_status): seasonal_model="additive" time_col=self.datetimefeature stationary_method='adfuller' if (isinstance(self.targetFeature,list)): target=self.targetFeature pass elif (isinstance(self.targetFeature,str)): target=list(self.targetFeature.split(',')) if (stationarity_status.lower()=="true"): stats_model,n_lags,p_value,stationary_result,stationary_combined_res=self.stationary_check(target,time_col,stationary_method) return stationary_result if (seasonality_status.lower()=="true"): df,decompose_result_mult,seasonality_result,seasonality_combined_res=self.seasonal_check(target,time_col,seasonal_model) return seasonality_result #Main fn for standalone test purpose if __name__=='__main__': print("Inside seasonality-stationary test main function...") print("Below code used for standalone test purpose.") ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json import os import rsa import boto3 #usnish import pandas as pd import time def add_new_azureStorage(request): try: file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','azurestorage.conf')) with open(file_path, 'r') as f: data = json.load(f) f.close() if data == '': data = [] except: data = [] if request.POST["azurename"] =='' or request.POST["azureaccountkey"] == '' or request.POST["containername"] == '' : return 'error' newdata = {} newdata['azurename'] = request.POST["azurename"] newdata['azureaccountkey'] = request.POST["azureaccountkey"] newdata['containername'] = request.POST["containername"] data.append(newdata) with open(file_path, 'w') as f: json.dump(data, f) f.close() return 'success' def get_azureStorage(): try: file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','azurestorage.conf')) with open(file_path, 'r') as f: data = json.load(f) except: data = [] return data def read_azureStorage(name,directoryname,DATA_FILE_PATH): try: file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','azurestorage.conf')) with open(file_path, 'r') as f: data = json.load(f) except: data = [] found = False for x in data: if x['azurename'] == name: storage_account_name = str(x['azurename']) storage_account_key = str(x['azureaccountkey']) azure_container_name = x['containername'] found = True break try: if found: root_dir = str(directoryname) from azure.storage.filedatalake import DataLakeServiceClient import io import pandavro as pdx from detect_delimiter import detect try: service_client = DataLakeServiceClient(account_url="{}://{}.dfs.core.windows.net".format("https", storage_account_name), credential=storage_account_key) print(azure_container_name) file_system_client = service_client.get_file_system_client(azure_container_name) print(root_dir) file_paths = file_system_client.get_paths(path=root_dir) main_df = pd.DataFrame() for path in file_paths: if not path.is_directory: file_client = file_system_client.get_file_client(path.name) file_ext = os.path.basename(path.name).split('.', 1)[1] if file_ext in ["csv", "tsv"]: with open(csv_local, "wb") as my_file: download = file_client.download_file() download.readinto(my_file) with open(csv_local, 'r') as file: data = file.read() row_delimiter = detect(text=data, default=None, whitelist=[',', ';', ':', '|', '\t']) processed_df = pd.read_csv(csv_local, sep=row_delimiter) if file_ext == "parquet": download = file_client.download_file() stream = io.BytesIO() download.readinto(stream) processed_df = pd.read_parquet(stream, engine='pyarrow') if file_ext == "avro": with open(avro_local, "wb") as my_file: download = file_client.download_file() download.readinto(my_file) processed_df = pdx.read_avro(avro_local) if not main_df.empty: main_df = main_df.append(processed_df, ignore_index=True) else: main_df = pd.DataFrame(processed_df) except Exception as e: print(e) return 'Success',main_df except Exception as e: print(e) return 'Error', pd.DataFrame() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from typing import Tuple, Union, List import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import SGDClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from flwr.common.logger import log from logging import INFO TRUE_FALSE_MAPPING = {'True':'False','true':'false',True:False,'y':'n','Y':'N','Yes':'No','yes':'no','YES':'NO'} XY = Tuple[np.ndarray, np.ndarray] Dataset = Tuple[XY, XY] LogRegParams = Union[XY, Tuple[np.ndarray]] XYList = List[XY] modelUsed=None modelname=None def setmodelName(modelselected): try: modelname=str(modelselected) print("setmodelName ,given modelname: \n",modelname) if (modelname.lower() == 'logisticregression'): modelUsed=LogisticRegression() return True elif (modelname.lower() == "naivebayes"): modelUsed = GaussianNB() return True elif (modelname.lower() == "sgdclassifier"): #from sklearn.linear_model import SGDClassifier modelUsed=SGDClassifier() return True elif (modelname.lower() == "knn"): modelUsed = KNeighborsClassifier() return True elif (modelname.lower() == "decisiontreeclassifier"): modelUsed = DecisionTreeClassifier() return True else: return False except Exception as e: log(INFO, "set fl model name fn issue: ",e) def get_model_parameters(model:modelUsed) -> LogRegParams: """Returns the paramters of a sklearn LogisticRegression model.""" model_name=model.__class__.__name__ if model.fit_intercept: params = (model.coef_, model.intercept_) else: params = (model.coef_,) return params def set_model_params( model:modelUsed, params: LogRegParams ) -> modelUsed: """Sets the parameters of a sklean LogisticRegression model.""" model.coef_ = params[0] model_name=model.__class__.__name__ try: if model.fit_intercept: model.intercept_ = params[1] except Exception as e: log(INFO, "set_model_params fn issue: ",e) pass return model def set_initial_params(model,no_classes,no_features): """Sets initial parameters as zeros Required since model params are uninitialized until model.fit is called. But server asks for initial parameters from clients at launch. Refer to sklearn.linear_model.LogisticRegression documentation for more information. """ n_classes = no_classes n_features = no_features model.classes_ = np.array([i for i in range(n_classes)]) model.coef_ = np.zeros((n_classes, n_features)) model_name=model.__class__.__name__ try: if model.fit_intercept: model.intercept_ = np.zeros((n_classes,)) except Exception as e: log(INFO, "set_initial_params fn issue: ",e) pass def shuffle(X: np.ndarray, y: np.ndarray) -> XY: """Shuffle X and y.""" rng = np.random.default_rng() idx = rng.permutation(len(X)) return X[idx], y[idx] def partition(X: np.ndarray, y: np.ndarray, num_partitions: int) -> XYList: """Split X and y into a number of partitions.""" return list( zip(np.array_split(X, num_partitions), np.array_split(y, num_partitions)) ) def get_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if v in TRUE_FALSE_MAPPING.keys(): return k return default_value def get_true_options( d): options = [] if isinstance(d, dict): for k,v in d.items(): if v in TRUE_FALSE_MAPPING.keys(): options.append(k) return options def set_true_option(d, key=None, value='True'): if key in d.keys(): if value in TRUE_FALSE_MAPPING.keys(): for k in d.keys(): d[ k] = TRUE_FALSE_MAPPING[ value] d[key] = value return d ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import os import sys import scipy.stats as st def DistributionFinder(data): try: distributionName = "" sse = 0.0 KStestStatic = 0.0 dataType = "" if (data.dtype == "float64"): dataType = "Continuous" elif (data.dtype == "int"): dataType = "Discrete" elif (data.dtype == "int64"): dataType = "Discrete" if (dataType == "Discrete"): distributions = [st.bernoulli, st.binom, st.geom, st.nbinom, st.poisson] index, counts = np.unique(data.astype(int), return_counts=True) if (len(index) >= 2): best_sse = np.inf y1 = [] total = sum(counts) mean = float(sum(index * counts)) / total variance = float((sum(index ** 2 * counts) - total * mean ** 2)) / (total - 1) dispersion = mean / float(variance) theta = 1 / float(dispersion) r = mean * (float(theta) / 1 - theta) for j in counts: y1.append(float(j) / total) pmf1 = st.bernoulli.pmf(index, mean) pmf2 = st.binom.pmf(index, len(index), p=mean / len(index)) pmf3 = st.geom.pmf(index, 1 / float(1 + mean)) pmf4 = st.nbinom.pmf(index, mean, r) pmf5 = st.poisson.pmf(index, mean) sse1 = np.sum(np.power(y1 - pmf1, 2.0)) sse2 = np.sum(np.power(y1 - pmf2, 2.0)) sse3 = np.sum(np.power(y1 - pmf3, 2.0)) sse4 = np.sum(np.power(y1 - pmf4, 2.0)) sse5 = np.sum(np.power(y1 - pmf5, 2.0)) sselist = [sse1, sse2, sse3, sse4, sse5] best_distribution = 'NA' for i in range(0, len(sselist)): if best_sse > sselist[i] > 0: best_distribution = distributions[i].name best_sse = sselist[i] elif (len(index) == 1): best_distribution = "Constant Data-No Distribution" best_sse = 0.0 distributionName = best_distribution sse = best_sse elif (dataType == "Continuous"): distributions = [st.uniform, st.expon, st.weibull_max, st.weibull_min, st.chi, st.norm, st.lognorm, st.t, st.gamma, st.beta] best_distribution = st.norm.name best_sse = np.inf datamin = data.min() datamax = data.max() nrange = datamax - datamin y, x = np.histogram(data.astype(float), bins='auto', density=True) x = (x + np.roll(x, -1))[:-1] / 2.0 for distribution in distributions: params = distribution.fit(data.astype(float)) arg = params[:-2] loc = params[-2] scale = params[-1] pdf = distribution.pdf(x, loc=loc, scale=scale, *arg) sse = np.sum(np.power(y - pdf, 2.0)) if (best_sse > sse > 0): best_distribution = distribution.name best_sse = sse distributionName = best_distribution sse = best_sse except: response = str(sys.exc_info()[0]) message = 'Job has Failed' + response exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) print(message) return distributionName, sse ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import socket import os import rsa from os.path import expanduser from pathlib import Path import requests import platform from appbe.dataPath import DATA_DIR import socket import getmac import subprocess import sys import json from datetime import datetime import binascii computername = socket.getfqdn() global_key = ''' -----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAzJcxqRiUpp7CzViyqNlYaeyceDh5y6Ib4SoxoyNkN3+k0q+cr1lb k0KdWTtHIVqH1wsLYofYjpB7X2RN0KYTv8VfwmfQNrpFEbiRz4gcAeuxGCPgGaue N1ttujQMWHWCcY+UH5Voh8YUfkW8P+T3zxvr1d30D+kVBJC59y/31JvTzr3Bw/T+ NYv6xiienYiEYtm9d5ATioEwZOXaQBrtVvRmqcod5A1h4kn1ZauLX2Ph8H4TAuit NLtw6xUCJNumphP7xdU+ca6P6a6eaLprgKhvky+nz16u9/AC2AazRQHKWf8orS6b fw16JDCRs0zU4mTQLCjkUUt0edOaRhUtcQIDAQAB -----END RSA PUBLIC KEY----- ''' quarter_key = ''' -----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAmKzOJxVEV9ulA+cjfxguAduLMD47OWjLcEAEmEuK8vR4O5f6e2h1 08NniGC+nkwqmM00U7JTVBkqnt9S/JgE3pAH2xwfWda2OvXNWisWmOQdqB0+XRHh NXsIG3yRk/sMlDpe7MJIyM5ADSu01PLn9FZTfmMq7lEp32tAf71cuUE/dwuWSvEQ WK2hn1L4D97O43XCd7FHtMSHfgtjdcCFgX9IRgWLKC8Bm3q5qcqF4v3cHuYTj3V9 njxPtRqPg6HJFiJrm9AX5bUEHAvbTcw4wAmsNTRQHPvVB+Lc+yGh5x8crhKjNB01 gdB5I3a4mPO7dKvadR6Mr28trr0Ff5t2HQIDAQAB -----END RSA PUBLIC KEY----- ''' halfYear_key=''' -----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAgrGNwl8CNYQmVxi8/GEgPjfL5aEmyPkDyaJb9h4hZDSZCeeKd7Rv wwhuRTdBBfOp0bQ7QS7NYMg38Xlc3x85I9RnxdQdDKn2nRuvG0hG3wMBFy/DCSXF tXbDjJkLijAhqcBNu8m+a2Gtn14ShC7TbcfY4iVXho3WFUrn0xq6S5ducqWCsLJh R+TNImCaMICqfoAzEDGC3ojO5Hi3vJmmyK5CVp6bt4wLRATQjcp1ujGW4Uv4kEgp 7TR077c226v1KOdKdyZPHJzT1MKwZrG2Gdluk3/Y1apbwyGzYqFdTCOAB+mE73Dn wFXURgDJQmaU2oxxaA13WRcELpnirm+aIwIDAQAB -----END RSA PUBLIC KEY----- ''' oneYear_key=''' -----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEA3GLqn+vkKn3fTNH3Bbb3Lq60pCoe+mn0KPz74Bp7p5OkZAUe14pP Tcf/UqdPwiENhSCseWtfZmfKDK8qYRHJ5xW02+AhHPPdiacS45X504/lGG3q/4SG ZgaFhMDvX+IH/ZH+qqbU3dRQhXJCCrAVAa7MonzM6yPiVeS2SdpMkNg1VDR1oTLB Pn+qSV6CnkK1cYtWCRQ23GH2Ru7fc09r7m8hVcifKJze84orpHC5FX0WScQuR8h/ fs1IbGkxTOxP8vplUj/cd4JjUxgd+w+8R4kcoPhdGZF5UGeZA8xMERzQLvh+4Ui0 KIvz5/iyKB/ozaeSG0OMwDAk3WDEnb1WqQIDAQAB -----END RSA PUBLIC KEY----- ''' full_key=''' -----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAhqfNMuYYLdVrePhkO9rU/qT6FgolzI0YyzIJ2OeJE+++JioYm6nn ohQU32iiE0DZlCCLrHJXOOIAz2Op80goX0lxtngyxVUPsiB5CI77sAC7x6K3anJ0 elpnQCC0+xV2ZL5eIMNQHLe+X6wJl/HGWqkUlxKpWr4/kBEB4EisW60OePfhntIN 4OUJ7iEq+sDdOM5WazJIXeNV1cig4i6057GE3k5ITcQUmw17DZu2+dqkIscckaG+ t5SF7Qnvt4IY8IeQp2htx3yD+CJCV0u2uKwoSFMGJn3OWdaixC3+eojyMXmfAWtQ Ee9NLNNaTCMIvQ8BeItJLQs2Htw3bZNMvwIDAQAB -----END RSA PUBLIC KEY----- ''' def validate_key_Pair(privatepath,publickey): with open(privatepath, 'rb') as privatefile: keydata = privatefile.read() privatefile.close() try: privkey = rsa.PrivateKey.load_pkcs1(keydata,'PEM') data = 'Validate Global License' signature = rsa.sign(data.encode('utf-8'), privkey, 'SHA-1') pubkey = rsa.PublicKey.load_pkcs1(publickey) except: return False try: rsa.verify(data.encode('utf-8'), signature, pubkey) return True except Exception as e: return False def updateDRecord(licensepath): domain_license_path = os.path.join(DATA_DIR,'License','license_domain.lic') if(os.path.isfile(licensepath)): with open(licensepath, 'rb') as f: licensekey = f.read() f.close() with open(domain_license_path, 'wb') as f: f.write(licensekey) f.close() if(validate_key_Pair(domain_license_path,global_key)): return True,'Valid Domain License' else: return False,'Invalid Domain License' else: return False,'File Not Exists' def generateLicenseKey(userKey): record = {'UserKey':userKey} record = json.dumps(record) status = 'Error' url = 'https://qw7e33htlk.execute-api.ap-south-1.amazonaws.com/default/aion_license' try: response = requests.post(url, data=record,headers={"x-api-key":"3cQKRkKA4S57pYrkFp1Dd9jRXt4xnFoB9iqhAQRM","Content-Type":"application/json",}) if response.status_code == 200: outputStr=response.content outputStr = outputStr.decode('utf-8','ignore') outputStr = outputStr.strip() license_dict = json.loads(str(outputStr)) if license_dict['status'] == 'success': status = 'Success' licenseKey = license_dict['msg'] else: status = 'Error' licenseKey = '' else: status = 'Error' licenseKey = '' except Exception as inst: print(inst) status = 'Error' licenseKey = '' msg = {'status':status,'key':userKey,'licenseKey':licenseKey,'link':''} return msg def updateRecord(licensepath): currentDirectory = os.path.dirname(os.path.abspath(__file__)) license_path = os.path.join(currentDirectory,'..','lic','license.lic') if(os.path.isfile(licensepath)): with open(licensepath, 'rb') as f: licensekey = f.read() f.close() with open(license_path, 'wb') as f: f.write(licensekey) f.close() status,msg = check_domain_license() if status: status,msg = getdaysfromstartdate() if status: status,msg = check_days_license(int(msg)) return status,msg else: return False,'File Not Exists' def check_domain_license(): if 'CORP.HCL.IN' in computername: return True,'HCL Domain' else: return True,'HCL Domain' def diff_month(d1, d2): return (d1.year - d2.year) * 12 + d1.month - d2.month def getdaysfromstartdate(): currentDirectory = os.path.dirname(os.path.abspath(__file__)) startdatePath = os.path.join(currentDirectory,'..','lic','startdate.txt') if(os.path.isfile(startdatePath)): with open(startdatePath, "rb") as fl: encrypted_message = fl.read() fl.close() privkey = '''-----BEGIN RSA PRIVATE KEY----- MIIEqwIBAAKCAQEAm75ZwaepuxGJjU1Slk1+IUO2E49Hy8i9dym5FUaBRyTRH6R+ GTF1kcpd+1QinIZDMIdsmAc95Y8pTufxY30QxCkOhVASitSQWHS/IiWQHmsTJwdr 38lqZnQQloOt/iPlhcavbxu/yKFzwBmp+nM+ErDTnCBh6EGCGrw1xWF30T2IBpmp WwMEoqZsFV69RzwQAw39KG1KCxi5uscrB62YPgUdlT2b4Yaa90egQhGLLVdnKvhP ORiGT9omCH90Dkm1oMMQ0Y2JBLezgXa/bunSqtTBxEwzlwUAX2JJcanFYrzKy2OL xzwNRlWUXilZ4R/1RHAgUdNyKbYxZqc24MApoQIDAQABAoIBAQCHZ/i7gNz10qqH 2qkqGlfF7gvYd6MRTwdDGlhbYgA17ZGP9EDaAIFabtpFEAJDmgvCnotQpkMvWcet XcUmHW89TQDd8R8d6u9QqLggpQ3nFGsDbNViLMjAKLrfUb8tjOIZ7ANNE5ArjAuK AgYhxJ48O9bPD+xvtLwip95PHxMMz1CF0vxrpCinvPdeC3HzcnLNZWN3ustbph/4 Tx8mrKDpAVIHVYVbY4CMtm7NbIBYdyR9Lokc4zBg/OTuLo+0QRVJ3GHAN6cGxTwY vLwN9iBBHyn9WBp5NIOSoCdob7+ce8y+X8yHmVhwRCfcrYphzfFNfP7SPNzV1dLs dFybn/h9AoGJALCOC7ss+PBXy5WrWVNRPzFO7KrJDl5q7s/gMk0PkB4i4XOKHDTl MhHZXhxp84HwpphwNxPHvpFe3pVZwwoe8LH1neoodlLOF0Kuk3jENh6cMhKFvcZ+ gxaBxGSCOXF/U307mh0i4AafClhVjxtLgBW5iJSVA9Brc7ZqVwxlUP7aYGzReIE1 uEMCeQDh0vq8NteUlkM/wpNzrHHqgtEzePbTYa+QcTm4xhARHR/cO+E0/mZIfltw 3NVWCIalMia+aKnvRHqHy/cQfEo2Uv/h8oARWnbrvicMRTwYL0w2GrP0f+aG0RqQ msLMzS3kp6szhM7C99reFxdlxJoWBKkp94psOksCgYkApB01zGRudkK17EcdvjPc sMHzfoFryNpPaI23VChuR4UW2mZ797NAypSqRXE7OALxaOuOVuWqP8jW0C9i/Cps hI+SnZHFAw2tU3+hd3Wz9NouNUd6c2MwCSDQ5LikGttHSTa49/JuGdmGLTxCzRVu V0NiMPMfW4I2Sk8o4U3gbzWgwiYohLrhrwJ5ANun/7IB2lIykvk7B3g1nZzRYDIk EFpuI3ppWA8NwOUUoj/zksycQ9tx5Pn0JCMKKgYXsS322ozc3B6o3AoSC5GpzDH4 UnAOwavvC0ZZNeoEX6ok8TP7EL3EOYW8s4zIa0KFgPac0Q0+T4tFhMG9qW+PWwhy Oxeo3wKBiCQ8LEgmHnXZv3UZvwcikj6oCrPy8fnhp5RZl2DPPlaqf3vokE6W5oEo LIKcWKvth3EU7HRKwYgaznj/Mw55aETx31R0FiXMG266B4V7QWPF/KuaR0GBsYfu +edGXQCnLgooKlMtQLdL5mcLXHc9x/0Z0iYEejJtbjcGR87WylSNaCH3hH703iQ= -----END RSA PRIVATE KEY----- ''' privkey = rsa.PrivateKey.load_pkcs1(privkey,'PEM') decrypted_message = rsa.decrypt(encrypted_message, privkey) decrypted_message = decrypted_message.decode() import datetime start_time = datetime.datetime.strptime(decrypted_message, '%Y-%m-%d') current_date = datetime.datetime.today().strftime('%Y-%m-%d') current_date = datetime.datetime.strptime(current_date, '%Y-%m-%d') Months = diff_month(current_date,start_time) return True,Months else: return False,'Start Date Not Exists' def check_days_license(months): currentDirectory = os.path.dirname(os.path.abspath(__file__)) license_path = os.path.join(currentDirectory,'..','lic','license.lic') if(os.path.isfile(license_path)): if(validate_key_Pair(license_path,full_key)): return True,'Valid License' elif(validate_key_Pair(license_path,oneYear_key)): if months <= 12: return True,'Valid License' else: return False,'License for AI.ON has expired. Please contact ERS Research for renewal.' elif(validate_key_Pair(license_path,halfYear_key)): if months <= 6: return True,'Valid License' else: return False,'License for AI.ON has expired. Please contact ERS Research for renewal.' elif(validate_key_Pair(license_path,quarter_key)): if months <= 3: return True,'Valid License' else: return False,'License for AI.ON has expired. Please contact ERS Research for renewal.' else: return False,'Invalid License' else: return False,'License Not exists.Please contact ERS Research for renewal.' def checklicense(): import binascii license_path = os.path.join(DATA_DIR,'License','license.lic') if(os.path.isfile(license_path)): try: with open(license_path, 'r') as privatefile: license_key = privatefile.read() privatefile.close() encrypted_message = binascii.unhexlify(license_key.encode()) privkey = '''-----BEGIN RSA PRIVATE KEY----- MIIEqQIBAAKCAQEAhqfNMuYYLdVrePhkO9rU/qT6FgolzI0YyzIJ2OeJE+++JioY m6nnohQU32iiE0DZlCCLrHJXOOIAz2Op80goX0lxtngyxVUPsiB5CI77sAC7x6K3 anJ0elpnQCC0+xV2ZL5eIMNQHLe+X6wJl/HGWqkUlxKpWr4/kBEB4EisW60OePfh ntIN4OUJ7iEq+sDdOM5WazJIXeNV1cig4i6057GE3k5ITcQUmw17DZu2+dqkIscc kaG+t5SF7Qnvt4IY8IeQp2htx3yD+CJCV0u2uKwoSFMGJn3OWdaixC3+eojyMXmf AWtQEe9NLNNaTCMIvQ8BeItJLQs2Htw3bZNMvwIDAQABAoIBAGGmuRnrYaeDeWAO CmqZxRMyQybOjyDrRgq9rAR/zJoHp8b3ikcBDTkuBQELWVZLFj7k50XU2cono9zC cxI5xwVrNqrUOkV+7VYJVJzPTFkT/xnEt+zbOfstKmmIDpdzthtTLuHlomhhHA83 rPFi5a0Dpynz35suEnm6ONxx4ICONa3xkQ51ALm8EEsdJ+qRQhi2HLTF/OVZMxSa A2DlFd4ChOEbYaN63xVCDxPXe9BfeHd/Rnim9x4xL9i2RL+mhARUy/ZP6LMHIPk7 NxTrGr4TuE/ETg8FZ3cywSnwsMlcplXo8Ar+5ths2XKxbmH1TI/vuQV1r7r0IeqV F4W/xOkCgYkAiDQy7/WyJWuT+rQ+gOjSUumXgWE3HO+vJAsy05cTZFSs+nUE4ctn FnvbBIRuClSr3zhcTtjaEaVnZ2OmGfOoAq0cvaXSlxqEs2456WQBf9oPHnvJEV07 AIqzo2EuDvGUh/bkFN3+djRRL9usNNplYA8jU3OQHGdeaS15ZikT+ZkQLXoHE0Oh vQJ5AP0W9Qouvc9jXRhjNNOWmgt+JiHw/oQts/LUWJ2T4UJ7wKAqGwsmgf0NbF2p aZ6AbMc7dHzCb52iLJRxlmlkJYzg449t0MgQVxTKQ5viIAdjkRBCIY2++GcYXb6k 6tUnF0Vm2kpffYUb5Lx5JoUE6IhMP0mEv3jKKwKBiCmvoC9lCUL+q+m9JKwbldOe fqowcMfAa+AiNUohIORCLjbxfa8Fq+VrvtqhFXS/+WJ2Q3o2UHe6Ie24x+uFcVRw Wy2IBO4ORbMM91iBLRxORvZTeHSCDj7aNKS6Z3hXY9hBLglc8DaJSJfXKdt7RC+k MnGmGuM2l+Sk8FTeGaj4ucTRZjz1JBkCeQDhNSV1GyShv4xeoCCoy1FmOqmZ+EWy vqxqv1PfXHDM5SwCGZWY9XokAGbWbWLjvOmO27QLNEV34pCCwxSR0aCsXI2B2rk2 3Xtvr5A7zRqtGIdEDWSoKjAGJSN9+mhQpglKI3zJQ3GBGdIPeEqzgSud5SNHu01a IaMCgYgyoxtqdWi90iE75/x+uIVGJRdHtWoL2dr8Ixu1bOMjKCR8gjneSRTqI1tA lbRH5K/jg6iccB/pQmBcIPIubF10Nv/ZQV760WK/h6ue2hOCaBLWT8EQEEfBfnp+ 9rfBfNQIQIkBFTfGIHXUUPb9sJgDP1boUxcqxr9bpKUrs1EMkUd+PrvpHIj2 -----END RSA PRIVATE KEY----- ''' privkey = rsa.PrivateKey.load_pkcs1(privkey,'PEM') decrypted_message = rsa.decrypt(encrypted_message, privkey) msg = decrypted_message.decode().split('####') product = msg[0] computernameLicense = msg[1] computername = socket.getfqdn() licenseValid = False if product.lower() == 'aion': if computernameLicense == computername: uuidlicense = msg[3] uuid = guid() if uuidlicense == uuid: current_date = datetime.now() license_expiry_date = msg[5] license_expiry_date = datetime.strptime(license_expiry_date,'%Y-%m-%d %H:%M:%S') if current_date > license_expiry_date: return False,'License Expire' else: return True,'' return False,'License Error' except Exception as e: print(e) return False,'License Error' else: return False,'Generate License' def generate_record_key(product,version): computername = socket.getfqdn() macaddress = getmac.get_mac_address() license_date = datetime.today().strftime('%Y-%m-%d %H:%M:%S') try: user = os.getlogin() except: user = 'NA' uuid = guid() msg = product+'###'+version+'###'+computername+'###'+macaddress+'###'+user+'###'+sys.platform+'###'+uuid+'###'+license_date pkeydata='''-----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAm75ZwaepuxGJjU1Slk1+IUO2E49Hy8i9dym5FUaBRyTRH6R+GTF1 kcpd+1QinIZDMIdsmAc95Y8pTufxY30QxCkOhVASitSQWHS/IiWQHmsTJwdr38lq ZnQQloOt/iPlhcavbxu/yKFzwBmp+nM+ErDTnCBh6EGCGrw1xWF30T2IBpmpWwME oqZsFV69RzwQAw39KG1KCxi5uscrB62YPgUdlT2b4Yaa90egQhGLLVdnKvhPORiG T9omCH90Dkm1oMMQ0Y2JBLezgXa/bunSqtTBxEwzlwUAX2JJcanFYrzKy2OLxzwN RlWUXilZ4R/1RHAgUdNyKbYxZqc24MApoQIDAQAB -----END RSA PUBLIC KEY----- ''' pubkey = rsa.PublicKey.load_pkcs1(pkeydata) encrypted_message = rsa.encrypt(msg.encode(), pubkey) encrypted_message = binascii.hexlify(encrypted_message).decode() return(encrypted_message) def run(cmd): try: return subprocess.run(cmd, shell=True, capture_output=True, check=True, encoding="utf-8").stdout.strip() except Exception as e: print(e) return None def guid(): if sys.platform == 'darwin': return run( "ioreg -d2 -c IOPlatformExpertDevice | awk -F\\\" '/IOPlatformUUID/{print $(NF-1)}'", ) if sys.platform == 'win32' or sys.platform == 'cygwin' or sys.platform == 'msys': return run('wmic csproduct get uuid').split('\n')[2].strip() if sys.platform.startswith('linux'): return run('cat /var/lib/dbus/machine-id') or \ run('cat /etc/machine-id') if sys.platform.startswith('openbsd') or sys.platform.startswith('freebsd'): return run('cat /etc/hostid') or \ run('kenv -q smbios.system.uuid') def updateLicense(licensekey): license_folder = os.path.join(DATA_DIR,'License') license_folder = Path(license_folder) license_folder.mkdir(parents=True, exist_ok=True) license_file = license_folder/'license.lic' with open(license_file, "w") as fl: fl.write(licensekey) fl.close() def enterRecord(version): validLicense,msg = checklicense() if not validLicense: key = generate_record_key('AION',version) msg = {'status':msg,'key':key,'licenseKey':'','link':''} return validLicense,msg ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' #from sklearn.externals import joblib import joblib # import pyreadstat # import sys # import math import time import pandas as pd import numpy as np from sklearn.metrics import confusion_matrix from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, confusion_matrix from sklearn.svm import SVC from sklearn.linear_model import LinearRegression import argparse import json import os import pathlib from tensorflow.keras.models import load_model # from tensorflow.keras import backend as K import tensorflow as tf # from sklearn.decomposition import LatentDirichletAllocation from pathlib import Path #from aionUQ import aionUQ from uq_main import aionUQ import os from datetime import datetime from sklearn.model_selection import train_test_split parser = argparse.ArgumentParser() parser.add_argument('savFile') parser.add_argument('csvFile') parser.add_argument('features') parser.add_argument('target') args = parser.parse_args() from appbe.dataPath import DEPLOY_LOCATION if ',' in args.features: args.features = [x.strip() for x in args.features.split(',')] else: args.features = args.features.split(",") models = args.savFile if Path(models).is_file(): # if Path(args.savFile.is_file()): model = joblib.load(args.savFile) # print(model.__class__.__name__) # print('class:',model.__class__) # print(type(model).__name__) # try: # print('Classess=',model.classes_) # except: # print("Classess=N/A") # print('params:',model.get_params()) # try: # print('fea_imp =',model.feature_importances_) # except: # print("fea_imp =N/A") ProblemName = model.__class__.__name__ Params = model.get_params() # print("ProblemName: \n",ProblemName) # print("Params: \n",Params) # print('ProblemName:',model.__doc__) # print(type(ProblemName)) if ProblemName in ['LogisticRegression','SGDClassifier','SVC','DecissionTreeClassifier','RandomForestClassifier','GaussianNB','KNeighboursClassifier','DecisionTreeClassifier','GradientBoostingClassifier']: Problemtype = 'Classification' else : Problemtype = 'Regression' if Problemtype == 'Classification': df = pd.read_csv(args.csvFile) object_cols = [col for col, col_type in df.dtypes.items() if col_type == 'object'] df = df.drop(object_cols, axis=1) df = df.dropna(axis=1) df = df.reset_index(drop=True) modelfeatures = args.features # dfp = df[modelfeatures] tar = args.target # target = df[tar] y=df[tar] X = df.drop(tar, axis=1) #for dummy test,train values pass X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) uqObj=aionUQ(df,X,y,ProblemName,Params,model,modelfeatures,tar) #accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertaitny_per=uqObj.uqMain_BBMClassification(X_train, X_test, y_train, y_test,"uqtest") accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertaitny_per=uqObj.uqMain_BBMClassification() # print("UQ Classification: \n",output_jsonobject) print(accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertaitny_per) print("End of UQ Classification.\n") else: df = pd.read_csv(args.csvFile) modelfeatures = args.features # print("modelfeatures: \n",modelfeatures) # print("type modelfeatures: \n",type(modelfeatures)) dfp = df[modelfeatures] tar = args.target target = df[tar] #Not used, just dummy X,y split y=df[tar] X = df.drop(tar, axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) uqObj=aionUQ(df,dfp,target,ProblemName,Params,model,modelfeatures,tar) total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject=uqObj.uqMain_BBMRegression() print(total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject) print("End of UQ reg\n") elif Path(models).is_dir(): os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices' os.environ['TF_CPP_MIN_LOG_LEVEL']='2' model = load_model(models) ProblemName = model.__class__.__name__ Problemtype = 'Classification' # print('class:',model.__class__) # print('class1',model.__class__.__name__) # print(model.summary()) # print('ProblemName1:',model.get_config()) def Params(model: tf.keras.Model): Params = [] model.Params(print_fn=lambda x: Params.append(x)) return '\n'.join(Params) df = pd.read_csv(args.csvFile) modelfeatures = args.features dfp = df[modelfeatures] tar = args.target target = df[tar] df3 = dfp.astype(np.float32) predic = model.predict(df3) if predic.shape[-1] > 1: predic = np.argmax(predic, axis=-1) else: predic = (predic > 0.5).astype("int32") matrixconfusion = pd.DataFrame(confusion_matrix(predic,target)) matrixconfusion = matrixconfusion.to_json(orient='index') classificationreport = pd.DataFrame(classification_report(target,predic,output_dict=True)).transpose() classificationreport = round(classificationreport,2) classificationreport = classificationreport.to_json(orient='index') output = {} output["Precision"] = "%.3f" % precision_score(target, predic,average='weighted') output["Recall"] = "%.3f" % recall_score(target, predic,average='weighted') output["Accuracy"] = "%.3f" % accuracy_score(target, predic) output["ProblemName"] = ProblemName output["Params"] = Params output["Problemtype"] = Problemtype output["Confusionmatrix"] = matrixconfusion output["classificationreport"] = classificationreport print(json.dumps(output)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import logging logging.getLogger('tensorflow').disabled = True import json #from nltk.corpus import stopwords from collections import Counter from matplotlib import pyplot import sys import os import json import matplotlib.pyplot as plt from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression from uq360.algorithms.ucc_recalibration import UCCRecalibration from sklearn import datasets from sklearn.model_selection import train_test_split import pandas as pd from uq360.metrics.regression_metrics import compute_regression_metrics import numpy as np from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import roc_curve # from math import sqrt from sklearn.metrics import r2_score,mean_squared_error, explained_variance_score,mean_absolute_error # from uq360.metrics import picp, mpiw, compute_regression_metrics, plot_uncertainty_distribution, plot_uncertainty_by_feature, plot_picp_by_feature from uq360.metrics import plot_uncertainty_by_feature, plot_picp_by_feature #Added libs from MLTest import sys import time from sklearn.metrics import confusion_matrix from pathlib import Path import logging # import json class aionUQ: # def __init__(self,uqdf,targetFeature,xtrain,ytrain,xtest,ytest,uqconfig_base,uqconfig_meta,deployLocation,saved_model): def __init__(self,df,dfp,target,ProblemName,Params,model,modelfeatures,targetfeature,deployLocation): # #printprint("Inside aionUQ \n") try: #print("Inside aionUQ init\n ") self.data=df self.dfFeatures=dfp self.uqconfig_base=Params self.uqconfig_meta=Params self.targetFeature=targetfeature self.target=target self.selectedfeature=modelfeatures self.y=self.target self.X=self.dfFeatures self.log = logging.getLogger('eion') self.basemodel=model self.model_name=ProblemName self.Deployment = os.path.join(deployLocation,'log','UQ') os.makedirs(self.Deployment,exist_ok=True) self.uqgraphlocation = os.path.join(self.Deployment,'UQgraph') os.makedirs(self.uqgraphlocation,exist_ok=True) except Exception as e: self.log.info('<!------------- UQ model INIT Error ---------------> '+str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def totalUncertainty(self,df,basemodel,model_params,xtrain, xtest, ytrain, ytest,aionstatus): from sklearn.model_selection import train_test_split # To get each class values and uncertainty if (aionstatus.lower() == 'aionuq'): X_train, X_test, y_train, y_test = xtrain, xtest, ytrain, ytest # y_val = y_train.append(y_test) else: # y_val = self.y df=self.data y=df[self.targetFeature] X = df.drop(self.targetFeature, axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) key = 'criterion' #if key in model_params: try: #if model_params.has_key(key): if key in model_params: if (model_params['criterion']): uq_scoring_param=model_params.get('criterion') elif(model_params['criterion'] == None): uq_scoring_param='picp' else: uq_scoring_param='picp' else: uq_scoring_param='picp' pass except Exception as inst: uq_scoring_param='picp' # from sklearn.tree import DecisionTreeRegressor # from sklearn.linear_model import LinearRegression,Lasso,Ridge # from sklearn import linear_model # from sklearn.ensemble import RandomForestRegressor if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']: uq_scoring_param=uq_scoring_param else: uq_scoring_param='picp' uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params) # this will fit both the base and the meta model uqmodel_fit = uq_model.fit(X_train, y_train) y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test) y_hat_total_mean=np.mean(y_hat) y_hat_lb_total_mean=np.mean(y_hat_lb) y_hat_ub_total_mean=np.mean(y_hat_ub) mpiw_20_per=(y_hat_total_mean*20/100) mpiw_lower_range = y_hat_total_mean - mpiw_20_per mpiw_upper_range = y_hat_total_mean + mpiw_20_per from uq360.metrics import picp, mpiw observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub) observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub) observed_alphas_picp=round(observed_alphas_picp,2) observed_widths_mpiw=round(observed_widths_mpiw,2) picp_percentage= round(observed_alphas_picp*100) Uncertainty_percentage=round(100-picp_percentage) self.log.info('Model total observed_widths_mpiw : '+str(observed_widths_mpiw)) self.log.info('Model mpiw_lower_range : '+str(mpiw_lower_range)) self.log.info('Model mpiw_upper_range : '+str(mpiw_upper_range)) self.log.info('Model total picp_percentage : '+str(picp_percentage)) return observed_alphas_picp,observed_widths_mpiw,picp_percentage,Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range def display_results(self,X_test, y_test, y_mean, y_lower, y_upper): try: global x_feature,y_feature if (isinstance(self.selectedfeature, list) or isinstance(self.selectedfeature, tuple)): x_feature=''.join(map(str, self.selectedfeature)) else: x_feature= str(self.selectedfeature) # self.selectedfeature=str(self.selectedfeature) X_test=np.squeeze(X_test) y_feature=str(self.targetFeature) pred_dict = {x_feature: X_test, 'y': y_test, 'y_mean': y_mean, 'y_upper': y_upper, 'y_lower': y_lower } pred_df = pd.DataFrame(data=pred_dict) pred_df_sorted = pred_df.sort_values(by=x_feature) plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y'], 'o', label='Observed') plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y_mean'], '-', lw=2, label='Predicted') plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y_upper'], 'r--', lw=2, label='Upper Bound') plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y_lower'], 'r--', lw=2, label='Lower Bound') plt.legend() plt.xlabel(x_feature) plt.ylabel(y_feature) plt.title('UQ Confidence Interval Plot.') # plt.savefig('uq_test_plt.png') if os.path.exists(str(self.uqgraphlocation)+'/uq_test_plt.png'): os.remove(str(self.uqgraphlocation)+'/uq_test_plt.png') plt.savefig(str(self.Deployment)+'/uq_test_plt.png') plt.savefig(str(self.uqgraphlocation)+'/uq_test_plt.png') plt.clf() plt.cla() plt.close() pltreg=plot_picp_by_feature(X_test, y_test, y_lower, y_upper, xlabel=x_feature) #pltreg.savefig('x.png') pltr=pltreg.figure if os.path.exists(str(self.uqgraphlocation)+'/picp_per_feature.png'): os.remove(str(self.uqgraphlocation)+'/picp_per_feature.png') pltr.savefig(str(self.Deployment)+'/picp_per_feature.png') pltr.savefig(str(self.uqgraphlocation)+'/picp_per_feature.png') plt.clf() plt.cla() plt.close() except Exception as e: # #print("display exception: \n",e) self.log.info('<!------------- UQ model Display Error ---------------> '+str(e)) def classUncertainty(self,pred,score): try: outuq = {} classes = np.unique(pred) for c in classes: ids = pred == c class_score = score[ids] predc = 'Class_'+str(c) outuq[predc]=np.mean(class_score) x = np.mean(class_score) #Uncertaininty in percentage x=x*100 self.log.info('----------------> Class '+str(c)+' Confidence Score '+str(round(x))) return outuq except Exception as e: # #print("display exception: \n",e) self.log.info('<!------------- UQ classUncertainty Error ---------------> '+str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def uqMain_BBMClassification(self,x_train, x_test, y_train, y_test,aionstatus): try: # print("Inside uqMain_BBMClassification\n") # print("lenth of x_train {}, x_test {}, y_train {}, y_test {}".format(x_train, x_test, y_train, y_test)) aionstatus = str(aionstatus) if (aionstatus.lower() == 'aionuq'): X_train, X_test, y_train, y_test = x_train, x_test, y_train, y_test else: X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, random_state=0) from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelClassification from uq360.metrics.classification_metrics import plot_reliability_diagram,area_under_risk_rejection_rate_curve,plot_risk_vs_rejection_rate,expected_calibration_error,compute_classification_metrics from sklearn.ensemble import GradientBoostingClassifier from sklearn.linear_model import LogisticRegression from sklearn.linear_model import SGDClassifier from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.svm import SVC from xgboost import XGBClassifier from lightgbm import LGBMClassifier from sklearn.neighbors import KNeighborsClassifier base_modelname=__class__.__name__ base_config = self.uqconfig_base meta_config = self.uqconfig_base model_name=self.basemodel.__class__.__name__ #print(model_name) try: #geting used features model_used_features=self.basemodel.feature_names_in_ self.log.info("Base model used training features are (UQ Testing): \n"+str(model_used_features)) except: pass model_params=self.basemodel.get_params() uq_scoring_param='accuracy' basemodel=None if (model_name == "GradientBoostingClassifier"): basemodel=GradientBoostingClassifier elif (model_name == "SGDClassifier"): basemodel=SGDClassifier elif (model_name == "GaussianNB"): basemodel=GaussianNB elif (model_name == "DecisionTreeClassifier"): basemodel=DecisionTreeClassifier elif(model_name == "RandomForestClassifier"): basemodel=RandomForestClassifier elif (model_name == "SVC"): basemodel=SVC elif(model_name == "KNeighborsClassifier"): basemodel=KNeighborsClassifier elif(model_name.lower() == "logisticregression"): basemodel=LogisticRegression elif(model_name == "XGBClassifier"): basemodel=XGBClassifier elif(model_name == "LGBMClassifier"): basemodel=LGBMClassifier else: basemodel=LogisticRegression calibrated_mdl=None if (model_name == "SVC"): from sklearn.calibration import CalibratedClassifierCV basemodel=SVC(**model_params) calibrated_mdl = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3) calibrated_mdl.fit(X_train, y_train) basepredict = calibrated_mdl.predict(X_test) predprob_base = calibrated_mdl.predict_proba(X_test)[:, :] elif (model_name == "SGDClassifier"): from sklearn.calibration import CalibratedClassifierCV basemodel=SGDClassifier(**model_params) calibrated_mdl = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3) calibrated_mdl.fit(X_train, y_train) basepredict = calibrated_mdl.predict(X_test) predprob_base = calibrated_mdl.predict_proba(X_test)[:, :] else: from sklearn.calibration import CalibratedClassifierCV base_mdl = basemodel(**model_params) calibrated_mdl = CalibratedClassifierCV(base_mdl,method='sigmoid',cv=3) basemodelfit = calibrated_mdl.fit(X_train, y_train) basepredict = calibrated_mdl.predict(X_test) predprob_base=calibrated_mdl.predict_proba(X_test)[:, :] cal_model_params=calibrated_mdl.get_params() acc_score_base=accuracy_score(y_test, basepredict) base_estimator_calibrate = cal_model_params['base_estimator'] uq_model = BlackboxMetamodelClassification(base_model=self.basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params) try: X_train=X_train[model_used_features] X_test=X_test[model_used_features] except: pass uqmodel_fit = uq_model.fit(X_train, y_train,base_is_prefitted=True,meta_train_data=(X_train, y_train)) # uqmodel_fit = uq_model.fit(X_train, y_train) y_t_pred, y_t_score = uq_model.predict(X_test) acc_score=accuracy_score(y_test, y_t_pred) test_accuracy_perc=round(100*acc_score) if(aionstatus == "aionuq"): test_accuracy_perc=round(test_accuracy_perc,2) #uq_aurrrc not used for any aion gui configuration, so it initialized as 0. if we use area_under_risk_rejection_rate_curve(), it shows plot in cmd prompt,so code execution interuupted.so we make it 0. uq_aurrrc=0 pass else: bbm_c_plot = plot_risk_vs_rejection_rate( y_true=y_test, y_prob=predprob_base, selection_scores=y_t_score, y_pred=y_t_pred, plot_label=['UQ_risk_vs_rejection'], risk_func=accuracy_score, num_bins = 10 ) # This done by kiran, need to uncomment for GUI integration. # bbm_c_plot_sub = bbm_c_plot[4] bbm_c_plot_sub = bbm_c_plot if os.path.exists(str(self.uqgraphlocation)+'/plot_risk_vs_rejection_rate.png'): os.remove(str(self.uqgraphlocation)+'/plot_risk_vs_rejection_rate.png') # bbm_c_plot_sub.savefig(str(self.uqgraphlocation)+'/plot_risk_vs_rejection_rate.png') re_plot=plot_reliability_diagram(y_true=y_test, y_prob=predprob_base, y_pred=y_t_pred, plot_label=['UQModel reliability_diagram'], num_bins=10 ) # This done by kiran, need to uncomment for GUI integration. # re_plot_sub = re_plot[4] re_plot_sub = re_plot if os.path.exists(str(self.uqgraphlocation)+'/plot_reliability_diagram.png'): os.remove(str(self.uqgraphlocation)+'/plot_reliability_diagram.png') # re_plot_sub.savefig(str(DEFAULT_FILE_PATH)+'/plot_reliability_diagram.png') uq_aurrrc=area_under_risk_rejection_rate_curve( y_true=y_test, y_prob=predprob_base, y_pred=y_t_pred, selection_scores=y_t_score, attributes=None, risk_func=accuracy_score,subgroup_ids=None, return_counts=False, num_bins=10) uq_aurrrc=uq_aurrrc test_accuracy_perc=round(test_accuracy_perc) #metric_all=compute_classification_metrics(y_test, y_prob, option='all') metric_all=compute_classification_metrics(y_test, predprob_base, option='accuracy') #expected_calibration_error uq_ece=expected_calibration_error(y_test, y_prob=predprob_base,y_pred=basepredict, num_bins=10, return_counts=False) # uq_aurrrc=uq_aurrrc confidence_score=acc_score_base-uq_ece ece_confidence_score=round(confidence_score,2) # Model uncertainty using ECE score # model_uncertainty_ece = 1-ece_confidence_score #Uncertainty Using model inherent predict probability mean_predprob_total=np.mean(y_t_score) model_confidence=mean_predprob_total model_uncertainty = 1-mean_predprob_total model_confidence = round(model_confidence,2) # To get each class values and uncertainty if (aionstatus.lower() == 'aionuq'): y_val = np.append(y_train,y_test) else: y_val = self.y self.log.info('------------------> Model Confidence Score '+str(model_confidence)) outuq = self.classUncertainty(y_t_pred,y_t_score) # Another way to get conf score model_uncertainty_per=round((model_uncertainty*100),2) model_confidence_per=round((model_confidence*100),2) acc_score_per = round((acc_score*100),2) uq_ece_per=round((uq_ece*100),2) output={} recommendation = "" if (uq_ece > 0.5): # RED text recommendation = 'Model has high ece (expected calibration error) score compare to threshold (0.5),not good to be deploy. need to be add more input data across all feature ranges to train base model, also try with different classification algorithms/ensembling to reduce ECE (ECE~0).' else: # self.log.info('Model has good ECE score and accuracy, ready to deploy.\n.') if (uq_ece <= 0.1 and model_confidence >= 0.9): # Green Text recommendation = 'Model has best calibration score (near to 0) and good confidence score , ready to deploy. ' else: # Orange recommendation = 'Model has good ECE score (between 0.1-0.5), but less confidence score compare to threshold (90%). If user wants,model can be improve by adding more input data across all feature ranges and could be evaluate with different algorithms/ensembling. ' #Adding each class uncertainty value classoutput = {} for k,v in outuq.items(): classoutput[k]=(str(round((v*100),2))) output['classes'] = classoutput output['ModelConfidenceScore']=(str(model_confidence_per)) output['ExpectedCalibrationError']=str(uq_ece_per) output['ModelUncertainty']=str(model_uncertainty_per) output['Recommendation']=recommendation # output['user_msg']='Please check the plot for more understanding of model uncertainty' #output['UQ_area_under_risk_rejection_rate_curve']=round(uq_aurrrc,4) output['Accuracy']=str(acc_score_per) output['Problem']= 'Classification' #self.log.info('Model Accuracy score in percentage : '+str(test_accuracy_perc)+str(' %')) # #print("Prediction mean for the given model:",np.mean(y_hat),"\n") #self.log.info(recommendation) #self.log.info("Model_confidence_score: " +str(confidence_score)) #self.log.info("Model_uncertainty: " +str(round(model_uncertainty,2))) #self.log.info('Please check the plot for more understanding of model uncertainty.\n.') uq_jsonobject = json.dumps(output) with open(str(self.Deployment)+"/uq_classification_log.json", "w") as f: json.dump(output, f) return test_accuracy_perc,uq_ece,output,model_confidence_per,model_uncertainty_per except Exception as inst: self.log.info('\n < ---------- UQ Model Execution Failed Start--------->') self.log.info('\n<------Model Execution failed!!!.' + str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) self.log.info('\n < ---------- Model Execution Failed End --------->') def aion_confidence_plot(self,df): df=df df = df.sort_values(by=self.selectedfeature) best_values=df.Best_values.to_list() best_upper=df.Best__upper.to_list() best_lower=df.Best__lower.to_list() Total_Upper_PI=df.Total_Upper_PI.to_list() Total_Low_PI=df.Total_Low_PI.to_list() Obseved = df.Observed.to_list() plt.plot(df[x_feature], df['Observed'], 'o', label='Observed') plt.plot(df[x_feature], df['Best__upper'],'r--', lw=2, color='grey') plt.plot(df[x_feature], df['Best__lower'],'r--', lw=2, color='grey') plt.plot(df[x_feature], df['Best_values'], 'r--', lw=2, label='MeanPrediction',color='red') plt.fill_between(df[x_feature], Total_Upper_PI, Total_Low_PI, label='Good Confidence', color='lightblue', alpha=.5) plt.fill_between(df[x_feature],best_lower, best_upper,label='Best Confidence', color='orange', alpha=.5) plt.legend() plt.xlabel(self.selectedfeature) plt.ylabel(self.targetFeature) plt.title('UQ Best & Good Area Plot') if os.path.exists(str(self.uqgraphlocation)+'/uq_confidence_plt.png'): os.remove(str(self.uqgraphlocation)+'/uq_confidence_plt.png') plt.savefig(str(self.uqgraphlocation)+'/uq_confidence_plt.png') plt.savefig(str(self.Deployment)+'/uq_confidence_plt.png') def uqMain_BBMRegression(self,x_train, x_test, y_train, y_test,aionstatus): aionstatus = str(aionstatus) # if (aionstatus.lower() == 'aionuq'): # X_train, X_test, y_train, y_test = x_train, x_test, y_train, y_test # total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,x_train, x_test, y_train, y_test,aionstatus) # else: # X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, random_state=0) # modelName = "" self.log.info('<!------------- Inside BlackBox MetaModel Regression process. ---------------> ') try: from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression import pandas as pd base_modelname=__class__.__name__ base_config = self.uqconfig_base meta_config = self.uqconfig_base model_name=self.basemodel.__class__.__name__ model_params=self.basemodel.get_params() # #print("model_params['criterion']: \n",model_params['criterion']) key = 'criterion' #if key in model_params: try: #if model_params.has_key(key): if key in model_params: if (model_params['criterion']): uq_scoring_param=model_params.get('criterion') elif(model_params['criterion'] == None): uq_scoring_param='picp' else: uq_scoring_param='picp' else: uq_scoring_param='picp' pass except Exception as inst: uq_scoring_param='picp' # modelname='sklearn.linear_model'+'.'+model_name # X_train, X_test, y_train, y_test = self.xtrain,self.xtest,self.ytrain,self.ytest #Geeting trained model name and to use the model in BlackboxMetamodelRegression from sklearn.tree import DecisionTreeRegressor from sklearn.linear_model import LinearRegression,Lasso,Ridge from sklearn.ensemble import RandomForestRegressor if (model_name == "DecisionTreeRegressor"): basemodel=DecisionTreeRegressor elif (model_name == "LinearRegression"): basemodel=LinearRegression elif (model_name == "Lasso"): basemodel=Lasso elif (model_name == "Ridge"): basemodel=Ridge elif(model_name == "RandomForestRegressor"): basemodel=RandomForestRegressor else: basemodel=LinearRegression if (aionstatus.lower() == 'aionuq'): X_train, X_test, y_train, y_test = x_train, x_test, y_train, y_test total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,x_train, x_test, y_train, y_test,aionstatus) else: X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, random_state=0) total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,None, None, None, None,aionstatus) if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']: uq_scoring_param=uq_scoring_param else: uq_scoring_param='picp' uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params) # this will fit both the base and the meta model uqmodel_fit = uq_model.fit(X_train, y_train) # #print("X_train.shape: \n",X_train.shape) y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test) from uq360.metrics import picp, mpiw observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub) observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub) picp_percentage= round(observed_alphas_picp*100) Uncertainty_percentage=round(100-picp_percentage) self.log.info('<!------------- observed_picp: ---------------> '+str(observed_alphas_picp)) self.log.info('<!------------- observed_widths_mpiw: ---------------> '+str(observed_widths_mpiw)) # UQ metamodel regression have metrics as follows, “rmse”, “nll”, “auucc_gain”, “picp”, “mpiw”, “r2” #metric_all=compute_regression_metrics(y_test, y_hat,y_hat_lb, y_hat_ub,option='all',nll_fn=None) #nll - Gaussian negative log likelihood loss. metric_all=compute_regression_metrics(y_test, y_hat,y_hat_lb, y_hat_ub,option=uq_scoring_param,nll_fn=None) metric_used='' for k,v in metric_all.items(): metric_used=str(round(v,2)) self.log.info('<!------------- Metric used for regression UQ: ---------------> '+str(metric_all)) # Determine the confidence level and recommentation to the tester # test_data=y_test observed_alphas_picp=round(observed_alphas_picp,2) observed_widths_mpiw=round(observed_widths_mpiw,2) #Calculate total uncertainty for all features # total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage = self.totalUncertainty(self.data) # df1=self.data total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,x_train, x_test, y_train, y_test,aionstatus) recommendation="" output={} if (observed_alphas_picp >= 0.95 and total_picp >= 0.75): # Add GREEN text self.log.info('Model has good confidence for the selected feature, ready to deploy.\n.') recommendation = "Model has good confidence score, ready to deploy." elif ((observed_alphas_picp >= 0.50 and observed_alphas_picp <= 0.95) and (total_picp >= 0.50)): # Orange recommendation = "Model has average confidence compare to threshold (95%), need to be add more input data across all feature ranges to train base model, also try with different regression algorithms/ensembling." self.log.info('Model has average confidence score compare to threshold, need to be add more input data for training base model and again try with UQ .') else: # RED text recommendation = "Model has less confidence compare to threshold (95%), need to be add more input data across all feature ranges to train base model, also try with different regression algorithms/ensembling." self.log.info('Model has less confidence score compare to threshold, need to be add more input data for training base model and again try with UQ .') #Build uq json info dict output['ModelConfidenceScore']=(str(total_picp_percentage)+'%') output['ModelUncertainty']=(str(total_Uncertainty_percentage)+'%') output['SelectedFeatureConfidence']=(str(picp_percentage)+'%') output['SelectedFeatureUncertainty']=(str(Uncertainty_percentage)+'%') output['PredictionIntervalCoverageProbability']=observed_alphas_picp output['MeanPredictionIntervalWidth']=round(observed_widths_mpiw) output['DesirableMPIWRange: ']=(str(round(mpiw_lower_range))+str(' - ')+str(round(mpiw_upper_range))) output['Recommendation']=str(recommendation) output['Metric']=uq_scoring_param output['Score']=metric_used output['Problemtype']= 'Regression' self.log.info('Model confidence in percentage is: '+str(picp_percentage)+str(' %')) self.log.info('Model Uncertainty is:: '+str(Uncertainty_percentage)+str(' %')) #self.log.info('Please check the plot for more understanding of model uncertainty.\n.') #self.display_results(X_test, y_test, y_mean=y_hat, y_lower=y_hat_lb, y_upper=y_hat_ub) uq_jsonobject = json.dumps(output) with open(str(self.Deployment)+"/uq_reg_log.json", "w") as f: json.dump(output, f) #To get best and medium UQ range of values from total predict interval y_hat_m=y_hat.tolist() y_hat_lb=y_hat_lb.tolist() upper_bound=y_hat_ub.tolist() y_hat_ub=y_hat_ub.tolist() for x in y_hat_lb: y_hat_ub.append(x) total_pi=y_hat_ub medium_UQ_range = y_hat_ub best_UQ_range= y_hat.tolist() ymean_upper=[] ymean_lower=[] y_hat_m=y_hat.tolist() for i in y_hat_m: y_hat_m_range= (i*20/100) x=i+y_hat_m_range y=i-y_hat_m_range ymean_upper.append(x) ymean_lower.append(y) min_best_uq_dist=round(min(best_UQ_range)) max_best_uq_dist=round(max(best_UQ_range)) # initializing ranges list_medium=list(filter(lambda x:not(min_best_uq_dist<=x<=max_best_uq_dist), total_pi)) list_best = y_hat_m X_test = np.squeeze(X_test) ''' uq_dict = {x_feature:X_test,'Observed':y_test,'Best_values': y_hat_m, 'Best__upper':ymean_upper, 'Best__lower':ymean_lower, 'Total_Low_PI': y_hat_lb, 'Total_Upper_PI': upper_bound, } print(uq_dict) uq_pred_df = pd.DataFrame(data=uq_dict) uq_pred_df_sorted = uq_pred_df.sort_values(by='Best_values') uq_pred_df_sorted.to_csv(str(self.Deployment)+"/uq_pred_df.csv",index = False) csv_path=str(self.Deployment)+"/uq_pred_df.csv" df=pd.read_csv(csv_path) self.log.info('uqMain() returns: observed_alphas_picp,observed_widths_mpiw,list_medium,list_best,metric_all.\n.') #Callconfidence olot fn only for UQTest interface if (aionstatus.lower() == 'aionuq'): #No need to showcase confidence plot for aion main pass else: self.aion_confidence_plot(df) ''' return total_picp_percentage,total_Uncertainty_percentage,list_medium,list_best,metric_all,json.loads(uq_jsonobject) except Exception as inst: exc = {"status":"FAIL","message":str(inst).strip('"')} out_exc = json.dumps(exc) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import logging logging.getLogger('tensorflow').disabled = True import json #from nltk.corpus import stopwords from collections import Counter from matplotlib import pyplot import sys import os import matplotlib.pyplot as plt from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression from sklearn import datasets from sklearn.model_selection import train_test_split import pandas as pd from uq360.metrics.regression_metrics import compute_regression_metrics import numpy as np from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import roc_curve from sklearn.metrics import r2_score,mean_squared_error, explained_variance_score,mean_absolute_error from uq360.metrics import plot_uncertainty_by_feature, plot_picp_by_feature import sys import time from sklearn.metrics import confusion_matrix from pathlib import Path import logging import logging.config from os.path import expanduser import platform from sklearn.utils import shuffle class aionUQ: # def __init__(self,uqdf,targetFeature,xtrain,ytrain,xtest,ytest,uqconfig_base,uqconfig_meta,deployLocation,saved_model): def __init__(self,df,dfp,target,ProblemName,Params,model,modelfeatures,targetfeature): try: self.data=df self.dfFeatures=dfp self.uqconfig_base=Params self.uqconfig_meta=Params self.targetFeature=targetfeature self.log = logging.getLogger('aionUQ') self.target=target self.selectedfeature=modelfeatures self.y=self.target self.X=self.dfFeatures from appbe.dataPath import DEPLOY_LOCATION self.Deployment = os.path.join(DEPLOY_LOCATION,('UQTEST_'+str(int(time.time())))) os.makedirs(self.Deployment,exist_ok=True) self.basemodel=model self.model_name=ProblemName # self.X, self.y = shuffle(self.X, self.y) X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.2, random_state=0) self.xtrain = X_train self.xtest = X_test self.ytrain = y_train self.ytest = y_test # self.deployLocation=deployLocation except Exception as e: # self.log.info('<!------------- UQ model INIT Error ---------------> '+str(e)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) # self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def totalUncertainty(self,df,basemodel,model_params): try: # from sklearn.model_selection import train_test_split # df=self.data # y=df[self.targetFeature] # X = df.drop(self.targetFeature, axis=1) if (isinstance(self.selectedfeature,list)): selectedfeature=[self.selectedfeature[0]] selectedfeature=' '.join(map(str,selectedfeature)) if (isinstance(self.targetFeature,list)): targetFeature=[self.targetFeature[0]] targetFeature=' '.join(map(str,targetFeature)) X = self.data[selectedfeature] y = self.data[targetFeature] X = X.values.reshape((-1,1)) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) key = 'criterion' #if key in model_params: try: #if model_params.has_key(key): if key in model_params: if (model_params['criterion']): uq_scoring_param=model_params.get('criterion') elif(model_params['criterion'] == None): uq_scoring_param='picp' else: uq_scoring_param='picp' else: uq_scoring_param='picp' pass except Exception as inst: uq_scoring_param='picp' # from sklearn.tree import DecisionTreeRegressor # from sklearn.linear_model import LinearRegression,Lasso,Ridge # from sklearn import linear_model # from sklearn.ensemble import RandomForestRegressor if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']: uq_scoring_param=uq_scoring_param else: uq_scoring_param='picp' uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params) # this will fit both the base and the meta model uqmodel_fit = uq_model.fit(X_train, y_train) y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test) y_hat_total_mean=np.mean(y_hat) y_hat_lb_total_mean=np.mean(y_hat_lb) y_hat_ub_total_mean=np.mean(y_hat_ub) mpiw_20_per=(y_hat_total_mean*20/100) mpiw_lower_range = y_hat_total_mean - mpiw_20_per mpiw_upper_range = y_hat_total_mean + mpiw_20_per from uq360.metrics import picp, mpiw observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub) observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub) observed_alphas_picp=round(observed_alphas_picp,2) observed_widths_mpiw=round(observed_widths_mpiw,2) picp_percentage= round(observed_alphas_picp*100) Uncertainty_percentage=round(100-picp_percentage) # self.log.info('Model total observed_widths_mpiw : '+str(observed_widths_mpiw)) # self.log.info('Model mpiw_lower_range : '+str(mpiw_lower_range)) # self.log.info('Model mpiw_upper_range : '+str(mpiw_upper_range)) # self.log.info('Model total picp_percentage : '+str(picp_percentage)) except Exception as e: print("totalUncertainty fn error: \n",e) return observed_alphas_picp,observed_widths_mpiw,picp_percentage,Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range def display_results(self,X_test, y_test, y_mean, y_lower, y_upper): try: global x_feature,y_feature if (isinstance(self.selectedfeature, list) or isinstance(self.selectedfeature, tuple)): x_feature=','.join(map(str, self.selectedfeature)) else: x_feature= str(self.selectedfeature) # self.selectedfeature=str(self.selectedfeature) X_test=np.squeeze(X_test) y_feature=str(self.targetFeature) pred_dict = {x_feature: X_test, 'y': y_test, 'y_mean': y_mean, 'y_upper': y_upper, 'y_lower': y_lower } pred_df = pd.DataFrame(data=pred_dict) x_feature1 = x_feature.split(',') pred_df_sorted = pred_df.sort_values(by=x_feature1) plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y'], 'o', label='Observed') plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y_mean'], '-', lw=2, label='Predicted') plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y_upper'], 'r--', lw=2, label='Upper Bound') plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y_lower'], 'r--', lw=2, label='Lower Bound') plt.legend() plt.xlabel(x_feature1[0]) plt.ylabel(y_feature) plt.title('UQ Confidence Interval Plot.') # plt.savefig('uq_test_plt.png') ''' if os.path.exists(str(DEFAULT_FILE_PATH)+'/uq_test_plt.png'): os.remove(str(DEFAULT_FILE_PATH)+'/uq_test_plt.png') ''' plt.savefig(str(self.Deployment)+'/uq_test_plt.png') #plt.savefig(str(DEFAULT_FILE_PATH)+'/uq_test_plt.png') confidencePlot = os.path.join(self.Deployment,'picp_per_feature.png') plt.clf() plt.cla() plt.close() pltreg=plot_picp_by_feature(X_test, y_test, y_lower, y_upper, xlabel=x_feature) #pltreg.savefig('x.png') pltr=pltreg.figure ''' if os.path.exists(str(DEFAULT_FILE_PATH)+'/picp_per_feature.png'): os.remove(str(DEFAULT_FILE_PATH)+'/picp_per_feature.png') ''' pltr.savefig(str(self.Deployment)+'/picp_per_feature.png') picpPlot = os.path.join(self.Deployment,'picp_per_feature.png') #pltr.savefig(str(DEFAULT_FILE_PATH)+'/picp_per_feature.png') plt.clf() plt.cla() plt.close() except Exception as e: print("display exception: \n",e) # self.log.info('<!------------- UQ model Display Error ---------------> '+str(e)) return confidencePlot,picpPlot def classUncertainty(self,predprob_base): # from collections import Counter predc="Class_" classes = np.unique(self.y) total = len(self.y) list_predprob=[] counter = Counter(self.y) #for loop for test class purpose for k,v in counter.items(): n_samples = len(self.y[self.y==k]) per = ((v/total) * 100) prob_c=predprob_base[:,int(k)] list_predprob.append(prob_c) # #print("Class_{} : {}/{} percentage={}% \n".format(k,n_samples,total,per )) outuq={} for k in classes: predc += str(k) mean_predprob_class=np.mean(list_predprob[int(k)]) uncertainty=1-mean_predprob_class predc+='_Uncertainty' outuq[predc]=uncertainty predc="Class_" return outuq def uqMain_BBMClassification(self): # self.log.info('<!------------- Inside BlackBox MetaModel Classification process. ---------------> ') # import matplotlib.pyplot as plt try: from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelClassification except: ##In latest UQ360, library changed from BlackboxMetamodelClassification to MetamodelClassification. from uq360.algorithms.blackbox_metamodel import MetamodelClassification # from uq360.metrics.classification_metrics import area_under_risk_rejection_rate_curve,plot_risk_vs_rejection_rate,expected_calibration_error,compute_classification_metrics from uq360.metrics.classification_metrics import plot_reliability_diagram,area_under_risk_rejection_rate_curve,plot_risk_vs_rejection_rate,expected_calibration_error,compute_classification_metrics # from sklearn import datasets # from sklearn.model_selection import train_test_split # from sklearn.metrics import accuracy_score from sklearn.ensemble import GradientBoostingClassifier from sklearn.linear_model import LogisticRegression from sklearn.linear_model import SGDClassifier from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier # from sklearn.linear_model import LogisticRegression # import pandas as pd base_modelname=__class__.__name__ base_config = self.uqconfig_base meta_config = self.uqconfig_base model_name=self.basemodel.__class__.__name__ model_params=self.basemodel.get_params() try: #geting used features model_used_features=self.basemodel.feature_names_in_ except: pass X_train, X_test, y_train, y_test = self.xtrain,self.xtest,self.ytrain,self.ytest uq_scoring_param='accuracy' basemodel=None if (model_name == "GradientBoostingClassifier"): basemodel=GradientBoostingClassifier elif (model_name == "SGDClassifier"): basemodel=SGDClassifier elif (model_name == "GaussianNB"): basemodel=GaussianNB elif (model_name == "DecisionTreeClassifier"): basemodel=DecisionTreeClassifier elif(model_name == "RandomForestClassifier"): basemodel=RandomForestClassifier elif (model_name == "SVC"): basemodel=SVC elif(model_name == "KNeighborsClassifier"): basemodel=KNeighborsClassifier elif(model_name == "LogisticRegression"): basemodel=LogisticRegression else: basemodel=LogisticRegression try: try: ##Removed meta_config because leave meta model config as default ml model params uq_model = BlackboxMetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params) except: uq_model = BlackboxMetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params, meta_config=model_params) except: ##In latest version BlackboxMetamodelClassification name modified as MetamodelClassification try: ##Removed meta_config because leave meta model config as default ml model params uq_model = MetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params) except: uq_model = MetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params, meta_config=model_params) # this will fit both the base and the meta model try: X_train=X_train[model_used_features] X_test=X_test[model_used_features] except: pass uqmodel_fit = uq_model.fit(X_train, y_train,base_is_prefitted=True,meta_train_data=(X_train, y_train)) # uqmodel_fit = uq_model.fit(X_train, y_train) #Test data pred, score y_t_pred, y_t_score = uq_model.predict(X_test) #predict probability # uq_pred_prob=uq_model.predict_proba(X_test) # predprob_base=basemodel.predict_proba(X_test)[:, :] #if (model_name == "SVC" or model_name == "SGDClassifier"): # if model_name in ['SVC','SGDClassifier']: if (model_name == "SVC"): from sklearn.calibration import CalibratedClassifierCV basemodel=SVC(**model_params) calibrated_svc = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3) calibrated_svc.fit(X_train, y_train) basepredict = basemodel.predict(X_test) predprob_base = calibrated_svc.predict_proba(X_test)[:, :] elif (model_name == "SGDClassifier"): from sklearn.calibration import CalibratedClassifierCV basemodel=SGDClassifier(**model_params) calibrated_svc = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3) calibrated_svc.fit(X_train, y_train) basepredict = basemodel.predict(X_test) predprob_base = calibrated_svc.predict_proba(X_test)[:, :] else: base_mdl = basemodel(**model_params) basemodelfit = base_mdl.fit(X_train, y_train) basepredict = base_mdl.predict(X_test) predprob_base=base_mdl.predict_proba(X_test)[:, :] acc_score=accuracy_score(y_test, y_t_pred) test_accuracy_perc=round(100*acc_score) ''' bbm_c_plot = plot_risk_vs_rejection_rate( y_true=y_test, y_prob=predprob_base, selection_scores=y_t_score, y_pred=y_t_pred, plot_label=['UQ_risk_vs_rejection'], risk_func=accuracy_score, num_bins = 10 ) # This done by kiran, need to uncomment for GUI integration. try: bbm_c_plot_sub = bbm_c_plot[4] bbm_c_plot.savefig(str(self.Deployment)+'/plot_risk_vs_rejection_rate.png') riskPlot = os.path.join(self.Deployment,'plot_risk_vs_rejection_rate.png') except Exception as e: print(e) pass riskPlot = '' ''' riskPlot = '' ''' try: re_plot=plot_reliability_diagram(y_true=y_test, y_prob=predprob_base, y_pred=y_t_pred, plot_label=['UQModel reliability_diagram'], num_bins=10) # This done by kiran, need to uncomment for GUI integration. re_plot_sub = re_plot[4] # re_plot_sub = re_plot re_plot_sub.savefig(str(self.Deployment)+'/plot_reliability_diagram.png') reliability_plot = os.path.join(self.Deployment,'plot_reliability_diagram.png') except Exception as e: print(e) pass reliability_plot = '' ''' reliability_plot = '' uq_aurrrc=area_under_risk_rejection_rate_curve( y_true=y_test, y_prob=predprob_base, y_pred=y_t_pred, selection_scores=y_t_score, attributes=None, risk_func=accuracy_score,subgroup_ids=None, return_counts=False, num_bins=10) uq_aurrrc=uq_aurrrc test_accuracy_perc=round(test_accuracy_perc) #metric_all=compute_classification_metrics(y_test, y_prob, option='all') metric_all=compute_classification_metrics(y_test, predprob_base, option='accuracy') #expected_calibration_error uq_ece=expected_calibration_error(y_test, y_prob=predprob_base,y_pred=y_t_pred, num_bins=10, return_counts=False) uq_aurrrc=uq_aurrrc confidence_score=acc_score-uq_ece ece_confidence_score=round(confidence_score,2) # Model uncertainty using ECE score # model_uncertainty_ece = 1-ece_confidence_score # #print("model_uncertainty1: \n",model_uncertainty_ece) #Uncertainty Using model inherent predict probability mean_predprob_total=np.mean(predprob_base) model_uncertainty = 1-mean_predprob_total model_confidence=mean_predprob_total model_confidence = round(model_confidence,2) # To get each class values and uncertainty outuq = self.classUncertainty(predprob_base) # Another way to get conf score model_uncertainty_per=round((model_uncertainty*100),2) # model_confidence_per=round((model_confidence*100),2) model_confidence_per=round((ece_confidence_score*100),2) acc_score_per = round((acc_score*100),2) uq_ece_per=round((uq_ece*100),2) output={} recommendation = "" if (uq_ece > 0.5): # RED text recommendation = 'Model has high ece (expected calibration error) score compare to threshold (50%),not good to deploy. Add more input data across all feature ranges to train base model, also try with different classification algorithms/ensembling to reduce ECE (ECE~0).' msg = 'Bad' else: # self.log.info('Model has good ECE score and accuracy, ready to deploy.\n.') if (uq_ece <= 0.1 and model_confidence >= 0.9): # Green Text recommendation = 'Model has best calibration score (near to 0) and good confidence score , ready to deploy. ' msg = 'Best' else: # Orange recommendation = 'Model has average confidence score (ideal is >90% confidence) and good ECE score (ideal is <10% error).Model can be improved by adding more training data across all feature ranges and re-training the model.' msg = 'Good' #Adding each class uncertainty value output['Problem']= 'Classification' output['recommend']= 'recommend' output['msg']= msg output['UQ_Area_Under_Risk_Rejection_Rate_Curve']=round(uq_aurrrc,4) output['Model_Total_Confidence']=(str(model_confidence_per)+str('%')) output['Expected_Calibration_Error']=(str(uq_ece_per)+str('%')) output['Model_Total_Uncertainty']=(str(model_uncertainty_per)+str('%')) # output['Risk Plot'] = str(riskPlot) # output['Reliability Plot'] = str(reliability_plot) for k,v in outuq.items(): output[k]=(str(round((v*100),2))+str(' %')) output['Recommendation']=recommendation # output['user_msg']='Please check the plot for more understanding of model uncertainty' output['Metric_Accuracy_Score']=(str(acc_score_per)+str(' %')) outputs = json.dumps(output) with open(str(self.Deployment)+"/uq_classification_log.json", "w") as f: json.dump(output, f) return test_accuracy_perc,uq_ece,outputs def aion_confidence_plot(self,df): try: global x_feature df=df df = df.sort_values(by=self.selectedfeature) best_values=df.Best_values.to_list() best_upper=df.Best__upper.to_list() best_lower=df.Best__lower.to_list() Total_Upper_PI=df.Total_Upper_PI.to_list() Total_Low_PI=df.Total_Low_PI.to_list() Obseved = df.Observed.to_list() x_feature1 = x_feature.split(',') plt.plot(df[x_feature1[0]], df['Observed'], 'o', label='Observed') plt.plot(df[x_feature1[0]], df['Best__upper'],'r--', lw=2, color='grey') plt.plot(df[x_feature1[0]], df['Best__lower'],'r--', lw=2, color='grey') plt.plot(df[x_feature1[0]], df['Best_values'], 'r--', lw=2, label='MeanPrediction',color='red') plt.fill_between(df[x_feature1[0]], Total_Upper_PI, Total_Low_PI, label='Good Confidence', color='lightblue', alpha=.5) plt.fill_between(df[x_feature1[0]],best_lower, best_upper,label='Best Confidence', color='orange', alpha=.5) plt.legend() plt.xlabel(x_feature1[0]) plt.ylabel(self.targetFeature) plt.title('UQ Best & Good Area Plot') ''' if os.path.exists(str(DEFAULT_FILE_PATH)+'/uq_confidence_plt.png'): os.remove(str(DEFAULT_FILE_PATH)+'/uq_confidence_plt.png') plt.savefig(str(DEFAULT_FILE_PATH)+'/uq_confidence_plt.png') ''' plt.savefig(str(self.Deployment)+'/uq_confidence_plt.png') uq_confidence_plt = os.path.join(str(self.Deployment),'uq_confidence_plt.png') except Exception as inst: print('-----------dsdas->',inst) uq_confidence_plt = '' return uq_confidence_plt def uqMain_BBMRegression(self): # modelName = "" # self.log.info('<!------------- Inside BlockBox MetaModel Regression process. ---------------> ') try: from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression import pandas as pd base_modelname=__class__.__name__ base_config = self.uqconfig_base meta_config = self.uqconfig_base model_name=self.basemodel.__class__.__name__ model_params=self.basemodel.get_params() # #print("model_params['criterion']: \n",model_params['criterion']) key = 'criterion' #if key in model_params: try: #if model_params.has_key(key): if key in model_params: if (model_params['criterion']): uq_scoring_param=model_params.get('criterion') elif(model_params['criterion'] == None): uq_scoring_param='picp' else: uq_scoring_param='picp' else: uq_scoring_param='picp' pass except Exception as inst: uq_scoring_param='picp' # modelname='sklearn.linear_model'+'.'+model_name # self.xtrain = self.xtrain.values.reshape((-1,1)) # self.xtest = self.xtest.values.reshape((-1,1)) if (isinstance(self.selectedfeature,list)): selectedfeature=[self.selectedfeature[0]] selectedfeature=' '.join(map(str,selectedfeature)) if (isinstance(self.targetFeature,list)): targetFeature=[self.targetFeature[0]] targetFeature=' '.join(map(str,targetFeature)) X = self.data[selectedfeature] y = self.data[targetFeature] X = X.values.reshape((-1,1)) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) #Geeting trained model name and to use the model in BlackboxMetamodelRegression from sklearn.tree import DecisionTreeRegressor from sklearn.linear_model import LinearRegression,Lasso,Ridge from sklearn.ensemble import RandomForestRegressor if (model_name == "DecisionTreeRegressor"): basemodel=DecisionTreeRegressor elif (model_name == "LinearRegression"): basemodel=LinearRegression elif (model_name == "Lasso"): basemodel=Lasso elif (model_name == "Ridge"): basemodel=Ridge elif(model_name == "RandomForestRegressor"): basemodel=RandomForestRegressor else: basemodel=LinearRegression if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']: if (uq_scoring_param.lower() == 'picp'): uq_scoring_param='prediction interval coverage probability score (picp)' else: uq_scoring_param=uq_scoring_param else: uq_scoring_param='prediction interval coverage probability score (picp)' uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params) # this will fit both the base and the meta model uqmodel_fit = uq_model.fit(X_train, y_train) y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test) from uq360.metrics import picp, mpiw observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub) observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub) picp_percentage= round(observed_alphas_picp*100) Uncertainty_percentage=round(100-picp_percentage) # UQ metamodel regression have metrics as follows, “rmse”, “nll”, “auucc_gain”, “picp”, “mpiw”, “r2” metric_all=compute_regression_metrics(y_test, y_hat,y_hat_lb, y_hat_ub,option=uq_scoring_param,nll_fn=None) metric_used='' for k,v in metric_all.items(): metric_used=str(round(v,2)) # Determine the confidence level and recommentation to the tester # test_data=y_test observed_alphas_picp=round(observed_alphas_picp,2) observed_widths_mpiw=round(observed_widths_mpiw,2) #Calculate total uncertainty for all features # total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage = self.totalUncertainty(self.data) # df1=self.data total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params) recommendation="" observed_widths_mpiw = round((observed_widths_mpiw/1000000)*100) if observed_widths_mpiw > 100: observed_widths_mpiw = 100 output={} if (observed_alphas_picp >= 0.90 and total_picp >= 0.75): # GREEN text recommendation = "Model has good confidence and MPIW score, ready to deploy." msg='Good' elif ((observed_alphas_picp >= 0.50 and observed_alphas_picp <= 0.90) and (total_picp >= 0.50)): # Orange recommendation = " Model has average confidence compare to threshold (ideal is both model confidence and MPIW should be >90%) .Model can be improved by adding more training data across all feature ranges and re-training the model." msg = 'Average' else: # RED text recommendation = "Model has less confidence compare to threshold (ideal is both model confidence and MPIW should be >90%), need to be add more input data across all feature ranges and retrain base model, also try with different regression algorithms/ensembling." msg = 'Bad' #Build uq json info dict output['Model_total_confidence']=(str(total_picp_percentage)+'%') output['Model_total_Uncertainty']=(str(total_Uncertainty_percentage)+'%') output['Selected_feature_confidence']=(str(picp_percentage)+'%') output['Selected_feature_Uncertainty']=(str(Uncertainty_percentage)+'%') output['Prediction_Interval_Coverage_Probability']=observed_alphas_picp output['Mean_Prediction_Interval_Width']=str(observed_widths_mpiw)+'%' output['Desirable_MPIW_range']=(str(round(mpiw_lower_range))+str(' - ')+str(round(mpiw_upper_range))) output['Recommendation']=str(recommendation) output['Metric_used']=uq_scoring_param output['Metric_value']=metric_used output['Problem']= 'Regression' output['recommend']= 'recommend' output['msg'] = msg with open(str(self.Deployment)+"/uq_reg_log.json", "w") as f: json.dump(output, f) #To get best and medium UQ range of values from total predict interval y_hat_m=y_hat.tolist() y_hat_lb=y_hat_lb.tolist() upper_bound=y_hat_ub.tolist() y_hat_ub=y_hat_ub.tolist() for x in y_hat_lb: y_hat_ub.append(x) total_pi=y_hat_ub medium_UQ_range = y_hat_ub best_UQ_range= y_hat.tolist() ymean_upper=[] ymean_lower=[] y_hat_m=y_hat.tolist() for i in y_hat_m: y_hat_m_range= (i*20/100) x=i+y_hat_m_range y=i-y_hat_m_range ymean_upper.append(x) ymean_lower.append(y) min_best_uq_dist=round(min(best_UQ_range)) max_best_uq_dist=round(max(best_UQ_range)) # initializing ranges list_medium=list(filter(lambda x:not(min_best_uq_dist<=x<=max_best_uq_dist), total_pi)) list_best = y_hat_m ''' print(X_test) print(X_test) X_test = np.squeeze(X_test) print(x_feature) ''' uq_dict = pd.DataFrame(X_test) #print(uq_dict) uq_dict['Observed'] = y_test uq_dict['Best_values'] = y_hat_m uq_dict['Best__upper'] = ymean_upper uq_dict['Best__lower'] = ymean_lower uq_dict['Total_Low_PI'] = y_hat_lb uq_dict['Total_Upper_PI'] = upper_bound ''' uq_dict = {x_feature:X_test,'Observed':y_test,'Best_values': y_hat_m, 'Best__upper':ymean_upper, 'Best__lower':ymean_lower, 'Total_Low_PI': y_hat_lb, 'Total_Upper_PI': upper_bound, }''' uq_pred_df = pd.DataFrame(data=uq_dict) uq_pred_df_sorted = uq_pred_df.sort_values(by='Best_values') uq_pred_df_sorted.to_csv(str(self.Deployment)+"/uq_pred_df.csv",index = False) csv_path=str(self.Deployment)+"/uq_pred_df.csv" df=pd.read_csv(csv_path) # self.log.info('uqMain() returns: observed_alphas_picp,observed_widths_mpiw,list_medium,list_best,metric_all.\n.') # confidenceplot = self.aion_confidence_plot(df) # output['Confidence Plot']= confidenceplot uq_jsonobject = json.dumps(output) print("UQ regression problem training completed...\n") return observed_alphas_picp,observed_widths_mpiw,list_medium,list_best,metric_all,uq_jsonobject except Exception as inst: print('-------',inst) exc = {"status":"FAIL","message":str(inst).strip('"')} out_exc = json.dumps(exc) import numpy as np from scipy.stats import norm from sklearn.metrics import mean_squared_error, r2_score from ..utils.misc import fitted_ucc_w_nullref def picp(y_true, y_lower, y_upper): """ Prediction Interval Coverage Probability (PICP). Computes the fraction of samples for which the grounds truth lies within predicted interval. Measures the prediction interval calibration for regression. Args: y_true: Ground truth y_lower: predicted lower bound y_upper: predicted upper bound Returns: float: the fraction of samples for which the grounds truth lies within predicted interval. """ satisfies_upper_bound = y_true <= y_upper satisfies_lower_bound = y_true >= y_lower return np.mean(satisfies_upper_bound * satisfies_lower_bound) def mpiw(y_lower, y_upper): """ Mean Prediction Interval Width (MPIW). Computes the average width of the the prediction intervals. Measures the sharpness of intervals. Args: y_lower: predicted lower bound y_upper: predicted upper bound Returns: float: the average width the prediction interval across samples. """ return np.mean(np.abs(y_lower - y_upper)) def auucc_gain(y_true, y_mean, y_lower, y_upper): """ Computes the Area Under the Uncertainty Characteristics Curve (AUUCC) gain wrt to a null reference with constant band. Args: y_true: Ground truth y_mean: predicted mean y_lower: predicted lower bound y_upper: predicted upper bound Returns: float: AUUCC gain """ u = fitted_ucc_w_nullref(y_true, y_mean, y_lower, y_upper) auucc = u.get_AUUCC() assert(isinstance(auucc, list) and len(auucc) == 2), "Failed to calculate auucc gain" assert (not np.isclose(auucc[1], 0.)), "Failed to calculate auucc gain" auucc_gain = (auucc[1]-auucc[0])/auucc[0] return auucc_gain def negative_log_likelihood_Gaussian(y_true, y_mean, y_lower, y_upper): """ Computes Gaussian negative_log_likelihood assuming symmetric band around the mean. Args: y_true: Ground truth y_mean: predicted mean y_lower: predicted lower bound y_upper: predicted upper bound Returns: float: nll """ y_std = (y_upper - y_lower) / 4.0 nll = np.mean(-norm.logpdf(y_true.squeeze(), loc=y_mean.squeeze(), scale=y_std.squeeze())) return nll def compute_regression_metrics(y_true, y_mean, y_lower, y_upper, option="all", nll_fn=None): """ Computes the metrics specified in the option which can be string or a list of strings. Default option `all` computes the ["rmse", "nll", "auucc_gain", "picp", "mpiw", "r2"] metrics. Args: y_true: Ground truth y_mean: predicted mean y_lower: predicted lower bound y_upper: predicted upper bound option: string or list of string contained the name of the metrics to be computed. nll_fn: function that evaluates NLL, if None, then computes Gaussian NLL using y_mean and y_lower. Returns: dict: dictionary containing the computed metrics. """ assert y_true.shape == y_mean.shape, "y_true shape: {}, y_mean shape: {}".format(y_true.shape, y_mean.shape) assert y_true.shape == y_lower.shape, "y_true shape: {}, y_mean shape: {}".format(y_true.shape, y_lower.shape) assert y_true.shape == y_upper.shape, "y_true shape: {}, y_mean shape: {}".format(y_true.shape, y_upper.shape) results = {} if not isinstance(option, list): if option == "all": option_list = ["rmse", "nll", "auucc_gain", "picp", "mpiw", "r2"] else: option_list = [option] if "rmse" in option_list: results["rmse"] = mean_squared_error(y_true, y_mean, squared=False) if "nll" in option_list: if nll_fn is None: nll = negative_log_likelihood_Gaussian(y_true, y_mean, y_lower, y_upper) results["nll"] = nll else: results["nll"] = np.mean(nll_fn(y_true)) if "auucc_gain" in option_list: gain = auucc_gain(y_true, y_mean, y_lower, y_upper) results["auucc_gain"] = gain if "picp" in option_list: results["picp"] = picp(y_true, y_lower, y_upper) if "mpiw" in option_list: results["mpiw"] = mpiw(y_lower, y_upper) if "r2" in option_list: results["r2"] = r2_score(y_true, y_mean) return results def _check_not_tuple_of_2_elements(obj, obj_name='obj'): """Check object is not tuple or does not have 2 elements.""" if not isinstance(obj, tuple) or len(obj) != 2: raise TypeError('%s must be a tuple of 2 elements.' % obj_name) def plot_uncertainty_distribution(dist, show_quantile_dots=False, qd_sample=20, qd_bins=7, ax=None, figsize=None, dpi=None, title='Predicted Distribution', xlims=None, xlabel='Prediction', ylabel='Density', **kwargs): """ Plot the uncertainty distribution for a single distribution. Args: dist: scipy.stats._continuous_distns. A scipy distribution object. show_quantile_dots: boolean. Whether to show quantil dots on top of the density plot. qd_sample: int. Number of dots for the quantile dot plot. qd_bins: int. Number of bins for the quantile dot plot. ax: matplotlib.axes.Axes or None, optional (default=None). Target axes instance. If None, new figure and axes will be created. figsize: tuple of 2 elements or None, optional (default=None). Figure size. dpi : int or None, optional (default=None). Resolution of the figure. title : string or None, optional (default=Prediction Distribution) Axes title. If None, title is disabled. xlims : tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.xlim()``. xlabel : string or None, optional (default=Prediction) X-axis title label. If None, title is disabled. ylabel : string or None, optional (default=Density) Y-axis title label. If None, title is disabled. Returns: matplotlib.axes.Axes: ax : The plot with prediction distribution. """ import matplotlib.pyplot as plt if ax is None: if figsize is not None: _check_not_tuple_of_2_elements(figsize, 'figsize') _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) x = np.linspace(dist.ppf(0.01), dist.ppf(0.99), 100) ax.plot(x, dist.pdf(x), **kwargs) if show_quantile_dots: from matplotlib.patches import Circle from matplotlib.collections import PatchCollection import matplotlib.ticker as ticker data = dist.rvs(size=10000) p_less_than_x = np.linspace(1 / qd_sample / 2, 1 - (1 / qd_sample / 2), qd_sample) x_ = np.percentile(data, p_less_than_x * 100) # Inverce CDF (ppf) # Create bins hist = np.histogram(x_, bins=qd_bins) bins, edges = hist radius = (edges[1] - edges[0]) / 2 ax2 = ax.twinx() patches = [] max_y = 0 for i in range(qd_bins): x_bin = (edges[i + 1] + edges[i]) / 2 y_bins = [(i + 1) * (radius * 2) for i in range(bins[i])] max_y = max(y_bins) if max(y_bins) > max_y else max_y for _, y_bin in enumerate(y_bins): circle = Circle((x_bin, y_bin), radius) patches.append(circle) p = PatchCollection(patches, alpha=0.4) ax2.add_collection(p) # Axis tweek y_scale = (max_y + radius) / max(dist.pdf(x)) ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x_ / y_scale)) ax2.yaxis.set_major_formatter(ticks_y) ax2.set_yticklabels([]) if xlims is not None: ax2.set_xlim(left=xlims[0], right=xlims[1]) else: ax2.set_xlim([min(x_) - radius, max(x) + radius]) ax2.set_ylim([0, max_y + radius]) ax2.set_aspect(1) if title is not None: ax.set_title(title) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: ax.set_ylabel(ylabel) return ax def plot_picp_by_feature(x_test, y_test, y_test_pred_lower_total, y_test_pred_upper_total, num_bins=10, ax=None, figsize=None, dpi=None, xlims=None, ylims=None, xscale="linear", title=None, xlabel=None, ylabel=None): """ Plot how prediction uncertainty varies across the entire range of a feature. Args: x_test: One dimensional ndarray. Feature column of the test dataset. y_test: One dimensional ndarray. Ground truth label of the test dataset. y_test_pred_lower_total: One dimensional ndarray. Lower bound of the total uncertainty range. y_test_pred_upper_total: One dimensional ndarray. Upper bound of the total uncertainty range. num_bins: int. Number of bins used to discritize x_test into equal-sample-sized bins. ax: matplotlib.axes.Axes or None, optional (default=None). Target axes instance. If None, new figure and axes will be created. figsize: tuple of 2 elements or None, optional (default=None). Figure size. dpi : int or None, optional (default=None). Resolution of the figure. xlims : tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.xlim()``. ylims: tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.ylim()``. xscale: Passed to ``ax.set_xscale()``. title : string or None, optional Axes title. If None, title is disabled. xlabel : string or None, optional X-axis title label. If None, title is disabled. ylabel : string or None, optional Y-axis title label. If None, title is disabled. Returns: matplotlib.axes.Axes: ax : The plot with PICP scores binned by a feature. """ from scipy.stats.mstats import mquantiles import matplotlib.pyplot as plt if ax is None: if figsize is not None: _check_not_tuple_of_2_elements(figsize, 'figsize') _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) x_uniques_sorted = np.sort(np.unique(x_test)) num_unique = len(x_uniques_sorted) sample_bin_ids = np.searchsorted(x_uniques_sorted, x_test) if len(x_uniques_sorted) > 10: # bin the values q_bins = mquantiles(x_test, np.histogram_bin_edges([], bins=num_bins-1, range=(0.0, 1.0))[1:]) q_sample_bin_ids = np.digitize(x_test, q_bins) picps = np.array([picp(y_test[q_sample_bin_ids==bin], y_test_pred_lower_total[q_sample_bin_ids==bin], y_test_pred_upper_total[q_sample_bin_ids==bin]) for bin in range(num_bins)]) unique_sample_bin_ids = np.digitize(x_uniques_sorted, q_bins) picp_replicated = [len(x_uniques_sorted[unique_sample_bin_ids == bin]) * [picps[bin]] for bin in range(num_bins)] picp_replicated = np.array([item for sublist in picp_replicated for item in sublist]) else: picps = np.array([picp(y_test[sample_bin_ids == bin], y_test_pred_lower_total[sample_bin_ids == bin], y_test_pred_upper_total[sample_bin_ids == bin]) for bin in range(num_unique)]) picp_replicated = picps ax.plot(x_uniques_sorted, picp_replicated, label='PICP') ax.axhline(0.95, linestyle='--', label='95%') ax.set_ylabel('PICP') ax.legend(loc='best') if title is None: title = 'Test data overall PICP: {:.2f} MPIW: {:.2f}'.format( picp(y_test, y_test_pred_lower_total, y_test_pred_upper_total), mpiw(y_test_pred_lower_total, y_test_pred_upper_total)) if xlims is not None: ax.set_xlim(left=xlims[0], right=xlims[1]) if ylims is not None: ax.set_ylim(bottom=ylims[0], top=ylims[1]) ax.set_title(title) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: ax.set_ylabel(ylabel) if xscale is not None: ax.set_xscale(xscale) return ax def plot_uncertainty_by_feature(x_test, y_test_pred_mean, y_test_pred_lower_total, y_test_pred_upper_total, y_test_pred_lower_epistemic=None, y_test_pred_upper_epistemic=None, ax=None, figsize=None, dpi=None, xlims=None, xscale="linear", title=None, xlabel=None, ylabel=None): """ Plot how prediction uncertainty varies across the entire range of a feature. Args: x_test: one dimensional ndarray. Feature column of the test dataset. y_test_pred_mean: One dimensional ndarray. Model prediction for the test dataset. y_test_pred_lower_total: One dimensional ndarray. Lower bound of the total uncertainty range. y_test_pred_upper_total: One dimensional ndarray. Upper bound of the total uncertainty range. y_test_pred_lower_epistemic: One dimensional ndarray. Lower bound of the epistemic uncertainty range. y_test_pred_upper_epistemic: One dimensional ndarray. Upper bound of the epistemic uncertainty range. ax: matplotlib.axes.Axes or None, optional (default=None). Target axes instance. If None, new figure and axes will be created. figsize: tuple of 2 elements or None, optional (default=None). Figure size. dpi : int or None, optional (default=None). Resolution of the figure. xlims : tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.xlim()``. xscale: Passed to ``ax.set_xscale()``. title : string or None, optional Axes title. If None, title is disabled. xlabel : string or None, optional X-axis title label. If None, title is disabled. ylabel : string or None, optional Y-axis title label. If None, title is disabled. Returns: matplotlib.axes.Axes: ax : The plot with model's uncertainty binned by a feature. """ import matplotlib.pyplot as plt if ax is None: if figsize is not None: _check_not_tuple_of_2_elements(figsize, 'figsize') _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) x_uniques_sorted = np.sort(np.unique(x_test)) y_pred_var = ((y_test_pred_upper_total - y_test_pred_lower_total) / 4.0)**2 agg_y_std = np.array([np.sqrt(np.mean(y_pred_var[x_test==x])) for x in x_uniques_sorted]) agg_y_mean = np.array([np.mean(y_test_pred_mean[x_test==x]) for x in x_uniques_sorted]) ax.plot(x_uniques_sorted, agg_y_mean, '-b', lw=2, label='mean prediction') ax.fill_between(x_uniques_sorted, agg_y_mean - 2.0 * agg_y_std, agg_y_mean + 2.0 * agg_y_std, alpha=0.3, label='total uncertainty') if y_test_pred_lower_epistemic is not None: y_pred_var_epistemic = ((y_test_pred_upper_epistemic - y_test_pred_lower_epistemic) / 4.0)**2 agg_y_std_epistemic = np.array([np.sqrt(np.mean(y_pred_var_epistemic[x_test==x])) for x in x_uniques_sorted]) ax.fill_between(x_uniques_sorted, agg_y_mean - 2.0 * agg_y_std_epistemic, agg_y_mean + 2.0 * agg_y_std_epistemic, alpha=0.3, label='model uncertainty') ax.legend(loc='best') if xlims is not None: ax.set_xlim(left=xlims[0], right=xlims[1]) if title is not None: ax.set_title(title) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: ax.set_ylabel(ylabel) if xscale is not None: ax.set_xscale(xscale) return ax import numpy as np import pandas as pd from scipy.stats import entropy from sklearn.metrics import roc_auc_score, log_loss, accuracy_score def entropy_based_uncertainty_decomposition(y_prob_samples): """ Entropy based decomposition [2]_ of predictive uncertainty into aleatoric and epistemic components. References: .. [2] Depeweg, S., Hernandez-Lobato, J. M., Doshi-Velez, F., & Udluft, S. (2018, July). Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning. In International Conference on Machine Learning (pp. 1184-1193). PMLR. Args: y_prob_samples: list of array-like of shape (n_samples, n_classes) containing class prediction probabilities corresponding to samples from the model posterior. Returns: tuple: - total_uncertainty: entropy of the predictive distribution. - aleatoric_uncertainty: aleatoric component of the total_uncertainty. - epistemic_uncertainty: epistemic component of the total_uncertainty. """ y_preds_samples_stacked = np.stack(y_prob_samples) preds_mean = np.mean(y_preds_samples_stacked, 0) total_uncertainty = entropy(preds_mean, axis=1) aleatoric_uncertainty = np.mean( np.concatenate([entropy(y_pred, axis=1).reshape(-1, 1) for y_pred in y_prob_samples], axis=1), axis=1) epistemic_uncertainty = total_uncertainty - aleatoric_uncertainty return total_uncertainty, aleatoric_uncertainty, epistemic_uncertainty def multiclass_brier_score(y_true, y_prob): """Brier score for multi-class. Args: y_true: array-like of shape (n_samples,) ground truth labels. y_prob: array-like of shape (n_samples, n_classes). Probability scores from the base model. Returns: float: Brier score. """ assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)" y_target = np.zeros_like(y_prob) y_target[:, y_true] = 1.0 return np.mean(np.sum((y_target - y_prob) ** 2, axis=1)) def area_under_risk_rejection_rate_curve(y_true, y_prob, y_pred=None, selection_scores=None, risk_func=accuracy_score, attributes=None, num_bins=10, subgroup_ids=None, return_counts=False): """ Computes risk vs rejection rate curve and the area under this curve. Similar to risk-coverage curves [3]_ where coverage instead of rejection rate is used. References: .. [3] Franc, Vojtech, and Daniel Prusa. "On discriminative learning of prediction uncertainty." In International Conference on Machine Learning, pp. 1963-1971. 2019. Args: y_true: array-like of shape (n_samples,) ground truth labels. y_prob: array-like of shape (n_samples, n_classes). Probability scores from the base model. y_pred: array-like of shape (n_samples,) predicted labels. selection_scores: scores corresponding to certainty in the predicted labels. risk_func: risk function under consideration. attributes: (optional) if risk function is a fairness metric also pass the protected attribute name. num_bins: number of bins. subgroup_ids: (optional) selectively compute risk on a subgroup of the samples specified by subgroup_ids. return_counts: set to True to return counts also. Returns: float or tuple: - aurrrc (float): area under risk rejection rate curve. - rejection_rates (list): rejection rates for each bin (returned only if return_counts is True). - selection_thresholds (list): selection threshold for each bin (returned only if return_counts is True). - risks (list): risk in each bin (returned only if return_counts is True). """ if selection_scores is None: assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)" selection_scores = y_prob[np.arange(y_prob.shape[0]), np.argmax(y_prob, axis=1)] if y_pred is None: assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)" y_pred = np.argmax(y_prob, axis=1) order = np.argsort(selection_scores)[::-1] rejection_rates = [] selection_thresholds = [] risks = [] for bin_id in range(num_bins): samples_in_bin = len(y_true) // num_bins selection_threshold = selection_scores[order[samples_in_bin * (bin_id+1)-1]] selection_thresholds.append(selection_threshold) ids = selection_scores >= selection_threshold if sum(ids) > 0: if attributes is None: if isinstance(y_true, pd.Series): y_true_numpy = y_true.values else: y_true_numpy = y_true if subgroup_ids is None: risk_value = 1.0 - risk_func(y_true_numpy[ids], y_pred[ids]) else: if sum(subgroup_ids & ids) > 0: risk_value = 1.0 - risk_func(y_true_numpy[subgroup_ids & ids], y_pred[subgroup_ids & ids]) else: risk_value = 0.0 else: risk_value = risk_func(y_true.iloc[ids], y_pred[ids], prot_attr=attributes) else: risk_value = 0.0 risks.append(risk_value) rejection_rates.append(1.0 - 1.0 * sum(ids) / len(y_true)) aurrrc = np.nanmean(risks) if not return_counts: return aurrrc else: return aurrrc, rejection_rates, selection_thresholds, risks def expected_calibration_error(y_true, y_prob, y_pred=None, num_bins=10, return_counts=False): """ Computes the reliability curve and the expected calibration error [1]_ . References: .. [1] Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger; Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1321-1330, 2017. Args: y_true: array-like of shape (n_samples,) ground truth labels. y_prob: array-like of shape (n_samples, n_classes). Probability scores from the base model. y_pred: array-like of shape (n_samples,) predicted labels. num_bins: number of bins. return_counts: set to True to return counts also. Returns: float or tuple: - ece (float): expected calibration error. - confidences_in_bins: average confidence in each bin (returned only if return_counts is True). - accuracies_in_bins: accuracy in each bin (returned only if return_counts is True). - frac_samples_in_bins: fraction of samples in each bin (returned only if return_counts is True). """ assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)" num_samples, num_classes = y_prob.shape top_scores = np.max(y_prob, axis=1) if y_pred is None: y_pred = np.argmax(y_prob, axis=1) if num_classes == 2: bins_edges = np.histogram_bin_edges([], bins=num_bins, range=(0.5, 1.0)) else: bins_edges = np.histogram_bin_edges([], bins=num_bins, range=(0.0, 1.0)) non_boundary_bin_edges = bins_edges[1:-1] bin_centers = (bins_edges[1:] + bins_edges[:-1])/2 sample_bin_ids = np.digitize(top_scores, non_boundary_bin_edges) num_samples_in_bins = np.zeros(num_bins) accuracies_in_bins = np.zeros(num_bins) confidences_in_bins = np.zeros(num_bins) for bin in range(num_bins): num_samples_in_bins[bin] = len(y_pred[sample_bin_ids == bin]) if num_samples_in_bins[bin] > 0: accuracies_in_bins[bin] = np.sum(y_true[sample_bin_ids == bin] == y_pred[sample_bin_ids == bin]) / num_samples_in_bins[bin] confidences_in_bins[bin] = np.sum(top_scores[sample_bin_ids == bin]) / num_samples_in_bins[bin] ece = np.sum( num_samples_in_bins * np.abs(accuracies_in_bins - confidences_in_bins) / num_samples ) frac_samples_in_bins = num_samples_in_bins / num_samples if not return_counts: return ece else: return ece, confidences_in_bins, accuracies_in_bins, frac_samples_in_bins, bin_centers def compute_classification_metrics(y_true, y_prob, option='all'): """ Computes the metrics specified in the option which can be string or a list of strings. Default option `all` computes the [aurrrc, ece, auroc, nll, brier, accuracy] metrics. Args: y_true: array-like of shape (n_samples,) ground truth labels. y_prob: array-like of shape (n_samples, n_classes). Probability scores from the base model. option: string or list of string contained the name of the metrics to be computed. Returns: dict: a dictionary containing the computed metrics. """ results = {} if not isinstance(option, list): if option == "all": option_list = ["aurrrc", "ece", "auroc", "nll", "brier", "accuracy"] else: option_list = [option] if "aurrrc" in option_list: results["aurrrc"] = area_under_risk_rejection_rate_curve(y_true=y_true, y_prob=y_prob) if "ece" in option_list: results["ece"] = expected_calibration_error(y_true=y_true, y_prob=y_prob) if "auroc" in option_list: results["auroc"], _ = roc_auc_score(y_true=y_true, y_score=y_prob) if "nll" in option_list: results["nll"] = log_loss(y_true=y_true, y_pred=np.argmax(y_prob, axis=1)) if "brier" in option_list: results["brier"] = multiclass_brier_score(y_true=y_true, y_prob=y_prob) if "accuracy" in option_list: results["accuracy"] = accuracy_score(y_true=y_true, y_pred=np.argmax(y_prob, axis=1)) return results def plot_reliability_diagram(y_true, y_prob, y_pred, plot_label=[""], num_bins=10): """ Plots the reliability diagram showing the calibration error for different confidence scores. Multiple curves can be plot by passing data as lists. Args: y_true: array-like or or a list of array-like of shape (n_samples,) ground truth labels. y_prob: array-like or or a list of array-like of shape (n_samples, n_classes). Probability scores from the base model. y_pred: array-like or or a list of array-like of shape (n_samples,) predicted labels. plot_label: (optional) list of names identifying each curve. num_bins: number of bins. Returns: tuple: - ece_list: ece: list containing expected calibration error for each curve. - accuracies_in_bins_list: list containing binned average accuracies for each curve. - frac_samples_in_bins_list: list containing binned sample frequencies for each curve. - confidences_in_bins_list: list containing binned average confidence for each curve. """ import matplotlib.pyplot as plt if not isinstance(y_true, list): y_true, y_prob, y_pred = [y_true], [y_prob], [y_pred] if len(plot_label) != len(y_true): raise ValueError('y_true and plot_label should be of same length.') ece_list = [] accuracies_in_bins_list = [] frac_samples_in_bins_list = [] confidences_in_bins_list = [] for idx in range(len(plot_label)): ece, confidences_in_bins, accuracies_in_bins, frac_samples_in_bins, bins = expected_calibration_error(y_true[idx], y_prob[idx], y_pred[idx], num_bins=num_bins, return_counts=True) ece_list.append(ece) accuracies_in_bins_list.append(accuracies_in_bins) frac_samples_in_bins_list.append(frac_samples_in_bins) confidences_in_bins_list.append(confidences_in_bins) fig = plt.figure(figsize=(12, 5)) plt.subplot(1, 2, 1) for idx in range(len(plot_label)): plt.plot(bins, frac_samples_in_bins_list[idx], 'o-', label=plot_label[idx]) plt.title("Confidence Histogram") plt.xlabel("Confidence") plt.ylabel("Fraction of Samples") plt.grid() plt.ylim([0.0, 1.0]) plt.legend() plt.subplot(1, 2, 2) for idx in range(len(plot_label)): plt.plot(bins, accuracies_in_bins_list[idx], 'o-', label="{} ECE = {:.2f}".format(plot_label[idx], ece_list[idx])) plt.plot(np.linspace(0, 1, 50), np.linspace(0, 1, 50), 'b.', label="Perfect Calibration") plt.title("Reliability Plot") plt.xlabel("Confidence") plt.ylabel("Accuracy") plt.grid() plt.legend() plt.show() return ece_list, accuracies_in_bins_list, frac_samples_in_bins_list, confidences_in_bins_list def plot_risk_vs_rejection_rate(y_true, y_prob, y_pred, selection_scores=None, plot_label=[""], risk_func=None, attributes=None, num_bins=10, subgroup_ids=None): """ Plots the risk vs rejection rate curve showing the risk for different rejection rates. Multiple curves can be plot by passing data as lists. Args: y_true: array-like or or a list of array-like of shape (n_samples,) ground truth labels. y_prob: array-like or or a list of array-like of shape (n_samples, n_classes). Probability scores from the base model. y_pred: array-like or or a list of array-like of shape (n_samples,) predicted labels. selection_scores: ndarray or a list of ndarray containing scores corresponding to certainty in the predicted labels. risk_func: risk function under consideration. attributes: (optional) if risk function is a fairness metric also pass the protected attribute name. num_bins: number of bins. subgroup_ids: (optional) ndarray or a list of ndarray containing subgroup_ids to selectively compute risk on a subgroup of the samples specified by subgroup_ids. Returns: tuple: - aurrrc_list: list containing the area under risk rejection rate curves. - rejection_rate_list: list containing the binned rejection rates. - selection_thresholds_list: list containing the binned selection thresholds. - risk_list: list containing the binned risks. """ import matplotlib.pyplot as plt if not isinstance(y_true, list): y_true, y_prob, y_pred, selection_scores, subgroup_ids = [y_true], [y_prob], [y_pred], [selection_scores], [subgroup_ids] if len(plot_label) != len(y_true): raise ValueError('y_true and plot_label should be of same length.') aurrrc_list = [] rejection_rate_list = [] risk_list = [] selection_thresholds_list = [] for idx in range(len(plot_label)): aursrc, rejection_rates, selection_thresholds, risks = area_under_risk_rejection_rate_curve( y_true[idx], y_prob[idx], y_pred[idx], selection_scores=selection_scores[idx], risk_func=risk_func, attributes=attributes, num_bins=num_bins, subgroup_ids=subgroup_ids[idx], return_counts=True ) aurrrc_list.append(aursrc) rejection_rate_list.append(rejection_rates) risk_list.append(risks) selection_thresholds_list.append(selection_thresholds) plt.figure(figsize=(12, 5)) plt.subplot(1, 2, 1) for idx in range(len(plot_label)): plt.plot(rejection_rate_list[idx], risk_list[idx], label="{} AURRRC={:.5f}".format(plot_label[idx], aurrrc_list[idx])) plt.legend(loc="best") plt.xlabel("Rejection Rate") if risk_func is None: ylabel = "Prediction Error Rate" else: if 'accuracy' in risk_func.__name__: ylabel = "1.0 - " + risk_func.__name__ else: ylabel = risk_func.__name__ plt.ylabel(ylabel) plt.title("Risk vs Rejection Rate Plot") plt.grid() plt.subplot(1, 2, 2) for idx in range(len(plot_label)): plt.plot(selection_thresholds_list[idx], risk_list[idx], label="{}".format(plot_label[idx])) plt.legend(loc="best") plt.xlabel("Selection Threshold") if risk_func is None: ylabel = "Prediction Error Rate" else: if 'accuracy' in risk_func.__name__: ylabel = "1.0 - " + risk_func.__name__ else: ylabel = risk_func.__name__ plt.ylabel(ylabel) plt.title("Risk vs Selection Threshold Plot") plt.grid() plt.show() return aurrrc_list, rejection_rate_list, selection_thresholds_list, risk_list from .classification_metrics import expected_calibration_error, area_under_risk_rejection_rate_curve, \ compute_classification_metrics, entropy_based_uncertainty_decomposition from .regression_metrics import picp, mpiw, compute_regression_metrics, plot_uncertainty_distribution, \ plot_uncertainty_by_feature, plot_picp_by_feature from .uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve from .uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve from copy import deepcopy import matplotlib.pyplot as plt import numpy as np from scipy.integrate import simps, trapz from sklearn.isotonic import IsotonicRegression DEFAULT_X_AXIS_NAME = 'excess' DEFAULT_Y_AXIS_NAME = 'missrate' class UncertaintyCharacteristicsCurve: """ Class with main functions of the Uncertainty Characteristics Curve (UCC). """ def __init__(self, normalize=True, precompute_bias_data=True): """ :param normalize: set initial axes normalization flag (can be changed via set_coordinates()) :param precompute_bias_data: if True, fit() will compute statistics necessary to generate bias-based UCCs (in addition to the scale-based ones). Skipping this precomputation may speed up the fit() call if bias-based UCC is not needed. """ self.axes_name2idx = {"missrate": 1, "bandwidth": 2, "excess": 3, "deficit": 4} self.axes_idx2descr = {1: "Missrate", 2: "Bandwidth", 3: "Excess", 4: "Deficit"} self.x_axis_idx = None self.y_axis_idx = None self.norm_x_axis = False self.norm_y_axis = False self.std_unit = None self.normalize = normalize self.d = None self.gt = None self.lb = None self.ub = None self.precompute_bias_data = precompute_bias_data self.set_coordinates(x_axis_name=DEFAULT_X_AXIS_NAME, y_axis_name=DEFAULT_Y_AXIS_NAME, normalize=normalize) def set_coordinates(self, x_axis_name=None, y_axis_name=None, normalize=None): """ Assigns user-specified type to the axes and normalization behavior (sticky). :param x_axis_name: None-> unchanged, or name from self.axes_name2idx :param y_axis_name: ditto :param normalize: True/False will activate/deactivate norming for specified axes. Behavior for Axes_name that are None will not be changed. Value None will leave norm status unchanged. Note, axis=='missrate' will never get normalized, even with normalize == True :return: none """ normalize = self.normalize if normalize is None else normalize if x_axis_name is None and self.x_axis_idx is None: raise ValueError("ERROR(UCC): x-axis has not been defined.") if y_axis_name is None and self.y_axis_idx is None: raise ValueError("ERROR(UCC): y-axis has not been defined.") if x_axis_name is None and y_axis_name is None and normalize is not None: # just set normalization on/off for both axes and return self.norm_x_axis = False if x_axis_name == 'missrate' else normalize self.norm_y_axis = False if y_axis_name == 'missrate' else normalize return if x_axis_name is not None: self.x_axis_idx = self.axes_name2idx[x_axis_name] self.norm_x_axis = False if x_axis_name == 'missrate' else normalize if y_axis_name is not None: self.y_axis_idx = self.axes_name2idx[y_axis_name] self.norm_y_axis = False if y_axis_name == 'missrate' else normalize def set_std_unit(self, std_unit=None): """ Sets the UCC's unit to be used when displaying normalized axes. :param std_unit: if None, the unit will be calculated as stddev of the ground truth data (ValueError raised if data has not been set at this point) or set to the user-specified value. :return: """ if std_unit is None: # set it to stddev of data if self.gt is None: raise ValueError("ERROR(UCC): No data specified - cannot set stddev unit.") self.std_unit = np.std(self.gt) if np.isclose(self.std_unit, 0.): print("WARN(UCC): data-based stddev is zero - resetting axes unit to 1.") self.std_unit = 1. else: self.std_unit = float(std_unit) def fit(self, X, gt): """ Calculates internal arrays necessary for other methods (plotting, auc, cost minimization). Re-entrant. :param X: [numsamples, 3] numpy matrix, or list of numpy matrices. Col 1: predicted values Col 2: lower band (deviate) wrt predicted value (always positive) Col 3: upper band wrt predicted value (always positive) If list is provided, all methods will output corresponding metrics as lists as well! :param gt: Ground truth array (i.e.,the 'actual' values corresponding to predictions in X :return: self """ if not isinstance(X, list): X = [X] newX = [] for x in X: assert (isinstance(x, np.ndarray) and len(x.shape) == 2 and x.shape[1] == 3 and x.shape[0] == len(gt)) newX.append(self._sanitize_input(x)) self.d = [gt - x[:, 0] for x in newX] self.lb = [x[:, 1] for x in newX] self.ub = [x[:, 2] for x in newX] self.gt = gt self.set_std_unit() self.plotdata_for_scale = [] self.plotdata_for_bias = [] # precompute plotdata: for i in range(len(self.d)): self.plotdata_for_scale.append(self._calc_plotdata(self.d[i], self.lb[i], self.ub[i], vary_bias=False)) if self.precompute_bias_data: self.plotdata_for_bias.append(self._calc_plotdata(self.d[i], self.lb[i], self.ub[i], vary_bias=True)) return self def minimize_cost(self, x_axis_cost=.5, y_axis_cost=.5, augment_cost_by_normfactor=True, search=('scale', 'bias')): """ Find minima of a linear cost function for each component. Cost function C = x_axis_cost * x_axis_value + y_axis_cost * y_axis_value. A minimum can occur in the scale-based or bias-based UCC (this can be constrained by the 'search' arg). The function returns a 'recipe' how to achieve the corresponding minimum, for each component. :param x_axis_cost: weight of one unit on x_axis :param y_axis_cost: weight of one unit on y_axis :param augment_cost_by_normfactor: when False, the cost multipliers will apply as is. If True, they will be pre-normed by the corresponding axis norm (where applicable), to account for range differences between axes. :param search: list of types over which minimization is to be performed, valid elements are 'scale' and 'bias'. :return: list of dicts - one per component, or a single dict, if there is only one component. Dict keys are - 'operation': can be 'bias' (additive) or 'scale' (multiplicative), 'modvalue': value to multiply by or to add to error bars to achieve the minimum, 'new_x'/'new_y': new coordinates (operating point) with that minimum, 'cost': new cost at minimum point, 'original_cost': original cost (original operating point). """ if self.d is None: raise ValueError("ERROR(UCC): call fit() prior to using this method.") if augment_cost_by_normfactor: if self.norm_x_axis: x_axis_cost /= self.std_unit if self.norm_y_axis: y_axis_cost /= self.std_unit print("INFO(UCC): Pre-norming costs by corresp. std deviation: new x_axis_cost = %.4f, y_axis_cost = %.4f" % (x_axis_cost, y_axis_cost)) if isinstance(search, tuple): search = list(search) if not isinstance(search, list): search = [search] min_costs = [] for d in range(len(self.d)): # original OP cost m, b, e, df = self._calc_missrate_bandwidth_excess_deficit(self.d[d], self.lb[d], self.ub[d]) original_cost = x_axis_cost * [0., m, b, e, df][self.x_axis_idx] + y_axis_cost * [0., m, b, e, df][ self.y_axis_idx] plotdata = self.plotdata_for_scale[d] cost_scale, minidx_scale = self._find_min_cost_in_component(plotdata, self.x_axis_idx, self.y_axis_idx, x_axis_cost, y_axis_cost) mcf_scale_multiplier = plotdata[minidx_scale][0] mcf_scale_x = plotdata[minidx_scale][self.x_axis_idx] mcf_scale_y = plotdata[minidx_scale][self.y_axis_idx] if 'bias' in search: if not self.precompute_bias_data: raise ValueError( "ERROR(UCC): Cannot perform minimization - instantiated without bias data computation") plotdata = self.plotdata_for_bias[d] cost_bias, minidx_bias = self._find_min_cost_in_component(plotdata, self.x_axis_idx, self.y_axis_idx, x_axis_cost, y_axis_cost) mcf_bias_add = plotdata[minidx_bias][0] mcf_bias_x = plotdata[minidx_bias][self.x_axis_idx] mcf_bias_y = plotdata[minidx_bias][self.y_axis_idx] if 'bias' in search and 'scale' in search: if cost_bias < cost_scale: min_costs.append({'operation': 'bias', 'cost': cost_bias, 'modvalue': mcf_bias_add, 'new_x': mcf_bias_x, 'new_y': mcf_bias_y, 'original_cost': original_cost}) else: min_costs.append({'operation': 'scale', 'cost': cost_scale, 'modvalue': mcf_scale_multiplier, 'new_x': mcf_scale_x, 'new_y': mcf_scale_y, 'original_cost': original_cost}) elif 'scale' in search: min_costs.append({'operation': 'scale', 'cost': cost_scale, 'modvalue': mcf_scale_multiplier, 'new_x': mcf_scale_x, 'new_y': mcf_scale_y, 'original_cost': original_cost}) elif 'bias' in search: min_costs.append({'operation': 'bias', 'cost': cost_bias, 'modvalue': mcf_bias_add, 'new_x': mcf_bias_x, 'new_y': mcf_bias_y, 'original_cost': original_cost}) else: raise ValueError("(ERROR): Unknown search element (%s) requested." % ",".join(search)) if len(min_costs) < 2: return min_costs[0] else: return min_costs def get_specific_operating_point(self, req_x_axis_value=None, req_y_axis_value=None, req_critical_value=None, vary_bias=False): """ Finds corresponding operating point on the current UCC, given a point on either x or y axis. Returns a list of recipes how to achieve the point (x,y), for each component. If there is only one component, returns a single recipe dict. :param req_x_axis_value: requested x value on UCC (normalization status is taken from current display) :param req_y_axis_value: requested y value on UCC (normalization status is taken from current display) :param vary_bias: set to True when referring to bias-induced UCC (scale UCC default) :return: list of dicts (recipes), or a single dict """ if self.d is None: raise ValueError("ERROR(UCC): call fit() prior to using this method.") if np.sum([req_x_axis_value is not None, req_y_axis_value is not None, req_critical_value is not None]) != 1: raise ValueError("ERROR(UCC): exactly one axis value must be requested at a time.") if vary_bias and not self.precompute_bias_data: raise ValueError("ERROR(UCC): Cannot vary bias - instantiated without bias data computation") xnorm = self.std_unit if self.norm_x_axis else 1. ynorm = self.std_unit if self.norm_y_axis else 1. recipe = [] for dc in range(len(self.d)): plotdata = self.plotdata_for_bias[dc] if vary_bias else self.plotdata_for_scale[dc] if req_x_axis_value is not None: tgtidx = self.x_axis_idx req_value = req_x_axis_value * xnorm elif req_y_axis_value is not None: tgtidx = self.y_axis_idx req_value = req_y_axis_value * ynorm elif req_critical_value is not None: req_value = req_critical_value tgtidx = 0 # first element in plotdata is always the critical value (scale of bias) else: raise RuntimeError("Unhandled case") closestidx = np.argmin(np.asarray([np.abs(p[tgtidx] - req_value) for p in plotdata])) recipe.append({'operation': ('bias' if vary_bias else 'scale'), 'modvalue': plotdata[closestidx][0], 'new_x': plotdata[closestidx][self.x_axis_idx] / xnorm, 'new_y': plotdata[closestidx][self.y_axis_idx] / ynorm}) if len(recipe) < 2: return recipe[0] else: return recipe def _find_min_cost_in_component(self, plotdata, idx1, idx2, cost1, cost2): """ Find s minimum cost function value and corresp. position index in plotdata :param plotdata: liste of tuples :param idx1: idx of x-axis item within the tuple :param idx2: idx of y-axis item within the tuple :param cost1: cost factor for x-axis unit :param cost2: cost factor for y-axis unit :return: min cost value, index within plotdata where minimum occurs """ raw = [cost1 * i[idx1] + cost2 * i[idx2] for i in plotdata] minidx = np.argmin(raw) return raw[minidx], minidx def _sanitize_input(self, x): """ Replaces problematic values in input data (e.g, zero error bars) :param x: single matrix of input data [n, 3] :return: sanitized version of x """ if np.isclose(np.sum(x[:, 1]), 0.): raise ValueError("ERROR(UCC): Provided lower bands are all zero.") if np.isclose(np.sum(x[:, 2]), 0.): raise ValueError("ERROR(UCC): Provided upper bands are all zero.") for i in [1, 2]: if any(np.isclose(x[:, i], 0.)): print("WARN(UCC): some band values are 0. - REPLACING with positive minimum") m = np.min(x[x[:, i] > 0, i]) x = np.where(np.isclose(x, 0.), m, x) return x def _calc_avg_excess(self, d, lb, ub): """ Excess is amount an error bar overshoots actual :param d: pred-actual array :param lb: lower band :param ub: upper band :return: average excess over array """ excess = np.zeros(d.shape) posidx = np.where(d >= 0)[0] excess[posidx] = np.where(ub[posidx] - d[posidx] < 0., 0., ub[posidx] - d[posidx]) negidx = np.where(d < 0)[0] excess[negidx] = np.where(lb[negidx] + d[negidx] < 0., 0., lb[negidx] + d[negidx]) return np.mean(excess) def _calc_avg_deficit(self, d, lb, ub): """ Deficit is error bar insufficiency: bar falls short of actual :param d: pred-actual array :param lb: lower band :param ub: upper band :return: average deficit over array """ deficit = np.zeros(d.shape) posidx = np.where(d >= 0)[0] deficit[posidx] = np.where(- ub[posidx] + d[posidx] < 0., 0., - ub[posidx] + d[posidx]) negidx = np.where(d < 0)[0] deficit[negidx] = np.where(- lb[negidx] - d[negidx] < 0., 0., - lb[negidx] - d[negidx]) return np.mean(deficit) def _calc_missrate_bandwidth_excess_deficit(self, d, lb, ub, scale=1.0, bias=0.0): """ Calculates recall at a given scale/bias, average bandwidth and average excess :param d: delta :param lb: lower band :param ub: upper band :param scale: scale * (x + bias) :param bias: :return: miss rate, average bandwidth, avg excess, avg deficit """ abslband = scale * np.where((lb + bias) < 0., 0., lb + bias) absuband = scale * np.where((ub + bias) < 0., 0., ub + bias) recall = np.sum((d >= - abslband) & (d <= absuband)) / len(d) avgbandwidth = np.mean([absuband, abslband]) avgexcess = self._calc_avg_excess(d, abslband, absuband) avgdeficit = self._calc_avg_deficit(d, abslband, absuband) return 1 - recall, avgbandwidth, avgexcess, avgdeficit def _calc_plotdata(self, d, lb, ub, vary_bias=False): """ Generates data necessary for various UCC metrics. :param d: delta (predicted - actual) vector :param ub: upper uncertainty bandwidth (above predicted) :param lb: lower uncertainty bandwidth (below predicted) - all positive (bandwidth) :param vary_bias: True will switch to additive bias instead of scale :return: list. Elements are tuples (varyvalue, missrate, bandwidth, excess, deficit) """ # step 1: collect critical scale or bias values critval = [] for i in range(len(d)): if not vary_bias: if d[i] >= 0: critval.append(d[i] / ub[i]) else: critval.append(-d[i] / lb[i]) else: if d[i] >= 0: critval.append(d[i] - ub[i]) else: critval.append(-lb[i] - d[i]) critval = sorted(critval) plotdata = [] for i in range(len(critval)): if not vary_bias: missrate, bandwidth, excess, deficit = self._calc_missrate_bandwidth_excess_deficit(d, lb, ub, scale=critval[i]) else: missrate, bandwidth, excess, deficit = self._calc_missrate_bandwidth_excess_deficit(d, lb, ub, bias=critval[i]) plotdata.append((critval[i], missrate, bandwidth, excess, deficit)) return plotdata def get_AUUCC(self, vary_bias=False, aucfct="trapz", partial_x=None, partial_y=None): """ returns approximate area under the curve on current coordinates, for each component. :param vary_bias: False == varies scale, True == varies bias :param aucfct: specifies AUC integrator (can be "trapz", "simps") :param partial_x: tuple (x_min, x_max) defining interval on x to calc a a partial AUC. The interval bounds refer to axes as visualized (ie. potentially normed) :param partial_y: tuple (y_min, y_max) defining interval on y to calc a a partial AUC. partial_x must be None. :return: list of floats with AUUCCs for each input component, or a single float, if there is only 1 component. """ if self.d is None: raise ValueError("ERROR(UCC): call fit() prior to using this method.") if vary_bias and not self.precompute_bias_data: raise ValueError("ERROR(UCC): Cannot vary bias - instantiated without bias data computation") if partial_x is not None and partial_y is not None: raise ValueError("ERROR(UCC): partial_x and partial_y can not be specified at the same time.") assert(partial_x is None or (isinstance(partial_x, tuple) and len(partial_x)==2)) assert(partial_y is None or (isinstance(partial_y, tuple) and len(partial_y)==2)) # find starting point (where the x axis value starts to actually change) rv = [] # do this for individual streams xind = self.x_axis_idx aucfct = simps if aucfct == "simps" else trapz for s in range(len(self.d)): plotdata = self.plotdata_for_bias[s] if vary_bias else self.plotdata_for_scale[s] prev = plotdata[0][xind] t = 1 cval = plotdata[t][xind] while cval == prev and t < len(plotdata) - 1: t += 1 prev = cval cval = plotdata[t][xind] startt = t - 1 # from here, it's a valid function endtt = len(plotdata) if startt >= endtt - 2: rvs = 0. # no area else: xnorm = self.std_unit if self.norm_x_axis else 1. ynorm = self.std_unit if self.norm_y_axis else 1. y=[(plotdata[i][self.y_axis_idx]) / ynorm for i in range(startt, endtt)] x=[(plotdata[i][self.x_axis_idx]) / xnorm for i in range(startt, endtt)] if partial_x is not None: from_i = self._find_closest_index(partial_x[0], x) to_i = self._find_closest_index(partial_x[1], x) + 1 elif partial_y is not None: from_i = self._find_closest_index(partial_y[0], y) to_i = self._find_closest_index(partial_y[1], y) if from_i > to_i: # y is in reverse order from_i, to_i = to_i, from_i to_i += 1 # as upper bound in array indexing else: from_i = 0 to_i = len(x) to_i = min(to_i, len(x)) if to_i < from_i: raise ValueError("ERROR(UCC): Failed to find an appropriate partial-AUC interval in the data.") if to_i - from_i < 2: raise RuntimeError("ERROR(UCC): There are too few samples (1) in the partial-AUC interval specified") rvs = aucfct(x=x[from_i:to_i], y=y[from_i:to_i]) rv.append(rvs) if len(rv) < 2: return rv[0] else: return rv @ staticmethod def _find_closest_index(value, array): """ Returns an index of the 'array' element closest in value to 'value' :param value: :param array: :return: """ return np.argmin(np.abs(np.asarray(array)-value)) def _get_single_OP(self, d, lb, ub, scale=1., bias=0.): """ Returns Operating Point for original input data, on coordinates currently set up, given a scale/bias. :param scale: :param bias: :return: single tuple (x point, y point, unit of x, unit of y) """ xnorm = self.std_unit if self.norm_x_axis else 1. ynorm = self.std_unit if self.norm_y_axis else 1. auxop = self._calc_missrate_bandwidth_excess_deficit(d, lb, ub, scale=scale, bias=bias) op = [0.] + [i for i in auxop] # mimic plotdata (first element ignored here) return (op[self.x_axis_idx] / xnorm, op[self.y_axis_idx] / ynorm, xnorm, ynorm) def get_OP(self, scale=1., bias=0.): """ Returns all Operating Points for original input data, on coordinates currently set up, given a scale/bias. :param scale: :param bias: :return: list of tuples (x point, y point, unit of x, unit of y) or a single tuple if there is only 1 component. """ if self.d is None: raise ValueError("ERROR(UCC): call fit() prior to using this method.") op = [] for dc in range(len(self.d)): op.append(self._get_single_OP(self.d[dc], self.lb[dc], self.ub[dc], scale=scale, bias=bias)) if len(op) < 2: return op[0] else: return op def plot_UCC(self, titlestr='', syslabel='model', outfn=None, vary_bias=False, markers=None, xlim=None, ylim=None, **kwargs): """ Will plot/display the UCC based on current data and coordinates. Multiple curves will be shown if there are multiple data components (via fit()) :param titlestr: Plot title string :param syslabel: list is label strings to appear in the plot legend. Can be single, if one component. :param outfn: base name of an image file to be created (will append .png before creating) :param vary_bias: True will switch to varying additive bias (default is multiplicative scale) :param markers: None or a list of marker styles to be used for each curve. List must be same or longer than number of components. Markers can be one among these ['o', 's', 'v', 'D', '+']. :param xlim: tuples or lists of specifying the range for the x axis, or None (auto) :param ylim: tuples or lists of specifying the range for the y axis, or None (auto) :param `**kwargs`: Additional arguments passed to the main plot call. :return: list of areas under the curve (or single area, if one data component) list of operating points (or single op): format of an op is tuple (xaxis value, yaxis value, xunit, yunit) """ if self.d is None: raise ValueError("ERROR(UCC): call fit() prior to using this method.") if vary_bias and not self.precompute_bias_data: raise ValueError("ERROR(UCC): Cannot vary bias - instantiated without bias data computation") if not isinstance(syslabel, list): syslabel = [syslabel] assert (len(syslabel) == len(self.d)) assert (markers is None or (isinstance(markers, list) and len(markers) >= len(self.d))) # main plot of (possibly multiple) datasets plt.figure() xnorm = self.std_unit if self.norm_x_axis else 1. ynorm = self.std_unit if self.norm_y_axis else 1. op_info = [] auucc = self.get_AUUCC(vary_bias=vary_bias) auucc = [auucc] if not isinstance(auucc, list) else auucc for s in range(len(self.d)): # original operating point x_op, y_op, x_unit, y_unit = self._get_single_OP(self.d[s], self.lb[s], self.ub[s]) op_info.append((x_op, y_op, x_unit, y_unit)) # display chart plotdata = self.plotdata_for_scale[s] if not vary_bias else self.plotdata_for_bias[s] axisX_data = [i[self.x_axis_idx] / xnorm for i in plotdata] axisY_data = [i[self.y_axis_idx] / ynorm for i in plotdata] marker = None if markers is not None: marker = markers[s] p = plt.plot(axisX_data, axisY_data, label=syslabel[s] + (" (AUC=%.3f)" % auucc[s]), marker=marker, **kwargs) if s + 1 == len(self.d): oplab = 'OP' else: oplab = None plt.plot(x_op, y_op, marker='o', color=p[0].get_color(), label=oplab, markerfacecolor='w', markeredgewidth=1.5, markeredgecolor=p[0].get_color()) axisX_label = self.axes_idx2descr[self.x_axis_idx] axisY_label = self.axes_idx2descr[self.y_axis_idx] axisX_units = "(raw)" if np.isclose(xnorm, 1.0) else "[in std deviations]" axisY_units = "(raw)" if np.isclose(ynorm, 1.0) else "[in std deviations]" axisX_label += ' ' + axisX_units axisY_label += ' ' + axisY_units if ylim is not None: plt.ylim(ylim) if xlim is not None: plt.xlim(xlim) plt.xlabel(axisX_label) plt.ylabel(axisY_label) plt.legend() plt.title(titlestr) plt.grid() if outfn is None: plt.show() else: plt.savefig(outfn) if len(auucc) < 2: auucc = auucc[0] op_info = op_info[0] return auucc, op_info import torch import torch.nn.functional as F from uq360.models.noise_models.heteroscedastic_noise_models import GaussianNoise class GaussianNoiseMLPNet(torch.nn.Module): def __init__(self, num_features, num_outputs, num_hidden): super(GaussianNoiseMLPNet, self).__init__() self.fc = torch.nn.Linear(num_features, num_hidden) self.fc_mu = torch.nn.Linear(num_hidden, num_outputs) self.fc_log_var = torch.nn.Linear(num_hidden, num_outputs) self.noise_layer = GaussianNoise() def forward(self, x): x = F.relu(self.fc(x)) mu = self.fc_mu(x) log_var = self.fc_log_var(x) return mu, log_var def loss(self, y_true=None, mu_pred=None, log_var_pred=None): return self.noise_layer.loss(y_true, mu_pred, log_var_pred, reduce_mean=True) """ Contains implementations of various utilities used by Horseshoe Bayesian layers """ import numpy as np import torch from torch.nn import Parameter td = torch.distributions gammaln = torch.lgamma def diag_gaussian_entropy(log_std, D): return 0.5 * D * (1.0 + torch.log(2 * np.pi)) + torch.sum(log_std) def inv_gamma_entropy(a, b): return torch.sum(a + torch.log(b) + torch.lgamma(a) - (1 + a) * torch.digamma(a)) def log_normal_entropy(log_std, mu, D): return torch.sum(log_std + mu + 0.5) + (D / 2) * np.log(2 * np.pi) class InvGammaHalfCauchyLayer(torch.nn.Module): """ Uses the inverse Gamma parameterization of the half-Cauchy distribution. a ~ C^+(0, b) <==> a^2 ~ IGamma(0.5, 1/lambda), lambda ~ IGamma(0.5, 1/b^2), where lambda is an auxiliary latent variable. Uses a factorized variational approximation q(ln a^2)q(lambda) = N(mu, sigma^2) IGamma(ahat, bhat). This layer places a half Cauchy prior on the scales of each output node of the layer. """ def __init__(self, out_features, b): """ :param out_fatures: number of output nodes in the layer. :param b: scale of the half Cauchy """ super(InvGammaHalfCauchyLayer, self).__init__() self.b = b self.out_features = out_features # variational parameters for q(ln a^2) self.mu = Parameter(torch.FloatTensor(out_features)) self.log_sigma = Parameter(torch.FloatTensor(out_features)) # self.log_sigma = torch.FloatTensor(out_features) # variational parameters for q(lambda). These will be updated via fixed point updates, hence not parameters. self.ahat = torch.FloatTensor([1.]) # The posterior parameter is always 1. self.bhat = torch.ones(out_features) * (1.0 / self.b ** 2) self.const = torch.FloatTensor([0.5]) self.initialize_from_prior() def initialize_from_prior(self): """ Initializes variational parameters by sampling from the prior. """ # sample from half cauchy and log to initialize the mean of the log normal sample = np.abs(self.b * (np.random.randn(self.out_features) / np.random.randn(self.out_features))) self.mu.data = torch.FloatTensor(np.log(sample)) self.log_sigma.data = torch.FloatTensor(np.random.randn(self.out_features) - 10.) def expectation_wrt_prior(self): """ Computes E[ln p(a^2 | lambda)] + E[ln p(lambda)] """ expected_a_given_lambda = -gammaln(self.const) - 0.5 * (torch.log(self.bhat) - torch.digamma(self.ahat)) + ( -0.5 - 1.) * self.mu - torch.exp(-self.mu + 0.5 * self.log_sigma.exp() ** 2) * (self.ahat / self.bhat) expected_lambda = -gammaln(self.const) - 2 * 0.5 * np.log(self.b) + (-self.const - 1.) * ( torch.log(self.bhat) - torch.digamma(self.ahat)) - (1. / self.b ** 2) * (self.ahat / self.bhat) return torch.sum(expected_a_given_lambda) + torch.sum(expected_lambda) def entropy(self): """ Computes entropy of q(ln a^2) and q(lambda) """ return self.entropy_lambda() + self.entropy_a2() def entropy_lambda(self): return inv_gamma_entropy(self.ahat, self.bhat) def entropy_a2(self): return log_normal_entropy(self.log_sigma, self.mu, self.out_features) def kl(self): """ Computes KL(q(ln(a^2)q(lambda) || IG(a^2 | 0.5, 1/lambda) IG(lambda | 0.5, 1/b^2)) """ return -self.expectation_wrt_prior() - self.entropy() def fixed_point_updates(self): # update lambda moments self.bhat = torch.exp(-self.mu + 0.5 * self.log_sigma.exp() ** 2) + (1. / self.b ** 2) class InvGammaLayer(torch.nn.Module): """ Approximates the posterior of c^2 with prior IGamma(c^2 | a , b) using a log Normal approximation q(ln c^2) = N(mu, sigma^2) """ def __init__(self, a, b, out_features=1): super(InvGammaLayer, self).__init__() self.a = torch.FloatTensor([a]) self.b = torch.FloatTensor([b]) # variational parameters for q(ln c^2) self.mu = Parameter(torch.FloatTensor(out_features)) self.log_sigma = Parameter(torch.FloatTensor(out_features)) self.out_features = out_features self.initialize_from_prior() def initialize_from_prior(self): """ Initializes variational parameters by sampling from the prior. """ self.mu.data = torch.log(self.b / (self.a + 1) * torch.ones(self.out_features)) # initialize at the mode self.log_sigma.data = torch.FloatTensor(np.random.randn(self.out_features) - 10.) def expectation_wrt_prior(self): """ Computes E[ln p(c^2 | a, b)] """ # return self.c_a * np.log(self.c_b) - gammaln(self.c_a) + ( # - self.c_a - 1) * c_mu - self.c_b * Ecinv return self.a * torch.log(self.b) - gammaln(self.a) + (- self.a - 1) \ * self.mu - self.b * torch.exp(-self.mu + 0.5 * self.log_sigma.exp() ** 2) def entropy(self): return log_normal_entropy(self.log_sigma, self.mu, 1) def kl(self): """ Computes KL(q(ln(c^2) || IG(c^2 | a, b)) """ return -self.expectation_wrt_prior().sum() - self.entropy() """ Contains implementations of various Bayesian layers """ import numpy as np import torch import torch.nn.functional as F from torch.nn import Parameter from uq360.models.bayesian_neural_networks.layer_utils import InvGammaHalfCauchyLayer, InvGammaLayer td = torch.distributions def reparam(mu, logvar, do_sample=True, mc_samples=1): if do_sample: std = torch.exp(0.5 * logvar) eps = torch.FloatTensor(std.size()).normal_() sample = mu + eps * std for _ in np.arange(1, mc_samples): sample += mu + eps * std return sample / mc_samples else: return mu class BayesianLinearLayer(torch.nn.Module): """ Affine layer with N(0, v/H) or N(0, user specified v) priors on weights and fully factorized variational Gaussian approximation """ def __init__(self, in_features, out_features, cuda=False, init_weight=None, init_bias=None, prior_stdv=None): super(BayesianLinearLayer, self).__init__() self.cuda = cuda self.in_features = in_features self.out_features = out_features # weight mean params self.weights = Parameter(torch.Tensor(out_features, in_features)) self.bias = Parameter(torch.Tensor(out_features)) # weight variance params self.weights_logvar = Parameter(torch.Tensor(out_features, in_features)) self.bias_logvar = Parameter(torch.Tensor(out_features)) # numerical stability self.fudge_factor = 1e-8 if not prior_stdv: # We will use a N(0, 1/num_inputs) prior over weights self.prior_stdv = torch.FloatTensor([1. / np.sqrt(self.weights.size(1))]) else: self.prior_stdv = torch.FloatTensor([prior_stdv]) # self.prior_stdv = torch.Tensor([1. / np.sqrt(1e+3)]) self.prior_mean = torch.FloatTensor([0.]) # for Bias use a prior of N(0, 1) self.prior_bias_stdv = torch.FloatTensor([1.]) self.prior_bias_mean = torch.FloatTensor([0.]) # init params either random or with pretrained net self.init_parameters(init_weight, init_bias) def init_parameters(self, init_weight, init_bias): # init means if init_weight is not None: self.weights.data = torch.Tensor(init_weight) else: self.weights.data.normal_(0, np.float(self.prior_stdv.numpy()[0])) if init_bias is not None: self.bias.data = torch.Tensor(init_bias) else: self.bias.data.normal_(0, 1) # init variances self.weights_logvar.data.normal_(-9, 1e-2) self.bias_logvar.data.normal_(-9, 1e-2) def forward(self, x, do_sample=True, scale_variances=False): # local reparameterization trick mu_activations = F.linear(x, self.weights, self.bias) var_activations = F.linear(x.pow(2), self.weights_logvar.exp(), self.bias_logvar.exp()) if scale_variances: activ = reparam(mu_activations, var_activations.log() - np.log(self.in_features), do_sample=do_sample) else: activ = reparam(mu_activations, var_activations.log(), do_sample=do_sample) return activ def kl(self): """ KL divergence (q(W) || p(W)) :return: """ weights_logvar = self.weights_logvar kld_weights = self.prior_stdv.log() - weights_logvar.mul(0.5) + \ (weights_logvar.exp() + (self.weights.pow(2) - self.prior_mean)) / ( 2 * self.prior_stdv.pow(2)) - 0.5 kld_bias = self.prior_bias_stdv.log() - self.bias_logvar.mul(0.5) + \ (self.bias_logvar.exp() + (self.bias.pow(2) - self.prior_bias_mean)) / ( 2 * self.prior_bias_stdv.pow(2)) \ - 0.5 return kld_weights.sum() + kld_bias.sum() class HorseshoeLayer(BayesianLinearLayer): """ Uses non-centered parametrization. w_k = v*tau_k*beta_k where k indexes an output unit and w_k and beta_k are vectors of all weights incident into the unit """ def __init__(self, in_features, out_features, cuda=False, scale=1.): super(HorseshoeLayer, self).__init__(in_features, out_features) self.cuda = cuda self.in_features = in_features self.out_features = out_features self.nodescales = InvGammaHalfCauchyLayer(out_features=out_features, b=1.) self.layerscale = InvGammaHalfCauchyLayer(out_features=1, b=scale) # prior on beta is N(0, I) when employing non centered parameterization self.prior_stdv = torch.Tensor([1]) self.prior_mean = torch.Tensor([0.]) def forward(self, x, do_sample=True, debug=False, eps_scale=None, eps_w=None): # At a particular unit k, preactivation_sample = scale_sample * pre_activation_sample # sample scales scale_mean = 0.5 * (self.nodescales.mu + self.layerscale.mu) scale_var = 0.25 * (self.nodescales.log_sigma.exp() ** 2 + self.layerscale.log_sigma.exp() ** 2) scale_sample = reparam(scale_mean, scale_var.log(), do_sample=do_sample).exp() # sample preactivations mu_activations = F.linear(x, self.weights, self.bias) var_activations = F.linear(x.pow(2), self.weights_logvar.exp(), self.bias_logvar.exp()) activ_sample = reparam(mu_activations, var_activations.log(), do_sample=do_sample) return scale_sample * activ_sample def kl(self): return super(HorseshoeLayer, self).kl() + self.nodescales.kl() + self.layerscale.kl() def fixed_point_updates(self): self.nodescales.fixed_point_updates() self.layerscale.fixed_point_updates() class RegularizedHorseshoeLayer(HorseshoeLayer): """ Uses the regularized Horseshoe distribution. The regularized Horseshoe soft thresholds the tails of the Horseshoe. For all weights w_k incident upon node k in the layer we have: w_k ~ N(0, (tau_k * v)^2 I) N(0, c^2 I), c^2 ~ InverseGamma(c_a, b). c^2 controls the scale of the thresholding. As c^2 -> infinity, the regularized Horseshoe -> Horseshoe. """ def __init__(self, in_features, out_features, cuda=False, scale=1., c_a=2., c_b=6.): super(RegularizedHorseshoeLayer, self).__init__(in_features, out_features, cuda=cuda, scale=scale) self.c = InvGammaLayer(a=c_a, b=c_b) def forward(self, x, do_sample=True, **kwargs): # At a particular unit k, preactivation_sample = scale_sample * pre_activation_sample # sample regularized scales scale_mean = self.nodescales.mu + self.layerscale.mu scale_var = self.nodescales.log_sigma.exp() ** 2 + self.layerscale.log_sigma.exp() ** 2 scale_sample = reparam(scale_mean, scale_var.log(), do_sample=do_sample).exp() c_sample = reparam(self.c.mu, 2 * self.c.log_sigma, do_sample=do_sample).exp() regularized_scale_sample = (c_sample * scale_sample) / (c_sample + scale_sample) # sample preactivations mu_activations = F.linear(x, self.weights, self.bias) var_activations = F.linear(x.pow(2), self.weights_logvar.exp(), self.bias_logvar.exp()) activ_sample = reparam(mu_activations, var_activations.log(), do_sample=do_sample) return torch.sqrt(regularized_scale_sample) * activ_sample def kl(self): return super(RegularizedHorseshoeLayer, self).kl() + self.c.kl() class NodeSpecificRegularizedHorseshoeLayer(RegularizedHorseshoeLayer): """ Uses the regularized Horseshoe distribution. The regularized Horseshoe soft thresholds the tails of the Horseshoe. For all weights w_k incident upon node k in the layer we have: w_k ~ N(0, (tau_k * v)^2 I) N(0, c_k^2 I), c_k^2 ~ InverseGamma(a, b). c_k^2 controls the scale of the thresholding. As c_k^2 -> infinity, the regularized Horseshoe -> Horseshoe Note that we now have a per-node c_k. """ def __init__(self, in_features, out_features, cuda=False, scale=1., c_a=2., c_b=6.): super(NodeSpecificRegularizedHorseshoeLayer, self).__init__(in_features, out_features, cuda=cuda, scale=scale) self.c = InvGammaLayer(a=c_a, b=c_b, out_features=out_features) import numpy as np import torch from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseFixedPrecision def compute_test_ll(y_test, y_pred_samples, std_y=1.): """ Computes test log likelihoods = (1 / Ntest) * \sum_n p(y_n | x_n, D_train) :param y_test: True y :param y_pred_samples: y^s = f(x_test, w^s); w^s ~ q(w). S x Ntest, where S is the number of samples q(w) is either a trained variational posterior or an MCMC approximation to p(w | D_train) :param std_y: True std of y (assumed known) """ S, _ = y_pred_samples.shape noise = GaussianNoiseFixedPrecision(std_y=std_y) ll = noise.loss(y_pred=y_pred_samples, y_true=y_test.unsqueeze(dim=0), reduce_sum=False) ll = torch.logsumexp(ll, dim=0) - np.log(S) # mean over num samples return torch.mean(ll) # mean over test points from abc import ABC import numpy as np import torch from torch import nn from uq360.models.bayesian_neural_networks.layers import HorseshoeLayer, BayesianLinearLayer, RegularizedHorseshoeLayer from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseGammaPrecision import numpy as np td = torch.distributions class HshoeBNN(nn.Module, ABC): """ Bayesian neural network with Horseshoe layers. """ def __init__(self, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1, hshoe_scale=1e-1, use_reg_hshoe=False): if use_reg_hshoe: layer = RegularizedHorseshoeLayer else: layer = HorseshoeLayer super(HshoeBNN, self).__init__() self.num_layers = num_layers if activation_type == 'relu': # activation self.activation = nn.ReLU() elif activation_type == 'tanh': self.activation = nn.Tanh() else: print("Activation Type not supported") self.fc_hidden = [] self.fc1 = layer(ip_dim, num_nodes, scale=hshoe_scale) for _ in np.arange(self.num_layers - 1): self.fc_hidden.append(layer(num_nodes, num_nodes)) self.fc_out = BayesianLinearLayer(num_nodes, op_dim) self.noise_layer = None def forward(self, x, do_sample=True): x = self.fc1(x, do_sample=do_sample) x = self.activation(x) for layer in self.fc_hidden: x = layer(x, do_sample=do_sample) x = self.activation(x) return self.fc_out(x, do_sample=do_sample, scale_variances=True) def kl_divergence_w(self): kld = self.fc1.kl() + self.fc_out.kl() for layer in self.fc_hidden: kld += layer.kl() return kld def fixed_point_updates(self): if hasattr(self.fc1, 'fixed_point_updates'): self.fc1.fixed_point_updates() if hasattr(self.fc_out, 'fixed_point_updates'): self.fc_out.fixed_point_updates() for layer in self.fc_hidden: if hasattr(layer, 'fixed_point_updates'): layer.fixed_point_updates() def prior_predictive_samples(self, n_sample=100): n_eval = 1000 x = torch.linspace(-2, 2, n_eval)[:, np.newaxis] y = np.zeros([n_sample, n_eval]) for i in np.arange(n_sample): y[i] = self.forward(x).data.numpy().ravel() return x.data.numpy(), y ### get and set weights ### def get_weights(self): assert len(self.fc_hidden) == 0 # only works for one layer networks. weight_dict = {} weight_dict['layerip_means'] = torch.cat([self.fc1.weights, self.fc1.bias.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerip_logvar'] = torch.cat([self.fc1.weights_logvar, self.fc1.bias_logvar.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerop_means'] = torch.cat([self.fc_out.weights, self.fc_out.bias.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerop_logvar'] = torch.cat([self.fc_out.weights_logvar, self.fc_out.bias_logvar.unsqueeze(1)], dim=1).data.numpy() return weight_dict def set_weights(self, weight_dict): assert len(self.fc_hidden) == 0 # only works for one layer networks. to_param = lambda x: nn.Parameter(torch.Tensor(x)) self.fc1.weights = to_param(weight_dict['layerip_means'][:, :-1]) self.fc1.weights = to_param(weight_dict['layerip_logvar'][:, :-1]) self.fc1.bias = to_param(weight_dict['layerip_means'][:, -1]) self.fc1.bias_logvar = to_param(weight_dict['layerip_logvar'][:, -1]) self.fc_out.weights = to_param(weight_dict['layerop_means'][:, :-1]) self.fc_out.weights = to_param(weight_dict['layerop_logvar'][:, :-1]) self.fc_out.bias = to_param(weight_dict['layerop_means'][:, -1]) self.fc_out.bias_logvar = to_param(weight_dict['layerop_logvar'][:, -1]) class HshoeRegressionNet(HshoeBNN, ABC): """ Horseshoe net with N(y_true | f(x, w), \lambda^-1); \lambda ~ Gamma(a, b) likelihoods. """ def __init__(self, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1, hshoe_scale=1e-5, use_reg_hshoe=False): super(HshoeRegressionNet, self).__init__(ip_dim=ip_dim, op_dim=op_dim, num_nodes=num_nodes, activation_type=activation_type, num_layers=num_layers, hshoe_scale=hshoe_scale, use_reg_hshoe=use_reg_hshoe) self.noise_layer = GaussianNoiseGammaPrecision(a0=6., b0=6.) def likelihood(self, x=None, y=None): out = self.forward(x) return -self.noise_layer.loss(y_pred=out, y_true=y) def neg_elbo(self, num_batches, x=None, y=None): # scale the KL terms by number of batches so that the minibatch elbo is an unbiased estiamte of the true elbo. Elik = self.likelihood(x, y) neg_elbo = (self.kl_divergence_w() + self.noise_layer.kl()) / num_batches - Elik return neg_elbo def mse(self, x, y): """ scaled rmse (scaled by 1 / std_y**2) """ E_noise_precision = 1. / self.noise_layer.get_noise_var() return (0.5 * E_noise_precision * (self.forward(x, do_sample=False) - y)**2).sum() def get_noise_var(self): return self.noise_layer.get_noise_var() class HshoeClassificationNet(HshoeBNN, ABC): """ Horseshoe net with Categorical(y_true | f(x, w)) likelihoods. Use for classification. """ def __init__(self, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1, hshoe_scale=1e-5, use_reg_hshoe=False): super(HshoeClassificationNet, self).__init__(ip_dim=ip_dim, op_dim=op_dim, num_nodes=num_nodes, activation_type=activation_type, num_layers=num_layers, hshoe_scale=hshoe_scale, use_reg_hshoe=use_reg_hshoe) self.noise_layer = torch.nn.CrossEntropyLoss(reduction='sum') def likelihood(self, x=None, y=None): out = self.forward(x) return -self.noise_layer(out, y) def neg_elbo(self, num_batches, x=None, y=None): # scale the KL terms by number of batches so that the minibatch elbo is an unbiased estiamte of the true elbo. Elik = self.likelihood(x, y) neg_elbo = (self.kl_divergence_w()) / num_batches - Elik return neg_elbo from abc import ABC import torch from torch import nn from uq360.models.bayesian_neural_networks.layers import BayesianLinearLayer from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseGammaPrecision import numpy as np td = torch.distributions class BayesianNN(nn.Module, ABC): """ Bayesian neural network with zero mean Gaussian priors over weights. """ def __init__(self, layer=BayesianLinearLayer, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1): super(BayesianNN, self).__init__() self.num_layers = num_layers if activation_type == 'relu': # activation self.activation = nn.ReLU() elif activation_type == 'tanh': self.activation = nn.Tanh() else: print("Activation Type not supported") self.fc_hidden = [] self.fc1 = layer(ip_dim, num_nodes,) for _ in np.arange(self.num_layers - 1): self.fc_hidden.append(layer(num_nodes, num_nodes, )) self.fc_out = layer(num_nodes, op_dim, ) self.noise_layer = None def forward(self, x, do_sample=True): x = self.fc1(x, do_sample=do_sample) x = self.activation(x) for layer in self.fc_hidden: x = layer(x, do_sample=do_sample) x = self.activation(x) return self.fc_out(x, do_sample=do_sample, scale_variances=True) def kl_divergence_w(self): kld = self.fc1.kl() + self.fc_out.kl() for layer in self.fc_hidden: kld += layer.kl() return kld def prior_predictive_samples(self, n_sample=100): n_eval = 1000 x = torch.linspace(-2, 2, n_eval)[:, np.newaxis] y = np.zeros([n_sample, n_eval]) for i in np.arange(n_sample): y[i] = self.forward(x).data.numpy().ravel() return x.data.numpy(), y ### get and set weights ### def get_weights(self): assert len(self.fc_hidden) == 0 # only works for one layer networks. weight_dict = {} weight_dict['layerip_means'] = torch.cat([self.fc1.weights, self.fc1.bias.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerip_logvar'] = torch.cat([self.fc1.weights_logvar, self.fc1.bias_logvar.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerop_means'] = torch.cat([self.fc_out.weights, self.fc_out.bias.unsqueeze(1)], dim=1).data.numpy() weight_dict['layerop_logvar'] = torch.cat([self.fc_out.weights_logvar, self.fc_out.bias_logvar.unsqueeze(1)], dim=1).data.numpy() return weight_dict def set_weights(self, weight_dict): assert len(self.fc_hidden) == 0 # only works for one layer networks. to_param = lambda x: nn.Parameter(torch.Tensor(x)) self.fc1.weights = to_param(weight_dict['layerip_means'][:, :-1]) self.fc1.weights = to_param(weight_dict['layerip_logvar'][:, :-1]) self.fc1.bias = to_param(weight_dict['layerip_means'][:, -1]) self.fc1.bias_logvar = to_param(weight_dict['layerip_logvar'][:, -1]) self.fc_out.weights = to_param(weight_dict['layerop_means'][:, :-1]) self.fc_out.weights = to_param(weight_dict['layerop_logvar'][:, :-1]) self.fc_out.bias = to_param(weight_dict['layerop_means'][:, -1]) self.fc_out.bias_logvar = to_param(weight_dict['layerop_logvar'][:, -1]) class BayesianRegressionNet(BayesianNN, ABC): """ Bayesian neural net with N(y_true | f(x, w), \lambda^-1); \lambda ~ Gamma(a, b) likelihoods. """ def __init__(self, layer=BayesianLinearLayer, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1): super(BayesianRegressionNet, self).__init__(layer=layer, ip_dim=ip_dim, op_dim=op_dim, num_nodes=num_nodes, activation_type=activation_type, num_layers=num_layers, ) self.noise_layer = GaussianNoiseGammaPrecision(a0=6., b0=6.) def likelihood(self, x=None, y=None): out = self.forward(x) return -self.noise_layer.loss(y_pred=out, y_true=y) def neg_elbo(self, num_batches, x=None, y=None): # scale the KL terms by number of batches so that the minibatch elbo is an unbiased estiamte of the true elbo. Elik = self.likelihood(x, y) neg_elbo = (self.kl_divergence_w() + self.noise_layer.kl()) / num_batches - Elik return neg_elbo def mse(self, x, y): """ scaled rmse (scaled by 1 / std_y**2) """ E_noise_precision = 1. / self.noise_layer.get_noise_var() return (0.5 * E_noise_precision * (self.forward(x, do_sample=False) - y)**2).sum() def get_noise_var(self): return self.noise_layer.get_noise_var() class BayesianClassificationNet(BayesianNN, ABC): """ Bayesian neural net with Categorical(y_true | f(x, w)) likelihoods. Use for classification. """ def __init__(self, layer=BayesianLinearLayer, ip_dim=1, op_dim=1, num_nodes=50, activation_type='relu', num_layers=1): super(BayesianClassificationNet, self).__init__(layer=layer, ip_dim=ip_dim, op_dim=op_dim, num_nodes=num_nodes, activation_type=activation_type, num_layers=num_layers) self.noise_layer = torch.nn.CrossEntropyLoss(reduction='sum') def likelihood(self, x=None, y=None): out = self.forward(x) return -self.noise_layer(out, y) def neg_elbo(self, num_batches, x=None, y=None): # scale the KL terms by number of batches so that the minibatch elbo is an unbiased estiamte of the true elbo. Elik = self.likelihood(x, y) neg_elbo = self.kl_divergence_w() / num_batches - Elik return neg_elbo import math import numpy as np import torch from scipy.special import gammaln from uq360.models.noise_models.noisemodel import AbstractNoiseModel from torch.nn import Parameter td = torch.distributions def transform(a): return torch.log(1 + torch.exp(a)) class GaussianNoiseGammaPrecision(torch.nn.Module, AbstractNoiseModel): """ N(y_true | f(x, w), \lambda^-1); \lambda ~ Gamma(a, b). Uses a variational approximation; q(lambda) = Gamma(ahat, bhat) """ def __init__(self, a0=6, b0=6, cuda=False): super(GaussianNoiseGammaPrecision, self).__init__() self.cuda = cuda self.a0 = a0 self.b0 = b0 self.const = torch.log(torch.FloatTensor([2 * math.pi])) # variational parameters self.ahat = Parameter(torch.FloatTensor([10.])) self.bhat = Parameter(torch.FloatTensor([3.])) def loss(self, y_pred=None, y_true=None): """ computes -1 * E_q(\lambda)[ln N (y_pred | y_true, \lambda^-1)], where q(lambda) = Gamma(ahat, bhat) :param y_pred: :param y_true: :return: """ n = y_pred.shape[0] ahat = transform(self.ahat) bhat = transform(self.bhat) return -1 * (-0.5 * n * self.const + 0.5 * n * (torch.digamma(ahat) - torch.log(bhat)) \ - 0.5 * (ahat/bhat) * ((y_pred - y_true) ** 2).sum()) def kl(self): ahat = transform(self.ahat) bhat = transform(self.bhat) return (ahat - self.a0) * torch.digamma(ahat) - torch.lgamma(ahat) + gammaln(self.a0) + \ self.a0 * (torch.log(bhat) - np.log(self.b0)) + ahat * (self.b0 - bhat) / bhat def get_noise_var(self): ahat = transform(self.ahat) bhat = transform(self.bhat) return (bhat / ahat).data.numpy()[0] class GaussianNoiseFixedPrecision(torch.nn.Module, AbstractNoiseModel): """ N(y_true | f(x, w), sigma_y**2); known sigma_y """ def __init__(self, std_y=1., cuda=False): super(GaussianNoiseFixedPrecision, self).__init__() self.cuda = cuda self.const = torch.log(torch.FloatTensor([2 * math.pi])) self.sigma_y = std_y def loss(self, y_pred=None, y_true=None): """ computes -1 * ln N (y_pred | y_true, sigma_y**2) :param y_pred: :param y_true: :return: """ ll = -0.5 * self.const - np.log(self.sigma_y) - 0.5 * (1. / self.sigma_y ** 2) * ((y_pred - y_true) ** 2) return -ll.sum(dim=0) def get_noise_var(self): return self.sigma_y ** 2 import math import numpy as np import torch from scipy.special import gammaln from uq360.models.noise_models.noisemodel import AbstractNoiseModel from torch.nn import Parameter td = torch.distributions def transform(a): return torch.log(1 + torch.exp(a)) class GaussianNoise(torch.nn.Module, AbstractNoiseModel): """ N(y_true | f_\mu(x, w), f_\sigma^2(x, w)) """ def __init__(self, cuda=False): super(GaussianNoise, self).__init__() self.cuda = cuda self.const = torch.log(torch.FloatTensor([2 * math.pi])) def loss(self, y_true=None, mu_pred=None, log_var_pred=None, reduce_mean=True): """ computes -1 * ln N (y_true | mu_pred, softplus(log_var_pred)) :param y_true: :param mu_pred: :param log_var_pred: :return: """ var_pred = transform(log_var_pred) ll = -0.5 * self.const - 0.5 * torch.log(var_pred) - 0.5 * (1. / var_pred) * ((mu_pred - y_true) ** 2) if reduce_mean: return -ll.mean(dim=0) else: return -ll.sum(dim=0) def get_noise_var(self, log_var_pred): return transform(log_var_pred) import abc import sys # Ensure compatibility with Python 2/3 if sys.version_info >= (3, 4): ABC = abc.ABC else: ABC = abc.ABCMeta(str('ABC'), (), {}) class AbstractNoiseModel(ABC): """ Abstract class. All noise models inherit from here. """ def __init__(self, *argv, **kwargs): """ Initialize an AbstractNoiseModel object. """ @abc.abstractmethod def loss(self, *argv, **kwargs): """ Compute loss given predictions and groundtruth labels """ raise NotImplementedError @abc.abstractmethod def get_noise_var(self, *argv, **kwargs): """ Return the current estimate of noise variance """ raise NotImplementedError import abc import sys # Ensure compatibility with Python 2/3 if sys.version_info >= (3, 4): ABC = abc.ABC else: ABC = abc.ABCMeta(str('ABC'), (), {}) class BuiltinUQ(ABC): """ BuiltinUQ is the base class for any algorithm that has UQ built into it. """ def __init__(self, *argv, **kwargs): """ Initialize a BuiltinUQ object. """ @abc.abstractmethod def fit(self, *argv, **kwargs): """ Learn the UQ related parameters.. """ raise NotImplementedError @abc.abstractmethod def predict(self, *argv, **kwargs): """ Method to obtain the predicitve uncertainty, this can return the total, epistemic and/or aleatoric uncertainty in the predictions. """ raise NotImplementedError def set_params(self, **parameters): for parameter, value in parameters.items(): setattr(self, parameter, value) return self import abc import sys # Ensure compatibility with Python 2/3 if sys.version_info >= (3, 4): ABC = abc.ABC else: ABC = abc.ABCMeta(str('ABC'), (), {}) class PostHocUQ(ABC): """ PostHocUQ is the base class for any algorithm that quantifies uncertainty of a pre-trained model. """ def __init__(self, *argv, **kwargs): """ Initialize a BuiltinUQ object. """ @abc.abstractmethod def _process_pretrained_model(self, *argv, **kwargs): """ Method to process the pretrained model that requires UQ. """ raise NotImplementedError @abc.abstractmethod def predict(self, *argv, **kwargs): """ Method to obtain the predicitve uncertainty, this can return the total, epistemic and/or aleatoric uncertainty in the predictions. """ raise NotImplementedError def set_params(self, **parameters): for parameter, value in parameters.items(): setattr(self, parameter, value) return self def get_params(self): """ This method should not take any arguments and returns a dict of the __init__ parameters. """ raise NotImplementedError from .ucc_recalibration import UCCRecalibration from collections import namedtuple from uq360.algorithms.posthocuq import PostHocUQ from uq360.utils.misc import form_D_for_auucc from uq360.metrics.uncertainty_characteristics_curve.uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve class UCCRecalibration(PostHocUQ): """ Recalibration a regression model to specified operating point using Uncertainty Characteristics Curve. """ def __init__(self, base_model): """ Args: base_model: pretrained model to be recalibrated. """ super(UCCRecalibration).__init__() self.base_model = self._process_pretrained_model(base_model) self.ucc = None def get_params(self, deep=True): return {"base_model": self.base_model} def _process_pretrained_model(self, base_model): return base_model def fit(self, X, y): """ Fit the Uncertainty Characteristics Curve. Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ y_pred_mean, y_pred_lower, y_pred_upper = self.base_model.predict(X)[:3] bwu = y_pred_upper - y_pred_mean bwl = y_pred_mean - y_pred_lower self.ucc = UncertaintyCharacteristicsCurve() self.ucc.fit(form_D_for_auucc(y_pred_mean, bwl, bwu), y.squeeze()) return self def predict(self, X, missrate=0.05): """ Generate prediction and uncertainty bounds for data X. Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. missrate: desired missrate of the new operating point, set to 0.05 by default. Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. """ C = self.ucc.get_specific_operating_point(req_y_axis_value=missrate, vary_bias=False) new_scale = C['modvalue'] y_pred_mean, y_pred_lower, y_pred_upper = self.base_model.predict(X)[:3] bwu = y_pred_upper - y_pred_mean bwl = y_pred_mean - y_pred_lower if C['operation'] == 'bias': calib_y_pred_upper = y_pred_mean + (new_scale + bwu) # lower bound width calib_y_pred_lower = y_pred_mean - (new_scale + bwl) # Upper bound width else: calib_y_pred_upper = y_pred_mean + (new_scale * bwu) # lower bound width calib_y_pred_lower = y_pred_mean - (new_scale * bwl) # Upper bound width Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_pred_mean, calib_y_pred_lower, calib_y_pred_upper) return res from .classification_calibration import ClassificationCalibration from collections import namedtuple import numpy as np from sklearn.calibration import CalibratedClassifierCV from sklearn.preprocessing import LabelEncoder from uq360.utils.misc import DummySklearnEstimator from uq360.algorithms.posthocuq import PostHocUQ class ClassificationCalibration(PostHocUQ): """Post hoc calibration of classification models. Currently wraps `CalibratedClassifierCV` from sklearn and allows non-sklearn models to be calibrated. """ def __init__(self, num_classes, fit_mode="features", method='isotonic', base_model_prediction_func=None): """ Args: num_classes: number of classes. fit_mode: features or probs. If probs the `fit` and `predict` operate on the base models probability scores, useful when these are precomputed. method: isotonic or sigmoid. base_model_prediction_func: the function that takes in the input features and produces base model's probability scores. This is ignored when operating in `probs` mode. """ super(ClassificationCalibration).__init__() if fit_mode == "probs": # In this case, the fit assumes that it receives the probability scores of the base model. # create a dummy estimator self.base_model = DummySklearnEstimator(num_classes, lambda x: x) else: self.base_model = DummySklearnEstimator(num_classes, base_model_prediction_func) self.method = method def get_params(self, deep=True): return {"num_classes": self.num_classes, "fit_mode": self.fit_mode, "method": self.method, "base_model_prediction_func": self.base_model_prediction_func} def _process_pretrained_model(self, base_model): return base_model def fit(self, X, y): """ Fits calibration model using the provided calibration set. Args: X: array-like of shape (n_samples, n_features) or (n_samples, n_classes). Features vectors of the training data or the probability scores from the base model. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ self.base_model.label_encoder_ = LabelEncoder().fit(y) self.calib_model = CalibratedClassifierCV(base_estimator=self.base_model, cv="prefit", method=self.method) self.calib_model.fit(X, y) return self def predict(self, X): """ Obtain calibrated predictions for the test points. Args: X: array-like of shape (n_samples, n_features) or (n_samples, n_classes). Features vectors of the training data or the probability scores from the base model. Returns: namedtuple: A namedtupe that holds y_pred: ndarray of shape (n_samples,) Predicted labels of the test points. y_prob: ndarray of shape (n_samples, n_classes) Predicted probability scores of the classes. """ y_prob = self.calib_model.predict_proba(X) if len(np.shape(y_prob)) == 1: y_pred_labels = y_prob > 0.5 else: y_pred_labels = np.argmax(y_prob, axis=1) Result = namedtuple('res', ['y_pred', 'y_prob']) res = Result(y_pred_labels, y_prob) return res from collections import namedtuple import numpy as np import torch import torch.nn.functional as F from scipy.stats import norm from torch.utils.data import DataLoader from torch.utils.data import TensorDataset from uq360.algorithms.builtinuq import BuiltinUQ np.random.seed(42) torch.manual_seed(42) class _MLPNet_Main(torch.nn.Module): def __init__(self, num_features, num_outputs, num_hidden): super(_MLPNet_Main, self).__init__() self.fc = torch.nn.Linear(num_features, num_hidden) self.fc_mu = torch.nn.Linear(num_hidden, num_outputs) self.fc_log_var = torch.nn.Linear(num_hidden, num_outputs) def forward(self, x): x = F.relu(self.fc(x)) mu = self.fc_mu(x) log_var = self.fc_log_var(x) return mu, log_var class _MLPNet_Aux(torch.nn.Module): def __init__(self, num_features, num_outputs, num_hidden): super(_MLPNet_Aux, self).__init__() self.fc = torch.nn.Linear(num_features, num_hidden) self.fc_log_var = torch.nn.Linear(num_hidden, num_outputs) def forward(self, x): x = F.relu(self.fc(x)) log_var = self.fc_log_var(x) return log_var class AuxiliaryIntervalPredictor(BuiltinUQ): """ Auxiliary Interval Predictor [1]_ uses an auxiliary model to encourage calibration of the main model. References: .. [1] Thiagarajan, J. J., Venkatesh, B., Sattigeri, P., & Bremer, P. T. (2020, April). Building calibrated deep models via uncertainty matching with auxiliary interval predictors. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 04, pp. 6005-6012). https://arxiv.org/abs/1909.04079 """ def __init__(self, model_type=None, main_model=None, aux_model=None, config=None, device=None, verbose=True): """ Args: model_type: The model type used to build the main model and the auxiliary model. Currently supported values are [mlp, custom]. `mlp` modeltype learns a mlp neural network using pytorch framework. For `custom` the user provide `main_model` and `aux_model`. main_model: (optional) The main prediction model. Currently support pytorch models that return mean and log variance. aux_model: (optional) The auxiliary prediction model. Currently support pytorch models that return calibrated log variance. config: dictionary containing the config parameters for the model. device: device used for pytorch models ignored otherwise. verbose: if True, print statements with the progress are enabled. """ super(AuxiliaryIntervalPredictor).__init__() self.config = config self.device = device self.verbose = verbose if model_type == "mlp": self.model_type = model_type self.main_model = _MLPNet_Main( num_features=self.config["num_features"], num_outputs=self.config["num_outputs"], num_hidden=self.config["num_hidden"], ) self.aux_model = _MLPNet_Aux( num_features=self.config["num_features"], num_outputs=self.config["num_outputs"], num_hidden=self.config["num_hidden"], ) elif model_type == "custom": self.model_type = model_type self.main_model = main_model self.aux_model = aux_model else: raise NotImplementedError def get_params(self, deep=True): return {"model_type": self.model_type, "config": self.config, "main_model": self.main_model, "aux_model": self.aux_model, "device": self.device, "verbose": self.verbose} def _main_model_loss(self, y_true, y_pred_mu, y_pred_log_var, y_pred_log_var_aux): r = torch.abs(y_true - y_pred_mu) # + 0.5 * y_pred_log_var + loss = torch.mean(0.5 * torch.exp(-y_pred_log_var) * r ** 2) + \ self.config["lambda_match"] * torch.mean(torch.abs(torch.exp(0.5 * y_pred_log_var) - torch.exp(0.5 * y_pred_log_var_aux))) return loss def _aux_model_loss(self, y_true, y_pred_mu, y_pred_log_var_aux): deltal = deltau = 2.0 * torch.exp(0.5 * y_pred_log_var_aux) upper = y_pred_mu + deltau lower = y_pred_mu - deltal width = upper - lower r = torch.abs(y_true - y_pred_mu) emce = torch.mean(torch.sigmoid((y_true - lower) * (upper - y_true) * 100000)) loss_emce = torch.abs(self.config["calibration_alpha"]-emce) loss_noise = torch.mean(torch.abs(0.5 * width - r)) loss_sharpness = torch.mean(torch.abs(upper - y_true)) + torch.mean(torch.abs(lower - y_true)) #print(emce) return loss_emce + self.config["lambda_noise"] * loss_noise + self.config["lambda_sharpness"] * loss_sharpness def fit(self, X, y): """ Fit the Auxiliary Interval Predictor model. Args: X: array-like of shape (n_samples, n_features). Features vectors of the training data. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ X = torch.from_numpy(X).float().to(self.device) y = torch.from_numpy(y).float().to(self.device) dataset_loader = DataLoader( TensorDataset(X,y), batch_size=self.config["batch_size"] ) optimizer_main_model = torch.optim.Adam(self.main_model.parameters(), lr=self.config["lr"]) optimizer_aux_model = torch.optim.Adam(self.aux_model.parameters(), lr=self.config["lr"]) for it in range(self.config["num_outer_iters"]): # Train the main model for epoch in range(self.config["num_main_iters"]): avg_mean_model_loss = 0.0 for batch_x, batch_y in dataset_loader: self.main_model.train() self.aux_model.eval() batch_y_pred_log_var_aux = self.aux_model(batch_x) batch_y_pred_mu, batch_y_pred_log_var = self.main_model(batch_x) main_loss = self._main_model_loss(batch_y, batch_y_pred_mu, batch_y_pred_log_var, batch_y_pred_log_var_aux) optimizer_main_model.zero_grad() main_loss.backward() optimizer_main_model.step() avg_mean_model_loss += main_loss.item()/len(dataset_loader) if self.verbose: print("Iter: {}, Epoch: {}, main_model_loss = {}".format(it, epoch, avg_mean_model_loss)) # Train the auxiliary model for epoch in range(self.config["num_aux_iters"]): avg_aux_model_loss = 0.0 for batch_x, batch_y in dataset_loader: self.aux_model.train() self.main_model.eval() batch_y_pred_log_var_aux = self.aux_model(batch_x) batch_y_pred_mu, batch_y_pred_log_var = self.main_model(batch_x) aux_loss = self._aux_model_loss(batch_y, batch_y_pred_mu, batch_y_pred_log_var_aux) optimizer_aux_model.zero_grad() aux_loss.backward() optimizer_aux_model.step() avg_aux_model_loss += aux_loss.item() / len(dataset_loader) if self.verbose: print("Iter: {}, Epoch: {}, aux_model_loss = {}".format(it, epoch, avg_aux_model_loss)) return self def predict(self, X, return_dists=False): """ Obtain predictions for the test points. In addition to the mean and lower/upper bounds, also returns full predictive distribution (return_dists=True). Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. return_dists: If True, the predictive distribution for each instance using scipy distributions is returned. Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. dists: list of predictive distribution as `scipy.stats` objects with length n_samples. Only returned when `return_dists` is True. """ self.main_model.eval() X = torch.from_numpy(X).float().to(self.device) dataset_loader = DataLoader( X, batch_size=self.config["batch_size"] ) y_mean_list = [] y_log_var_list = [] for batch_x in dataset_loader: batch_y_pred_mu, batch_y_pred_log_var = self.main_model(batch_x) y_mean_list.append(batch_y_pred_mu.data.cpu().numpy()) y_log_var_list.append(batch_y_pred_log_var.data.cpu().numpy()) y_mean = np.concatenate(y_mean_list) y_log_var = np.concatenate(y_log_var_list) y_std = np.sqrt(np.exp(y_log_var)) y_lower = y_mean - 2.0*y_std y_upper = y_mean + 2.0*y_std Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) if return_dists: dists = [norm(loc=y_mean[i], scale=y_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_dists',)) res = Result(*res, y_dists=dists) return res from .auxiliary_interval_predictor import AuxiliaryIntervalPredictor import copy from collections import namedtuple import numpy as np import torch import torch.nn.functional as F from torch.utils.data import DataLoader import torch.utils.data as data_utils from scipy.stats import norm from sklearn.preprocessing import StandardScaler from uq360.algorithms.builtinuq import BuiltinUQ from uq360.models.bayesian_neural_networks.bnn_models import horseshoe_mlp, bayesian_mlp class BnnRegression(BuiltinUQ): """ Variationally trained BNNs with Gaussian and Horseshoe [6]_ priors for regression. References: .. [6] Ghosh, Soumya, Jiayu Yao, and Finale Doshi-Velez. "Structured variational learning of Bayesian neural networks with horseshoe priors." International Conference on Machine Learning. PMLR, 2018. """ def __init__(self, config, prior="Gaussian"): """ Args: config: a dictionary specifying network and learning hyperparameters. prior: BNN priors specified as a string. Supported priors are Gaussian, Hshoe, RegHshoe """ super(BnnRegression, self).__init__() self.config = config if prior == "Gaussian": self.net = bayesian_mlp.BayesianRegressionNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers']) self.config['use_reg_hshoe'] = None elif prior == "Hshoe": self.net = horseshoe_mlp.HshoeRegressionNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers'], hshoe_scale=config['hshoe_scale']) self.config['use_reg_hshoe'] = False elif prior == "RegHshoe": self.net = horseshoe_mlp.HshoeRegressionNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers'], hshoe_scale=config['hshoe_scale'], use_reg_hshoe=config['use_reg_hshoe']) self.config['use_reg_hshoe'] = True else: raise NotImplementedError("'prior' must be a string. It can be one of Gaussian, Hshoe, RegHshoe") def get_params(self, deep=True): return {"prior": self.prior, "config": self.config} def fit(self, X, y): """ Fit the BNN regression model. Args: X: array-like of shape (n_samples, n_features). Features vectors of the training data. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ torch.manual_seed(1234) optimizer = torch.optim.Adam(self.net.parameters(), lr=self.config['step_size']) neg_elbo = torch.zeros([self.config['num_epochs'], 1]) params_store = {} for epoch in range(self.config['num_epochs']): loss = self.net.neg_elbo(num_batches=1, x=X, y=y.float().unsqueeze(dim=1)) / X.shape[0] optimizer.zero_grad() loss.backward() optimizer.step() if hasattr(self.net, 'fixed_point_updates'): # for hshoe or regularized hshoe nets self.net.fixed_point_updates() neg_elbo[epoch] = loss.item() if (epoch + 1) % 10 == 0: # print ((net.noise_layer.bhat/net.noise_layer.ahat).data.numpy()[0]) print('Epoch[{}/{}], neg elbo: {:.6f}, noise var: {:.6f}' .format(epoch + 1, self.config['num_epochs'], neg_elbo[epoch].item() / X.shape[0], self.net.get_noise_var())) params_store[epoch] = copy.deepcopy(self.net.state_dict()) # for small nets we can just store all. best_model_id = neg_elbo.argmin() # loss_val_store.argmin() # self.net.load_state_dict(params_store[best_model_id.item()]) return self def predict(self, X, mc_samples=100, return_dists=False, return_epistemic=True, return_epistemic_dists=False): """ Obtain predictions for the test points. In addition to the mean and lower/upper bounds, also returns epistemic uncertainty (return_epistemic=True) and full predictive distribution (return_dists=True). Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. mc_samples: Number of Monte-Carlo samples. return_dists: If True, the predictive distribution for each instance using scipy distributions is returned. return_epistemic: if True, the epistemic upper and lower bounds are returned. return_epistemic_dists: If True, the epistemic distribution for each instance using scipy distributions is returned. Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. y_lower_epistemic: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of epistemic component of the predictive distribution of the test points. Only returned when `return_epistemic` is True. y_upper_epistemic: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of epistemic component of the predictive distribution of the test points. Only returned when `return_epistemic` is True. dists: list of predictive distribution as `scipy.stats` objects with length n_samples. Only returned when `return_dists` is True. """ epistemic_out = np.zeros([mc_samples, X.shape[0]]) total_out = np.zeros([mc_samples, X.shape[0]]) for s in np.arange(mc_samples): pred = self.net(X).data.numpy().ravel() epistemic_out[s] = pred total_out[s] = pred + np.sqrt(self.net.get_noise_var()) * np.random.randn(pred.shape[0]) y_total_std = np.std(total_out, axis=0) y_epi_std = np.std(epistemic_out, axis=0) y_mean = np.mean(total_out, axis=0) y_lower = y_mean - 2 * y_total_std y_upper = y_mean + 2 * y_total_std y_epi_lower = y_mean - 2 * y_epi_std y_epi_upper = y_mean + 2 * y_epi_std Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) if return_epistemic: Result = namedtuple('res', Result._fields + ('lower_epistemic', 'upper_epistemic',)) res = Result(*res, lower_epistemic=y_epi_lower, upper_epistemic=y_epi_upper) if return_dists: dists = [norm(loc=y_mean[i], scale=y_total_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_dists',)) res = Result(*res, y_dists=dists) if return_epistemic_dists: epi_dists = [norm(loc=y_mean[i], scale=y_epi_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_epistemic_dists',)) res = Result(*res, y_epistemic_dists=epi_dists) return res class BnnClassification(BuiltinUQ): """ Variationally trained BNNs with Gaussian and Horseshoe [6]_ priors for classification. """ def __init__(self, config, prior="Gaussian", device=None): """ Args: config: a dictionary specifying network and learning hyperparameters. prior: BNN priors specified as a string. Supported priors are Gaussian, Hshoe, RegHshoe """ super(BnnClassification, self).__init__() self.config = config self.device = device if prior == "Gaussian": self.net = bayesian_mlp.BayesianClassificationNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers']) self.config['use_reg_hshoe'] = None elif prior == "Hshoe": self.net = horseshoe_mlp.HshoeClassificationNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers'], hshoe_scale=config['hshoe_scale']) self.config['use_reg_hshoe'] = False elif prior == "RegHshoe": self.net = horseshoe_mlp.HshoeClassificationNet(ip_dim=config['ip_dim'], op_dim=config['op_dim'], num_nodes=config['num_nodes'], num_layers=config['num_layers'], hshoe_scale=config['hshoe_scale'], use_reg_hshoe=config['use_reg_hshoe']) self.config['use_reg_hshoe'] = True else: raise NotImplementedError("'prior' must be a string. It can be one of Gaussian, Hshoe, RegHshoe") if "batch_size" not in self.config: self.config["batch_size"] = 50 self.net = self.net.to(device) def get_params(self, deep=True): return {"prior": self.prior, "config": self.config, "device": self.device} def fit(self, X=None, y=None, train_loader=None): """ Fits BNN regression model. Args: X: array-like of shape (n_samples, n_features) or (n_samples, n_classes). Features vectors of the training data or the probability scores from the base model. Ignored if train_loader is not None. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Ignored if train_loader is not None. train_loader: pytorch train_loader object. Returns: self """ if train_loader is None: train = data_utils.TensorDataset(torch.Tensor(X), torch.Tensor(y.values).long()) train_loader = data_utils.DataLoader(train, batch_size=self.config['batch_size'], shuffle=True) torch.manual_seed(1234) optimizer = torch.optim.Adam(self.net.parameters(), lr=self.config['step_size']) neg_elbo = torch.zeros([self.config['num_epochs'], 1]) params_store = {} for epoch in range(self.config['num_epochs']): avg_loss = 0.0 for batch_x, batch_y in train_loader: loss = self.net.neg_elbo(num_batches=len(train_loader), x=batch_x, y=batch_y) / batch_x.size(0) optimizer.zero_grad() loss.backward() optimizer.step() if hasattr(self.net, 'fixed_point_updates'): # for hshoe or regularized hshoe nets self.net.fixed_point_updates() avg_loss += loss.item() neg_elbo[epoch] = avg_loss / len(train_loader) if (epoch + 1) % 10 == 0: # print ((net.noise_layer.bhat/net.noise_layer.ahat).data.numpy()[0]) print('Epoch[{}/{}], neg elbo: {:.6f}' .format(epoch + 1, self.config['num_epochs'], neg_elbo[epoch].item())) params_store[epoch] = copy.deepcopy(self.net.state_dict()) # for small nets we can just store all. best_model_id = neg_elbo.argmin() # loss_val_store.argmin() # self.net.load_state_dict(params_store[best_model_id.item()]) return self def predict(self, X, mc_samples=100): """ Obtain calibrated predictions for the test points. Args: X: array-like of shape (n_samples, n_features) or (n_samples, n_classes). Features vectors of the training data or the probability scores from the base model. mc_samples: Number of Monte-Carlo samples. Returns: namedtuple: A namedtupe that holds y_pred: ndarray of shape (n_samples,) Predicted labels of the test points. y_prob: ndarray of shape (n_samples, n_classes) Predicted probability scores of the classes. y_prob_var: ndarray of shape (n_samples,) Variance of the prediction on the test points. y_prob_samples: ndarray of shape (mc_samples, n_samples, n_classes) Samples from the predictive distribution. """ X = torch.Tensor(X) y_prob_samples = [F.softmax(self.net(X), dim=1).detach().numpy() for _ in np.arange(mc_samples)] y_prob_samples_stacked = np.stack(y_prob_samples) prob_mean = np.mean(y_prob_samples_stacked, 0) prob_var = np.std(y_prob_samples_stacked, 0) ** 2 if len(np.shape(prob_mean)) == 1: y_pred_labels = prob_mean > 0.5 else: y_pred_labels = np.argmax(prob_mean, axis=1) Result = namedtuple('res', ['y_pred', 'y_prob', 'y_prob_var', 'y_prob_samples']) res = Result(y_pred_labels, prob_mean, prob_var, y_prob_samples) return res from collections import namedtuple import botorch import gpytorch import numpy as np import torch from botorch.models import SingleTaskGP from botorch.utils.transforms import normalize from gpytorch.constraints import GreaterThan from scipy.stats import norm from sklearn.preprocessing import StandardScaler from uq360.algorithms.builtinuq import BuiltinUQ np.random.seed(42) torch.manual_seed(42) class HomoscedasticGPRegression(BuiltinUQ): """ A wrapper around Botorch SingleTask Gaussian Process Regression [1]_ with homoscedastic noise. References: .. [1] https://botorch.org/api/models.html#singletaskgp """ def __init__(self, kernel=gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel()), likelihood=None, config=None): """ Args: kernel: gpytorch kernel function with default set to `RBFKernel` with output scale. likelihood: gpytorch likelihood function with default set to `GaussianLikelihood`. config: dictionary containing the config parameters for the model. """ super(HomoscedasticGPRegression).__init__() self.config = config self.kernel = kernel self.likelihood = likelihood self.model = None self.scaler = StandardScaler() self.X_bounds = None def get_params(self, deep=True): return {"kernel": self.kernel, "likelihood": self.likelihood, "config": self.config} def fit(self, X, y, **kwargs): """ Fit the GP Regression model. Additional arguments relevant for SingleTaskGP fitting can be passed to this function. Args: X: array-like of shape (n_samples, n_features). Features vectors of the training data. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values **kwargs: Additional arguments relevant for SingleTaskGP fitting. Returns: self """ y = self.scaler.fit_transform(y) X, y = torch.tensor(X), torch.tensor(y) self.X_bounds = X_bounds = torch.stack([X.min() * torch.ones(X.shape[1]), X.max() * torch.ones(X.shape[1])]) X = normalize(X, X_bounds) model_homo = SingleTaskGP(train_X=X, train_Y=y, covar_module=self.kernel, likelihood=self.likelihood, **kwargs) model_homo.likelihood.noise_covar.register_constraint("raw_noise", GreaterThan(1e-5)) model_homo_marginal_log_lik = gpytorch.mlls.ExactMarginalLogLikelihood(model_homo.likelihood, model_homo) botorch.fit.fit_gpytorch_model(model_homo_marginal_log_lik) model_homo_marginal_log_lik.eval() self.model = model_homo_marginal_log_lik self.inferred_observation_noise = self.scaler.inverse_transform(self.model.likelihood.noise.detach().numpy()[0].reshape(1,1)).squeeze() return self def predict(self, X, return_dists=False, return_epistemic=False, return_epistemic_dists=False): """ Obtain predictions for the test points. In addition to the mean and lower/upper bounds, also returns epistemic uncertainty (return_epistemic=True) and full predictive distribution (return_dists=True). Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. return_dists: If True, the predictive distribution for each instance using scipy distributions is returned. return_epistemic: if True, the epistemic upper and lower bounds are returned. return_epistemic_dists: If True, the epistemic distribution for each instance using scipy distributions is returned. Returns: namedtuple: A namedtuple that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. y_lower_epistemic: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of epistemic component of the predictive distribution of the test points. Only returned when `return_epistemic` is True. y_upper_epistemic: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of epistemic component of the predictive distribution of the test points. Only returned when `return_epistemic` is True. dists: list of predictive distribution as `scipy.stats` objects with length n_samples. Only returned when `return_dists` is True. """ X = torch.tensor(X) X_test_norm = normalize(X, self.X_bounds) self.model.eval() with torch.no_grad(): posterior = self.model.model.posterior(X_test_norm) y_mean = posterior.mean #y_epi_std = torch.sqrt(posterior.variance) y_lower_epistemic, y_upper_epistemic = posterior.mvn.confidence_region() predictive_posterior = self.model.model.posterior(X_test_norm, observation_noise=True) #y_std = torch.sqrt(predictive_posterior.variance) y_lower_total, y_upper_total = predictive_posterior.mvn.confidence_region() y_mean, y_lower, y_upper, y_lower_epistemic, y_upper_epistemic = self.scaler.inverse_transform(y_mean.numpy()).squeeze(), \ self.scaler.inverse_transform(y_lower_total.numpy()).squeeze(),\ self.scaler.inverse_transform(y_upper_total.numpy()).squeeze(),\ self.scaler.inverse_transform(y_lower_epistemic.numpy()).squeeze(),\ self.scaler.inverse_transform(y_upper_epistemic.numpy()).squeeze() y_epi_std = (y_upper_epistemic - y_lower_epistemic) / 4.0 y_std = (y_upper_total - y_lower_total) / 4.0 Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) if return_epistemic: Result = namedtuple('res', Result._fields + ('y_lower_epistemic', 'y_upper_epistemic',)) res = Result(*res, y_lower_epistemic=y_lower_epistemic, y_upper_epistemic=y_upper_epistemic) if return_dists: dists = [norm(loc=y_mean[i], scale=y_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_dists',)) res = Result(*res, y_dists=dists) if return_epistemic_dists: epi_dists = [norm(loc=y_mean[i], scale=y_epi_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_epistemic_dists',)) res = Result(*res, y_epistemic_dists=epi_dists) return res from .homoscedastic_gaussian_process_regression import HomoscedasticGPRegression from collections import namedtuple from sklearn.ensemble import GradientBoostingRegressor from uq360.algorithms.builtinuq import BuiltinUQ class QuantileRegression(BuiltinUQ): """Quantile Regression uses quantile loss and learns two separate models for the upper and lower quantile to obtain the prediction intervals. """ def __init__(self, model_type="gbr", config=None): """ Args: model_type: The base model used for predicting a quantile. Currently supported values are [gbr]. gbr is sklearn GradientBoostingRegressor. config: dictionary containing the config parameters for the model. """ super(QuantileRegression).__init__() if config is not None: self.config = config else: self.config = {} if "alpha" not in self.config: self.config["alpha"] = 0.95 if model_type == "gbr": self.model_type = model_type self.model_mean = GradientBoostingRegressor( loss='ls', n_estimators=self.config["n_estimators"], max_depth=self.config["max_depth"], learning_rate=self.config["learning_rate"], min_samples_leaf=self.config["min_samples_leaf"], min_samples_split=self.config["min_samples_split"] ) self.model_upper = GradientBoostingRegressor( loss='quantile', alpha=self.config["alpha"], n_estimators=self.config["n_estimators"], max_depth=self.config["max_depth"], learning_rate=self.config["learning_rate"], min_samples_leaf=self.config["min_samples_leaf"], min_samples_split=self.config["min_samples_split"] ) self.model_lower = GradientBoostingRegressor( loss='quantile', alpha=1.0 - self.config["alpha"], n_estimators=self.config["n_estimators"], max_depth=self.config["max_depth"], learning_rate=self.config["learning_rate"], min_samples_leaf=self.config["min_samples_leaf"], min_samples_split=self.config["min_samples_split"]) else: raise NotImplementedError def get_params(self, deep=True): return {"model_type": self.model_type, "config": self.config} def fit(self, X, y): """ Fit the Quantile Regression model. Args: X: array-like of shape (n_samples, n_features). Features vectors of the training data. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ self.model_mean.fit(X, y) self.model_lower.fit(X, y) self.model_upper.fit(X, y) return self def predict(self, X): """ Obtain predictions for the test points. In addition to the mean and lower/upper bounds, also returns epistemic uncertainty (return_epistemic=True) and full predictive distribution (return_dists=True). Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. """ y_mean = self.model_mean.predict(X) y_lower = self.model_lower.predict(X) y_upper = self.model_upper.predict(X) Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) return res from .quantile_regression import QuantileRegression from .infinitesimal_jackknife import InfinitesimalJackknife from collections import namedtuple import numpy as np from uq360.algorithms.posthocuq import PostHocUQ class InfinitesimalJackknife(PostHocUQ): """ Performs a first order Taylor series expansion around MLE / MAP fit. Requires the model being probed to be twice differentiable. """ def __init__(self, params, gradients, hessian, config): """ Initialize IJ. Args: params: MLE / MAP fit around which uncertainty is sought. d*1 gradients: Per data point gradients, estimated at the MLE / MAP fit. d*n hessian: Hessian evaluated at the MLE / MAP fit. d*d """ super(InfinitesimalJackknife).__init__() self.params_one = params self.gradients = gradients self.hessian = hessian self.d, self.n = gradients.shape self.dParams_dWeights = -np.linalg.solve(self.hessian, self.gradients) self.approx_dParams_dWeights = -np.linalg.solve(np.diag(np.diag(self.hessian)), self.gradients) self.w_one = np.ones([self.n]) self.config = config def get_params(self, deep=True): return {"params": self.params, "config": self.config, "gradients": self.gradients, "hessian": self.hessian} def _process_pretrained_model(self, *argv, **kwargs): pass def get_parameter_uncertainty(self): if (self.config['resampling_strategy'] == "jackknife") or (self.config['resampling_strategy'] == "jackknife+"): w_query = np.ones_like(self.w_one) resampled_params = np.zeros([self.n, self.d]) for i in np.arange(self.n): w_query[i] = 0 resampled_params[i] = self.ij(w_query) w_query[i] = 1 return np.cov(resampled_params), resampled_params elif self.config['resampling_strategy'] == "bootstrap": pass else: raise NotImplementedError("Only jackknife, jackknife+, and bootstrap resampling strategies are supported") def predict(self, X, model): """ Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. model: model object, must implement a set_parameters function Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. """ n, _ = X.shape y_all = model.predict(X) _, d_out = y_all.shape params_cov, params = self.get_parameter_uncertainty() if d_out > 1: print("Quantiles are computed independently for each dimension. May not be accurate.") y = np.zeros([params.shape[0], n, d_out]) for i in np.arange(params.shape[0]): model.set_parameters(params[i]) y[i] = model.predict(X) y_lower = np.quantile(y, q=0.5 * self.config['alpha'], axis=0) y_upper = np.quantile(y, q=(1. - 0.5 * self.config['alpha']), axis=0) y_mean = y.mean(axis=0) Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) return res def ij(self, w_query): """ Args: w_query: A n*1 vector to query parameters at. Return: new parameters at w_query """ assert w_query.shape[0] == self.n return self.params_one + self.dParams_dWeights @ (w_query-self.w_one).T def approx_ij(self, w_query): """ Args: w_query: A n*1 vector to query parameters at. Return: new parameters at w_query """ assert w_query.shape[0] == self.n return self.params_one + self.approx_dParams_dWeights @ (w_query-self.w_one).T import inspect from collections import namedtuple import numpy as np from sklearn.ensemble import GradientBoostingClassifier from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.exceptions import NotFittedError from uq360.algorithms.posthocuq import PostHocUQ class BlackboxMetamodelClassification(PostHocUQ): """ Extracts confidence scores from black-box classification models using a meta-model [4]_ . References: .. [4] Chen, Tongfei, et al. "Confidence scoring using whitebox meta-models with linear classifier probes." The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 2019. """ def _create_named_model(self, mdltype, config): """ Instantiates a model by name passed in 'mdltype'. Args: mdltype: string with name (must be supported) config: dict with args passed in the instantiation call Returns: mdl instance """ assert (isinstance(mdltype, str)) if mdltype == 'lr': mdl = LogisticRegression(**config) elif mdltype == 'gbm': mdl = GradientBoostingClassifier(**config) else: raise NotImplementedError("ERROR: Requested model type unknown: \"%s\"" % mdltype) return mdl def _get_model_instance(self, model, config): """ Returns an instance of a model based on (a) a desired name or (b) passed in class, or (c) passed in instance. :param model: string, class, or instance. Class and instance must have certain methods callable. :param config: dict with args passed in during the instantiation :return: model instance """ assert (model is not None and config is not None) if isinstance(model, str): # 'model' is a name, create it mdl = self._create_named_model(model, config) elif inspect.isclass(model): # 'model' is a class, instantiate it mdl = model(**config) else: # 'model' is an instance, register it mdl = model if not all([hasattr(mdl, key) and callable(getattr(mdl, key)) for key in self.callable_keys]): raise ValueError("ERROR: Passed model/method failed the interface test. Methods required: %s" % ','.join(self.callable_keys)) return mdl def __init__(self, base_model=None, meta_model=None, base_config=None, meta_config=None, random_seed=42): """ :param base_model: Base model. Can be: (1) None (default mdl will be set up), (2) Named model (e.g., logistic regression 'lr' or gradient boosting machine 'gbm'), (3) Base model class declaration (e.g., sklearn.linear_model.LogisticRegression). Will instantiate. (4) Model instance (instantiated outside). Will be re-used. Must have certain callable methods. Note: user-supplied classes and models must have certain callable methods ('predict', 'fit') and be capable of raising NotFittedError. :param meta_model: Meta model. Same values possible as with 'base_model' :param base_config: None or a params dict to be passed to 'base_model' at instantiation :param meta_config: None or a params dict to be passed to 'meta_model' at instantiation :param random_seed: seed used in the various pipeline steps """ super(BlackboxMetamodelClassification).__init__() self.random_seed = random_seed self.callable_keys = ['predict', 'fit'] # required methods - must be present in models passed in self.base_model_default = 'gbm' self.meta_model_default = 'lr' self.base_config_default = {'n_estimators': 300, 'max_depth': 10, 'learning_rate': 0.001, 'min_samples_leaf': 10, 'min_samples_split': 10, 'random_state': self.random_seed} self.meta_config_default = {'penalty': 'l1', 'C': 1, 'solver': 'liblinear', 'random_state': self.random_seed} self.base_config = base_config if base_config is not None else self.base_config_default self.meta_config = meta_config if meta_config is not None else self.meta_config_default self.base_model = None self.meta_model = None self.base_model = self._get_model_instance(base_model if base_model is not None else self.base_model_default, self.base_config) self.meta_model = self._get_model_instance(meta_model if meta_model is not None else self.meta_model_default, self.meta_config) def get_params(self, deep=True): return {"base_model": self.base_model, "meta_model": self.meta_model, "base_config": self.base_config, "meta_config": self.meta_config, "random_seed": self.random_seed} def _process_pretrained_model(self, X, y_hat_proba): """ Given the original input features and the base output probabilities, generate input features to train a meta model. Current implementation copies all input features and appends. :param X: numpy [nsamples, dim] :param y_hat_proba: [nsamples, nclasses] :return: array with new features [nsamples, newdim] """ assert (len(y_hat_proba.shape) == 2) assert (X.shape[0] == y_hat_proba.shape[0]) # sort the probs sample by sample faux1 = np.sort(y_hat_proba, axis=-1) # add delta between top and second candidate faux2 = np.expand_dims(faux1[:, -1] - faux1[:, -2], axis=-1) return np.hstack([X, faux1, faux2]) def fit(self, X, y, meta_fraction=0.2, randomize_samples=True, base_is_prefitted=False, meta_train_data=(None, None)): """ Fit base and meta models. :param X: input to the base model, array-like of shape (n_samples, n_features). Features vectors of the training data. :param y: ground truth for the base model, array-like of shape (n_samples,) :param meta_fraction: float in [0,1] - a fractional size of the partition carved out to train the meta model (complement will be used to train the base model) :param randomize_samples: use shuffling when creating partitions :param base_is_prefitted: Setting True will skip fitting the base model (useful for base models that have been instantiated outside/by the user and are already fitted. :param meta_train_data: User supplied data to train the meta model. Note that this option should only be used with 'base_is_prefitted'==True. Pass a tuple meta_train_data=(X_meta, y_meta) to activate. Note that (X,y,meta_fraction, randomize_samples) will be ignored in this mode. :return: self """ X = np.asarray(X) y = np.asarray(y) assert (len(meta_train_data) == 2) if meta_train_data[0] is None: X_base, X_meta, y_base, y_meta = train_test_split(X, y, shuffle=randomize_samples, test_size=meta_fraction, random_state=self.random_seed) else: if not base_is_prefitted: raise ValueError("ERROR: fit(): base model must be pre-fitted to use the 'meta_train_data' option") X_base = y_base = None X_meta = meta_train_data[0] y_meta = meta_train_data[1] # fit the base model if not base_is_prefitted: self.base_model.fit(X_base, y_base) # get input for the meta model from the base try: y_hat_meta_proba = self.base_model.predict_proba(X_meta) # determine correct-incorrect outcome - these are targets for the meta model trainer # y_hat_meta_targets = np.asarray((y_meta == np.argmax(y_hat_meta_proba, axis=-1)), dtype=np.int) -- Fix for python 3.8.11 update (in 2.9.0.8) y_hat_meta_targets = np.asarray((y_meta == np.argmax(y_hat_meta_proba, axis=-1)), dtype=int) except NotFittedError as e: raise RuntimeError("ERROR: fit(): The base model appears not pre-fitted (%s)" % repr(e)) # get input features for meta training X_meta_in = self._process_pretrained_model(X_meta, y_hat_meta_proba) # train meta model to predict 'correct' vs. 'incorrect' of the base self.meta_model.fit(X_meta_in, y_hat_meta_targets) return self def predict(self, X): """ Generate a base prediction along with uncertainty/confidence for data X. :param X: array-like of shape (n_samples, n_features). Features vectors of the test points. :return: namedtuple: A namedtuple that holds y_pred: ndarray of shape (n_samples,) Predicted labels of the test points. y_score: ndarray of shape (n_samples,) Confidence score the test points. """ y_hat_proba = self.base_model.predict_proba(X) y_hat = np.argmax(y_hat_proba, axis=-1) X_meta_in = self._process_pretrained_model(X, y_hat_proba) z_hat = self.meta_model.predict_proba(X_meta_in) index_of_class_1 = np.where(self.meta_model.classes_ == 1)[0][0] # class 1 corresponds to probab of positive/correct outcome Result = namedtuple('res', ['y_pred', 'y_score']) res = Result(y_hat, z_hat[:, index_of_class_1]) return res from .blackbox_metamodel_regression import BlackboxMetamodelRegression from .blackbox_metamodel_classification import BlackboxMetamodelClassification import inspect from collections import namedtuple import numpy as np from sklearn.ensemble import GradientBoostingRegressor from sklearn.model_selection import train_test_split from sklearn.exceptions import NotFittedError from uq360.algorithms.posthocuq import PostHocUQ class BlackboxMetamodelRegression(PostHocUQ): """ Extracts confidence scores from black-box regression models using a meta-model [2]_ . References: .. [2] Chen, Tongfei, et al. Confidence scoring using whitebox meta-models with linear classifier probes. The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 2019. """ def _create_named_model(self, mdltype, config): """ Instantiates a model by name passed in 'mdltype' :param mdltype: string with name (must be supprted) :param config: dict with args passed in the instantiation call :return: mdl instance """ assert (isinstance(mdltype, str)) if mdltype == 'gbr': mdl = GradientBoostingRegressor(**config) else: raise NotImplementedError("ERROR: Requested model type unknown: \"%s\"" % mdltype) return mdl def _get_model_instance(self, model, config): """ Returns an instance of a model based on (a) a desired name or (b) passed in class, or (c) passed in instance :param model: string, class, or instance. Class and instance must have certain methods callable. :param config: dict with args passed in during the instantiation :return: model instance """ assert (model is not None and config is not None) if isinstance(model, str): # 'model' is a name, create it mdl = self._create_named_model(model, config) elif inspect.isclass(model): # 'model' is a class, instantiate it mdl = model(**config) else: # 'model' is an instance, register it mdl = model if not all([hasattr(mdl, key) and callable(getattr(mdl, key)) for key in self.callable_keys]): raise ValueError("ERROR: Passed model/method failed the interface test. Methods required: %s" % ','.join(self.callable_keys)) return mdl def __init__(self, base_model=None, meta_model=None, base_config=None, meta_config=None, random_seed=42): """ :param base_model: Base model. Can be: (1) None (default mdl will be set up), (2) Named model (e.g., 'gbr'), (3) Base model class declaration (e.g., sklearn.linear_model.LinearRegressor). Will instantiate. (4) Model instance (instantiated outside). Will be re-used. Must have required callable methods. Note: user-supplied classes and models must have certain callable methods ('predict', 'fit') and be capable of raising NotFittedError. :param meta_model: Meta model. Same values possible as with 'base_model' :param base_config: None or a params dict to be passed to 'base_model' at instantiation :param meta_config: None or a params dict to be passed to 'meta_model' at instantiation :param random_seed: seed used in the various pipeline steps """ super(BlackboxMetamodelRegression).__init__() self.random_seed = random_seed self.callable_keys = ['predict', 'fit'] # required methods - must be present in models passed in self.base_model_default = 'gbr' self.meta_model_default = 'gbr' self.base_config_default = {'loss': 'ls', 'n_estimators': 300, 'max_depth': 10, 'learning_rate': 0.001, 'min_samples_leaf': 10, 'min_samples_split': 10, 'random_state': self.random_seed} self.meta_config_default = {'loss': 'quantile', 'alpha': 0.95, 'n_estimators': 300, 'max_depth': 10, 'learning_rate': 0.001, 'min_samples_leaf': 10, 'min_samples_split': 10, 'random_state': self.random_seed} self.base_config = base_config if base_config is not None else self.base_config_default self.meta_config = meta_config if meta_config is not None else self.meta_config_default self.base_model = None self.meta_model = None self.base_model = self._get_model_instance(base_model if base_model is not None else self.base_model_default, self.base_config) self.meta_model = self._get_model_instance(meta_model if meta_model is not None else self.meta_model_default, self.meta_config) def get_params(self, deep=True): return {"base_model": self.base_model, "meta_model": self.meta_model, "base_config": self.base_config, "meta_config": self.meta_config, "random_seed": self.random_seed} def fit(self, X, y, meta_fraction=0.2, randomize_samples=True, base_is_prefitted=False, meta_train_data=(None, None)): """ Fit base and meta models. :param X: input to the base model :param y: ground truth for the base model :param meta_fraction: float in [0,1] - a fractional size of the partition carved out to train the meta model (complement will be used to train the base model) :param randomize_samples: use shuffling when creating partitions :param base_is_prefitted: Setting True will skip fitting the base model (useful for base models that have been instantiated outside/by the user and are already fitted. :param meta_train_data: User supplied data to train the meta model. Note that this option should only be used with 'base_is_prefitted'==True. Pass a tuple meta_train_data=(X_meta, y_meta) to activate. Note that (X,y,meta_fraction, randomize_samples) will be ignored in this mode. :return: self """ X = np.asarray(X) y = np.asarray(y) assert(len(meta_train_data)==2) if meta_train_data[0] is None: X_base, X_meta, y_base, y_meta = train_test_split(X, y, shuffle=randomize_samples, test_size=meta_fraction, random_state=self.random_seed) else: if not base_is_prefitted: raise ValueError("ERROR: fit(): base model must be pre-fitted to use the 'meta_train_data' option") X_base = y_base = None X_meta = meta_train_data[0] y_meta = meta_train_data[1] # fit the base model if not base_is_prefitted: self.base_model.fit(X_base, y_base) # get input for the meta model from the base try: y_hat_meta = self.base_model.predict(X_meta) except NotFittedError as e: raise RuntimeError("ERROR: fit(): The base model appears not pre-fitted (%s)" % repr(e)) # used base input and output as meta input X_meta_in = self._process_pretrained_model(X_meta, y_hat_meta) # train meta model to predict abs diff self.meta_model.fit(X_meta_in, np.abs(y_hat_meta - y_meta)) return self def _process_pretrained_model(self, X, y_hat): """ Given the original input features and the base output probabilities, generate input features to train a meta model. Current implementation copies all input features and appends. :param X: numpy [nsamples, dim] :param y_hat: [nsamples,] :return: array with new features [nsamples, newdim] """ y_hat_meta_prime = np.expand_dims(y_hat, -1) if len(y_hat.shape) < 2 else y_hat X_meta_in = np.hstack([X, y_hat_meta_prime]) return X_meta_in def predict(self, X): """ Generate prediction and uncertainty bounds for data X. :param X: input features :return: namedtuple: A namedtuple that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. """ y_hat = self.base_model.predict(X) y_hat_prime = np.expand_dims(y_hat, -1) if len(y_hat.shape) < 2 else y_hat X_meta_in = np.hstack([X, y_hat_prime]) z_hat = self.meta_model.predict(X_meta_in) Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_hat, y_hat - z_hat, y_hat + z_hat) return res from .heteroscedastic_regression import HeteroscedasticRegression from collections import namedtuple import numpy as np import torch from scipy.stats import norm from torch.utils.data import DataLoader from torch.utils.data import TensorDataset from uq360.algorithms.builtinuq import BuiltinUQ from uq360.models.heteroscedastic_mlp import GaussianNoiseMLPNet as _MLPNet np.random.seed(42) torch.manual_seed(42) class HeteroscedasticRegression(BuiltinUQ): """ Wrapper for heteroscedastic regression. We learn to predict targets given features, assuming that the targets are noisy and that the amount of noise varies between data points. https://en.wikipedia.org/wiki/Heteroscedasticity """ def __init__(self, model_type=None, model=None, config=None, device=None, verbose=True): """ Args: model_type: The base model architecture. Currently supported values are [mlp]. mlp modeltype learns a multi-layer perceptron with a heteroscedastic Gaussian likelihood. Both the mean and variance of the Gaussian are functions of the data point ->git N(y_n | mlp_mu(x_n), mlp_var(x_n)) model: (optional) The prediction model. Currently support pytorch models that returns mean and log variance. config: dictionary containing the config parameters for the model. device: device used for pytorch models ignored otherwise. verbose: if True, print statements with the progress are enabled. """ super(HeteroscedasticRegression).__init__() self.config = config self.device = device self.verbose = verbose if model_type == "mlp": self.model_type = model_type self.model = _MLPNet( num_features=self.config["num_features"], num_outputs=self.config["num_outputs"], num_hidden=self.config["num_hidden"], ) elif model_type == "custom": self.model_type = model_type self.model = model else: raise NotImplementedError def get_params(self, deep=True): return {"model_type": self.model_type, "config": self.config, "model": self.model, "device": self.device, "verbose": self.verbose} def _loss(self, y_true, y_pred_mu, y_pred_log_var): return torch.mean(0.5 * torch.exp(-y_pred_log_var) * torch.abs(y_true - y_pred_mu) ** 2 + 0.5 * y_pred_log_var) def fit(self, X, y): """ Fit the Heteroscedastic Regression model. Args: X: array-like of shape (n_samples, n_features). Features vectors of the training data. y: array-like of shape (n_samples,) or (n_samples, n_targets) Target values Returns: self """ X = torch.from_numpy(X).float().to(self.device) y = torch.from_numpy(y).float().to(self.device) dataset_loader = DataLoader( TensorDataset(X,y), batch_size=self.config["batch_size"] ) optimizer = torch.optim.Adam(self.model.parameters(), lr=self.config["lr"]) for epoch in range(self.config["num_epochs"]): avg_loss = 0.0 for batch_x, batch_y in dataset_loader: self.model.train() batch_y_pred_mu, batch_y_pred_log_var = self.model(batch_x) loss = self.model.loss(batch_y, batch_y_pred_mu, batch_y_pred_log_var) optimizer.zero_grad() loss.backward() optimizer.step() avg_loss += loss.item()/len(dataset_loader) if self.verbose: print("Epoch: {}, loss = {}".format(epoch, avg_loss)) return self def predict(self, X, return_dists=False): """ Obtain predictions for the test points. In addition to the mean and lower/upper bounds, also returns epistemic uncertainty (return_epistemic=True) and full predictive distribution (return_dists=True). Args: X: array-like of shape (n_samples, n_features). Features vectors of the test points. return_dists: If True, the predictive distribution for each instance using scipy distributions is returned. Returns: namedtuple: A namedtupe that holds y_mean: ndarray of shape (n_samples, [n_output_dims]) Mean of predictive distribution of the test points. y_lower: ndarray of shape (n_samples, [n_output_dims]) Lower quantile of predictive distribution of the test points. y_upper: ndarray of shape (n_samples, [n_output_dims]) Upper quantile of predictive distribution of the test points. dists: list of predictive distribution as `scipy.stats` objects with length n_samples. Only returned when `return_dists` is True. """ self.model.eval() X = torch.from_numpy(X).float().to(self.device) dataset_loader = DataLoader( X, batch_size=self.config["batch_size"] ) y_mean_list = [] y_log_var_list = [] for batch_x in dataset_loader: batch_y_pred_mu, batch_y_pred_log_var = self.model(batch_x) y_mean_list.append(batch_y_pred_mu.data.cpu().numpy()) y_log_var_list.append(batch_y_pred_log_var.data.cpu().numpy()) y_mean = np.concatenate(y_mean_list) y_log_var = np.concatenate(y_log_var_list) y_std = np.sqrt(np.exp(y_log_var)) y_lower = y_mean - 2.0*y_std y_upper = y_mean + 2.0*y_std Result = namedtuple('res', ['y_mean', 'y_lower', 'y_upper']) res = Result(y_mean, y_lower, y_upper) if return_dists: dists = [norm(loc=y_mean[i], scale=y_std[i]) for i in range(y_mean.shape[0])] Result = namedtuple('res', Result._fields + ('y_dists',)) res = Result(*res, y_dists=dists) return res from .meps_dataset import MEPSDataset # Adapted from https://github.com/Trusted-AI/AIX360/blob/master/aix360/datasets/meps_dataset.py # Utilization target is kept as a continuous target. import os import pandas as pd def default_preprocessing(df): """ 1.Create a new column, RACE that is 'White' if RACEV2X = 1 and HISPANX = 2 i.e. non Hispanic White and 'non-White' otherwise 2. Restrict to Panel 19 3. RENAME all columns that are PANEL/ROUND SPECIFIC 4. Drop rows based on certain values of individual features that correspond to missing/unknown - generally < -1 5. Compute UTILIZATION. """ def race(row): if ((row['HISPANX'] == 2) and (row['RACEV2X'] == 1)): #non-Hispanic Whites are marked as WHITE; all others as NON-WHITE return 'White' return 'Non-White' df['RACEV2X'] = df.apply(lambda row: race(row), axis=1) df = df.rename(columns = {'RACEV2X' : 'RACE'}) df = df[df['PANEL'] == 19] # RENAME COLUMNS df = df.rename(columns = {'FTSTU53X' : 'FTSTU', 'ACTDTY53' : 'ACTDTY', 'HONRDC53' : 'HONRDC', 'RTHLTH53' : 'RTHLTH', 'MNHLTH53' : 'MNHLTH', 'CHBRON53' : 'CHBRON', 'JTPAIN53' : 'JTPAIN', 'PREGNT53' : 'PREGNT', 'WLKLIM53' : 'WLKLIM', 'ACTLIM53' : 'ACTLIM', 'SOCLIM53' : 'SOCLIM', 'COGLIM53' : 'COGLIM', 'EMPST53' : 'EMPST', 'REGION53' : 'REGION', 'MARRY53X' : 'MARRY', 'AGE53X' : 'AGE', 'POVCAT15' : 'POVCAT', 'INSCOV15' : 'INSCOV'}) df = df[df['REGION'] >= 0] # remove values -1 df = df[df['AGE'] >= 0] # remove values -1 df = df[df['MARRY'] >= 0] # remove values -1, -7, -8, -9 df = df[df['ASTHDX'] >= 0] # remove values -1, -7, -8, -9 df = df[(df[['FTSTU','ACTDTY','HONRDC','RTHLTH','MNHLTH','HIBPDX','CHDDX','ANGIDX','EDUCYR','HIDEG', 'MIDX','OHRTDX','STRKDX','EMPHDX','CHBRON','CHOLDX','CANCERDX','DIABDX', 'JTPAIN','ARTHDX','ARTHTYPE','ASTHDX','ADHDADDX','PREGNT','WLKLIM', 'ACTLIM','SOCLIM','COGLIM','DFHEAR42','DFSEE42','ADSMOK42', 'PHQ242','EMPST','POVCAT','INSCOV']] >= -1).all(1)] #for all other categorical features, remove values < -1 def utilization(row): return row['OBTOTV15'] + row['OPTOTV15'] + row['ERTOT15'] + row['IPNGTD15'] + row['HHTOTD15'] df['TOTEXP15'] = df.apply(lambda row: utilization(row), axis=1) df = df.rename(columns = {'TOTEXP15' : 'UTILIZATION'}) df = df[['REGION','AGE','SEX','RACE','MARRY', 'FTSTU','ACTDTY','HONRDC','RTHLTH','MNHLTH','HIBPDX','CHDDX','ANGIDX', 'MIDX','OHRTDX','STRKDX','EMPHDX','CHBRON','CHOLDX','CANCERDX','DIABDX', 'JTPAIN','ARTHDX','ARTHTYPE','ASTHDX','ADHDADDX','PREGNT','WLKLIM', 'ACTLIM','SOCLIM','COGLIM','DFHEAR42','DFSEE42','ADSMOK42','PCS42', 'MCS42','K6SUM42','PHQ242','EMPST','POVCAT','INSCOV','UTILIZATION','PERWT15F']] return df class MEPSDataset(): """ The Medical Expenditure Panel Survey (MEPS) [#]_ data consists of large scale surveys of families and individuals, medical providers, and employers, and collects data on health services used, costs & frequency of services, demographics, health status and conditions, etc., of the respondents. This specific dataset contains MEPS survey data for calendar year 2015 obtained in rounds 3, 4, and 5 of Panel 19, and rounds 1, 2, and 3 of Panel 20. See :file:`uq360/datasets/data/meps_data/README.md` for more details on the dataset and instructions on downloading/processing the data. References: .. [#] `Medical Expenditure Panel Survey data <https://meps.ahrq.gov/mepsweb/>`_ """ def __init__(self, custom_preprocessing=default_preprocessing, dirpath=None): self._dirpath = dirpath if not self._dirpath: self._dirpath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data', 'meps_data') self._filepath = os.path.join(self._dirpath, 'h181.csv') try: df = pd.read_csv(self._filepath, sep=',', na_values=[]) except IOError as err: print("IOError: {}".format(err)) print("To use this class, please place the heloc_dataset.csv:") print("file, as-is, in the folder:") print("\n\t{}\n".format(os.path.abspath(os.path.join( os.path.abspath(__file__), 'data', 'meps_data')))) import sys sys.exit(1) if custom_preprocessing: self._data = custom_preprocessing(df) def data(self): return self._data import autograd import autograd.numpy as np import numpy.random as npr import scipy.optimize sigmoid = lambda x: 0.5 * (np.tanh(x / 2.) + 1) get_num_train = lambda inputs: inputs.shape[0] logistic_predictions = lambda params, inputs: sigmoid(np.dot(inputs, params)) class LogisticRegression: def __init__(self): self.params = None def set_parameters(self, params): self.params = params def predict(self, X): if self.params is not None: # Outputs probability of a label being true according to logistic model return np.atleast_2d(sigmoid(np.dot(X, self.params))).T else: raise RuntimeError("Params need to be fit before predictions can be made.") def loss(self, params, weights, inputs, targets): # Training loss is the negative log-likelihood of the training labels. preds = logistic_predictions(params, inputs) label_probabilities = preds * targets + (1 - preds) * (1 - targets) return -np.sum(weights * np.log(label_probabilities + 1e-16)) def fit(self, weights, init_params, inputs, targets, verbose=True): training_loss_fun = lambda params: self.loss(params, weights, inputs, targets) # Define a function that returns gradients of training loss using Autograd. training_gradient_fun = autograd.grad(training_loss_fun, 0) # optimize params if verbose: print("Initial loss:", self.loss(init_params, weights, inputs, targets)) # opt_params = sgd(training_gradient_fun, params, hyper=1, num_iters=5000, step_size=0.1) res = scipy.optimize.minimize(fun=training_loss_fun, jac=training_gradient_fun, x0=init_params, tol=1e-10, options={'disp': verbose}) opt_params = res.x if verbose: print("Trained loss:", self.loss(opt_params, weights, inputs, targets)) self.params = opt_params return opt_params def get_test_acc(self, params, test_targets, test_inputs): preds = np.round(self.predict(test_inputs).T).astype(np.int) err = np.abs(test_targets - preds).sum() return 1 - err/ test_targets.shape[1] #### Required for IJ computation ### def compute_hessian(self, params_one, weights_one, inputs, targets): return autograd.hessian(self.loss, argnum=0)(params_one, weights_one, inputs, targets) def compute_jacobian(self, params_one, weights_one, inputs, targets): return autograd.jacobian(autograd.jacobian(self.loss, argnum=0), argnum=1)\ (params_one, weights_one, inputs, targets).squeeze() ################################################### @staticmethod def synthetic_lr_data(N=10000, D=10): x = 1. * npr.randn(N, D) x_test = 1. * npr.randn(int(0.3 * N), D) w = npr.randn(D, 1) y = sigmoid((x @ w)).ravel() y = npr.binomial(n=1, p=y) # corrupt labels y_test = sigmoid(x_test @ w).ravel() # y_test = np.round(y_test) y_test = npr.binomial(n=1, p=y_test) return x, np.atleast_2d(y), x_test, np.atleast_2d(y_test) import autograd import autograd.numpy as np import scipy.optimize from autograd import grad from autograd.scipy.special import logsumexp from sklearn.cluster import KMeans class HMM: """ A Hidden Markov Model with Gaussian observations with unknown means and known precisions. """ def __init__(self, X, config_dict=None): self.N, self.T, self.D = X.shape self.K = config_dict['K'] # number of HMM states self.I = np.eye(self.K) self.Precision = np.zeros([self.D, self.D, self.K]) self.X = X if config_dict['precision'] is None: for k in np.arange(self.K): self.Precision[:, :, k] = np.eye(self.D) else: self.Precision = config_dict['precision'] self.dParams_dWeights = None self.alphaT = None # Store the final beliefs. self.beta1 = None # store the first timestep beliefs from the beta recursion. self.forward_trellis = {} # stores \alpha self.backward_trellis = {} # stores \beta def initialize_params(self, seed=1234): np.random.seed(seed) param_dict = {} A = np.random.randn(self.K, self.K) # use k-means to initialize the mean parameters X = self.X.reshape([-1, self.D]) kmeans = KMeans(n_clusters=self.K, random_state=seed, n_init=15).fit(X) labels = kmeans.labels_ _, counts = np.unique(labels, return_counts=True) pi = counts phi = kmeans.cluster_centers_ param_dict['A'] = np.exp(A) param_dict['pi0'] = pi param_dict['phi'] = phi return self.pack_params(param_dict) def unpack_params(self, params): param_dict = dict() K = self.K # For unpacking simplex parameters: have packed them as # log(pi[:-1]) - log(pi[-1]). unnorm_A = np.exp(np.append(params[:K**2-K].reshape(K, K-1), np.zeros((K, 1)), axis=1) ) Z = np.sum(unnorm_A[:, :-1], axis=1) unnorm_A /= Z[:, np.newaxis] norm_A = unnorm_A / unnorm_A.sum(axis=1, keepdims=True) param_dict['A'] = norm_A unnorm_pi = np.exp(np.append(params[K**2-K:K**2-1], 0.0)) Z = np.sum(unnorm_pi[:-1]) unnorm_pi /= Z param_dict['pi0'] = unnorm_pi / unnorm_pi.sum() param_dict['phi'] = params[K**2-K+K-1:].reshape(self.D, K) return param_dict def weighted_alpha_recursion(self, xseq, pi, phi, Sigma, A, wseq, store_belief=False): """ Computes the weighted marginal probability of the sequence xseq given parameters; weights wseq turn on or off the emissions p(x_t | z_t) (weighting scheme B) :param xseq: T * D :param pi: K * 1 :param phi: D * K :param wseq: T * 1 :param A: :return: """ ll = self.log_obs_lik(xseq[:, :, np.newaxis], phi[np.newaxis, :, :], Sigma) alpha = np.log(pi.ravel()) + wseq[0] * ll[0] if wseq[0] == 0: self.forward_trellis[0] = alpha[:, np.newaxis] for t in np.arange(1, self.T): alpha = logsumexp(alpha[:, np.newaxis] + np.log(A), axis=0) + wseq[t] * ll[t] if wseq[t] == 0: # store the trellis, would be used to compute the posterior z_t | x_1...x_t-1, x_t+1, ...x_T self.forward_trellis[t] = alpha[:, np.newaxis] if store_belief: # store the final belief self.alphaT = alpha return logsumexp(alpha) def weighted_beta_recursion(self, xseq, pi, phi, Sigma, A, wseq, store_belief=False): """ Runs beta recursion; weights wseq turn on or off the emissions p(x_t | z_t) (weighting scheme B) :param xseq: T * D :param pi: K * 1 :param phi: D * K :param wseq: T * 1 :param A: :return: """ ll = self.log_obs_lik(xseq[:, :, np.newaxis], phi[np.newaxis, :, :], Sigma) beta = np.zeros_like(pi.ravel()) # log(\beta) of all ones. max_t = ll.shape[0] if wseq[max_t - 1] == 0: # store the trellis, would be used to compute the posterior z_t | x_1...x_t-1, x_t+1, ...x_T self.backward_trellis[max_t - 1] = beta[:, np.newaxis] for i in np.arange(1, max_t): t = max_t - i - 1 beta = logsumexp((beta + wseq[t + 1] * ll[t + 1])[np.newaxis, :] + np.log(A), axis=1) if wseq[t] == 0: # store the trellis, would be used to compute the posterior z_t | x_1...x_t-1, x_t+1, ...x_T self.backward_trellis[t] = beta[:, np.newaxis] # account for the init prob beta = (beta + wseq[0] * ll[0]) + np.log(pi.ravel()) if store_belief: # store the final belief self.beta1 = beta return logsumexp(beta) def weighted_loss(self, params, weights): """ For LOOCV / IF computation within a single sequence. Uses weighted alpha recursion :param params: :param weights: :return: """ param_dict = self.unpack_params(params) logp = self.get_prior_contrib(param_dict) logp = logp + self.weighted_alpha_recursion(self.X[0], param_dict['pi0'], param_dict['phi'], self.Precision, param_dict['A'], weights) return -logp def loss_at_missing_timesteps(self, weights, params): """ :param weights: zeroed out weights indicate missing values :param params: packed parameters :return: """ # empty forward and backward trellis self.clear_trellis() param_dict = self.unpack_params(params) # populate forward and backward trellis lpx = self.weighted_alpha_recursion(self.X[0], param_dict['pi0'], param_dict['phi'], self.Precision, param_dict['A'], weights, store_belief=True ) lpx_alt = self.weighted_beta_recursion(self.X[0], param_dict['pi0'], param_dict['phi'], self.Precision, param_dict['A'], weights, store_belief=True) assert np.allclose(lpx, lpx_alt) # sanity check test_ll = [] # compute loo likelihood ll = self.log_obs_lik(self.X[0][:, :, np.newaxis], param_dict['phi'], self.Precision) # compute posterior p(z_t | x_1,...t-1, t+1,...T) \forall missing t tsteps = [] for t in self.forward_trellis.keys(): lpz_given_x = self.forward_trellis[t] + self.backward_trellis[t] - lpx test_ll.append(logsumexp(ll[t] + lpz_given_x.ravel())) tsteps.append(t) # empty forward and backward trellis self.clear_trellis() return -np.array(test_ll) def fit(self, weights, init_params=None, num_random_restarts=1, verbose=False, maxiter=None): if maxiter: options_dict = {'disp': verbose, 'gtol': 1e-10, 'maxiter': maxiter} else: options_dict = {'disp': verbose, 'gtol': 1e-10} # Define a function that returns gradients of training loss using Autograd. training_loss_fun = lambda params: self.weighted_loss(params, weights) training_gradient_fun = grad(training_loss_fun, 0) if init_params is None: init_params = self.initialize_params() if verbose: print("Initial loss: ", training_loss_fun(init_params)) res = scipy.optimize.minimize(fun=training_loss_fun, jac=training_gradient_fun, x0=init_params, tol=1e-10, options=options_dict) if verbose: print('grad norm =', np.linalg.norm(res.jac)) return res.x def clear_trellis(self): self.forward_trellis = {} self.backward_trellis = {} #### Required for IJ computation ### def compute_hessian(self, params_one, weights_one): return autograd.hessian(self.weighted_loss, argnum=0)(params_one, weights_one) def compute_jacobian(self, params_one, weights_one): return autograd.jacobian(autograd.jacobian(self.weighted_loss, argnum=0), argnum=1)\ (params_one, weights_one).squeeze() ################################################### @staticmethod def log_obs_lik(x, phi, Sigma): """ :param x: T*D*1 :param phi: 1*D*K :param Sigma: D*D*K --- precision matrices per state :return: ll """ centered_x = x - phi ll = -0.5 * np.einsum('tdk, tdk, ddk -> tk', centered_x, centered_x, Sigma ) return ll @staticmethod def pack_params(params_dict): param_list = [(np.log(params_dict['A'][:, :-1]) - np.log(params_dict['A'][:, -1])[:, np.newaxis]).ravel(), np.log(params_dict['pi0'][:-1]) - np.log(params_dict['pi0'][-1]), params_dict['phi'].ravel()] return np.concatenate(param_list) @staticmethod def get_prior_contrib(param_dict): logp = 0.0 # Prior logp += -0.5 * (np.linalg.norm(param_dict['phi'], axis=0) ** 2).sum() logp += (1.1 - 1) * np.log(param_dict['A']).sum() logp += (1.1 - 1) * np.log(param_dict['pi0']).sum() return logp @staticmethod def get_indices_in_held_out_fold(T, pct_to_drop, contiguous=False): """ :param T: length of the sequence :param pct_to_drop: % of T in the held out fold :param contiguous: if True generate a block of indices to drop else generate indices by iid sampling :return: o (the set of indices in the fold) """ if contiguous: l = np.floor(pct_to_drop / 100. * T) anchor = np.random.choice(np.arange(l + 1, T)) o = np.arange(anchor - l, anchor).astype(int) else: # i.i.d LWCV o = np.random.choice(T - 2, size=np.int(pct_to_drop / 100. * T), replace=False) + 1 return o @staticmethod def synthetic_hmm_data(K, T, D, sigma0=None, seed=1234, varainces_of_mean=1.0, diagonal_upweight=False): """ :param K: Number of HMM states :param T: length of the sequence """ N = 1 # For structured IJ we will remove data / time steps from a single sequence np.random.seed(seed) if sigma0 is None: sigma0 = np.eye(D) A = np.random.dirichlet(alpha=np.ones(K), size=K) if diagonal_upweight: A = A + 3 * np.eye(K) # add 3 to the diagonal and renormalize to encourage self transitions A = A / A.sum(axis=1) pi0 = np.random.dirichlet(alpha=np.ones(K)) mus = np.random.normal(size=(K, D), scale=np.sqrt(varainces_of_mean)) zs = np.empty((N, T), dtype=np.int) X = np.empty((N, T, D)) for n in range(N): zs[n, 0] = int(np.random.choice(np.arange(K), p=pi0)) X[n, 0] = np.random.multivariate_normal(mean=mus[zs[n, 0]], cov=sigma0) for t in range(1, T): zs[n, t] = int(np.random.choice(np.arange(K), p=A[zs[n, t - 1], :])) X[n, t] = np.random.multivariate_normal(mean=mus[zs[n, t]], cov=sigma0) return {'X': X, 'state_assignments': zs, 'A': A, 'initial_state_assignment': pi0, 'means': mus} import abc import sys # Ensure compatibility with Python 2/3 if sys.version_info >= (3, 4): ABC = abc.ABC else: ABC = abc.ABCMeta(str('ABC'), (), {}) from copy import deepcopy import numpy as np import numpy.random as npr def make_batches(n_data, batch_size): return [slice(i, min(i+batch_size, n_data)) for i in range(0, n_data, batch_size)] def generate_regression_data(seed, data_count=500): """ Generate data from a noisy sine wave. :param seed: random number seed :param data_count: number of data points. :return: """ np.random.seed(seed) noise_var = 0.1 x = np.linspace(-4, 4, data_count) y = 1*np.sin(x) + np.sqrt(noise_var)*npr.randn(data_count) train_count = int (0.2 * data_count) idx = npr.permutation(range(data_count)) x_train = x[idx[:train_count], np.newaxis ] x_test = x[ idx[train_count:], np.newaxis ] y_train = y[ idx[:train_count] ] y_test = y[ idx[train_count:] ] mu = np.mean(x_train, 0) std = np.std(x_train, 0) x_train = (x_train - mu) / std x_test = (x_test - mu) / std mu = np.mean(y_train, 0) std = np.std(y_train, 0) y_train = (y_train - mu) / std train_stats = dict() train_stats['mu'] = mu train_stats['sigma'] = std return x_train, y_train, x_test, y_test, train_stats def form_D_for_auucc(yhat, zhatl, zhatu): # a handy routine to format data as needed by the UCC fit() method D = np.zeros([yhat.shape[0], 3]) D[:, 0] = yhat.squeeze() D[:, 1] = zhatl.squeeze() D[:, 2] = zhatu.squeeze() return D def fitted_ucc_w_nullref(y_true, y_pred_mean, y_pred_lower, y_pred_upper): """ Instantiates an UCC object for the target predictor plus a 'null' (constant band) reference :param y_pred_lower: :param y_pred_mean: :param y_pred_upper: :param y_true: :return: ucc object fitted for two systems: target + null reference """ # form matrix for ucc: X_for_ucc = form_D_for_auucc(y_pred_mean.squeeze(), y_pred_mean.squeeze() - y_pred_lower.squeeze(), y_pred_upper.squeeze() - y_pred_mean.squeeze()) # form matrix for a 'null' system (constant band) X_null = deepcopy(X_for_ucc) X_null[:,1:] = np.std(y_pred_mean) # can be set to any other constant (no effect on AUUCC) # create an instance of ucc and fit data from uq360.metrics.uncertainty_characteristics_curve import UncertaintyCharacteristicsCurve as ucc u = ucc() u.fit([X_for_ucc, X_null], y_true.squeeze()) return u def make_sklearn_compatible_scorer(task_type, metric, greater_is_better=True, **kwargs): """ Args: task_type: (str) regression or classification. metric: (str): choice of metric can be one of these - [aurrrc, ece, auroc, nll, brier, accuracy] for classification and ["rmse", "nll", "auucc_gain", "picp", "mpiw", "r2"] for regression. greater_is_better: is False the scores are negated before returning. **kwargs: additional arguments specific to some metrics. Returns: sklearn compatible scorer function. """ from uq360.metrics.classification_metrics import compute_classification_metrics from uq360.metrics.regression_metrics import compute_regression_metrics def sklearn_compatible_score(model, X, y_true): """ Args: model: The model being scored. Currently uq360 and sklearn models are supported. X: Input features. y_true: ground truth values for the target. Returns: Computed score of the model. """ from uq360.algorithms.builtinuq import BuiltinUQ from uq360.algorithms.posthocuq import PostHocUQ if isinstance(model, BuiltinUQ) or isinstance(model, PostHocUQ): # uq360 models if task_type == "classification": score = compute_classification_metrics( y_true=y_true, y_prob=model.predict(X).y_prob, option=metric, **kwargs )[metric] elif task_type == "regression": y_mean, y_lower, y_upper = model.predict(X) score = compute_regression_metrics( y_true=y_true, y_mean=y_mean, y_lower=y_lower, y_upper=y_upper, option=metric, **kwargs )[metric] else: raise NotImplementedError else: # sklearn models if task_type == "classification": score = compute_classification_metrics( y_true=y_true, y_prob=model.predict_proba(X), option=metric, **kwargs )[metric] else: if metric in ["rmse", "r2"]: score = compute_regression_metrics( y_true=y_true, y_mean=model.predict(X), y_lower=None, y_upper=None, option=metric, **kwargs )[metric] else: raise NotImplementedError("{} is not supported for sklearn regression models".format(metric)) if not greater_is_better: score = -score return score return sklearn_compatible_score class DummySklearnEstimator(ABC): def __init__(self, num_classes, base_model_prediction_fn): self.base_model_prediction_fn = base_model_prediction_fn self.classes_ = [i for i in range(num_classes)] def fit(self): pass def predict_proba(self, X): return self.base_model_prediction_fn(X) from builtins import range import autograd.numpy as np def adam(grad, x, callback=None, num_iters=100, step_size=0.001, b1=0.9, b2=0.999, eps=10**-8, polyak=False): """Adapted from autograd.misc.optimizers""" m = np.zeros(len(x)) v = np.zeros(len(x)) for i in range(num_iters): g = grad(x, i) if callback: callback(x, i, g, polyak) m = (1 - b1) * g + b1 * m # First moment estimate. v = (1 - b2) * (g**2) + b2 * v # Second moment estimate. mhat = m / (1 - b1**(i + 1)) # Bias correction. vhat = v / (1 - b2**(i + 1)) x = x - step_size*mhat/(np.sqrt(vhat) + eps) return x import matplotlib.pyplot as plt import numpy as np import numpy.random as npr import torch as torch def make_data_gap(seed, data_count=100): import GPy npr.seed(0) x = np.hstack([np.linspace(-5, -2, int(data_count/2)), np.linspace(2, 5, int(data_count/2))]) x = x[:, np.newaxis] k = GPy.kern.RBF(input_dim=1, variance=1., lengthscale=1.) K = k.K(x) L = np.linalg.cholesky(K + 1e-5 * np.eye(data_count)) # draw a noise free random function from a GP eps = np.random.randn(data_count) f = L @ eps # use a homoskedastic Gaussian noise model N(f(x)_i, \sigma^2). \sigma^2 = 0.1 eps_noise = np.sqrt(0.1) * np.random.randn(data_count) y = f + eps_noise y = y[:, np.newaxis] plt.plot(x, f, 'ko', ms=2) plt.plot(x, y, 'ro') plt.title("GP generated Data") plt.pause(1) return torch.FloatTensor(x), torch.FloatTensor(y), torch.FloatTensor(x), torch.FloatTensor(y) def make_data_sine(seed, data_count=450): # fix the random seed np.random.seed(seed) noise_var = 0.1 X = np.linspace(-4, 4, data_count) y = 1*np.sin(X) + np.sqrt(noise_var)*npr.randn(data_count) train_count = int (0.2 * data_count) idx = npr.permutation(range(data_count)) X_train = X[idx[:train_count], np.newaxis ] X_test = X[ idx[train_count:], np.newaxis ] y_train = y[ idx[:train_count] ] y_test = y[ idx[train_count:] ] mu = np.mean(X_train, 0) std = np.std(X_train, 0) X_train = (X_train - mu) / std X_test = (X_test - mu) / std mu = np.mean(y_train, 0) std = np.std(y_train, 0) # mu = 0 # std = 1 y_train = (y_train - mu) / std y_test = (y_test -mu) / std train_stats = dict() train_stats['mu'] = torch.FloatTensor([mu]) train_stats['sigma'] = torch.FloatTensor([std]) return torch.FloatTensor(X_train), torch.FloatTensor(y_train), torch.FloatTensor(X_test), torch.FloatTensor(y_test),\ train_stats ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' #System imports import os import sys import json import datetime,time,timeit import itertools import numpy as np import pandas as pd import math from sklearn.preprocessing import MinMaxScaler,StandardScaler from sklearn.preprocessing import PowerTransformer import logging class dataTransformer(): def __init__(self): self.log = logging.getLogger('eion') def startTransformer(self,df,features,target,transType): scaler ='None' if target in features: features.remove(target) transFeatures=features transDfColumns=[] dataframe=df[transFeatures] #targetArray=np.array(df[target]) #targetArray.shape = (len(targetArray), 1) self.log.info("Data Normalization has started") if transType.lower() =='standardscaler': scaler = StandardScaler().fit(dataframe) transDf = scaler.transform(dataframe) elif transType.lower() =='minmax': scaler=MinMaxScaler().fit(dataframe) transDf = scaler.transform(dataframe) elif transType.lower() =='lognormal': print(dataframe) scaler = PowerTransformer(method='yeo-johnson', standardize=False).fit(dataframe) transDf = scaler.transform(dataframe) else: self.log.info("Need to implement") #features.append(target) #scaledDf = pd.DataFrame(np.hstack((transDf, targetArray)),columns=features) return transDf,features,scaler import pandas as pd tab = ' ' VALID_AGGREGATION_METHODS = ['mean','sum'] VALID_GRANULARITY_UNITS = ['second','minute','hour','day','week','month','year'] VALID_INTERPOLATE_KWARGS = {'linear':{},'spline':{'order':5},'timebased':{}} VALID_INTERPOLATE_METHODS = list( VALID_INTERPOLATE_KWARGS.keys()) def get_one_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def get_boolean(value): if (isinstance(value, str) and value.lower() == 'true') or (isinstance(value, bool) and value == True): return True else: return False def get_source_delta( data: pd.DataFrame): MAX_SAMPLE_TRY = 20 if len( data) <= 1: return None time_delta = data.index[-1] - data.index[-2] count = {} for i in range(len(data)): if i == MAX_SAMPLE_TRY or i == data.index[-1]: break delta = data.index[i+1] - data.index[i] if delta not in count.keys(): count[delta] = 1 else: count[delta] += 1 if count: return max(count, key=count.get) else: return None class timeSeries(): def __init__( self, config, datetime, log=None): self.datetime = datetime self.validate_config(config) self.log = log def validate_config( self, config): if not self.datetime or self.datetime.lower() == 'na': raise ValueError('date time feature is not provided') self.config = {} method = get_one_true_option(config.get('interpolation',None)) self.config['interpolate'] = {} self.config['interpolate']['enabled'] = method in VALID_INTERPOLATE_METHODS self.config['interpolate']['method'] = method self.config['rolling'] = {} self.config['rolling']['enabled'] = get_boolean( config.get('rollingWindow',False)) self.config['rolling']['size'] = int( config.get('rollingWindowSize',1)) if self.config['rolling']['size'] < 1: raise ValueError('Rolling window size should be greater than 0.') self.config['aggregation'] = {} aggregation = config.get('aggregation',{}) agg_method = get_one_true_option(aggregation['type']) self.config['aggregation'] = {} self.config['aggregation']['enabled'] = agg_method in VALID_AGGREGATION_METHODS self.config['aggregation']['method'] = agg_method granularity = aggregation.get('granularity',{}) granularity_unit = get_one_true_option( granularity.get('unit',None)) if granularity_unit in VALID_GRANULARITY_UNITS: granularity_mapping = {'second':'S','minute':'Min','hour':'H','day':'D','week':'W','month':'M','year':'Y'} size = int(granularity.get('size',10)) granularity_unit = granularity_mapping.get(granularity_unit,granularity_unit) self.config['aggregation']['granularity'] = {} self.config['aggregation']['granularity']['unit'] = granularity_unit self.config['aggregation']['granularity']['size'] = size def log_info(self, msg, type='info'): if self.log: if type == 'error': self.log.error( msg) else: self.log.info( msg) else: print( msg) def is_down_sampling(self, data, size, granularity_unit): down_sampling = False if granularity_unit in ['M', 'Y']: return True else: target_delta = pd.Timedelta(size , granularity_unit) source_delta = get_source_delta(data) if not source_delta: raise ValueError('Could not find the data frame time frequency') return source_delta < target_delta def run( self, data): if self.datetime not in data.columns: raise ValueError(f"Date time feature '{self.datetime}' is not present in data") try: # data[self.datetime] = pd.to_datetime( data[self.datetime]) ##For bug 13513 - If the datetime needs UTC timestamp process, except part will handle. try: #for non utc timestamp data[self.datetime] = pd.to_datetime( data[self.datetime]) except: #for utc timestamp data[self.datetime] = pd.to_datetime( data[self.datetime],utc=True) data.set_index( self.datetime, inplace=True) except: raise ValueError(f"can not convert '{self.datetime}' to dateTime") if self.config.get('interpolate',{}).get('enabled',False): method = self.config['interpolate']['method'] self.log_info(f"Applying Interpolation using {method}") methods_mapping = {'timebased': 'time'} self.config['interpolate']['mapped_method'] = methods_mapping.get(method, method) data.interpolate(method=self.config['interpolate']['mapped_method'], inplace=True, **VALID_INTERPOLATE_KWARGS[method]) if self.config.get('rolling',{}).get('enabled',False): if self.config['rolling']['size'] > len( data): raise ValueError('Rolling window size is greater than dataset size') self.log_info(f"Applying rolling window( moving avg) with size {self.config['rolling']['size']}") data = data.rolling( self.config['rolling']['size']).mean() data = data.iloc[self.config['rolling']['size'] - 1:] aggregation = self.config.get('aggregation',{}) if aggregation.get('enabled',False): method = aggregation.get('method','mean') self.rule = str(aggregation['granularity']['size']) + aggregation['granularity']['unit'] if self.is_down_sampling(data, aggregation['granularity']['size'], aggregation['granularity']['unit']): self.log_info(f"Applying down sampling( {self.rule})") if method == 'mean': data = data.resample( self.rule).mean() elif method == 'sum': data = data.resample( self.rule).sum() else: self.log_info(f"Applying up sampling using forward fill method( {self.rule})") data = data.resample( self.rule).ffill() data.reset_index( inplace=True, names=self.datetime) return data def get_code(self, indent=0): tab = ' ' code = '' code += f""" def preprocess( data): try: #for non utc timestamp data['{self.datetime}'] = pd.to_datetime( data['{self.datetime}']) except: data['{self.datetime}'] = pd.to_datetime( data['{self.datetime}'],utc=True) data.set_index( '{self.datetime}', inplace=True) """ if self.config.get('interpolate',{}).get('enabled',False): code += tab + f"data.interpolate(method='{self.config['interpolate']['mapped_method']}', inplace=True, **{VALID_INTERPOLATE_KWARGS[self.config['interpolate']['method']]})\n" if self.config.get('rolling',{}).get('enabled',False): code += tab + f"data = data.rolling( {self.config['rolling']['size']}).mean().iloc[{self.config['rolling']['size'] - 1}:]\n" if self.config.get('aggregation',{}).get('enabled',False): code += tab + f"data = data.resample( '{self.rule}').{self.config.get('aggregation',{}).get('method','mean')}()\n" code += tab + f"data.reset_index( inplace=True, names='{self.datetime}')\n" code += tab + "return data\n" return code ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import os import sys import string import spacy #import en_core_web_sm from spacy.lang.en.stop_words import STOP_WORDS from spacy.lang.en import English try: from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS except: from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer from sklearn.base import TransformerMixin from nltk.stem import WordNetLemmatizer import re from collections import defaultdict from nltk.corpus import wordnet as wn from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import LabelBinarizer from nltk.tokenize import word_tokenize from nltk import pos_tag from nltk.corpus import stopwords class textDataProfiler(): def __init__(self): self.data=None #self.nlp=en_core_web_sm.load() self.punctuations = string.punctuation self.stopwords = list(STOP_WORDS) def startTextProfiler(self,df,target): try: dataColumns = list(df.columns) print(' \n No of rows and columns in dataFrame',df.shape) print('\n features in dataFrame',dataColumns) dataFDtypes=self.dataFramecolType(df) print('\n feature types in dataFrame',dataFDtypes) trainX=df['text'] trainY=df[target] return trainX,trainY except Exception as inst: print('startTextProfiler code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) def dataFramecolType(self,dataFrame): dataFDtypes=[] try: dataColumns=list(dataFrame.columns) for i in dataColumns: dataType=dataFrame[i].dtypes dataFDtypes.append(tuple([i,str(dataType)])) return dataFDtypes except Exception as e: print("error in dataFramecolyType",e) return dataFDtypes def textTokenizer(self,text): try: parser = English() tokens = parser(text) tokens = [ word.lemma_.lower().strip() if word.lemma_ != "-PRON-" else word.lower_ for word in tokens ] tokens = [ word for word in tokens if word not in self.stopwords and word not in self.punctuations ] return tokens except Exception as inst: print('textDataProfiler code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) return {} def cleanText(self,text): try: text=str(text).strip().lower() for punctuation in string.punctuation: text = text.replace(punctuation, '') return text except Exception as inst: print('cleanText code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) def textTokenization(self,text): try: tokenizedText=word_tokenize(text) return tokenizedText except Exception as inst: print('textDataProfiler code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) return {} def textLemmitizer(self,text): try: tag_map = defaultdict(lambda : wn.NOUN) tag_map['J'] = wn.ADJ tag_map['V'] = wn.VERB tag_map['R'] = wn.ADV Final_words = [] word_Lemmatized = WordNetLemmatizer() for word, tag in pos_tag(text): if word not in stopwords.words('english') and word.isalpha(): word_Final = word_Lemmatized.lemmatize(word,tag_map[tag[0]]) Final_words.append(word_Final) return str(Final_words) except Exception as inst: print('textLemmitizer code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) return {} class TextCleaner(TransformerMixin): def clean_text(self,text): try: text=str(text).strip().lower() text = text.replace("isn't", "is not") text = text.replace("aren't", "are not") text = text.replace("ain't", "am not") text = text.replace("won't", "will not") text = text.replace("didn't", "did not") text = text.replace("shan't", "shall not") text = text.replace("haven't", "have not") text = text.replace("hadn't", "had not") text = text.replace("hasn't", "has not") text = text.replace("don't", "do not") text = text.replace("wasn't", "was not") text = text.replace("weren't", "were not") text = text.replace("doesn't", "does not") text = text.replace("'s", " is") text = text.replace("'re", " are") text = text.replace("'m", " am") text = text.replace("'d", " would") text = text.replace("'ll", " will") text = re.sub(r'^https?:\/\/.*[\r\n]*', ' ', text, flags=re.MULTILINE) text = re.sub(r'[\w\.-]+@[\w\.-]+', ' ', text, flags=re.MULTILINE) for punctuation in string.punctuation: text = text.replace(punctuation,' ') text = re.sub(r'[^A-Za-z0-9\s]',r' ',text) text = re.sub(r'\n',r' ',text) text = re.sub(r'[0-9]',r' ',text) wordnet_lemmatizer = WordNetLemmatizer() text = " ".join([wordnet_lemmatizer.lemmatize(w, pos='v') for w in text.split()]) return text except Exception as inst: print('TextCleaner clean_text code execution failed !....',inst) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(exc_type, fname, exc_tb.tb_lineno) def text_cleaner(self,text): text = self.clean_text(text) stop_words = set(stopwords.words('english')) text_tokens = word_tokenize(text) out=' '.join(str(j) for j in text_tokens if j not in stop_words and (len(j)!=1)) return(out) def transform(self, X, **transform_params): # Cleaning Text return [self.clean_text(text) for text in X] def fit(self, X, y=None, **fit_params): return self def get_params(self, deep=True): return {} ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import random from matplotlib import pyplot as plt import cv2 import albumentations as A import os import pandas as pd from pathlib import Path class ImageAugmentation(): def __init__(self, dataLocation, csvFile): self.AugmentationOptions = {"Flip": {"operation": A.HorizontalFlip, "suffix":"_flip"}, "Rotate": {"operation": A.Rotate, "suffix":"_rotate"}, "Shift": {"operation": A.RGBShift, "suffix":"_shift"}, "Crop": {"operation": [A.CenterCrop, A.RandomSizedBBoxSafeCrop], "suffix":"_crop"}, "Contrast": {"operation": A.RandomContrast, "suffix":"_cont"}, "Brightness": {"operation": A.RandomBrightness, "suffix":"_bright"}, "Blur": {"operation": A.GaussianBlur, "suffix":"_blur"} } self.dataLocation = dataLocation self.csvFile = csvFile def __applyAugmentationClass(self, image, augmentation,limit): if augmentation in list(self.AugmentationOptions.keys()): if augmentation == "Crop": height, width, _ = image.shape crop_percentage = random.uniform(0.6, 0.9) transform = self.AugmentationOptions[augmentation]["operation"][0](height=int(height*crop_percentage), width=int(width*crop_percentage) ) elif augmentation == "Blur": transform = self.AugmentationOptions[augmentation]["operation"](blur_limit = limit) elif augmentation in ["Contrast","Brightness"]: transform = self.AugmentationOptions[augmentation]["operation"](limit = limit) else: transform = self.AugmentationOptions[augmentation]["operation"]() return transform(image=image) def __applyAugmentation(self, image, augmentation,limit,bboxes=None, category_ids=None, seed=7): transformOptions = [] if bboxes: bbox_params = A.BboxParams(format='pascal_voc', label_fields=['category_ids']) else: bbox_params = None if augmentation in list(self.AugmentationOptions.keys()): if augmentation == "Crop": height, width, _ = image.shape crop_percentage = random.uniform(0.6, 0.9) transformOptions.append(self.AugmentationOptions[augmentation]["operation"][1](height=int(height*crop_percentage), width=int(width*crop_percentage) )) elif augmentation == "Blur": transformOptions.append(self.AugmentationOptions[augmentation]["operation"](blur_limit = limit)) elif augmentation in ["Contrast","Brightness"]: transformOptions.append(self.AugmentationOptions[augmentation]["operation"](limit = limit)) else: transformOptions.append(self.AugmentationOptions[augmentation]["operation"]()) transform = A.Compose( transformOptions, bbox_params=bbox_params, ) random.seed(seed) return transform(image=image, bboxes=bboxes, category_ids=category_ids) else: return None def getBBox(self, df, imageLoc, category_name_to_id): subDf = df[df['loc']==imageLoc] boxes = [] category = [] for index, row in subDf.iterrows(): boxes.append( [row['xmin'],row['ymin'],row['xmax'],row['ymax']]) category.append(category_name_to_id[row['Label']]) return boxes, category def __objAug(self, imageLoc, df, classes_names, category_id_to_name, category_name_to_id,limit,numberofImages,op): for x in range(numberofImages): bbox, category_ids = self.getBBox(df, imageLoc, category_name_to_id) image = cv2.imread(imageLoc) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) transformed = self.__applyAugmentation(image, op,limit,bbox, category_ids) transformed['image'] = cv2.cvtColor(transformed['image'], cv2.COLOR_RGB2BGR) count = 1 row = df[df['loc']==imageLoc].iloc[0] filename = (Path(imageLoc).stem +'_'+str(x)+ self.AugmentationOptions[op]["suffix"] + Path(imageLoc).suffix) newImage = str(Path(imageLoc).parent/filename) for index,bbox in enumerate(transformed['bboxes']): data = {'File':filename, 'xmin':bbox[0],'ymin':bbox[1],'xmax':bbox[2],'ymax':bbox[3],'Label':category_id_to_name[transformed['category_ids'][index]],'id':count,'height':row['height'],'width':row['width'], 'angle':0.0, 'loc': newImage, 'AugmentedImage': True} count += 1 df=df.append(data, ignore_index=True) cv2.imwrite(newImage, transformed['image']) return df def __objectDetection(self, images, df, optionDf, classes_names, suffix='',augConf={}): category_id_to_name = {v+1:k for v,k in enumerate(classes_names)} category_name_to_id = {k:v+1 for v,k in enumerate(classes_names)} for i, imageLoc in enumerate(images): for key in optionDf.columns: if optionDf.iloc[i][key]: if key in augConf: limit = eval(augConf[key].get('limit','0.2')) numberofImages = int(augConf[key].get('noOfImages',1)) else: limit = 0.2 numberofImages = 1 df = self.__objAug(imageLoc, df, classes_names, category_id_to_name,category_name_to_id,limit,numberofImages,op=key) return df def __augClassificationImage(self, imageLoc, df,limit,imageindex,op): data = {} image = cv2.imread(imageLoc) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) transformed = self.__applyAugmentationClass(image, op,limit) transformed['image'] = cv2.cvtColor(transformed['image'], cv2.COLOR_RGB2BGR) location = Path(imageLoc).parent filename = (Path(imageLoc).stem +'_'+'str(imageindex)'+ self.AugmentationOptions[op]["suffix"] + Path(imageLoc).suffix) cv2.imwrite(str(location/'AION'/'AugumentedImages'/filename), transformed['image']) data['File'] = filename data['Label'] = df[df['File']==Path(imageLoc).name]["Label"].iloc[0] data['AugmentedImage'] = True data['loc'] = str(location/filename) return data def __classification(self, images, df, optionDf,augConf,csv_file=None, outputDir=None): for i, imageLoc in enumerate(images): for key in optionDf.columns: if optionDf.iloc[i][key]: if key in augConf: limit = eval(augConf[key].get('limit','0.2')) numberofImages = int(augConf[key].get('noOfImages',1)) else: limit = 0.2 numberofImages = 1 for x in range(numberofImages): rows = self.__augClassificationImage(imageLoc, df,limit,x,op=key) df=df.append(rows, ignore_index=True) return df def removeAugmentedImages(self, df): removeDf = df[df['AugmentedImage'] == True]['loc'].unique().tolist() #df[df['imageAugmentationOriginalImage'] != True][loocationField].apply(lambda x: Path(x).unlink()) for file in removeDf: if file: Path(file).unlink() def augment(self, modelType="imageclassification",params=None,csvSavePath = None,augConf={}): if isinstance(params, dict) and any(params.values()): df = pd.read_csv(self.csvFile) if not self.dataLocation.endswith('/'): images = self.dataLocation+'/' else: images = self.dataLocation if modelType == "imageclassification": images = images + df['File'] else: images = images + df['File'] df['loc'] = images images = set(images.tolist()) option = {} for key in list(self.AugmentationOptions.keys()): option[key] = params.get(key, False) optionDf = pd.DataFrame(columns=list(option.keys())) for i in range(len(images)): optionDf = optionDf.append(option, ignore_index=True) if modelType == "imageclassification": df = self.__classification(images, df, optionDf,augConf) else: classes_names = sorted(df['Label'].unique().tolist()) df = self.__objectDetection(images, df, optionDf, classes_names,'',augConf) df.to_csv(self.csvFile, index=False) return self.csvFile ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' #System imports import logging from distutils.util import strtobool import pandas as pd from text import TextProcessing def get_one_true_option(d, default_value): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value class textProfiler(): def __init__(self): self.log = logging.getLogger('eion') def textCleaning(self, textCorpus): textProcessor = TextProcessing.TextProcessing() textCorpus = textProcessor.transform(textCorpus) return(textCorpus) def textProfiler(self, textCorpus, conf_json, pipeList, max_features): cleaning_kwargs = {} textCleaning = conf_json.get('textCleaning') self.log.info("Text Preprocessing config: ",textCleaning) cleaning_kwargs['fRemoveNoise'] = strtobool(textCleaning.get('removeNoise', 'True')) cleaning_kwargs['fNormalize'] = strtobool(textCleaning.get('normalize', 'True')) cleaning_kwargs['fReplaceAcronym'] = strtobool(textCleaning.get('replaceAcronym', 'False')) cleaning_kwargs['fCorrectSpelling'] = strtobool(textCleaning.get('correctSpelling', 'False')) cleaning_kwargs['fRemoveStopwords'] = strtobool(textCleaning.get('removeStopwords', 'True')) cleaning_kwargs['fRemovePunctuation'] = strtobool(textCleaning.get('removePunctuation', 'True')) cleaning_kwargs['fRemoveNumericTokens'] = strtobool(textCleaning.get('removeNumericTokens', 'True')) cleaning_kwargs['normalizationMethod'] = get_one_true_option(textCleaning.get('normalizeMethod'), 'lemmatization').capitalize() removeNoiseConfig = textCleaning.get('removeNoiseConfig') if type(removeNoiseConfig) is dict: cleaning_kwargs['removeNoise_fHtmlDecode'] = strtobool(removeNoiseConfig.get('decodeHTML', 'True')) cleaning_kwargs['removeNoise_fRemoveHyperLinks'] = strtobool(removeNoiseConfig.get('removeHyperLinks', 'True')) cleaning_kwargs['removeNoise_fRemoveMentions'] = strtobool(removeNoiseConfig.get('removeMentions', 'True')) cleaning_kwargs['removeNoise_fRemoveHashtags'] = strtobool(removeNoiseConfig.get('removeHashtags', 'True')) cleaning_kwargs['removeNoise_RemoveOrReplaceEmoji'] = 'remove' if strtobool(removeNoiseConfig.get('removeEmoji', 'True')) else 'replace' cleaning_kwargs['removeNoise_fUnicodeToAscii'] = strtobool(removeNoiseConfig.get('unicodeToAscii', 'True')) cleaning_kwargs['removeNoise_fRemoveNonAscii'] = strtobool(removeNoiseConfig.get('removeNonAscii', 'True')) acronymConfig = textCleaning.get('acronymConfig') if type(acronymConfig) is dict: cleaning_kwargs['acronymDict'] = acronymConfig.get('acronymDict', None) stopWordsConfig = textCleaning.get('stopWordsConfig') if type(stopWordsConfig) is dict: cleaning_kwargs['stopwordsList'] = stopWordsConfig.get('stopwordsList', []) cleaning_kwargs['extend_or_replace_stopwordslist'] = 'extend' if strtobool(stopWordsConfig.get('extend', 'True')) else 'replace' removeNumericConfig = textCleaning.get('removeNumericConfig') if type(removeNumericConfig) is dict: cleaning_kwargs['removeNumeric_fIncludeSpecialCharacters'] = strtobool(removeNumericConfig.get('removeNumeric_IncludeSpecialCharacters', 'True')) removePunctuationConfig = textCleaning.get('removePunctuationConfig') if type(removePunctuationConfig) is dict: cleaning_kwargs['fRemovePuncWithinTokens'] = strtobool(removePunctuationConfig.get('removePuncWithinTokens', 'False')) cleaning_kwargs['fExpandContractions'] = strtobool(textCleaning.get('expandContractions', 'False')) if cleaning_kwargs['fExpandContractions']: cleaning_kwargs['expandContractions_googleNewsWordVectorPath'] = GOOGLE_NEWS_WORD_VECTORS_PATH libConfig = textCleaning.get('libConfig') if type(libConfig) is dict: cleaning_kwargs['tokenizationLib'] = get_one_true_option(libConfig.get('tokenizationLib'), 'nltk') cleaning_kwargs['lemmatizationLib'] = get_one_true_option(libConfig.get('lemmatizationLib'), 'nltk') cleaning_kwargs['stopwordsRemovalLib'] = get_one_true_option(libConfig.get('stopwordsRemovalLib'), 'nltk') textProcessor = TextProcessing.TextProcessing(**cleaning_kwargs) textCorpus = textProcessor.transform(textCorpus) pipeList.append(("TextProcessing",textProcessor)) textFeatureExtraction = conf_json.get('textFeatureExtraction') if strtobool(textFeatureExtraction.get('pos_tags', 'False')): pos_tags_lib = get_one_true_option(textFeatureExtraction.get('pos_tags_lib'), 'nltk') posTagger = TextProcessing.PosTagging( pos_tags_lib) textCorpus = posTagger.transform(textCorpus) pipeList.append(("posTagger",posTagger)) ngram_min = 1 ngram_max = 1 if strtobool(textFeatureExtraction.get('n_grams', 'False')): n_grams_config = textFeatureExtraction.get("n_grams_config") ngram_min = int(n_grams_config.get('min_n', 1)) ngram_max = int(n_grams_config.get('max_n', 1)) if (ngram_min < 1) or ngram_min > ngram_max: ngram_min = 1 ngram_max = 1 invalidNgramWarning = 'WARNING : invalid ngram config.\nUsing the default values min_n={}, max_n={}'.format(ngram_min, ngram_max) self.log.info(invalidNgramWarning) ngram_range_tuple = (ngram_min, ngram_max) textConversionMethod = conf_json.get('textConversionMethod') conversion_method = get_one_true_option(textConversionMethod, None) if conversion_method.lower() == "countvectors": X, vectorizer = TextProcessing.ExtractFeatureCountVectors(textCorpus, ngram_range=ngram_range_tuple, max_features=max_features) pipeList.append(("vectorizer",vectorizer)) df1 = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names()) df1 = df1.add_suffix('_vect') self.log.info('----------> Conversion Method: CountVectors') elif conversion_method.lower() in ["word2vec","fasttext","glove"]: embedding_method = conversion_method wordEmbeddingVecotrizer = TextProcessing.wordEmbedding(embedding_method) wordEmbeddingVecotrizer.checkAndDownloadPretrainedModel() X = wordEmbeddingVecotrizer.transform(textCorpus) df1 = pd.DataFrame(X) df1 = df1.add_suffix('_vect') pipeList.append(("vectorizer",wordEmbeddingVecotrizer)) self.log.info('----------> Conversion Method: '+str(conversion_method)) elif conversion_method.lower() == "sentencetransformer": from sentence_transformers import SentenceTransformer model = SentenceTransformer('sentence-transformers/msmarco-distilroberta-base-v2') X = model.encode(textCorpus) df1 = pd.DataFrame(X) df1 = df1.add_suffix('_vect') pipeList.append(("vectorizer",model)) self.log.info('----------> Conversion Method: SentenceTransformer') elif conversion_method.lower() == 'tf_idf': X, vectorizer = TextProcessing.ExtractFeatureTfIdfVectors(textCorpus,ngram_range=ngram_range_tuple, max_features=max_features) pipeList.append(("vectorizer",vectorizer)) df1 = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names()) df1 = df1.add_suffix('_vect') self.log.info('----------> Conversion Method: TF_IDF') else: df1 = pd.DataFrame() df1['tokenize'] = textCorpus self.log.info('----------> Conversion Method: NA') return df1, pipeList,conversion_method ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import glob import pandas as pd import io import xml.etree.ElementTree as ET import argparse os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) import tensorflow as tf from PIL import Image from object_detection.utils import dataset_util, label_map_util from collections import namedtuple from pathlib import Path def class_text_to_int(row_label, label_map_dict): return label_map_dict[row_label] def split(df, group): data = namedtuple('data', ['File', 'object']) gb = df.groupby(group) return [data(File, gb.get_group(x)) for File, x in zip(gb.groups.keys(), gb.groups)] def create_tf_example(group, path, label_map_dict): with tf.io.gfile.GFile(os.path.join(path, '{}'.format(group.File)), 'rb') as fid: encoded_jpg = fid.read() encoded_jpg_io = io.BytesIO(encoded_jpg) image = Image.open(encoded_jpg_io) width, height = image.size File = group.File.encode('utf8') image_format = b'jpg' xmins = [] xmaxs = [] ymins = [] ymaxs = [] classes_text = [] classes = [] for index, row in group.object.iterrows(): xmin_n = min(row['xmin'], row['xmax']) xmax_n = max(row['xmin'], row['xmax']) ymin_n = min(row['ymin'], row['ymax']) ymax_n = max(row['ymin'], row['ymax']) xmin_new = min(xmin_n, width) xmax_new = min(xmax_n, width) ymin_new = min(ymin_n, height) ymax_new = min(ymax_n, height) xmn = xmin_new / width xmins.append(xmn) xmx = xmax_new / width xmaxs.append(xmx) ymn = ymin_new / height ymins.append(ymn) ymx = ymax_new / height ymaxs.append(ymx) classes_text.append(row['Label'].encode('utf8')) classes.append(class_text_to_int(row['Label'], label_map_dict)) tf_example = tf.train.Example(features=tf.train.Features(feature={ 'image/height': dataset_util.int64_feature(height), 'image/width': dataset_util.int64_feature(width), 'image/filename': dataset_util.bytes_feature(File), 'image/source_id': dataset_util.bytes_feature(File), 'image/encoded': dataset_util.bytes_feature(encoded_jpg), 'image/format': dataset_util.bytes_feature(image_format), 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins), 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs), 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins), 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs), 'image/object/class/text': dataset_util.bytes_list_feature(classes_text), 'image/object/class/label': dataset_util.int64_list_feature(classes), })) return tf_example def labelFile(classes_names, label_map_path): pbtxt_content = "" for i, class_name in enumerate(classes_names): pbtxt_content = ( pbtxt_content + "item {{\n id: {0}\n name: '{1}'\n}}\n\n".format(i + 1, class_name) ) pbtxt_content = pbtxt_content.strip() with open(label_map_path, "w") as f: f.write(pbtxt_content) def createLabelFile(train_df, save_path): labelmap_path = str(Path(save_path)/ 'label_map.pbtxt') classes_names = sorted(train_df['Label'].unique().tolist()) labelFile(classes_names, labelmap_path) return labelmap_path, len(classes_names) def generate_TF_record(image_dir, output_dir, train_df, test_df, labelmap_path): outputPath = str(Path(output_dir)/ 'train.tfrecord') writer = tf.io.TFRecordWriter( outputPath) grouped = split(train_df, 'File') label_map = label_map_util.load_labelmap(labelmap_path ) label_map_dict = label_map_util.get_label_map_dict(label_map) for group in grouped: tf_example = create_tf_example(group, image_dir, label_map_dict) writer.write(tf_example.SerializeToString()) writer.close() if len(test_df): outputPath = str(Path(output_dir)/ 'test.tfrecord') writer = tf.io.TFRecordWriter( outputPath) grouped = split(test_df, 'File') for group in grouped: tf_example = create_tf_example(group, image_dir, label_map_dict) writer.write(tf_example.SerializeToString()) writer.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import io import json import logging import pandas as pd import sys import numpy as np from pathlib import Path from word2number import w2n from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OrdinalEncoder from sklearn.preprocessing import OneHotEncoder from sklearn.impute import SimpleImputer, KNNImputer from sklearn.pipeline import Pipeline, FeatureUnion from sklearn.preprocessing import FunctionTransformer from sklearn.preprocessing import MinMaxScaler,StandardScaler from sklearn.preprocessing import PowerTransformer from sklearn.compose import ColumnTransformer from sklearn.base import TransformerMixin from sklearn.ensemble import IsolationForest from category_encoders import TargetEncoder try: import transformations.data_profiler_functions as cs except: import data_profiler_functions as cs if 'AION' in sys.modules: try: from appbe.app_config import DEBUG_ENABLED except: DEBUG_ENABLED = False else: DEBUG_ENABLED = False log_suffix = f'[{Path(__file__).stem}] ' class profiler(): def __init__(self, xtrain, ytrain=None, target=None, encode_target = False, config={}, keep_unprocessed=[],data_path=None,log=None): if not isinstance(xtrain, pd.DataFrame): raise ValueError(f'{log_suffix}supported data type is pandas.DataFrame but provide data is of {type(xtrain)} type') if xtrain.empty: raise ValueError(f'{log_suffix}Data frame is empty') if target and target in xtrain.columns: self.target = xtrain[target] xtrain.drop(target, axis=1, inplace=True) self.target_name = target elif ytrain: self.target = ytrain self.target_name = 'target' else: self.target = pd.Series() self.target_name = None self.data_path = data_path self.encode_target = encode_target self.label_encoder = None self.data = xtrain self.keep_unprocessed = keep_unprocessed self.colm_type = {} for colm, infer_type in zip(self.data.columns, self.data.dtypes): self.colm_type[colm] = infer_type self.numeric_feature = [] self.cat_feature = [] self.text_feature = [] self.wordToNumericFeatures = [] self.added_features = [] self.pipeline = [] self.dropped_features = {} self.train_features_type={} self.__update_type() self.config = config self.featureDict = config.get('featureDict', []) self.output_columns = [] self.feature_expender = [] self.text_to_num = {} self.force_numeric_conv = [] if log: self.log = log else: self.log = logging.getLogger('eion') self.type_conversion = {} self.log_input_feat_info() def log_input_feat_info(self): if self.featureDict: feature_df = pd.DataFrame(self.featureDict) log_text = '\nPreprocessing options:' log_text += '\n\t'+str(feature_df.head( len(self.featureDict))).replace('\n','\n\t') self.log.info(log_text) def log_dataframe(self, msg=None): buffer = io.StringIO() self.data.info(buf=buffer) if msg: log_text = f'Data frame after {msg}:' else: log_text = 'Data frame:' log_text += '\n\t'+str(self.data.head(2)).replace('\n','\n\t') log_text += ('\n\t' + buffer.getvalue().replace('\n','\n\t')) self.log.info(log_text) def transform(self): if self.is_target_available(): if self.target_name: self.log.info(f"Target feature name: '{self.target_name}'") self.log.info(f"Target feature size: {len(self.target)}") else: self.log.info(f"Target feature not present") self.log_dataframe() print(self.data.info()) try: self.process() except Exception as e: self.log.error(e, exc_info=True) raise pipe = FeatureUnion(self.pipeline) try: if self.text_feature: from text.textProfiler import set_pretrained_model set_pretrained_model(pipe) conversion_method = self.get_conversion_method() process_data = pipe.fit_transform(self.data, y=self.target) # save for testing if DEBUG_ENABLED: if not isinstance(process_data, np.ndarray): process_data = process_data.toarray() df = pd.DataFrame(process_data) df.to_csv('debug_preprocessed.csv', index=False) if self.text_feature and conversion_method == 'latentsemanticanalysis': n_size = self.get_tf_idf_output_size( pipe) dimensions = self.get_tf_idf_dimensions() if n_size < dimensions or n_size > dimensions: dimensions = n_size from sklearn.decomposition import TruncatedSVD reducer = TruncatedSVD( n_components = dimensions) reduced_data = reducer.fit_transform( process_data[:,-n_size:]) text_process_idx = [t[0] for t in pipe.transformer_list].index('text_process') pipe.transformer_list[text_process_idx][1].steps.append(('feature_reducer',reducer)) if not isinstance(process_data, np.ndarray): process_data = process_data.toarray() process_data = np.concatenate((process_data[:,:-n_size], reduced_data), axis=1) last_step = self.feature_expender.pop() self.feature_expender.append({'feature_reducer':list(last_step.values())[0]}) except EOFError as e: if "Compressed file ended before the end-of-stream marker was reached" in str(e): raise EOFError('Pretrained model is not downloaded properly') self.update_output_features_names(pipe) if not isinstance(process_data, np.ndarray): process_data = process_data.toarray() df = pd.DataFrame(process_data, index=self.data.index, columns=self.output_columns) if self.is_target_available() and self.target_name: df[self.target_name] = self.target if self.keep_unprocessed: df[self.keep_unprocessed] = self.data[self.keep_unprocessed] self.log_numerical_fill() self.log_categorical_fill() self.log_normalization() return df, pipe, self.label_encoder def log_type_conversion(self): if self.log: self.log.info('----------- Inspecting Features -----------') self.log.info('----------- Type Conversion -----------') count = 0 for k, v in self.type_conversion.items(): if v[0] != v[1]: self.log.info(f'-------> {k} -> from {v[0]} to {v[1]} : {v[2]}') self.log.info('Status:- |... Feature inspection done') def check_config(self): removeDuplicate = self.config.get('removeDuplicate', False) self.config['removeDuplicate'] = cs.get_boolean(removeDuplicate) self.config['misValueRatio'] = float(self.config.get('misValueRatio', cs.default_config['misValueRatio'])) self.config['numericFeatureRatio'] = float(self.config.get('numericFeatureRatio', cs.default_config['numericFeatureRatio'])) self.config['categoryMaxLabel'] = int(self.config.get('categoryMaxLabel', cs.default_config['categoryMaxLabel'])) featureDict = self.config.get('featureDict', []) if isinstance(featureDict, dict): self.config['featureDict'] = [] if isinstance(featureDict, str): self.config['featureDict'] = [] def process(self): #remove duplicate not required at the time of prediction self.check_config() self.remove_constant_feature() self.remove_empty_feature(self.config['misValueRatio']) self.remove_index_features() self.dropna() if self.config['removeDuplicate']: self.drop_duplicate() #self.check_categorical_features() #self.string_to_numeric() self.process_target() self.train_features_type = {k:v for k,v in zip(self.data.columns, self.data.dtypes)} self.parse_process_step_config() self.process_drop_fillna() self.log_type_conversion() self.update_num_fill_dict() if DEBUG_ENABLED: print(self.num_fill_method_dict) self.update_cat_fill_dict() self.create_pipeline() self.text_pipeline(self.config) self.apply_outlier() if DEBUG_ENABLED: self.log.info(self.process_method) self.log.info(self.pipeline) def is_target_available(self): return (isinstance(self.target, pd.Series) and not self.target.empty) or len(self.target) def process_target(self, operation='encode', arg=None): if self.is_target_available(): # drop null values self.__update_index( self.target.notna(), 'target') if self.encode_target: self.label_encoder = LabelEncoder() self.target = self.label_encoder.fit_transform(self.target) return self.label_encoder return None def is_target_column(self, column): return column == self.target_name def fill_default_steps(self): num_fill_method = cs.get_one_true_option(self.config.get('numericalFillMethod',{})) normalization_method = cs.get_one_true_option(self.config.get('normalization',{}),'none') for colm in self.numeric_feature: if num_fill_method: self.fill_missing_value_method(colm, num_fill_method.lower()) if normalization_method: self.fill_normalizer_method(colm, normalization_method.lower()) cat_fill_method = cs.get_one_true_option(self.config.get('categoricalFillMethod',{})) cat_encode_method = cs.get_one_true_option(self.config.get('categoryEncoding',{})) for colm in self.cat_feature: if cat_fill_method: self.fill_missing_value_method(colm, cat_fill_method.lower()) if cat_encode_method: self.fill_encoder_value_method(colm, cat_encode_method.lower(), default=True) def parse_process_step_config(self): self.process_method = {} user_provided_data_type = {} for feat_conf in self.featureDict: colm = feat_conf.get('feature', '') if not self.is_target_column(colm): if colm in self.data.columns: user_provided_data_type[colm] = feat_conf['type'] if user_provided_data_type: self.update_user_provided_type(user_provided_data_type) self.fill_default_steps() for feat_conf in self.featureDict: colm = feat_conf.get('feature', '') if not self.is_target_column(colm): if colm in self.data.columns: if feat_conf.get('fillMethod', None): self.fill_missing_value_method(colm, feat_conf['fillMethod'].lower()) if feat_conf.get('categoryEncoding', None): self.fill_encoder_value_method(colm, feat_conf['categoryEncoding'].lower()) if feat_conf.get('normalization', None): self.fill_normalizer_method(colm, feat_conf['normalization'].lower()) if feat_conf.get('outlier', None): self.fill_outlier_method(colm, feat_conf['outlier'].lower()) if feat_conf.get('outlierOperation', None): self.fill_outlier_process(colm, feat_conf['outlierOperation'].lower()) def get_tf_idf_dimensions(self): dim = cs.get_one_true_option(self.config.get('embeddingSize',{}).get('TF_IDF',{}), 'default') return {'default': 300, '50d':50, '100d':100, '200d':200, '300d':300}[dim] def get_tf_idf_output_size(self, pipe): start_index = {} for feat_expender in self.feature_expender: if feat_expender: step_name = list(feat_expender.keys())[0] index = list(feat_expender.values())[0] for transformer_step in pipe.transformer_list: if transformer_step[1].steps[-1][0] in step_name: start_index[index] = {transformer_step[1].steps[-1][0]: transformer_step[1].steps[-1][1].get_feature_names_out()} if start_index: for key,value in start_index.items(): for k,v in value.items(): if k == 'vectorizer': return len(v) return 0 def update_output_features_names(self, pipe): columns = self.output_columns start_index = {} index_shifter = 0 for feat_expender in self.feature_expender: if feat_expender: step_name = list(feat_expender.keys())[0] for key,value in start_index.items(): for k,v in value.items(): index_shifter += len(v) index = list(feat_expender.values())[0] for transformer_step in pipe.transformer_list: if transformer_step[1].steps[-1][0] in step_name: start_index[index + index_shifter] = {transformer_step[1].steps[-1][0]: transformer_step[1].steps[-1][1].get_feature_names_out()} #print(start_index) if start_index: for key,value in start_index.items(): for k,v in value.items(): if k == 'vectorizer': v = [f'{x}_vect' for x in v] self.output_columns[key:key] = v self.added_features = [*self.added_features, *v] def text_pipeline(self, conf_json): if self.text_feature: from text.textProfiler import textProfiler from text.textProfiler import textCombine pipeList = [] text_pipe = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", self.text_feature) ], remainder="drop")), ("text_fillNa",SimpleImputer(strategy='constant', fill_value='')), ("merge_text_feature", textCombine())]) obj = textProfiler() pipeList = obj.cleaner(conf_json, pipeList, self.data_path) pipeList = obj.embedding(conf_json, pipeList) last_step = "merge_text_feature" for pipe_elem in pipeList: text_pipe.steps.append((pipe_elem[0], pipe_elem[1])) last_step = pipe_elem[0] text_transformer = ('text_process', text_pipe) self.pipeline.append(text_transformer) self.feature_expender.append({last_step:len(self.output_columns)}) def create_pipeline(self): num_pipe = {} for k,v in self.num_fill_method_dict.items(): for k1,v1 in v.items(): if k1 and k1 != 'none': num_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_num_imputer(k)), (k1, self.get_num_scaler(k1)) ]) else: num_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_num_imputer(k)) ]) self.output_columns.extend(v1) cat_pipe = {} for k,v in self.cat_fill_method_dict.items(): for k1,v1 in v.items(): cat_pipe[f'{k}_{k1}'] = Pipeline([ ('selector', ColumnTransformer([ ("selector", "passthrough", v1) ], remainder="drop")), (k, self.get_cat_imputer(k)), (k1, self.get_cat_encoder(k1)) ]) if k1 not in ['onehotencoding']: self.output_columns.extend(v1) else: self.feature_expender.append({k1:len(self.output_columns)}) for key, pipe in num_pipe.items(): self.pipeline.append((key, pipe)) for key, pipe in cat_pipe.items(): self.pipeline.append((key, pipe)) "Drop: feature during training but replace with zero during prediction " def process_drop_fillna(self): drop_column = [] if 'numFill' in self.process_method.keys(): for col, method in self.process_method['numFill'].items(): if method == 'drop': self.process_method['numFill'][col] = 'zero' drop_column.append(col) if 'catFill' in self.process_method.keys(): for col, method in self.process_method['catFill'].items(): if method == 'drop': self.process_method['catFill'][col] = 'zero' drop_column.append(col) if drop_column: self.data.dropna(subset=drop_column, inplace=True) def update_num_fill_dict(self): self.num_fill_method_dict = {} if 'numFill' in self.process_method.keys(): for f in cs.supported_method['fillNa']['numeric']: self.num_fill_method_dict[f] = {} for en in cs.supported_method['normalization']: self.num_fill_method_dict[f][en] = [] for col in self.numeric_feature: numFillDict = self.process_method.get('numFill',{}) normalizationDict = self.process_method.get('normalization',{}) if f == numFillDict.get(col, '') and en == normalizationDict.get(col,''): self.num_fill_method_dict[f][en].append(col) if not self.num_fill_method_dict[f][en] : del self.num_fill_method_dict[f][en] if not self.num_fill_method_dict[f]: del self.num_fill_method_dict[f] def update_cat_fill_dict(self): self.cat_fill_method_dict = {} if 'catFill' in self.process_method.keys(): for f in cs.supported_method['fillNa']['categorical']: self.cat_fill_method_dict[f] = {} for en in cs.supported_method['categoryEncoding']: self.cat_fill_method_dict[f][en] = [] for col in self.cat_feature: catFillDict = self.process_method.get('catFill',{}) catEncoderDict = self.process_method.get('catEncoder',{}) if f == catFillDict.get(col, '') and en == catEncoderDict.get(col,''): self.cat_fill_method_dict[f][en].append(col) if not self.cat_fill_method_dict[f][en] : del self.cat_fill_method_dict[f][en] if not self.cat_fill_method_dict[f]: del self.cat_fill_method_dict[f] def __update_type(self): self.numeric_feature = list( set(self.data.select_dtypes(include='number').columns.tolist()) - set(self.keep_unprocessed)) self.cat_feature = list( set(self.data.select_dtypes(include='category').columns.tolist()) - set(self.keep_unprocessed)) self.text_feature = list( set(self.data.select_dtypes(include='object').columns.tolist()) - set(self.keep_unprocessed)) self.datetime_feature = list( set(self.data.select_dtypes(include='datetime').columns.tolist()) - set(self.keep_unprocessed)) def update_user_provided_type(self, data_types): allowed_types = ['numerical','categorical', 'text'] skipped_types = ['date','index'] type_mapping = {'numerical': np.dtype('float'), 'float': np.dtype('float'),'categorical': 'category', 'text':np.dtype('object'),'date':'datetime64[ns]','index': np.dtype('int64'),} mapped_type = {k:type_mapping[v] for k,v in data_types.items() if v in allowed_types} skipped_features = [k for k,v in data_types.items() if v in skipped_types] if skipped_features: self.keep_unprocessed.extend( skipped_features) self.keep_unprocessed = list(set(self.keep_unprocessed)) self.update_type(mapped_type, 'user provided data type') def get_type(self, as_list=False): if as_list: return [self.colm_type.values()] else: return self.colm_type def update_type(self, data_types={}, reason=''): invalid_features = [x for x in data_types.keys() if x not in self.data.columns] if invalid_features: valid_feat = list(set(data_types.keys()) - set(invalid_features)) valid_feat_type = {k:v for k,v in data_types if k in valid_feat} else: valid_feat_type = data_types for k,v in valid_feat_type.items(): if v != self.colm_type[k].name: try: self.data.astype({k:v}) self.colm_type.update({k:self.data[k].dtype}) self.type_conversion[k] = (self.colm_type[k] , v, 'Done', reason) except: self.type_conversion[k] = (self.colm_type[k] , v, 'Fail', reason) if v == np.dtype('float64') and self.colm_type[k].name == 'object': if self.check_numeric( k): self.data[ k] = pd.to_numeric(self.data[ k], errors='coerce') self.type_conversion[k] = (self.colm_type[k] , v, 'Done', reason) self.force_numeric_conv.append( k) else: raise ValueError(f"Can not convert '{k}' feature to 'numeric' as numeric values are less than {self.config['numericFeatureRatio'] * 100}%") self.data = self.data.astype(valid_feat_type) self.__update_type() def check_numeric(self, feature): col_values = self.data[feature].copy() col_values = pd.to_numeric(col_values, errors='coerce') if col_values.count() >= (self.config['numericFeatureRatio'] * len(col_values)): return True return False def string_to_numeric(self): def to_number(x): try: return w2n.word_to_num(x) except: return np.nan for col in self.text_feature: col_values = self.data[col].copy() col_values = pd.to_numeric(col_values, errors='coerce') if col_values.count() >= (self.config['numericFeatureRatio'] * len(col_values)): self.text_to_num[col] = 'float64' self.wordToNumericFeatures.append(col) if self.text_to_num: columns = list(self.text_to_num.keys()) self.data[columns] = self.data[columns].apply(lambda x: to_number(x), axis=1, result_type='broadcast') self.update_type(self.text_to_num) self.log.info('----------- Inspecting Features -----------') for col in self.text_feature: self.log.info(f'-------> Feature : {col}') if col in self.text_to_num: self.log.info('----------> Numeric Status :Yes') self.log.info('----------> Data Type Converting to numeric :Yes') else: self.log.info('----------> Numeric Status :No') self.log.info(f'\nStatus:- |... Feature inspection done for numeric data: {len(self.text_to_num)} feature(s) converted to numeric') self.log.info(f'\nStatus:- |... Feature word to numeric treatment done: {self.text_to_num}') self.log.info('----------- Inspecting Features End -----------') def check_categorical_features(self): num_data = self.data.select_dtypes(include='number') num_data_unique = num_data.nunique() num_to_cat_col = {} for i, value in enumerate(num_data_unique): if value < self.config['categoryMaxLabel']: num_to_cat_col[num_data_unique.index[i]] = 'category' if num_to_cat_col: self.update_type(num_to_cat_col, 'numerical to categorical') str_to_cat_col = {} str_data = self.data.select_dtypes(include='object') str_data_unique = str_data.nunique() for i, value in enumerate(str_data_unique): if value < self.config['categoryMaxLabel']: str_to_cat_col[str_data_unique.index[i]] = 'category' for colm in str_data.columns: if self.data[colm].str.len().max() < cs.default_config['str_to_cat_len_max']: str_to_cat_col[colm] = 'category' if str_to_cat_col: self.update_type(str_to_cat_col, 'text to categorical') def drop_features(self, features=[], reason='unspecified'): if isinstance(features, str): features = [features] feat_to_remove = [x for x in features if x in self.data.columns] if feat_to_remove: self.data.drop(feat_to_remove, axis=1, inplace=True) for feat in feat_to_remove: self.dropped_features[feat] = reason self.log_drop_feature(feat_to_remove, reason) self.__update_type() def __update_index(self, indices, reason=''): if isinstance(indices, (bool, pd.core.series.Series)) and len(indices) == len(self.data): if not indices.all(): self.data = self.data[indices] if self.is_target_available(): self.target = self.target[indices] self.log_update_index((indices == False).sum(), reason) def dropna(self): self.data.dropna(how='all',inplace=True) if self.is_target_available(): self.target = self.target[self.data.index] def drop_duplicate(self): index = self.data.duplicated(keep='first') self.__update_index( ~index, reason='duplicate') def log_drop_feature(self, columns, reason): self.log.info(f'---------- Dropping {reason} features ----------') self.log.info(f'\nStatus:- |... {reason} feature treatment done: {len(columns)} {reason} feature(s) found') self.log.info(f'-------> Drop Features: {columns}') self.log.info(f'Data Frame Shape After Dropping (Rows,Columns): {self.data.shape}') def log_update_index(self,count, reason): if count: if reason == 'target': self.log.info('-------> Null Target Rows Drop:') self.log.info(f'-------> Dropped rows count: {count}') elif reason == 'duplicate': self.log.info('-------> Duplicate Rows Drop:') self.log.info(f'-------> Dropped rows count: {count}') elif reason == 'outlier': self.log.info(f'-------> Dropped rows count: {count}') self.log.info('Status:- |... Outlier treatment done') self.log.info(f'-------> Data Frame Shape After Dropping samples(Rows,Columns): {self.data.shape}') def log_normalization(self): if self.process_method.get('normalization', None): self.log.info(f'\nStatus:- !... Normalization treatment done') for method in cs.supported_method['normalization']: cols = [] for col, m in self.process_method['normalization'].items(): if m == method: cols.append(col) if cols and method != 'none': self.log.info(f'Running {method} on features: {cols}') def log_numerical_fill(self): if self.process_method.get('numFill', None): self.log.info(f'\nStatus:- !... Fillna for numeric feature done') for method in cs.supported_method['fillNa']['numeric']: cols = [] for col, m in self.process_method['numFill'].items(): if m == method: cols.append(col) if cols: self.log.info(f'-------> Running {method} on features: {cols}') def log_categorical_fill(self): if self.process_method.get('catFill', None): self.log.info(f'\nStatus:- !... FillNa for categorical feature done') for method in cs.supported_method['fillNa']['categorical']: cols = [] for col, m in self.process_method['catFill'].items(): if m == method: cols.append(col) if cols: self.log.info(f'-------> Running {method} on features: {cols}') def remove_constant_feature(self): unique_values = self.data.nunique() constant_features = [] for i, value in enumerate(unique_values): if value == 1: constant_features.append(unique_values.index[i]) if constant_features: self.drop_features(constant_features, "constant") def remove_empty_feature(self, misval_ratio=1.0): missing_ratio = self.data.isnull().sum() / len(self.data) missing_ratio = {k:v for k,v in zip(self.data.columns, missing_ratio)} empty_features = [k for k,v in missing_ratio.items() if v > misval_ratio] if empty_features: self.drop_features(empty_features, "empty") def remove_index_features(self): index_feature = [] for feat in self.numeric_feature: if self.data[feat].nunique() == len(self.data): #if (self.data[feat].sum()- sum(self.data.index) == (self.data.iloc[0][feat]-self.data.index[0])*len(self.data)): # index feature can be time based count = (self.data[feat] - self.data[feat].shift() == 1).sum() if len(self.data) - count == 1: index_feature.append(feat) self.drop_features(index_feature, "index") def fill_missing_value_method(self, colm, method): if colm in self.numeric_feature: if method in cs.supported_method['fillNa']['numeric']: if 'numFill' not in self.process_method.keys(): self.process_method['numFill'] = {} if method == 'na' and self.process_method['numFill'].get(colm, None): pass # don't overwrite else: self.process_method['numFill'][colm] = method if colm in self.cat_feature: if method in cs.supported_method['fillNa']['categorical']: if 'catFill' not in self.process_method.keys(): self.process_method['catFill'] = {} if method == 'na' and self.process_method['catFill'].get(colm, None): pass else: self.process_method['catFill'][colm] = method def check_encoding_method(self, method, colm,default=False): if not self.is_target_available() and (method.lower() == list(cs.target_encoding_method_change.keys())[0]): method = cs.target_encoding_method_change[method.lower()] if default: self.log.info(f"Applying Label encoding instead of Target encoding on feature '{colm}' as target feature is not present") return method def fill_encoder_value_method(self,colm, method, default=False): if colm in self.cat_feature: if method.lower() in cs.supported_method['categoryEncoding']: if 'catEncoder' not in self.process_method.keys(): self.process_method['catEncoder'] = {} if method == 'na' and self.process_method['catEncoder'].get(colm, None): pass else: self.process_method['catEncoder'][colm] = self.check_encoding_method(method, colm,default) else: self.log.info(f"-------> categorical encoding method '{method}' is not supported. supported methods are {cs.supported_method['categoryEncoding']}") def fill_normalizer_method(self,colm, method): if colm in self.numeric_feature: if method in cs.supported_method['normalization']: if 'normalization' not in self.process_method.keys(): self.process_method['normalization'] = {} if (method == 'na' or method == 'none') and self.process_method['normalization'].get(colm, None): pass else: self.process_method['normalization'][colm] = method else: self.log.info(f"-------> Normalization method '{method}' is not supported. supported methods are {cs.supported_method['normalization']}") def apply_outlier(self): inlier_indice = np.array([True] * len(self.data)) if self.process_method.get('outlier', None): self.log.info('-------> Feature wise outlier detection:') for k,v in self.process_method['outlier'].items(): if k in self.numeric_feature: if v == 'iqr': index = cs.findiqrOutlier(self.data[k]) elif v == 'zscore': index = cs.findzscoreOutlier(self.data[k]) elif v == 'disable': index = None if k in self.process_method['outlierOperation'].keys(): if self.process_method['outlierOperation'][k] == 'dropdata': inlier_indice = np.logical_and(inlier_indice, index) elif self.process_method['outlierOperation'][k] == 'average': mean = self.data[k].mean() index = ~index self.data.loc[index,[k]] = mean self.log.info(f'-------> {k}: Replaced by Mean {mean}: total replacement {index.sum()}') elif self.process_method['outlierOperation'][k] == 'nochange' and v != 'disable': self.log.info(f'-------> Total outliers in "{k}": {(~index).sum()}') if self.config.get('outlierDetection',None): if self.config['outlierDetection'].get('IsolationForest','False') == 'True': if self.numeric_feature: index = cs.findiforestOutlier(self.data[self.numeric_feature]) inlier_indice = np.logical_and(inlier_indice, index) self.log.info(f'-------> Numeric feature based Outlier detection(IsolationForest):') if inlier_indice.sum() != len(self.data): self.__update_index(inlier_indice, 'outlier') def fill_outlier_method(self,colm, method): if colm in self.numeric_feature: if method in cs.supported_method['outlier_column_wise']: if 'outlier' not in self.process_method.keys(): self.process_method['outlier'] = {} if method not in ['Disable', 'na']: self.process_method['outlier'][colm] = method else: self.log.info(f"-------> outlier detection method '{method}' is not supported for column wise. supported methods are {cs.supported_method['outlier_column_wise']}") def fill_outlier_process(self,colm, method): if colm in self.numeric_feature: if method in cs.supported_method['outlierOperation']: if 'outlierOperation' not in self.process_method.keys(): self.process_method['outlierOperation'] = {} self.process_method['outlierOperation'][colm] = method else: self.log.info(f"-------> outlier process method '{method}' is not supported for column wise. supported methods are {cs.supported_method['outlierOperation']}") def get_cat_imputer(self,method): if method == 'mode': return SimpleImputer(strategy='most_frequent') elif method == 'zero': return SimpleImputer(strategy='constant', fill_value=0) def get_cat_encoder(self,method): if method == 'labelencoding': return OrdinalEncoder() elif method == 'onehotencoding': return OneHotEncoder(sparse=False,handle_unknown="ignore") elif method == 'targetencoding': if not self.is_target_available(): raise ValueError('Can not apply Target Encoding when target feature is not present') return TargetEncoder() def get_num_imputer(self,method): if method == 'mode': return SimpleImputer(strategy='most_frequent') elif method == 'mean': return SimpleImputer(strategy='mean') elif method == 'median': return SimpleImputer(strategy='median') elif method == 'knnimputer': return KNNImputer() elif method == 'zero': return SimpleImputer(strategy='constant', fill_value=0) def get_num_scaler(self,method): if method == 'minmax': return MinMaxScaler() elif method == 'standardscaler': return StandardScaler() elif method == 'lognormal': return PowerTransformer(method='yeo-johnson', standardize=False) def recommenderStartProfiler(self,modelFeatures): return cs.recommenderStartProfiler(self,modelFeatures) def folderPreprocessing(self,folderlocation,folderdetails,deployLocation): return cs.folderPreprocessing(self,folderlocation,folderdetails,deployLocation) def textSimilarityStartProfiler(self, doc_col_1, doc_col_2): return cs.textSimilarityStartProfiler(self, doc_col_1, doc_col_2) def get_conversion_method(self): return cs.get_one_true_option(self.config.get('textConversionMethod','')).lower() def set_features(features,profiler=None): return cs.set_features(features,profiler) import os import sys import numpy as np import scipy import pandas as pd from pathlib import Path default_config = { 'misValueRatio': '1.0', 'numericFeatureRatio': '1.0', 'categoryMaxLabel': '20', 'str_to_cat_len_max': 10 } target_encoding_method_change = {'targetencoding': 'labelencoding'} supported_method = { 'fillNa': { 'categorical' : ['mode','zero','na'], 'numeric' : ['median','mean','knnimputer','zero','drop','na'], }, 'categoryEncoding': ['labelencoding','targetencoding','onehotencoding','na','none'], 'normalization': ['standardscaler','minmax','lognormal', 'na','none'], 'outlier_column_wise': ['iqr','zscore', 'disable', 'na'], 'outlierOperation': ['dropdata', 'average', 'nochange'] } def findiqrOutlier(df): Q1 = df.quantile(0.25) Q3 = df.quantile(0.75) IQR = Q3 - Q1 index = ~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))) return index def findzscoreOutlier(df): z = np.abs(scipy.stats.zscore(df)) index = (z < 3) return index def findiforestOutlier(df): from sklearn.ensemble import IsolationForest isolation_forest = IsolationForest(n_estimators=100) isolation_forest.fit(df) y_pred_train = isolation_forest.predict(df) return y_pred_train == 1 def get_one_true_option(d, default_value=None): if isinstance(d, dict): for k,v in d.items(): if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True): return k return default_value def get_boolean(value): if (isinstance(value, str) and value.lower() == 'true') or (isinstance(value, bool) and value == True): return True else: return False def recommenderStartProfiler(self,modelFeatures): try: self.log.info('----------> FillNA:0') self.data = self.data.fillna(value=0) self.log.info('Status:- !... Missing value treatment done') self.log.info('----------> Remove Empty Row') self.data = self.data.dropna(axis=0,how='all') self.log.info('Status:- !... Empty feature treatment done') userId,itemId,rating = modelFeatures.split(',') self.data[itemId] = self.data[itemId].astype(np.int32) self.data[userId] = self.data[userId].astype(np.int32) self.data[rating] = self.data[rating].astype(np.float32) return self.data except Exception as inst: self.log.info("Error: dataProfiler failed "+str(inst)) return(self.data) def folderPreprocessing(self,folderlocation,folderdetails,deployLocation): try: dataset_directory = Path(folderlocation) dataset_csv_file = dataset_directory/folderdetails['label_csv_file_name'] tfrecord_directory = Path(deployLocation)/'Video_TFRecord' from savp import PreprocessSAVP import csv csvfile = open(dataset_csv_file, newline='') csv_reader = csv.DictReader(csvfile) PreprocessSAVP(dataset_directory,csv_reader,tfrecord_directory) dataColumns = list(self.data.columns) VideoProcessing = True return dataColumns,VideoProcessing,tfrecord_directory except Exception as inst: self.log.info("Error: dataProfiler failed "+str(inst)) def textSimilarityStartProfiler(self, doc_col_1, doc_col_2): import os try: features = [doc_col_1, doc_col_2] pipe = None dataColumns = list(self.data.columns) self.numofCols = self.data.shape[1] self.numOfRows = self.data.shape[0] from transformations.textProfiler import textProfiler self.log.info('-------> Execute Fill NA With Empty String') self.data = self.data.fillna(value=" ") self.log.info('Status:- |... Missing value treatment done') self.data[doc_col_1] = textProfiler().textCleaning(self.data[doc_col_1]) self.data[doc_col_2] = textProfiler().textCleaning(self.data[doc_col_2]) self.log.info('-------> Concatenate: ' + doc_col_1 + ' ' + doc_col_2) self.data['text'] = self.data[[doc_col_1, doc_col_2]].apply(lambda row: ' '.join(row.values.astype(str)), axis=1) from tensorflow.keras.preprocessing.text import Tokenizer pipe = Tokenizer() pipe.fit_on_texts(self.data['text'].values) self.log.info('-------> Tokenizer: Fit on Concatenate Field') self.log.info('Status:- |... Tokenizer the text') self.data[doc_col_1] = self.data[doc_col_1].astype(str) self.data[doc_col_1] = self.data[doc_col_1].astype(str) return (self.data, pipe, self.target_name, features) except Exception as inst: self.log.info("StartProfiler failed " + str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno)) def set_features(features,profiler=None): if profiler: features = [x for x in features if x not in profiler.added_features] return features + profiler.text_feature return features ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import pandas as pd import sys import os import warnings import logging from pathlib import Path import random from sklearn.model_selection import train_test_split import operator import re import pdfplumber class dataReader(): def __init__(self): self.dataDf =None self.log = logging.getLogger('eion') def readCsv(self,dataPath,featureList,targetColumn): data=pd.read_csv(dataPath) dataDf=data[featureList] predictDf=data[targetColumn] return dataDf,predictDf def rowsfilter(self,filters,dataframe): self.log.info('\n-------> No of rows before filtering: '+str(dataframe.shape[0])) #task-13479 filterexpression='' firstexpressiondone = False for x in filters: if firstexpressiondone: filterexpression += ' ' if x['combineOperator'].lower() == 'and': filterexpression += '&' elif x['combineOperator'].lower() == 'or': filterexpression += '|' filterexpression += ' ' firstexpressiondone = True filterexpression += x['feature'] filterexpression += ' ' if x['condition'].lower() == 'equals': filterexpression += '==' elif x['condition'].lower() == 'notequals': filterexpression += '!=' elif x['condition'].lower() == 'lessthan': filterexpression += '<' elif x['condition'].lower() == 'lessthanequalto': filterexpression += '<=' elif x['condition'].lower() == 'greaterthan': filterexpression += '>' elif x['condition'].lower() == 'greaterthanequalto': filterexpression += '>=' filterexpression += ' ' if dataframe[x['feature']].dtype in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']: filterexpression += x['value'] else: filterexpression += '\''+x['value']+'\'' dataframe = dataframe.query(filterexpression) self.log.info('-------> Row filter: '+str(filterexpression)) #task-13479 self.log.info('-------> No of rows after filtering: '+str(dataframe.shape[0])) return dataframe,filterexpression def grouping(self,grouper,dataframe): grouperbyjson= {} groupbyfeatures = grouper['groupby'] dataframe = dataframe.reset_index() features = dataframe.columns.tolist() aggjson = {} for feature, featureType in zip(features,dataframe.dtypes): if feature == groupbyfeatures or feature == 'index': continue if dataframe[feature].empty == True: continue if dataframe[feature].isnull().all() == True: continue if featureType in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']: temp = {} temp[feature+'_size'] = 'size' temp[feature+'_sum'] = 'sum' temp[feature+'_max'] = 'max' temp[feature+'_min'] = 'min' temp[feature+'_mean'] = 'mean' aggjson[feature] = temp else: temp = {} temp[feature+'_size'] = 'size' temp[feature+'_unique'] = 'nunique' aggjson[feature] = temp groupbystring = 'groupby([\''+groupbyfeatures+'\']).agg('+str(aggjson)+')' grouperbyjson['groupbystring'] = groupbystring dataframe = dataframe.groupby([groupbyfeatures]).agg(aggjson) dataframe.columns = dataframe.columns.droplevel(0) dataframe = dataframe.reset_index() ''' if operation.lower() == 'size': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).size() elif operation.lower() == 'mean': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).mean() elif operation.lower() == 'max': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).max() elif operation.lower() == 'min': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).min() dataframe = dataframe.rename("groupby_value") dataframe = dataframe.to_frame() dataframe = dataframe.reset_index() ''' return dataframe,grouperbyjson def timeGrouping(self,timegrouper,dataframe): grouperbyjson= {} dateTime = timegrouper['dateTime'] frequency = timegrouper['freq'] groupbyfeatures = timegrouper['groupby'] grouperbyjson['datetime'] = dateTime if dataframe[dateTime].dtypes in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']: dtlenth = dataframe[dateTime].iloc[0] dtlenth = np.int64(dtlenth) dtlenth = len(str(dtlenth)) if dtlenth == 13: dataframe['date'] = pd.to_datetime(dataframe[dateTime],unit='ms') grouperbyjson['unit'] = 'ms' elif dtlenth == 10: dataframe['date'] = pd.to_datetime(dataframe[dateTime],unit='s') grouperbyjson['unit'] = 's' else: dataframe['date'] = pd.to_datetime(dataframe[dateTime]) grouperbyjson['unit'] = '' else: dataframe['date'] = pd.to_datetime(dataframe[dateTime]) grouperbyjson['unit'] = '' dataframe = dataframe.reset_index() dataframe.set_index('date',inplace=True) features = dataframe.columns.tolist() aggjson = {} for feature, featureType in zip(features,dataframe.dtypes): if feature == groupbyfeatures or feature == dateTime or feature == 'index': continue if dataframe[feature].empty == True: continue if dataframe[feature].isnull().all() == True: continue if featureType in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']: temp = {'size','sum','max','min','mean'} aggjson[feature] = temp else: temp = {'size','nunique'} aggjson[feature] = temp if groupbyfeatures == '': groupbystring = 'groupby([pd.Grouper(freq=\''+frequency+'\')]).agg('+str(aggjson)+')' else: groupbystring = 'groupby([pd.Grouper(freq=\''+frequency+'\'),\''+groupbyfeatures+'\']).agg('+str(aggjson)+')' grouperbyjson['groupbystring'] = groupbystring print(grouperbyjson) if groupbyfeatures == '': dataframe = dataframe.groupby([pd.Grouper(freq=frequency)]).agg(aggjson) else: dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).agg(aggjson) dataframe.columns = ['_'.join(col) for col in dataframe.columns] dataframe = dataframe.reset_index() self.log.info(dataframe.head(10)) ''' if operation.lower() == 'size': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).size() elif operation.lower() == 'mean': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).mean() elif operation.lower() == 'max': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).max() elif operation.lower() == 'min': dataframe = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).min() dataframe = dataframe.rename("groupby_value") dataframe = dataframe.to_frame() dataframe = dataframe.reset_index() ''' return dataframe,grouperbyjson def readDf(self,dataF,featureList,targetColumn): dataDf = dataF[featureList] predictDf =dataF[targetColumn] return dataDf,predictDf def csvTodf(self,dataPath,delimiter,textqualifier): ''' if os.path.splitext(dataPath)[1] == ".tsv": dataFrame=pd.read_csv(dataPath,encoding='latin1',sep='\t') else: dataFrame=pd.read_csv(dataPath,encoding='latin1') ''' if os.path.splitext(dataPath)[1] == ".py": f = open(dataPath, "r") pythoncode = f.read() f.close() ldict = {} exec(pythoncode, globals(), ldict) dataFrame = ldict['dfpy'] else: dataFrame=pd.read_csv(dataPath,encoding='utf-8',sep=delimiter,quotechar=textqualifier, skipinitialspace = True,na_values=['-','?'],encoding_errors= 'replace') dataFrame.rename(columns=lambda x: x.strip(), inplace=True) return dataFrame def read_file(self, fileName): fileName = Path(fileName) if fileName.suffix == '.pdf': pdf = pdfplumber.open(fileName) text = '' for index, page in enumerate(pdf.pages): if index: text += ' ' text += page.extract_text() else: with open(fileName, "r",encoding="utf-8") as f: text = f.read() return text def documentsTodf(self,folderlocation,labelFilePath): dataDf = pd.DataFrame() error_message = "" dataset_csv_file = os.path.join(folderlocation,labelFilePath) labels = pd.read_csv(dataset_csv_file) dataDict = {} keys = ["File","Label"] for key in keys: dataDict[key] = [] for i in range(len(labels)): filename = os.path.join(folderlocation,labels.loc[i,"File"]) dataDict["File"].append(self.read_file(filename)) dataDict["Label"].append(labels.loc[i,"Label"]) dataDf = pd.DataFrame.from_dict(dataDict) error_message = "" return dataDf, error_message def removeFeatures(self,df,datetimeFeature,indexFeature,modelFeatures,targetFeature): self.log.info("\n---------- Prepare Features ----------") if(str(datetimeFeature).lower() != 'na'): datetimeFeature = datetimeFeature.split(",") datetimeFeature = list(map(str.strip, datetimeFeature)) for dtfeature in datetimeFeature: if dtfeature in df.columns: self.log.info("-------> Remove Date Time Feature: "+dtfeature) df = df.drop(columns=dtfeature) if(str(indexFeature).lower() != 'na'): indexFeature = indexFeature.split(",") indexFeature = list(map(str.strip, indexFeature)) for ifeature in indexFeature: if ifeature in df.columns: self.log.info("-------> Remove Index Feature: "+ifeature) df = df.drop(columns=ifeature) if(str(modelFeatures).lower() != 'na'): self.log.info("-------> Model Features: "+str(modelFeatures)) modelFeatures = modelFeatures.split(",") modelFeatures = list(map(str.strip, modelFeatures)) if(targetFeature != '' and str(targetFeature).lower() != 'na'): targetFeature = targetFeature.split(",") targetFeature = list(map(str.strip, targetFeature)) for ifeature in targetFeature: if ifeature not in modelFeatures: modelFeatures.append(ifeature) if(str(indexFeature).lower() != 'na'): for ifeature in indexFeature: if ifeature in modelFeatures: modelFeatures.remove(ifeature) if(str(datetimeFeature).lower() != 'na'): for dtfeature in datetimeFeature: if dtfeature in modelFeatures: modelFeatures.remove(dtfeature) df = df[modelFeatures] self.log.info("---------- Prepare Features End ----------") return(df) def splitImageDataset(self, df, ratio, modelType): if modelType.lower() == "objectdetection": images = df['File'].unique().tolist() trainImages = random.sample(images, int(len(images) * ratio)) mask = [0] * len(df) for i in range(len(df)): mask[i] = df.iloc[i]['File'] in trainImages trainDf = df.iloc[mask] testDf = df.iloc[[not elem for elem in mask]] return trainDf, testDf else: return train_test_split(df, test_size=(1 - ratio)) def createTFRecord(self, train_image_dir, output_dir, csv_file, testPercentage, AugEnabled,keepAugImages,operations, modelType,augConf={}): from transformations import generate_tfrecord from transformations.imageAug import ImageAugmentation if isinstance(csv_file, pd.DataFrame): df = csv_file else: df = pd.read_csv(os.path.join(train_image_dir,csv_file)) labelmap_path, num_classes = generate_tfrecord.createLabelFile(df, output_dir) train_df, test_df = self.splitImageDataset(df, testPercentage/100.0, modelType) if AugEnabled: augFile = os.path.join(output_dir,"tempTrainDf.csv") train_df.to_csv(augFile) ia = ImageAugmentation(train_image_dir, augFile) augFile = ia.augment(modelType, operations,None,augConf) train_df = pd.read_csv(augFile) generate_tfrecord.generate_TF_record(train_image_dir, output_dir, train_df, test_df, labelmap_path) if AugEnabled and not keepAugImages: ia.removeAugmentedImages(train_df) return train_df, num_classes ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import sys from pathlib import Path import urllib.request import tarfile import json import subprocess import os from os.path import expanduser import platform class ODpretrainedModels(): def __init__(self, location=None): if location: if isinstance(location, Path): self.pretrained_models_location = location.as_posix() else: self.pretrained_models_location = location else: p = subprocess.run([sys.executable, "-m", "pip","show","AION"],capture_output=True, text=True) if p.returncode == 0: Output = p.stdout.split('\n') for x in Output: y = x.split(':',1) if(y[0]=='Location'): self.pretrained_models_location = y[1].strip()+"/AION/pretrained_models/object_detection" break if Path(self.pretrained_models_location).is_dir(): self.config_file_location = self.pretrained_models_location+'/supported_models.json' with open(self.config_file_location) as json_data: self.supportedModels = json.load(json_data) home = expanduser("~") if platform.system() == 'Windows': self.modelsPath = os.path.join(home,'AppData','Local','HCLT','AION','PreTrainedModels','ObjectDetection') else: self.modelsPath = os.path.join(home,'HCLT','AION','PreTrainedModels','ObjectDetection') if os.path.isdir(self.modelsPath) == False: os.makedirs(self.modelsPath) def __save_config(self): with open(self.config_file_location, 'w') as json_file: json.dump(self.supportedModels, json_file) def __download(self, modelName): try: url = self.supportedModels[modelName]["url"] file = self.supportedModels[modelName]["file"] local_file_path = Path(self.modelsPath)/(file+".tar.gz") urllib.request.urlretrieve(url, local_file_path) except: raise ValueError("{} model download error, check your internet connection".format(modelName)) return local_file_path def __extract(self, modelName, file_location, extract_dir): try: tarFile = tarfile.open(file_location) tarFile.extractall(extract_dir) tarFile.close() Path.unlink(file_location) return True except: return False def download(self, modelName): if modelName in list(self.supportedModels.keys()): p = Path(self.modelsPath).glob('**/*') modelsDownloaded = [x.name for x in p if x.is_dir()] if self.supportedModels[modelName]['file'] not in modelsDownloaded: file = self.__download(modelName) self.supportedModels[modelName]["downloaded"] = True if self.__extract(modelName, file, self.modelsPath): self.supportedModels[modelName]["extracted"] = True self.__save_config() else: self.__save_config() raise ValueError("{} model downloaded but extraction failed,please try again".format(modelName)) else: raise ValueError("{} is not supported for object detection".format(modelName)) return self.supportedModels[modelName] def get_info(self,modeltype): models_info = {} p = Path(self.pretrained_models_location) downloaded_models = [x.name for x in p.iterdir() if x.is_dir()] for model in list(self.supportedModels.keys()): if (self.supportedModels[model]['type'] == modeltype) or (modeltype == ''): models_info[model] = self.supportedModels[model]['extracted'] return models_info def is_model_exist(self, model_name): models = self.get_info('') status = "NOT_SUPPORTED" if model_name in models: if self.supportedModels[model_name]['extracted']: status = "READY" else: status = "NOT_READY" return status def clear_config(self, model_name): self.supportedModels[model_name]['extracted'] = False self.supportedModels[model_name]['downloaded'] = False self.__save_config() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json from pathlib import Path def get_metrics(request): output = {} output_path = Path(request.session['deploypath'])/"etc"/"output.json" if not output_path.exists(): raise ValueError('output json path does not exist, something unexpected happen') with open(output_path) as file: config = json.load(file) output['problem_type'] = config.get('data',{}).get('ModelType') output['best_model'] = config.get('data',{}).get('BestModel') output['hyper_params'] = config.get('data',{}).get('params') output['best_score'] = str(round(float(config.get('data',{}).get('BestScore')), 2)) output['scoring_method'] = config.get('data',{}).get('ScoreType') if output['problem_type'] == 'classification': output['mcc_score'] = str(round(float(config.get('data',{}).get('matrix',{}).get('MCC_SCORE', 0.0)), 2)) else: output['mcc_score'] = 'NA' return output import json import os def get_brier_score(request): try: displaypath = os.path.join(request.session['deploypath'], "etc", "output.json") with open(displaypath) as file: config = json.load(file) problem_type = config["data"]["ModelType"] brier_score = config["data"]["matrix"]["BRIER_SCORE"] print(problem_type,brier_score) except Exception as e: #print(str(e)) raise ValueError(str(e)) return problem_type, brier_score ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import numpy as np from appbe.eda import ux_eda from sklearn.preprocessing import LabelEncoder import json import matplotlib.pyplot as plt import os import mpld3 import subprocess import os import sys import re import json import pandas as pd from appbe.eda import ux_eda from aif360.datasets import StandardDataset from aif360.metrics import ClassificationMetric from aif360.datasets import BinaryLabelDataset def get_metrics(request): dataFile = os.path.join(request.session['deploypath'], "data", "preprocesseddata.csv.gz") predictionScriptPath = os.path.join(request.session['deploypath'], 'aion_predict.py') displaypath = os.path.join(request.session['deploypath'], "etc", "display.json") f = open(displaypath, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) Target_feature = configSettings['targetFeature'] outputStr = subprocess.check_output([sys.executable, predictionScriptPath, dataFile]) outputStr = outputStr.decode('utf-8') outputStr = re.search(r'predictions:(.*)', str(outputStr), re.IGNORECASE).group(1) outputStr = outputStr.strip() predict_dict = json.loads(outputStr) df = pd.read_csv(dataFile) df_p = pd.DataFrame.from_dict(predict_dict['data']) d3_url = request.GET.get('d3_url') mpld3_url = request.GET.get('mpld3_url') df_temp = request.GET.get('feature') global metricvalue metricvalue = request.GET.get('metricvalue') Protected_feature = df_temp df_p = df_p.drop(columns=[Target_feature, 'remarks', 'probability']) df_p.rename(columns={'prediction': Target_feature}, inplace=True) eda_obj = ux_eda(dataFile, optimize=1) features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catfeatures = eda_obj.getFeatures() features_to_Encode = features categorical_names = {} encoders = {} for feature in features_to_Encode: le = LabelEncoder() le.fit(df[feature]) df[feature] = le.transform(df[feature]) le.fit(df_p[feature]) df_p[feature] = le.transform(df_p[feature]) categorical_names[feature] = le.classes_ encoders[feature] = le new_list = [item for item in categorical_names[Protected_feature] if not(pd.isnull(item)) == True] claas_size = len(new_list) if claas_size > 10: return 'HeavyFeature' metrics = fair_metrics(categorical_names, Protected_feature,Target_feature, claas_size, df, df_p) figure = plot_fair_metrics(metrics) html_graph = mpld3.fig_to_html(figure,d3_url=d3_url,mpld3_url=mpld3_url) return html_graph def fair_metrics(categorical_names, Protected_feature,Target_feature, claas_size, df, df_p): cols = [metricvalue] obj_fairness = [[0]] fair_metrics = pd.DataFrame(data=obj_fairness, index=['objective'], columns=cols) for indx in range(claas_size): priv_group = categorical_names[Protected_feature][indx] privileged_class = np.where(categorical_names[Protected_feature] == priv_group)[0] data_orig = StandardDataset(df, label_name=Target_feature, favorable_classes=[1], protected_attribute_names=[Protected_feature], privileged_classes=[privileged_class]) attr = data_orig.protected_attribute_names[0] idx = data_orig.protected_attribute_names.index(attr) privileged_groups = [{attr:data_orig.privileged_protected_attributes[idx][0]}] unprivileged_size = data_orig.unprivileged_protected_attributes[0].size unprivileged_groups = [] for idx2 in range(unprivileged_size): unprivileged_groups.extend([{attr:data_orig.unprivileged_protected_attributes[idx][idx2]}]) bld = BinaryLabelDataset(df=df, label_names=[Target_feature], protected_attribute_names=[Protected_feature]) bld_p = BinaryLabelDataset(df=df_p, label_names=[Target_feature], protected_attribute_names=[Protected_feature]) ClsMet = ClassificationMetric(bld, bld_p,unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) if metricvalue == "Theil Index": row = pd.DataFrame([[ClsMet.theil_index()]], columns = cols , index = [priv_group]) elif metricvalue == "Equal Opportunity Difference": row = pd.DataFrame([[ClsMet.equal_opportunity_difference()]], columns = cols , index = [priv_group]) elif metricvalue == "Disparate Impact": row = pd.DataFrame([[ClsMet.disparate_impact()]], columns = cols , index = [priv_group]) elif metricvalue == "Statistical Parity Difference": row = pd.DataFrame([[ClsMet.statistical_parity_difference()]], columns = cols , index = [priv_group]) #fair_metrics = fair_metrics.append(row) fair_metrics = pd.concat([fair_metrics,row]) return fair_metrics def plot_fair_metrics(fair_metrics): import matplotlib.patches as patches plt.style.use('default') import seaborn as sns fig, ax = plt.subplots(figsize=(10,4), ncols=1, nrows=1) plt.subplots_adjust( left = 0.125, bottom = 0.1, right = 0.9, top = 0.9, wspace = .5, hspace = 1.1 ) y_title_margin = 1.2 plt.suptitle("Fairness metrics", y = 1.09, fontsize=20) sns.set(style="dark") cols = fair_metrics.columns.values obj = fair_metrics.loc['objective'] if metricvalue == "Theil Index": size_rect = [0.5] rect = [-0.1] bottom = [-0.1] top = [2] bound = [[-0.1,0.1]] elif metricvalue == "Equal Opportunity Difference": size_rect = [0.2] rect = [-0.1] bottom = [-1] top = [1] bound = [[-0.1,0.1]] elif metricvalue == "Disparate Impact": size_rect = [0.4] rect = [0.8] bottom = [0] top = [2] bound = [[-0.1,0.1]] elif metricvalue == "Statistical Parity Difference": size_rect = [0.2] rect = [-0.1] bottom = [-1] top = [1] bound = [[-0.1,0.1]] for attr in fair_metrics.index[1:len(fair_metrics)].values: check = [bound[i][0] < fair_metrics.loc[attr][i] < bound[i][1] for i in range(0,1)] for i in range(0,1): plt.subplot(1, 1, i+1) xx = fair_metrics.index[1:len(fair_metrics)].values.tolist() yy = fair_metrics.iloc[1:len(fair_metrics)][cols[i]].values.tolist() palette = sns.color_palette('husl', len(xx)) ax = sns.pointplot(x=fair_metrics.index[1:len(fair_metrics)], y=yy, palette=palette, hue=xx) index = 0 for p in zip(ax.get_xticks(), yy): if (p[1] > 2.0): _color = palette.as_hex()[index] _val = 'Outlier(' + str(round(p[1],3)) + ')' ax.text(p[0]-0.5, 0.02, _val, color=_color) else: ax.text(p[0], p[1]+0.05, round(p[1],3), color='k') index = index + 1 plt.ylim(bottom[i], top[i]) plt.setp(ax.patches, linewidth=0) ax.get_xaxis().set_visible(False) ax.legend(loc='right', bbox_to_anchor=(1, 0.8), ncol=1) ax.add_patch(patches.Rectangle((-5,rect[i]), 10, size_rect[i], alpha=0.3, facecolor="green", linewidth=1, linestyle='solid')) # plt.axhline(obj[i], color='black', alpha=0.3) plt.title(cols[i], fontname="Times New Roman", size=20,fontweight="bold") ax.set_ylabel('') ax.set_xlabel('') return fig import base64 import io import json import os import urllib import joblib import numpy as np import pandas as pd from SALib.analyze import sobol class sensitivityAnalysis(): def __init__(self, model, problemType, data, target, featureName): self.model = model self.probemType = problemType self.data = data self.target = target self.featureName = featureName self.paramvales = [] self.X = [] self.Y = [] self.problem = {} def preprocess(self): self.X = self.data[self.featureName].values self.Y = self.data[self.target].values bounds = [[np.min(self.X[:, i]), np.max(self.X[:, i])] for i in range(self.X.shape[1])] self.problem = { 'num_vars': self.X.shape[1], 'names': self.featureName, 'bounds': bounds } def generate_samples(self,size): from SALib.sample import sobol self.param_values = sobol.sample(self.problem, size) def calSiClass(self, satype,isML,isDL): try: D = self.problem['num_vars'] S = np.zeros(self.X.shape[1]) for class_label in np.unique(self.Y): if isML: y_pred_poba = self.model.predict_proba(self.param_values)[:, class_label] if isDL: y_pred_poba = self.model.predict(self.param_values)[:,class_label] if not y_pred_poba.size % (2 * D + 2) == 0: lim = y_pred_poba.size - y_pred_poba.size % (2 * D + 2) y_pred_poba = y_pred_poba[:lim] Si = sobol.analyze(self.problem, y_pred_poba) if satype.lower() == 'first': S += Si['S1'] else: S += Si['ST'] S /= len(np.unique(self.Y)) return S except Exception as e: print('Error in calculating Si for Classification: ', str(e)) raise ValueError(str(e)) def calSiReg(self, satype,isML,isDL): try: D = self.problem['num_vars'] Y = np.array([self.model.predict(X_sample.reshape(1, -1)) for X_sample in self.param_values]) Y = Y.reshape(-1) if not Y.size % (2 * D + 2) == 0: lim = Y.size - Y.size % (2 * D + 2) Y = Y[:lim] Si = sobol.analyze(self.problem, Y) if satype.lower() == 'first': S = Si['S1'] else: S = Si['ST'] return S except Exception as e: print('Error in calculating Si for Regression: ', str(e)) raise ValueError(str(e)) def plotSi(self, S, saType): try: import matplotlib.pyplot as plt if saType.lower() == 'first': title, label = 'Sensitivity Analysis', 'First order' else: title, label = 'Sensitivity Analysis', 'Total order' x = np.arange(len(self.problem['names'])) width = 0.35 fig, ax = plt.subplots() ax.bar(x - width / 2, S, width, label=label) ax.set_xticks(x) ax.set_xlabel('Features') ax.set_ylabel('Sensitivity Indices') ax.set_title(title) ax.set_xticklabels(self.problem['names'], rotation=45, ha="right") ax.legend() plt.tight_layout() image = io.BytesIO() plt.savefig(image, format='png') image.seek(0) string = base64.b64encode(image.read()) SAimage = 'data:image/png;base64,' + urllib.parse.quote(string) except Exception as e: print(e) SAimage = '' return SAimage def checkModelType(modelName): isML= False isDL = False if modelName in ["Neural Network", "Convolutional Neural Network (1D)", "Recurrent Neural Network","Recurrent Neural Network (GRU)", "Recurrent Neural Network (LSTM)", "Neural Architecture Search", "Deep Q Network", "Dueling Deep Q Network"]: isDL = True elif modelName in ["Linear Regression","Lasso","Ridge","Logistic Regression", "Naive Bayes", "Decision Tree", "Random Forest", "Support Vector Machine", "K Nearest Neighbors", "Gradient Boosting", "Extreme Gradient Boosting (XGBoost)", "Light Gradient Boosting (LightGBM)", "Categorical Boosting (CatBoost)","Bagging (Ensemble)"]: isML = True return isML,isDL def startSA(request): try: displaypath = os.path.join(request.session['deploypath'], "etc", "display.json") if not os.path.exists(displaypath): raise Exception('Config file not found.') with open(displaypath) as file: config = json.load(file) probelmType = config['problemType'] if probelmType.lower() not in ['classification','regression']: raise Exception(f"Probolem Type: {probelmType} not supported") isML,isDL = checkModelType(config['modelname']) sample_size = 1024 if isML: model = joblib.load(os.path.join(request.session['deploypath'], 'model', config['saved_model'])) sample_size = 2048 if isDL: from tensorflow.keras.models import load_model model = load_model(os.path.join(request.session['deploypath'], 'model', config['saved_model'])) sample_size = 512 target = config['targetFeature'] featureName = config['modelFeatures'] dataPath = os.path.join(request.session['deploypath'], 'data', 'postprocesseddata.csv.gz') if not os.path.exists(dataPath): raise Exception('Data file not found.') from utils.file_ops import read_df_compressed read_status,dataFrame = read_df_compressed(dataPath) obj = sensitivityAnalysis(model, probelmType, dataFrame, target, featureName) obj.preprocess() obj.generate_samples(sample_size) submitType = str(request.GET.get('satype')) saType = 'first' if submitType == 'first' else 'total' if probelmType.lower() == 'classification': SA_values = obj.calSiClass(saType,isML,isDL) else: SA_values = obj.calSiReg(saType,isML,isDL) if SA_values.size and saType: graph = obj.plotSi(SA_values, saType) if graph: outputJson = {'Status': "Success", "graph": graph} else: outputJson = {'Status': "Error", "graph": '','reason':'Error in Plotting Graph'} else: outputJson = {'Status': "Error", "graph": '','reason':'Error in calculating Si values'} output_json = json.dumps(outputJson) return output_json except Exception as e: print(str(e)) raise ValueError(str(e)) import numpy as np import joblib import pandas as pd from appbe.eda import ux_eda from sklearn.preprocessing import MinMaxScaler, LabelEncoder # from pathlib import Path import configparser import json import matplotlib.pyplot as plt import numpy as np import os def trustedai_uq(request): try: displaypath = os.path.join(request.session['deploypath'], "etc", "display.json") f = open(displaypath, "r") configSettings = f.read() f.close() configSettings = json.loads(configSettings) TargetFeature = configSettings['targetFeature'] problemType = configSettings['problemType'] raw_data_loc = configSettings['preprocessedData'] dataLocation = configSettings['postprocessedData'] selectedfeatures = request.GET.get('values') if problemType.lower() == "classification": model = (os.path.join(request.session['deploypath'], 'model', configSettings['saved_model'])) df = pd.read_csv(dataLocation) trainfea = df.columns.tolist() feature = json.loads(selectedfeatures) # feature = ",".join(featurs) # features = ['PetalLengthCm','PetalWidthCm'] targ = TargetFeature tar =[targ] from bin.aion_uncertainties import aion_uq outputStr = aion_uq(model,dataLocation,feature,tar) return outputStr if problemType.lower() == "regression": model = (os.path.join(request.session['deploypath'], 'model', configSettings['saved_model'])) df = pd.read_csv(dataLocation) trainfea = df.columns.tolist() feature = json.loads(selectedfeatures) # feature = ",".join(featurs) # features = ['PetalLengthCm','PetalWidthCm'] targ = TargetFeature tar =[targ] from bin.aion_uncertainties import aion_uq outputStr = aion_uq(model,dataLocation,feature,tar) print(outputStr) return outputStr except Exception as e: print('error',e) return e ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import warnings import numpy as np import pandas as pd import sklearn.metrics as metrics from collections import defaultdict from sklearn.metrics import confusion_matrix import re import shutil import scipy.stats as st import json import os,sys import glob import logging from utils.file_ops import read_df_compressed class Visualization(): def __init__(self,usecasename,version,dataframe,visualizationJson,dateTimeColumn,deployPath,dataFolderLocation,numericContinuousFeatures,discreteFeatures,categoricalFeatures,modelFeatures,targetFeature,modeltype,original_data_file,profiled_data_file,trained_data_file,predicted_data_file,labelMaps,vectorizerFeatures,textFeatures,numericalFeatures,nonNumericFeatures,emptyFeatures,nrows,ncols,saved_model,scoreParam,learner_type,modelname,featureReduction,reduction_data_file): self.dataframe = dataframe self.displayjson = {} self.visualizationJson = visualizationJson self.dateTimeColumn = dateTimeColumn self.deployPath = deployPath #shutil.copy2(os.path.join(os.path.dirname(os.path.abspath(__file__)),'aion_portal.py'),self.deployPath) if learner_type == 'ML' and modelname != 'Neural Architecture Search': if(os.path.isfile(os.path.join(self.deployPath,'explainable_ai.py'))): os.remove(os.path.join(self.deployPath,'explainable_ai.py')) shutil.copy2(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','utilities','xai','explainable_ai.py'),self.deployPath) # os.rename(os.path.join(self.deployPath,'explainable_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) try: os.rename(os.path.join(self.deployPath,'explainable_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) except FileExistsError: os.remove(os.path.join(self.deployPath,'aion_xai.py')) os.rename(os.path.join(self.deployPath,'explainable_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) elif learner_type == 'DL' or modelname == 'Neural Architecture Search': if(os.path.isfile(os.path.join(self.deployPath,'explainable_ai.py'))): os.remove(os.path.join(self.deployPath,'explainable_ai.py')) shutil.copy2(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','utilities','xai','explainabledl_ai.py'),self.deployPath) # os.rename(os.path.join(self.deployPath,'explainabledl_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) try: os.rename(os.path.join(self.deployPath,'explainabledl_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) except FileExistsError: os.remove(os.path.join(self.deployPath,'aion_xai.py')) os.rename(os.path.join(self.deployPath,'explainabledl_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) self.jsondeployPath = deployPath #self.deployPath = self.deployPath+'visualization/' self.dataFolderLocation = dataFolderLocation self.vectorizerFeatures = vectorizerFeatures self.textFeatures = textFeatures self.emptyFeatures = emptyFeatures ''' try: os.makedirs(self.deployPath) except OSError as e: print("\nFolder Already Exists") ''' self.numericContinuousFeatures = numericContinuousFeatures self.discreteFeatures = discreteFeatures self.categoricalFeatures = categoricalFeatures self.modelFeatures = modelFeatures self.modeltype = modeltype self.targetFeature = targetFeature self.displayjson['usecasename'] = str(usecasename) self.displayjson['version'] = str(version) self.displayjson['problemType'] = str(self.modeltype) self.displayjson['targetFeature'] = self.targetFeature self.displayjson['numericalFeatures'] = numericalFeatures self.displayjson['nonNumericFeatures'] = nonNumericFeatures self.displayjson['modelFeatures'] = self.modelFeatures self.displayjson['textFeatures'] = self.textFeatures self.displayjson['emptyFeatures'] = self.emptyFeatures self.displayjson['modelname']= str(modelname) self.displayjson['preprocessedData'] = str(original_data_file) self.displayjson['nrows'] = str(nrows) self.displayjson['ncols'] = str(ncols) self.displayjson['saved_model'] = str(saved_model) self.displayjson['scoreParam'] = str(scoreParam) self.displayjson['labelMaps'] = eval(str(labelMaps)) self.original_data_file = original_data_file self.displayjson['featureReduction'] = featureReduction if featureReduction == 'True': self.displayjson['reduction_data_file'] = reduction_data_file else: self.displayjson['reduction_data_file'] = '' self.pred_filename = predicted_data_file self.profiled_data_file = profiled_data_file self.displayjson['predictedData'] = predicted_data_file self.displayjson['postprocessedData'] = profiled_data_file #self.trained_data_file = trained_data_file #self.displayjson['trainingData'] = trained_data_file #self.displayjson['categorialFeatures']=categoricalFeatures #self.displayjson['discreteFeatures']=discreteFeatures #self.displayjson['continuousFeatures']=numericContinuousFeatures #y = json.dumps(self.displayjson) #print(y) self.labelMaps = labelMaps self.log = logging.getLogger('eion') def visualizationrecommandsystem(self): try: import tensorflow.keras.utils as kutils datasetid = self.visualizationJson['datasetid'] self.log.info('\n================== Data Profiling Details==================') datacolumns=list(self.dataframe.columns) self.log.info('================== Data Profiling Details End ==================\n') self.log.info('================== Features Correlation Details ==================\n') self.log.info('\n================== Model Performance Analysis ==================') if os.path.exists(self.pred_filename): try: status,df=read_df_compressed(self.pred_filename) if self.modeltype == 'Classification' or self.modeltype == 'ImageClassification' or self.modeltype == 'anomaly_detection': y_actual = df['actual'].values y_predict = df['predict'].values y_actual = kutils.to_categorical(y_actual) y_predict = kutils.to_categorical(y_predict) classes = df.actual.unique() n_classes = y_actual.shape[1] self.log.info('-------> ROC AUC CURVE') roc_curve_dict = [] for i in classes: try: classname = i if str(self.labelMaps) != '{}': inv_map = {v: k for k, v in self.labelMaps.items()} classname = inv_map[i] fpr, tpr, threshold = metrics.roc_curve(y_actual[:,i],y_predict[:,i]) roc_auc = metrics.auc(fpr, tpr) class_roc_auc_curve = {} class_roc_auc_curve['class'] = str(classname) fprstring = ','.join(str(v) for v in fpr) tprstring = ','.join(str(v) for v in tpr) class_roc_auc_curve['FP'] = str(fprstring) class_roc_auc_curve['TP'] = str(tprstring) roc_curve_dict.append(class_roc_auc_curve) self.log.info('----------> Class: '+str(classname)) self.log.info('------------> ROC_AUC: '+str(roc_auc)) self.log.info('------------> False Positive Rate (x Points): '+str(fpr)) self.log.info('------------> True Positive Rate (y Points): '+str(tpr)) except: pass self.displayjson['ROC_AUC_CURVE'] = roc_curve_dict self.log.info('-------> Precision Recall CURVE') precision_recall_curve_dict = [] for i in range(n_classes): try: lr_precision, lr_recall, threshold = metrics.precision_recall_curve(y_actual[:,i],y_predict[:,i]) classname = i if str(self.labelMaps) != '{}': inv_map = {v: k for k, v in self.labelMaps.items()} classname = inv_map[i] roc_auc = metrics.auc(lr_recall,lr_precision) class_precision_recall_curve = {} class_precision_recall_curve['class'] = str(classname) Precisionstring = ','.join(str(round(v,2)) for v in lr_precision) Recallstring = ','.join(str(round(v,2)) for v in lr_recall) class_precision_recall_curve['Precision'] = str(Precisionstring) class_precision_recall_curve['Recall'] = str(Recallstring) precision_recall_curve_dict.append(class_precision_recall_curve) except: pass self.log.info('----------> Class: '+str(classname)) self.log.info('------------> ROC_AUC: '+str(roc_auc)) self.log.info('------------> Recall (x Points): '+str(lr_precision)) self.log.info('------------> Precision (y Points): '+str(lr_recall)) self.displayjson['PRECISION_RECALL_CURVE'] = precision_recall_curve_dict status,predictdataFrame=read_df_compressed(self.displayjson['predictedData']) except Exception as e: self.log.info('================== Error in Calculation ROC_AUC/Recall Precision Curve '+str(e)) self.log.info('================== Model Performance Analysis End ==================\n') self.log.info('\n================== For Descriptive Analysis of Model Features ==================') outputfile = os.path.join(self.jsondeployPath,'etc','display.json') with open(outputfile, 'w') as fp: json.dump(self.displayjson, fp) self.log.info('================== For Descriptive Analysis of Model Features End ==================\n') except Exception as inst: self.log.info('Visualization Failed !....'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def drawlinechart(self,xcolumn,ycolumn,deploy_path,datasetid): title = 'aion_visualization_'+xcolumn+"_"+ycolumn+"_linechart" yaxisname = 'Average '+ycolumn datasetindex = datasetid visulizationjson = '[{"_id": "543234","_type": "visualization","_source": {"title": "'+title+'",' visulizationjson = visulizationjson+'"visState": "{\\"title\\":\\"'+title+'\\",' visulizationjson = visulizationjson+'\\"type\\":\\"line\\",\\"params\\":{\\"type\\":\\"line\\",\\"grid\\":{\\"categoryLines\\":false,\\"style\\":{\\"color\\":\\"#eee\\"}},\\"categoryAxes\\":[{\\"id\\":\\"CategoryAxis-1\\",\\"type\\":\\"category\\",\\"position\\":\\"bottom\\",\\"show\\":true,\\"style\\":{},\\"scale\\":{\\"type\\":\\"linear\\"},\\"labels\\":{\\"show\\":true,\\"truncate\\":100},\\"title\\":{}}],\\"valueAxes\\":[{\\"id\\":\\"ValueAxis-1\\",\\"name\\":\\"LeftAxis-1\\",\\"type\\":\\"value\\",\\"position\\":\\"left\\",\\"show\\":true,\\"style\\":{},\\"scale\\":{\\"type\\":\\"linear\\",\\"mode\\":\\"normal\\"},\\"labels\\":{\\"show\\":true,\\"rotate\\":0,\\"filter\\":false,\\"truncate\\":100},\\"title\\":' visulizationjson = visulizationjson+'{\\"text\\":\\"'+yaxisname+'\\"}}],\\"seriesParams\\":[{\\"show\\":\\"true\\",\\"type\\":\\"line\\",\\"mode\\":\\"normal\\",\\"data\\":' visulizationjson = visulizationjson+'{\\"label\\":\\"'+yaxisname+'\\",\\"id\\":\\"1\\"},\\"valueAxis\\":\\"ValueAxis-1\\",\\"drawLinesBetweenPoints\\":true,\\"showCircles\\":true}],\\"addTooltip\\":true,\\"addLegend\\":true,\\"legendPosition\\":\\"right\\",\\"times\\":[],\\"addTimeMarker\\":false},\\"aggs\\":[{\\"id\\":\\"1\\",\\"enabled\\":true,\\"type\\":\\"avg\\",\\"schema\\":\\"metric\\",\\"params\\":{\\"field\\":\\"'+str(ycolumn)+'\\"}},{\\"id\\":\\"2\\",\\"enabled\\":true,\\"type\\":\\"terms\\",\\"schema\\":\\"segment\\",\\"params\\":{\\"field\\":\\"'+xcolumn+'\\",\\"size\\":100,\\"order\\":\\"desc\\",\\"orderBy\\":\\"1\\",\\"otherBucket\\":false,\\"otherBucketLabel\\":\\"Other\\",\\"missingBucket\\":false,\\"missingBucketLabel\\":\\"Missing\\"}}]}","uiStateJSON": "{}", "description": "","version": 1,"kibanaSavedObjectMeta": {"searchSourceJSON": "{\\"index\\":\\"'+datasetindex+'\\",\\"query\\":{\\"query\\":\\"\\",\\"language\\":\\"lucene\\"},\\"filter\\":[]}"}},"_migrationVersion": {"visualization": "6.7.2"}}]' filename = deploy_path+title+'.json' f = open(filename, "w") f.write(str(visulizationjson)) f.close() def drawbarchart(self,xcolumn,ycolumn,deploy_path,datasetid): title = 'aion_visualization_'+xcolumn+"_"+ycolumn+"_barchart" yaxisname = 'Average '+ycolumn datasetindex = datasetid visulizationjson = '[{"_id": "123456","_type": "visualization","_source": {"title":"'+title+'",' visulizationjson = visulizationjson+'"visState": "{\\"title\\":\\"'+title+'\\",' visulizationjson = visulizationjson+'\\"type\\":\\"histogram\\",\\"params\\":{\\"addLegend\\":true,\\"addTimeMarker\\":false,\\"addTooltip\\":true,\\"categoryAxes\\":[{\\"id\\":\\"CategoryAxis-1\\",\\"labels\\":{\\"show\\":true,\\"truncate\\":100},\\"position\\":\\"bottom\\",\\"scale\\":{\\"type\\":\\"linear\\"},\\"show\\":true,\\"style\\":{},\\"title\\":{},\\"type\\":\\"category\\"}],\\"grid\\":{\\"categoryLines\\":false,\\"style\\":{\\"color\\":\\"#eee\\"}},\\"legendPosition\\":\\"right\\",\\"seriesParams\\":[{\\"data\\":{\\"id\\":\\"1\\",' visulizationjson = visulizationjson+'\\"label\\":\\"'+yaxisname+'\\"},' visulizationjson = visulizationjson+'\\"drawLinesBetweenPoints\\":true,\\"mode\\":\\"stacked\\",\\"show\\":\\"true\\",\\"showCircles\\":true,\\"type\\":\\"histogram\\",\\"valueAxis\\":\\"ValueAxis-1\\"}],\\"times\\":[],\\"type\\":\\"histogram\\",\\"valueAxes\\":[{\\"id\\":\\"ValueAxis-1\\",\\"labels\\":{\\"filter\\":false,\\"rotate\\":0,\\"show\\":true,\\"truncate\\":100},\\"name\\":\\"LeftAxis-1\\",\\"position\\":\\"left\\",\\"scale\\":{\\"mode\\":\\"normal\\",\\"type\\":\\"linear\\"},\\"show\\":true,\\"style\\":{},\\"title\\":' visulizationjson = visulizationjson+'{\\"text\\":\\"'+yaxisname+'\\"},' visulizationjson = visulizationjson+'\\"type\\":\\"value\\"}]},\\"aggs\\":[{\\"id\\":\\"1\\",\\"enabled\\":true,\\"type\\":\\"avg\\",\\"schema\\":\\"metric\\",\\"params\\":{\\"field\\":\\"'+str(xcolumn)+'\\"}},{\\"id\\":\\"2\\",\\"enabled\\":true,\\"type\\":\\"terms\\",\\"schema\\":\\"segment\\",\\"params\\":{\\"field\\":\\"'+ycolumn+'\\",\\"size\\":100,\\"order\\":\\"asc\\",\\"orderBy\\":\\"1\\",\\"otherBucket\\":false,\\"otherBucketLabel\\":\\"Other\\",\\"missingBucket\\":false,\\"missingBucketLabel\\":\\"Missing\\"}}]}","uiStateJSON":"{}","description": "","version": 1,"kibanaSavedObjectMeta": {' visulizationjson = visulizationjson+'"searchSourceJSON": "{\\"index\\":\\"'+datasetindex+'\\",\\"query\\":{\\"language\\":\\"lucene\\",\\"query\\":\\"\\"},\\"filter\\":[]}"}},"_migrationVersion":{"visualization": "6.7.2"}}]' filename = deploy_path+title+'.json' f = open(filename, "w") f.write(str(visulizationjson)) f.close() def drawpiechart(self,xcolumn,deploy_path,datasetid): title = 'aion_visualization_'+xcolumn+"_piechart" datasetindex = datasetid visulizationjson = '[{"_id": "123456","_type": "visualization","_source": {"title":"'+title+'",' visulizationjson = visulizationjson+'"visState": "{\\"title\\":\\"'+title+'\\",' visulizationjson = visulizationjson+'\\"type\\":\\"pie\\",\\"params\\":{\\"type\\":\\"pie\\",\\"addTooltip\\":true,\\"addLegend\\":true,\\"legendPosition\\":\\"right\\",\\"isDonut\\":true,\\"labels\\":{\\"show\\":false,\\"values\\":true,\\"last_level\\":true,\\"truncate\\":100}},\\"aggs\\":[{\\"id\\":\\"1\\",\\"enabled\\":true,\\"type\\":\\"count\\",\\"schema\\":\\"metric\\",\\"params\\":{}},{\\"id\\":\\"2\\",\\"enabled\\":true,\\"type\\":\\"terms\\",\\"schema\\":\\"segment\\",\\"params\\":{\\"field\\":\\"'+xcolumn+'\\",\\"size\\":100,\\"order\\":\\"asc\\",\\"orderBy\\":\\"1\\",\\"otherBucket\\":false,\\"otherBucketLabel\\":\\"Other\\",\\"missingBucket\\":false,\\"missingBucketLabel\\":\\"Missing\\"}}]}",' visulizationjson = visulizationjson+'"uiStateJSON": "{}","description": "","version": 1,"kibanaSavedObjectMeta": {"searchSourceJSON":"{\\"index\\":\\"'+datasetid+'\\",\\"query\\":{\\"query\\":\\"\\",\\"language\\":\\"lucene\\"},\\"filter\\":[]}"}},"_migrationVersion": {"visualization": "6.7.2"}}]' filename = deploy_path+title+'.json' f = open(filename, "w") f.write(str(visulizationjson)) f.close() def get_confusion_matrix(self,df): setOfyTrue = set(df['actual']) unqClassLst = list(setOfyTrue) if(str(self.labelMaps) != '{}'): inv_mapping_dict = {v: k for k, v in self.labelMaps.items()} unqClassLst2 = (pd.Series(unqClassLst)).map(inv_mapping_dict) unqClassLst2 = list(unqClassLst2) else: unqClassLst2 = unqClassLst indexName = [] columnName = [] for item in unqClassLst2: indexName.append("act:"+str(item)) columnName.append("pre:"+str(item)) result = pd.DataFrame(confusion_matrix(df['actual'], df['predict'], labels = unqClassLst),index = indexName, columns = columnName) resultjson = result.to_json(orient='index') return(resultjson) def DistributionFinder(self,data): try: distributionName ="" sse =0.0 KStestStatic=0.0 dataType="" if(data.dtype == "float64"): dataType ="Continuous" elif(data.dtype =="int" or data.dtype =="int64"): dataType="Discrete" if(dataType == "Discrete"): distributions= [st.bernoulli,st.binom,st.geom,st.nbinom,st.poisson] index, counts = np.unique(abs(data.astype(int)),return_counts=True) if(len(index)>=2): best_sse = np.inf y1=[] total=sum(counts) mean=float(sum(index*counts))/total variance=float((sum(index**2*counts) -total*mean**2))/(total-1) dispersion=mean/float(variance) theta=1/float(dispersion) r=mean*(float(theta)/1-theta) for j in counts: y1.append(float(j)/total) pmf1=st.bernoulli.pmf(index,mean) pmf2=st.binom.pmf(index,len(index),p=mean/len(index)) pmf3=st.geom.pmf(index,1/float(1+mean)) pmf4=st.nbinom.pmf(index,mean,r) pmf5=st.poisson.pmf(index,mean) sse1 = np.sum(np.power(y1 - pmf1, 2.0)) sse2 = np.sum(np.power(y1 - pmf2, 2.0)) sse3 = np.sum(np.power(y1 - pmf3, 2.0)) sse4 = np.sum(np.power(y1 - pmf4, 2.0)) sse5 = np.sum(np.power(y1- pmf5, 2.0)) sselist=[sse1,sse2,sse3,sse4,sse5] for i in range(0,len(sselist)): if best_sse > sselist[i] > 0: best_distribution = distributions[i].name best_sse = sselist[i] elif (len(index) == 1): best_distribution = "Constant Data-No Distribution" best_sse = 0.0 distributionName =best_distribution sse=best_sse elif(dataType == "Continuous"): distributions = [st.uniform,st.expon,st.weibull_max,st.weibull_min,st.chi,st.norm,st.lognorm,st.t,st.gamma,st.beta] best_distribution = st.norm.name best_sse = np.inf datamin=data.min() datamax=data.max() nrange=datamax-datamin y, x = np.histogram(data.astype(float), bins='auto', density=True) x = (x + np.roll(x, -1))[:-1] / 2.0 for distribution in distributions: with warnings.catch_warnings(): warnings.filterwarnings('ignore') params = distribution.fit(data.astype(float)) # Separate parts of parameters arg = params[:-2] loc = params[-2] scale = params[-1] # Calculate fitted PDF and error with fit in distribution pdf = distribution.pdf(x, loc=loc, scale=scale, *arg) sse = np.sum(np.power(y - pdf, 2.0)) if(best_sse >sse > 0): best_distribution = distribution.name best_sse = sse distributionName =best_distribution sse=best_sse except: response = str(sys.exc_info()[0]) message='Job has Failed'+response print(message) return distributionName,sse ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd class cat_to_str: def __init__(self): pass def convert(self, x): return pd.DataFrame(x).astype(str) import os from pathlib import Path import pandas as pd import numpy as np import json def listToStringWithDelimiter(s, vectorDBFeatureDelimitInDoc): #lenght sLen = len(s) # initialize an empty string str1 = "" # traverse in the string for i in range(0, sLen-1): str1 +=str(s[i])+vectorDBFeatureDelimitInDoc str1 +=str(s[sLen-1]) # return string return str1 def save_csv(df, fileLocation, encoding=None): #import pdb;pdb.set_trace(); try: parent_dir = Path(fileLocation).parent parent_dir.mkdir(parents=True, exist_ok=True) if encoding: df.to_csv(fileLocation, encoding=encoding, index=False,) else: df.to_csv(fileLocation, index=False) return True, '' except Exception as e: print(e) return False, str(e) def save_csv_compressed(df, fileLocation, encoding=None): try: parent_dir = Path(fileLocation).parent parent_dir.mkdir(parents=True, exist_ok=True) if encoding: df.to_csv(fileLocation, encoding=encoding, index=False, compression='gzip') else: df.to_csv(fileLocation, index=False, compression='gzip') return True, '' except Exception as e: print(e) return False, str(e) def read_df(fileLocation,encoding=None, nrows=None): parent_dir = Path(fileLocation).parent if parent_dir.exists(): try: if encoding and nrows: df = pd.read_csv(fileLocation, encoding=encoding,nrows=nrows,encoding_errors= 'replace') elif encoding: df = pd.read_csv(fileLocation, encoding=encoding,encoding_errors= 'replace') elif nrows: df = pd.read_csv(fileLocation, nrows=nrows) return True, df except Exception as e: df = pd.read_csv(fileLocation, encoding="utf-8",encoding_errors= 'replace') print(e) return True,df else: print("parent fails") def read_df_compressed(fileLocation, encoding=None, nrows=None): parent_dir = Path(fileLocation).parent if parent_dir.exists(): try: if encoding: df = pd.read_csv(fileLocation, encoding=encoding, compression="gzip",encoding_errors= 'replace') if nrows: df = pd.read_csv(fileLocation, nrows=nrows, compression="gzip") else: df = pd.read_csv(fileLocation, encoding="utf-8", compression="gzip",encoding_errors= 'replace') return True, df except Exception as e: df = pd.read_csv(fileLocation, encoding="utf-8",encoding_errors= 'replace') print(e) return True,df else: print("parent fails") def save_chromadb(df, config_obj, fileLocation, modelFeatures): import chromadb #from chromadb.config import Settings try: parent_dir = Path(fileLocation).parent parent_dir.mkdir(parents=True, exist_ok=True) vectorDBFeatureDelimitInDoc = config_obj.getVectorDBFeatureDelimitInDoc() persist_directory = os.path.dirname(os.path.abspath(fileLocation)) # client = chromadb.Client( # Settings( # persist_directory=persist_directory, # chroma_db_impl="duckdb+parquet", # ) # ) client = chromadb.PersistentClient(path=persist_directory) # Create a new chroma collection collection_name = os.path.basename(fileLocation).split('/')[-1] collection_name = collection_name.replace('.csv', '') collection_name = collection_name + 'VecDB' collection = client.create_collection( name=collection_name, metadata={"hnsw:space": "cosine"} ) features = modelFeatures.split(",") dftxt = pd.concat([df.pop(x) for x in features], axis=1) stepSize = 500 for i in range(0, len(df),stepSize): start = i end = i+ stepSize dfembdary = df.iloc[start:end].to_numpy() dftxtary = dftxt.iloc[start:end].to_numpy() idxary = df.iloc[start:end].index.values #convert to string idxary = [str(x) for x in idxary] dftxtary = [listToStringWithDelimiter(x.tolist(), vectorDBFeatureDelimitInDoc) for x in dftxtary] collection.add( embeddings=dfembdary.tolist(), ids=idxary, documents= dftxtary ) client.persist() return True, '' except Exception as e: return False, str(e) import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__)))) from .cat_type_str import cat_to_str __version__ = "1.0" import pandas as pd def dataGarbageValue(dataFrame,datetimeFeature): if datetimeFeature == '' or datetimeFeature.lower() == 'na': return 'Success','' try: features = datetimeFeature.split(',') for dtfeature in features: dataFrame[dtfeature] = pd.to_datetime(dataFrame[dtfeature],errors='coerce') if pd.isnull(dataFrame[dtfeature]).sum() > 0: return 'Error',dtfeature+' feature have some garbage values' except Exception as e: print(e) return 'Error', 'Datetime features validation error' return 'Success','' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import logging class VideoTraining(object): def __init__(self): self.log = logging.getLogger('eion') def train_model(self,model,modelParam,outputLocation,tfrecord_directory): print(model) print(modelParam) print(outputLocation) print(tfrecord_directory) from savp import TrainSAVP TrainSAVP(tfrecord_directory,outputLocation,modelParam,model) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import logging import numpy as np import pandas as pd from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report from sklearn.metrics import roc_curve, auc from sklearn.metrics import roc_auc_score from sklearn.preprocessing import LabelBinarizer from imblearn.over_sampling import RandomOverSampler,SMOTE from imblearn.under_sampling import RandomUnderSampler from imblearn.under_sampling import TomekLinks from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error,make_scorer from sklearn.metrics import mean_squared_error from sklearn.metrics import log_loss import tensorflow as tf from tensorflow.keras import backend as K from tensorflow.keras.models import load_model from dlearning.Classification import DLClassificationModel from dlearning.Regression import DLRegressionModel from learner.machinelearning import machinelearning from sklearn.metrics import matthews_corrcoef, brier_score_loss import os def recall_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return recall def precision_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + K.epsilon()) return precision def f1_m(y_true, y_pred): precision = precision_m(y_true, y_pred) recall = recall_m(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def rmse_m(y_true, y_pred): return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1)) def r_square(y_true, y_pred): SS_res = K.sum(K.square(y_true-y_pred)) SS_tot = K.sum(K.square(y_true-K.mean(y_true))) return (1 - SS_res/(SS_tot+K.epsilon())) class deeplearning(object): def __init__(self): self.log = logging.getLogger('eion') def getDLPredictionData(self,model_dl,hist_reloaded,X): if model_dl == "Neural Network": XSNN = X.values predictedData = hist_reloaded.predict(XSNN) else: X1 = np.expand_dims(X, axis=2) predictedData = hist_reloaded.predict(X1) return(predictedData) def getPredictionData(self,model_dl,hist_reloaded,X): if model_dl == "Neural Network": XSNN = X.values #predictedData = hist_reloaded.predict_classes(XSNN) predict_x=hist_reloaded.predict(XSNN) predictedData=np.argmax(predict_x,axis=1) else: X1 = np.expand_dims(X, axis=2) #predictedData = hist_reloaded.predict_classes(X1) predict_x=hist_reloaded.predict(X1) predictedData=np.argmax(predict_x,axis=1) return(predictedData, predict_x) def LoadDL_Regression_Model(self,filename_dl,scoreParam,loss_matrix,optimizer): if(scoreParam.lower() == 'rmse'): hist_reloaded = load_model(filename_dl,custom_objects={"rmse": rmse_m},compile=False) hist_reloaded.compile(loss=loss_matrix,optimizer=optimizer, metrics=[rmse_m]) elif(scoreParam.lower() == 'r2'): hist_reloaded = load_model(filename_dl,custom_objects={"r2": r_square},compile=False) hist_reloaded.compile(loss=loss_matrix,optimizer=optimizer, metrics=[r_square]) else: hist_reloaded = load_model(filename_dl) return(hist_reloaded) def startLearning(self,model_type,modelList, modelParams, scoreParam, cvSplit, xtrain,ytrain,xtest,ytest,method,randomMethod,roundLimit,labelMaps,df_test,deployLocation,modelName,modelVersion,best_feature_model): mlobj = machinelearning() if model_type == 'Classification': self.log.info('\n------ Training DL: Classification ----') objClf = DLClassificationModel(modelList, modelParams, scoreParam, cvSplit, xtrain,ytrain,xtest,ytest,method,randomMethod,roundLimit,best_feature_model) dftrain = xtrain.copy() dftrain['Target'] = ytrain model_dl,score_dl,best_model_dl,params_dl,X1,XSNN,model_tried_dl,loss_matrix,optimizer = objClf.TalosScan(objClf) self.log.info('------ Training DL: Classification End----\n') saved_model_dl = 'dl_'+modelName+'_'+modelVersion+'.sav' filename_dl = os.path.join(deployLocation,'model',saved_model_dl) best_model_dl.save(filename_dl) hist_reloaded = self.LoadDL_Classification_Model(filename_dl,scoreParam,loss_matrix,optimizer) self.log.info('\n--------- Performance Matrix with Train Data ---------') predictedData, prob = self.getPredictionData(model_dl,hist_reloaded,xtrain) trainingperformancematrix = mlobj.getClassificationPerformaceMatrix(ytrain, predictedData, prob,labelMaps) self.log.info('\n--------- Performance Matrix with Train Data End ---------') predictedData, prob = self.getPredictionData(model_dl,hist_reloaded,xtest) df_test['predict'] = predictedData self.log.info('\n--------- Performance Matrix with Test Data ---------') performancematrix = mlobj.getClassificationPerformaceMatrix(ytest, predictedData, prob,labelMaps) self.log.info('\n--------- Performance Matrix with Test Data End ---------') return(model_dl,score_dl,best_model_dl,params_dl,X1,XSNN,model_tried_dl,loss_matrix,optimizer,saved_model_dl,filename_dl,dftrain,df_test,performancematrix,trainingperformancematrix) else: objReg = DLRegressionModel(modelList, modelParams, scoreParam, cvSplit, xtrain,ytrain,xtest,ytest,method,randomMethod,roundLimit,best_feature_model) dftrain = xtrain.copy() dftrain['Target'] = ytrain model_dl,score_dl,best_model_dl,params_dl,X1,XSNN,model_tried_dl,loss_matrix,optimizer = objReg.TalosScan(objReg) self.log.info('------ Training DL: Regression End----\n') self.log.info('\n------- Best DL Model and its parameters -------------') self.log.info('-------> Best Model: '+str(model_dl)) self.log.info('-------> Best Score: '+str(score_dl)) self.log.info('-------> Best Params: '+str(params_dl)) self.log.info('------- Best DL Model and its parameters End-------------\n') saved_model_dl = 'dl_'+modelName+'_'+modelVersion+'.sav' filename_dl = os.path.join(deployLocation,'model',saved_model_dl) best_model_dl.save(filename_dl) hist_reloaded=self.LoadDL_Regression_Model(filename_dl,scoreParam,loss_matrix,optimizer) predictedData = self.getDLPredictionData(model_dl,hist_reloaded,xtrain) self.log.info('\n--------- Performance Matrix with Train Data ---------') trainingperformancematrix = mlobj.get_regression_matrix(ytrain, predictedData) self.log.info('--------- Performance Matrix with Train Data End---------\n') predictedData = self.getDLPredictionData(model_dl,hist_reloaded,xtest) df_test['predict'] = predictedData self.log.info('\n--------- Performance Matrix with Test Data ---------') performancematrix = mlobj.get_regression_matrix(ytest, predictedData) self.log.info('--------- Performance Matrix with Test Data End---------\n') return(model_dl,score_dl,best_model_dl,params_dl,X1,XSNN,model_tried_dl,loss_matrix,optimizer,saved_model_dl,filename_dl,dftrain,df_test,performancematrix,trainingperformancematrix) def LoadDL_Classification_Model(self,filename_dl,scoreParam,loss_matrix,optimizer): if(scoreParam.lower() == 'recall'): hist_reloaded = load_model(filename_dl,custom_objects={"recall": recall_m},compile=False) hist_reloaded.compile(loss=loss_matrix,optimizer=optimizer, metrics=[recall_m]) elif(scoreParam.lower() == 'precision'): hist_reloaded = load_model(filename_dl,custom_objects={"precision": precision_m},compile=False) hist_reloaded.compile(loss=loss_matrix,optimizer=optimizer, metrics=[precision_m]) elif(scoreParam.lower() == 'roc_auc'): hist_reloaded = load_model(filename_dl,compile=False) hist_reloaded.compile(loss=loss_matrix,optimizer=optimizer, metrics=[tf.keras.metrics.AUC()]) elif(scoreParam.lower() == 'f1_score'): hist_reloaded = load_model(filename_dl,custom_objects={"f1_score": f1_m},compile=False) hist_reloaded.compile(loss=loss_matrix,optimizer=optimizer, metrics=[f1_m]) else: hist_reloaded = load_model(filename_dl) return(hist_reloaded) def getClassificationPerformaceMatrix(self,le_trainY,predictedData,prob,labelMaps): setOfyTrue = set(le_trainY) unqClassLst = list(setOfyTrue) if(str(labelMaps) != '{}'): inv_mapping_dict = {v: k for k, v in labelMaps.items()} unqClassLst2 = (pd.Series(unqClassLst)).map(inv_mapping_dict) unqClassLst2 = list(unqClassLst2) else: unqClassLst2 = unqClassLst indexName = [] columnName = [] for item in unqClassLst2: indexName.append("true:"+str(item)) columnName.append(str(item)) matrixconfusion = pd.DataFrame(confusion_matrix(le_trainY,predictedData, labels = unqClassLst),index = indexName, columns = columnName) self.log.info('\n <--- Confusion Matrix --->') self.log.info(matrixconfusion) classificationreport = pd.DataFrame(classification_report(le_trainY, predictedData, output_dict=True)) self.log.info('\n <--- Classification Report --->') self.log.info(classificationreport) lb = LabelBinarizer() lb.fit(le_trainY) transformTarget= lb.transform(le_trainY) if transformTarget.shape[-1] == 1: transformTarget = le_trainY prob = np.delete( prob, 0, 1) rocaucscore = roc_auc_score(transformTarget,prob,average="macro") brier_score = None mcc_score = matthews_corrcoef(le_trainY,predictedData) if len(unqClassLst) > 2: brier_score = np.mean(np.sum(np.square(prob - transformTarget), axis=1)) else: brier_score = brier_score_loss(transformTarget,prob) self.log.info('-------> ROC AUC SCORE :'+str(rocaucscore)) self.log.info(f'-------> Matthews correlation coefficient SCORE : {mcc_score}') self.log.info(f'-------> BRIER SCORE : {brier_score}') matrixconfusion = matrixconfusion.to_json(orient='index') classificationreport = classificationreport.to_json(orient='index') matrix = f'"ConfusionMatrix": {matrixconfusion},"ClassificationReport": {classificationreport},"ROC_AUC_SCORE": {rocaucscore},"MCC_SCORE": {mcc_score},"BRIER_SCORE": {brier_score}' return(matrix) def split_into_train_test_data(self,featureData,targetData,cvSplit,testPercentage,modelType='classification'): ''' if cvSplit == None: ''' testSize=testPercentage/100 if modelType == 'regression': xtrain,xtest,ytrain,ytest=train_test_split(featureData,targetData,test_size=testSize,shuffle=True) else: try: xtrain,xtest,ytrain,ytest=train_test_split(featureData,targetData,stratify=targetData,test_size=testSize,shuffle=True) except: xtrain,xtest,ytrain,ytest=train_test_split(featureData,targetData,test_size=testSize,shuffle=True) self.log.info('\n<-------------- Test Train Split ---------------->\n') self.log.info('\n<-------- Train Data Shape '+str(xtrain.shape)+' ---------->\n') self.log.info('\n<-------- Test Data Shape '+str(xtest.shape)+' ---------->\n') ''' else: xtrain=featureData ytrain=targetData xtest=featureData ytest=targetData ''' return(xtrain,ytrain,xtest,ytest) def checkForClassBalancing(self,targetData): imbalancedCount=0 valueCount=targetData.value_counts() self.log.info("<------ Categories and Count ------>") self.log.info(valueCount) categoryList=valueCount.keys().tolist() categoryCountList=valueCount.tolist() for i in range(0,len(categoryCountList)): if float(categoryCountList[i])<=float(0.3*max(categoryCountList)): self.log.info("<------ Imbalanced class ------>"+str(categoryCountList[i])+' '+str(categoryList[i])) imbalancedCount=imbalancedCount+1 return(imbalancedCount) def setScoreParams(self,scoreParam,problem_type): if problem_type.lower() == 'classification' or problem_type.lower() == 'textclassification': allowedmatrix = ['accuracy','recall','precision','f1_score','roc_auc'] if(scoreParam.lower() not in allowedmatrix): scoreParam = 'accuracy' elif scoreParam.lower() == 'none': scoreParam = 'accuracy' else: scoreParam = scoreParam.lower() else: allowedmatrix = ['mse','rmse','r2','mae'] if(scoreParam.lower() not in allowedmatrix): scoreParam = 'mse' elif scoreParam.lower() == 'none': scoreParam = 'mse' else: scoreParam = scoreParam.lower() return(scoreParam) def ExecuteClassBalancing(self,featureData,targetData,balancingMethod): if balancingMethod.lower() == "oversample": self.log.info("<------ Balancing data using SMOTE OverSampling Technique ------>") oversample = SMOTE() balfeatureData, baltargetData = oversample.fit_resample(featureData, targetData) self.log.info(baltargetData.value_counts()) elif balancingMethod.lower() == "undersample": self.log.info("<------ Balancing data using Tomelinks UnderSampling Technique ------>") tLinks = TomekLinks() balfeatureData, baltargetData= tLinks.fit_sample(featureData, targetData) self.log.info(baltargetData.value_counts()) else: balfeatureData = featureData baltargetData = targetData self.log.info("<------ No balancing technique has been defined ,using imbalanced data for classification ------>") return(balfeatureData,baltargetData) def get_regression_matrix(self,targetData,predictedData): try: self.log.info('\n <--------- r2_score-------------- --->') r2score=r2_score(targetData, predictedData) self.log.info(r2score) except Exception as e: self.log.info('\n--------- r2_score ',str(e)) r2score = 0 try: self.log.info('\n <--- Mean Absolute Error --->') meanabsoluteerror=(mean_absolute_error(targetData, predictedData)) self.log.info(meanabsoluteerror) except Exception as e: self.log.info('\n---------Error: meanabsoluteerror ',str(e)) meanabsoluteerror = 0 try: self.log.info("<------------mean_squared_error--------------->") meanssquatederror=mean_squared_error(targetData, predictedData) self.log.info(meanssquatederror) except Exception as e: self.log.info('\n---------Error: meanssquatederror ',str(e)) meanssquatederror = 0 try: self.log.info("<------------root mean_squared_error--------------->") rootmeanssquatederror=mean_squared_error(targetData, predictedData,squared=False) self.log.info(rootmeanssquatederror) except Exception as e: self.log.info('\n---------Error: rootmeanssquatederror ',str(e)) rootmeanssquatederror = 0 try: self.log.info('\n <--- Mean Absolute Percentage Error --->') targetArray, predictedArray = np.array(targetData), np.array(predictedData) meanpercentageerror=np.mean(np.abs((targetArray - predictedArray) / targetArray))*100 self.log.info(meanpercentageerror) except Exception as e: self.log.info('\n---------Error: meanpercentageerror ',str(e)) meanpercentageerror = 0 matrix = '"MAE":'+str(meanabsoluteerror)+',"R2Score":'+str(r2score)+',"MSE":'+str(meanssquatederror)+',"MAPE":'+str(meanpercentageerror)+',"RMSE":'+str(rootmeanssquatederror) return matrix ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import pandas as pd import talos import json import sys import time import os from sklearn.metrics import recall_score from sklearn.metrics import precision_score from sklearn.metrics import accuracy_score from sklearn.metrics import roc_auc_score from sklearn.metrics import f1_score import tensorflow.keras.utils as kutils from talos.model.normalizers import lr_normalizer from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense,Dropout,LSTM,GRU,SimpleRNN,Flatten, Input from sklearn.model_selection import train_test_split from tensorflow.keras.layers import Conv1D,MaxPooling1D from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold from tensorflow.keras.optimizers import Adam from tensorflow.keras.optimizers import Nadam from tensorflow.keras.optimizers import RMSprop from tensorflow.keras.optimizers import SGD import logging import tensorflow as tf import tensorflow.keras.backend as K def recall_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return recall def precision_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + K.epsilon()) return precision def f1_m(y_true, y_pred): precision = precision_m(y_true, y_pred) recall = recall_m(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) class DLClassificationModel: def __init__(self,modelList, modelParams, scoreParam, cvSplit, featuresData, targetData,testX,testY, method,randomMethod,roundLimit,best_feature_model): self.modelList =modelList self.modelParams =modelParams self.scoreParam = scoreParam self.cvSplit =cvSplit self.featuresData =featuresData self.targetData = targetData self.testX = testX self.testY = testY self.method =method self.randomMethod=randomMethod self.roundLimit=roundLimit self.best_feature_model = best_feature_model self.log = logging.getLogger('eion') def RNNClassification(self,x_train,y_train,x_val,y_val,params): tf.keras.backend.clear_session() x_train = K.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) x_val = K.reshape(x_val, (x_val.shape[0], x_val.shape[1], 1)) model = Sequential() if params['RNNType'] == "LSTM" : if params['numRNNLayers'] > 1: model.add(LSTM(params['first_neuron'],return_sequences=True,input_shape=(x_train.shape[1],1))) for x in range(1,params['numRNNLayers']): model.add(LSTM(params['first_neuron'])) else: model.add(LSTM(params['first_neuron'],input_shape=(x_train.shape[1],1))) elif params['RNNType'] == "GRU" : if params['numRNNLayers'] > 1: model.add(GRU(params['first_neuron'],return_sequences=True,input_shape=(x_train.shape[1],1))) for x in range(1,params['numRNNLayers']): model.add(GRU(params['first_neuron'])) else: model.add(GRU(params['first_neuron'],input_shape=(x_train.shape[1],1))) elif params['RNNType'] == "SimpleRNN" : if params['numRNNLayers'] > 1: model.add(SimpleRNN(params['first_neuron'],return_sequences=True,input_shape=(x_train.shape[1],1))) for x in range(1,params['numRNNLayers']): model.add(SimpleRNN(params['first_neuron'])) else: model.add(SimpleRNN(params['first_neuron'],input_shape=(x_train.shape[1],1))) talos.utils.hidden_layers(model, params, x_train.shape[1]) model.add(Dense(y_train.shape[1],activation=params['last_activation'])) model.compile(loss=params['losses'],optimizer=params['optimizer'],metrics=['acc',f1_m,precision_m,recall_m,tf.keras.metrics.AUC()]) out = model.fit(x_train, y_train, validation_data=(x_val, y_val), batch_size=params['batch_size'],epochs=params['epochs'],verbose=0,shuffle=True) return out, model def SNNClassification(self,x_train,y_train,x_val,y_val,params): tf.keras.backend.clear_session() model = Sequential() model.add(Dense(params['first_neuron'], input_dim=x_train.shape[1], activation=params['activation'])) talos.utils.hidden_layers(model, params,1) model.add(Dropout(params['dropout'])) model.add(Dense(y_train.shape[1], activation=params['last_activation'])) model.compile(loss=params['losses'], optimizer=params['optimizer'], metrics=['acc',f1_m,precision_m,recall_m,tf.keras.metrics.AUC()]) out = model.fit(x=x_train, y=y_train, validation_data=(x_val, y_val), epochs=params['epochs'], batch_size=params['batch_size'], verbose=0) return out, model def CNNClassification(self,x_train,y_train,x_val,y_val,params): tf.keras.backend.clear_session() x_train = K.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) x_val = K.reshape(x_val, (x_val.shape[0], x_val.shape[1], 1)) model = Sequential() model.add(Conv1D(filters=params['first_neuron'], kernel_size=(3), activation=params['activation'], input_shape=(x_train.shape[1],1),padding='same') ) if params['numConvLayers'] > 1: for x in range(1,params['numConvLayers']): if params['MaxPool'] == "True": model.add(MaxPooling1D(pool_size=2,padding='same')) model.add(Conv1D(filters=8, kernel_size=3, activation=params['activation'],padding='same')) talos.utils.hidden_layers(model, params, x_train.shape[1]) model.add(MaxPooling1D(pool_size=2,padding='same')) model.add(Flatten()) model.add(Dense(y_train.shape[1],activation=params['last_activation'])) model.compile(loss=params['losses'],optimizer=params['optimizer'],metrics=['acc',f1_m,precision_m,recall_m,tf.keras.metrics.AUC()]) out = model.fit(x_train, y_train, validation_data=(x_val, y_val), batch_size=params['batch_size'], epochs=params['epochs'],verbose=0,shuffle=True) return out, model def TalosScan(self,modelObj): try: #dataPath = pd.read_csv(self.dataLocation) #X = dataPath.drop(self.targetData, axis=1) loss_matrix='binary_crossentropy' optimizer='Nadam' X = self.featuresData x = X.values Y = self.targetData scoredetails = '' #Y= dataPath[self.targetData] y = Y.values y = kutils.to_categorical(y) XSNN = X.values X1 = np.expand_dims(X, axis=2) kf = KFold(n_splits = self.cvSplit) for train_index, test_index in kf.split(X): X_train, X_test = x[train_index], x[test_index] y_train, y_test = y[train_index], y[test_index] data = self.modelParams models = data.keys() start = time.time() scoreSNN = [] scoreRNN = [] scoreCNN = [] scoreRNNGRU = [] scoreRNNLSTM = [] best_paramsSNN = {} best_paramsRNN = {} best_paramsRNNGRU = {} best_paramsRNNLSTM = {} best_paramsCNN = {} if "Neural Network"in self.modelList: self.log.info("-------> Model Name: Neural Network") start = time.time() data = self.modelParams["Neural Network"] p = {"activation":data["activation"].split(","), "last_activation":data["last_activation"].split(","), "optimizer":data["optimizer"].split(","), "losses":data["losses"].split(","), "first_neuron":[int(n) for n in data["first_layer"].split(",")], "shapes": data["shapes"].split(","), "hidden_layers":[int(n) for n in data["hidden_layers"].split(",")], "dropout": [float(n) for n in data["dropout"].split(",")], "lr": [float(n) for n in data["learning_rate"].split(",")], "batch_size": [int(n) for n in data["batch_size"].split(",")], "epochs": [int(n) for n in data["epochs"].split(",")] } param_combinations = int(np.prod([len(x.split(',')) for x in p])) round_limit = self.roundLimit if not self.roundLimit else min(self.roundLimit, param_combinations) scan_object = talos.Scan(x=X_train, y=y_train, x_val = X_test, y_val = y_test, model = modelObj.SNNClassification, experiment_name='SNN', params=p, round_limit=round_limit, random_method=self.randomMethod ) matrix_type = 'val_acc' if self.scoreParam.lower() == 'accuracy': matrix_type = 'val_acc' elif(self.scoreParam.lower() == 'roc_auc'): matrix_type = 'val_auc' elif(self.scoreParam.lower() == 'recall'): matrix_type = 'val_recall_m' elif(self.scoreParam.lower() == 'precision'): matrix_type = 'val_precision_m' elif(self.scoreParam.lower() == 'f1_score'): matrix_type = 'val_f1_m' analyze_objectSNN = talos.Analyze(scan_object) highValAccSNN = analyze_objectSNN.high(matrix_type) dfSNN = analyze_objectSNN.data #pd.set_option('display.max_columns',20) #print(dfSNN) #pd.reset_option('display.max_columns') newdfSNN = dfSNN.loc[dfSNN[matrix_type] == highValAccSNN] if(len(newdfSNN) > 1): lowLoss = analyze_objectSNN.low('val_loss') newdfSNN = newdfSNN.loc[newdfSNN['val_loss'] == lowLoss] best_paramsSNN["activation"] = list(newdfSNN["activation"])[0] best_paramsSNN["optimizer"] = list(newdfSNN["optimizer"])[0] best_paramsSNN["losses"] = list(newdfSNN["losses"])[0] best_paramsSNN["first_layer"] = list(newdfSNN["first_neuron"])[0] best_paramsSNN["shapes"] = list(newdfSNN["shapes"])[0] best_paramsSNN["hidden_layers"] = list(newdfSNN["hidden_layers"])[0] best_paramsSNN["dropout"] = list(newdfSNN["dropout"])[0] best_paramsSNN["batch_size"] = list(newdfSNN["batch_size"])[0] best_paramsSNN["epochs"] = list(newdfSNN["epochs"])[0] best_paramsSNN["lr"] = list(newdfSNN["lr"])[0] best_paramsSNN["last_activation"] = list(newdfSNN["last_activation"])[0] best_modelSNN = scan_object.best_model(metric=matrix_type) try: if(len(best_paramsSNN["losses"]) == 0): loss_matrix = 'binary_crossentropy' else: loss_matrix = best_paramsSNN["losses"] if(len(best_paramsSNN["optimizer"]) == 0): optimizer = 'Nadam' else: optimizer = best_paramsSNN["optimizer"] if best_paramsSNN["batch_size"] == 0: batchsize = 32 else: batchsize = best_paramsSNN["batch_size"] except: loss_matrix = 'binary_crossentropy' optimizer = 'Nadam' batchsize = 32 if self.scoreParam == 'accuracy': best_modelSNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['accuracy']) elif self.scoreParam == 'roc_auc': best_modelSNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[tf.keras.metrics.AUC()]) elif self.scoreParam == 'recall': best_modelSNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[recall_m]) elif self.scoreParam == 'precision': best_modelSNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[precision_m]) elif self.scoreParam == 'f1_score': best_modelSNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[f1_m]) scoreSNN = best_modelSNN.evaluate(XSNN,y, batch_size=batchsize) self.log.info("----------> Score Matrix: "+str(best_modelSNN.metrics_names)) self.log.info("----------> Score: "+str(scoreSNN)) self.log.info("----------> Model Params: "+str(best_paramsSNN)) executionTime=time.time() - start XSNN = self.testX.values #predict_x=best_modelSNN.predict(XSNN) predictedData=np.argmax(best_modelSNN.predict(XSNN),axis=1) #predictedData = best_modelSNN.predict_classes(XSNN) #print(predictedData) #predictedData = best_modelSNN.predict(self.testX) if 'accuracy' in str(self.scoreParam): score = accuracy_score(self.testY,predictedData) elif 'recall' in str(self.scoreParam): score = recall_score(self.testY,predictedData, average='macro') elif 'precision' in str(self.scoreParam): score = precision_score(self.testY,predictedData,average='macro') elif 'f1_score' in str(self.scoreParam): score = f1_score(self.testY,predictedData, average='macro') elif 'roc_auc' in str(self.scoreParam): score = roc_auc_score(self.testY,predictedData,average="macro") score = round((score*100),2) self.log.info("----------> Testing Score: "+str(score)) self.log.info('----------> Total Execution: '+str(executionTime)+'\n') scoreSNN[1] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"Neural Network","FeatureEngineering":"'+str(self.best_feature_model)+'","Score":'+str(scoreSNN[1])+'}' self.log.info('Status:- |... DL Algorithm applied: Neural Network') self.log.info('Status:- |... Score after hyperparameter tuning: '+str(round(score,2))) if "Recurrent Neural Network"in self.modelList: self.log.info("-------> Model Name: Recurrent Neural Network") start = time.time() data = self.modelParams["Recurrent Neural Network"] p = {"RNNType":["SimpleRNN"], "numRNNLayers":[int(n) for n in data["numRNNLayers"].split(",")], "activation":data["activation"].split(","), "last_activation":data["last_activation"].split(","), "optimizer":data["optimizer"].split(","), "losses":data["losses"].split(","), "first_neuron":[int(n) for n in data["first_layer"].split(",")], "shapes": data["shapes"].split(","), "hidden_layers":[int(n) for n in data["hidden_layers"].split(",")], "dropout": [float(n) for n in data["dropout"].split(",")], "lr": [float(n) for n in data["learning_rate"].split(",")], "batch_size": [int(n) for n in data["batch_size"].split(",")], "epochs": [int(n) for n in data["epochs"].split(",")]} param_combinations = int(np.prod([len(x.split(',')) for x in p])) round_limit = self.roundLimit if not self.roundLimit else min(self.roundLimit, param_combinations) scan_object = talos.Scan(x=X_train, y=y_train, x_val = X_test, y_val = y_test, model = modelObj.RNNClassification, experiment_name='RNN', params=p, round_limit=round_limit, random_method=self.randomMethod ) matrix_type = 'val_acc' if self.scoreParam.lower() == 'accuracy': matrix_type = 'val_acc' elif(self.scoreParam.lower() == 'roc_auc'): matrix_type = 'val_auc' elif(self.scoreParam.lower() == 'recall'): matrix_type = 'val_recall_m' elif(self.scoreParam.lower() == 'precision'): matrix_type = 'val_precision_m' elif(self.scoreParam.lower() == 'f1_score'): matrix_type = 'val_f1_m' analyze_objectRNN = talos.Analyze(scan_object) highValAccRNN = analyze_objectRNN.high(matrix_type) dfRNN = analyze_objectRNN.data newdfRNN = dfRNN.loc[dfRNN[matrix_type] == highValAccRNN] if(len(newdfRNN) > 1): lowLoss = analyze_objectRNN.low('val_loss') newdfRNN = newdfRNN.loc[newdfRNN['val_loss'] == lowLoss] best_paramsRNN["RNNType"] = list(newdfRNN["RNNType"])[0] best_paramsRNN["numRNNLayers"] = list(newdfRNN["numRNNLayers"])[0] best_paramsRNN["activation"] = list(newdfRNN["activation"])[0] best_paramsRNN["optimizer"] = list(newdfRNN["optimizer"])[0] best_paramsRNN["losses"] = list(newdfRNN["losses"])[0] best_paramsRNN["first_layer"] = list(newdfRNN["first_neuron"])[0] best_paramsRNN["shapes"] = list(newdfRNN["shapes"])[0] best_paramsRNN["hidden_layers"] = list(newdfRNN["hidden_layers"])[0] best_paramsRNN["dropout"] = list(newdfRNN["dropout"])[0] best_paramsRNN["batch_size"] = list(newdfRNN["batch_size"])[0] best_paramsRNN["epochs"] = list(newdfRNN["epochs"])[0] best_paramsRNN["lr"] = list(newdfRNN["lr"])[0] best_paramsRNN["last_activation"] = list(newdfRNN["last_activation"])[0] best_modelRNN = scan_object.best_model(metric=matrix_type, asc=False) try: if(len(best_paramsRNN["losses"]) == 0): loss_matrix = 'binary_crossentropy' else: loss_matrix = best_paramsRNN["losses"][0] if(len(best_paramsRNN["optimizer"]) == 0): optimizer = 'Nadam' else: optimizer = best_paramsRNN["optimizer"][0] if(best_paramsRNN["batch_size"] == 0): batchsize = 32 else: batchsize = best_paramsRNN["batch_size"][0] except: loss_matrix = 'binary_crossentropy' optimizer = 'Nadam' batchsize = 32 if self.scoreParam == 'accuracy': best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['accuracy']) elif self.scoreParam == 'recall': best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[recall_m]) elif self.scoreParam == 'roc_auc': best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[tf.keras.metrics.AUC()]) elif self.scoreParam == 'precision': best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[precision_m]) elif self.scoreParam == 'f1_score': best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[f1_m]) self.log.info("----------> Score Matrix: "+str(best_modelRNN.metrics_names)) scoreRNN = best_modelRNN.evaluate(X1,y, batch_size=batchsize) self.log.info("----------> Score: "+str(scoreRNN)) self.log.info("----------> Model Params: "+str(best_paramsRNN)) executionTime=time.time() - start self.log.info('----------> Total Execution: '+str(executionTime)+'\n') XSNN = np.expand_dims(self.testX, axis=2) #predictedData = best_modelRNN.predict_classes(XSNN) predictedData=np.argmax(best_modelRNN.predict(XSNN),axis=1) #predictedData = best_modelSNN.predict(self.testX) if 'accuracy' in str(self.scoreParam): score = accuracy_score(self.testY,predictedData) elif 'recall' in str(self.scoreParam): score = recall_score(self.testY,predictedData, average='macro') elif 'precision' in str(self.scoreParam): score = precision_score(self.testY,predictedData,average='macro') elif 'f1_score' in str(self.scoreParam): score = f1_score(self.testY,predictedData, average='macro') elif 'roc_auc' in str(self.scoreParam): score = roc_auc_score(self.testY,predictedData,average="macro") score = round((score*100),2) self.log.info("----------> Testing Score: "+str(score)) scoreRNN[1] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"Recurrent Neural Network","FeatureEngineering":"'+str(self.best_feature_model)+'","Score":'+str(scoreRNN[1])+'}' self.log.info('Status:- |... DL Algorithm applied: Recurrent Neural Network') self.log.info('Status:- |... Score after hyperparameter tuning: '+str(round(score,2))) if "Recurrent Neural Network (GRU)"in self.modelList: self.log.info("-------> Model Name: Recurrent Neural Network (GRU)") start = time.time() data = self.modelParams["Recurrent Neural Network (GRU)"] print(data) p = {"RNNType":["GRU"], "numRNNLayers":[int(n) for n in data["numRNNLayers"].split(",")], "activation":data["activation"].split(","), "last_activation":data["last_activation"].split(","), "optimizer":data["optimizer"].split(","), "losses":data["losses"].split(","), "first_neuron":[int(n) for n in data["first_layer"].split(",")], "shapes": data["shapes"].split(","), "hidden_layers":[int(n) for n in data["hidden_layers"].split(",")], "dropout": [float(n) for n in data["dropout"].split(",")], "lr": [float(n) for n in data["learning_rate"].split(",")], "batch_size": [int(n) for n in data["batch_size"].split(",")], "epochs": [int(n) for n in data["epochs"].split(",")]} param_combinations = int(np.prod([len(x.split(',')) for x in p])) round_limit = self.roundLimit if not self.roundLimit else min(self.roundLimit, param_combinations) scan_object = talos.Scan(x=X_train, y=y_train, x_val = X_test, y_val = y_test, model = modelObj.RNNClassification, experiment_name='RNN', params=p, round_limit=round_limit, random_method=self.randomMethod ) matrix_type = 'val_acc' if self.scoreParam.lower() == 'accuracy': matrix_type = 'val_acc' elif(self.scoreParam.lower() == 'roc_auc'): matrix_type = 'val_auc' elif(self.scoreParam.lower() == 'recall'): matrix_type = 'val_recall_m' elif(self.scoreParam.lower() == 'precision'): matrix_type = 'val_precision_m' elif(self.scoreParam.lower() == 'f1_score'): matrix_type = 'val_f1_m' analyze_objectRNNGRU = talos.Analyze(scan_object) highValAccRNNGRU = analyze_objectRNNGRU.high(matrix_type) dfRNNGRU = analyze_objectRNNGRU.data newdfRNNGRU = dfRNNGRU.loc[dfRNNGRU[matrix_type] == highValAccRNNGRU] if(len(newdfRNNGRU) > 1): lowLoss = analyze_objectRNNGRU.low('val_loss') newdfRNNGRU = newdfRNNGRU.loc[newdfRNNGRU['val_loss'] == lowLoss] best_paramsRNNGRU["RNNType"] = "GRU" best_paramsRNNGRU["numRNNLayers"] = list(newdfRNNGRU["numRNNLayers"])[0] best_paramsRNNGRU["activation"] = list(newdfRNNGRU["activation"])[0] best_paramsRNNGRU["optimizer"] = list(newdfRNNGRU["optimizer"])[0] best_paramsRNNGRU["losses"] = list(newdfRNNGRU["losses"])[0] best_paramsRNNGRU["first_layer"] = list(newdfRNNGRU["first_neuron"])[0] best_paramsRNNGRU["shapes"] = list(newdfRNNGRU["shapes"])[0] best_paramsRNNGRU["hidden_layers"] = list(newdfRNNGRU["hidden_layers"])[0] best_paramsRNNGRU["dropout"] = list(newdfRNNGRU["dropout"])[0] best_paramsRNNGRU["batch_size"] = list(newdfRNNGRU["batch_size"])[0] best_paramsRNNGRU["epochs"] = list(newdfRNNGRU["epochs"])[0] best_paramsRNNGRU["lr"] = list(newdfRNNGRU["lr"])[0] best_paramsRNNGRU["last_activation"] = list(newdfRNNGRU["last_activation"])[0] best_modelRNNGRU = scan_object.best_model(metric=matrix_type, asc=False) try: if(len(best_paramsRNNGRU["losses"]) == 0): loss_matrix = 'binary_crossentropy' else: loss_matrix = best_paramsRNNGRU["losses"][0] if(len(best_paramsRNNGRU["optimizer"]) == 0): optimizer = 'Nadam' else: optimizer = best_paramsRNNGRU["optimizer"][0] if(best_paramsRNNGRU["batch_size"]== 0): batchsize = 32 else: batchsize = best_paramsRNNGRU["batch_size"][0] except: loss_matrix = 'binary_crossentropy' optimizer = 'Nadam' batchsize = 32 if self.scoreParam == 'accuracy': best_modelRNNGRU.compile(loss=loss_matrix,optimizer=optimizer, metrics=['accuracy']) elif self.scoreParam == 'recall': best_modelRNNGRU.compile(loss=loss_matrix,optimizer=optimizer, metrics=[recall_m]) elif self.scoreParam == 'roc_auc': best_modelRNNGRU.compile(loss=loss_matrix,optimizer=optimizer, metrics=[tf.keras.metrics.AUC()]) elif self.scoreParam == 'precision': best_modelRNNGRU.compile(loss=loss_matrix,optimizer=optimizer, metrics=[precision_m]) elif self.scoreParam == 'f1_score': best_modelRNNGRU.compile(loss=loss_matrix,optimizer=optimizer, metrics=[f1_m]) self.log.info("----------> Score Matrix: "+str(best_modelRNNGRU.metrics_names)) scoreRNNGRU = best_modelRNNGRU.evaluate(X1,y, batch_size=batchsize) self.log.info("----------> Score: "+str(scoreRNNGRU)) self.log.info("----------> Model Params: "+str(best_paramsRNNGRU)) executionTime=time.time() - start self.log.info('----------> Total Execution: '+str(executionTime)+'\n') XSNN = np.expand_dims(self.testX, axis=2) #predictedData = best_modelRNNGRU.predict_classes(XSNN) predictedData=np.argmax(best_modelRNNGRU.predict(XSNN),axis=1) #predictedData = best_modelSNN.predict(self.testX) if 'accuracy' in str(self.scoreParam): score = accuracy_score(self.testY,predictedData) elif 'recall' in str(self.scoreParam): score = recall_score(self.testY,predictedData, average='macro') elif 'precision' in str(self.scoreParam): score = precision_score(self.testY,predictedData,average='macro') elif 'f1_score' in str(self.scoreParam): score = f1_score(self.testY,predictedData, average='macro') elif 'roc_auc' in str(self.scoreParam): score = roc_auc_score(self.testY,predictedData,average="macro") score = round((score*100),2) self.log.info("----------> Testing Score: "+str(score)) scoreRNNGRU[1] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"Recurrent Neural Network (GRU)","FeatureEngineering":"'+str(self.best_feature_model)+'","Score":'+str(scoreRNNGRU[1])+'}' self.log.info('Status:- |... DL Algorithm applied: Recurrent Neural Network (GRU)') self.log.info('Status:- |... Score after hyperparameter tuning: '+str(round(score,2))) if "Recurrent Neural Network (LSTM)"in self.modelList: self.log.info("-------> Model Name: Recurrent Neural Network (LSTM)") start = time.time() data = self.modelParams["Recurrent Neural Network (LSTM)"] p = {"RNNType":["LSTM"], "numRNNLayers":[int(n) for n in data["numRNNLayers"].split(",")], "activation":data["activation"].split(","), "last_activation":data["last_activation"].split(","), "optimizer":data["optimizer"].split(","), "losses":data["losses"].split(","), "first_neuron":[int(n) for n in data["first_layer"].split(",")], "shapes": data["shapes"].split(","), "hidden_layers":[int(n) for n in data["hidden_layers"].split(",")], "dropout": [float(n) for n in data["dropout"].split(",")], "lr": [float(n) for n in data["learning_rate"].split(",")], "batch_size": [int(n) for n in data["batch_size"].split(",")], "epochs": [int(n) for n in data["epochs"].split(",")]} param_combinations = int(np.prod([len(x.split(',')) for x in p])) round_limit = self.roundLimit if not self.roundLimit else min(self.roundLimit, param_combinations) scan_object = talos.Scan(x=X_train, y=y_train, x_val = X_test, y_val = y_test, model = modelObj.RNNClassification, experiment_name='RNN', params=p, round_limit=round_limit, random_method=self.randomMethod ) matrix_type = 'val_acc' if self.scoreParam.lower() == 'accuracy': matrix_type = 'val_acc' elif(self.scoreParam.lower() == 'roc_auc'): matrix_type = 'val_auc' elif(self.scoreParam.lower() == 'recall'): matrix_type = 'val_recall_m' elif(self.scoreParam.lower() == 'precision'): matrix_type = 'val_precision_m' elif(self.scoreParam.lower() == 'f1_score'): matrix_type = 'val_f1_m' analyze_objectRNNLSTM = talos.Analyze(scan_object) highValAccRNNLSTM = analyze_objectRNNLSTM.high(matrix_type) dfRNNLSTM = analyze_objectRNNLSTM.data newdfRNNLSTM = dfRNNLSTM.loc[dfRNNLSTM[matrix_type] == highValAccRNNLSTM] if(len(newdfRNNLSTM) > 1): lowLoss = analyze_objectRNNLSTM.low('val_loss') newdfRNNLSTM = newdfRNNLSTM.loc[newdfRNNLSTM['val_loss'] == lowLoss] best_paramsRNNLSTM["RNNType"] = "LSTM" best_paramsRNNLSTM["numRNNLayers"] = list(newdfRNNLSTM["numRNNLayers"])[0] best_paramsRNNLSTM["activation"] = list(newdfRNNLSTM["activation"])[0] best_paramsRNNLSTM["optimizer"] = list(newdfRNNLSTM["optimizer"])[0] best_paramsRNNLSTM["losses"] = list(newdfRNNLSTM["losses"])[0] best_paramsRNNLSTM["first_layer"] = list(newdfRNNLSTM["first_neuron"])[0] best_paramsRNNLSTM["shapes"] = list(newdfRNNLSTM["shapes"])[0] best_paramsRNNLSTM["hidden_layers"] = list(newdfRNNLSTM["hidden_layers"])[0] best_paramsRNNLSTM["dropout"] = list(newdfRNNLSTM["dropout"])[0] best_paramsRNNLSTM["batch_size"] = list(newdfRNNLSTM["batch_size"])[0] best_paramsRNNLSTM["epochs"] = list(newdfRNNLSTM["epochs"])[0] best_paramsRNNLSTM["lr"] = list(newdfRNNLSTM["lr"])[0] best_paramsRNNLSTM["last_activation"] = list(newdfRNNLSTM["last_activation"])[0] best_modelRNNLSTM = scan_object.best_model(metric=matrix_type, asc=False) try: if(len(best_paramsRNNLSTM["losses"]) == 0): loss_matrix = 'binary_crossentropy' else: loss_matrix = best_paramsRNNLSTM["losses"][0] if(len(best_paramsRNNLSTM["optimizer"]) == 0): optimizer = 'Nadam' else: optimizer = best_paramsRNNLSTM["optimizer"][0] if(best_paramsRNNLSTM["batch_size"] == 0): batchsize = 32 else: batchsize = best_paramsRNNLSTM["batch_size"][0] except: loss_matrix = 'binary_crossentropy' optimizer = 'Nadam' batchsize = 32 if self.scoreParam == 'accuracy': best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=['accuracy']) elif self.scoreParam == 'recall': best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=[recall_m]) elif self.scoreParam == 'roc_auc': best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=[tf.keras.metrics.AUC()]) elif self.scoreParam == 'precision': best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=[precision_m]) elif self.scoreParam == 'f1_score': best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=[f1_m]) self.log.info("----------> Score Matrix: "+str(best_modelRNNLSTM.metrics_names)) scoreRNNLSTM = best_modelRNNLSTM.evaluate(X1,y, batch_size=batchsize) self.log.info("----------> Score: "+str(scoreRNNLSTM)) self.log.info("----------> Model Params: "+str(best_paramsRNNLSTM)) executionTime=time.time() - start self.log.info('----------> Total Execution: '+str(executionTime)+'\n') XSNN = np.expand_dims(self.testX, axis=2) #predictedData = best_modelRNNLSTM.predict_classes(XSNN) predictedData=np.argmax(best_modelRNNLSTM.predict(XSNN),axis=1) #predictedData = best_modelSNN.predict(self.testX) if 'accuracy' in str(self.scoreParam): score = accuracy_score(self.testY,predictedData) elif 'recall' in str(self.scoreParam): score = recall_score(self.testY,predictedData, average='macro') elif 'precision' in str(self.scoreParam): score = precision_score(self.testY,predictedData,average='macro') elif 'f1_score' in str(self.scoreParam): score = f1_score(self.testY,predictedData, average='macro') elif 'roc_auc' in str(self.scoreParam): score = roc_auc_score(self.testY,predictedData,average="macro") score = round((score*100),2) self.log.info("----------> Testing Score: "+str(score)) scoreRNNLSTM[1] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"Recurrent Neural Network (LSTM)","FeatureEngineering":"'+str(self.best_feature_model)+'","Score":'+str(scoreRNNLSTM[1])+'}' self.log.info('Status:- |... DL Algorithm applied: Recurrent Neural Network (LSTM)') self.log.info('Status:- |... Score after hyperparameter tuning: '+str(round(score,2))) if "Convolutional Neural Network (1D)"in self.modelList: self.log.info("-------> Model Name: CNN") start = time.time() data = self.modelParams["Convolutional Neural Network (1D)"] p = {"activation":data["activation"].split(","), "last_activation":data["last_activation"].split(","), "numConvLayers":[int(n) for n in data["numConvLayers"].split(",")], "MaxPool":data["activation"].split(","), "optimizer":data["optimizer"].split(","), "losses":data["losses"].split(","), "first_neuron":[int(n) for n in data["first_layer"].split(",")], "shapes": data["shapes"].split(","), "hidden_layers":[int(n) for n in data["hidden_layers"].split(",")], "dropout": [float(n) for n in data["dropout"].split(",")], "lr": [float(n) for n in data["learning_rate"].split(",")], "batch_size": [int(n) for n in data["batch_size"].split(",")], "epochs": [int(n) for n in data["epochs"].split(",")]} param_combinations = int(np.prod([len(x.split(',')) for x in p])) round_limit = self.roundLimit if not self.roundLimit else min(self.roundLimit, param_combinations) scan_object = talos.Scan(x=X_train, y=y_train, x_val = X_test, y_val = y_test, model = modelObj.CNNClassification, experiment_name='CNN', params=p, round_limit=round_limit, random_method=self.randomMethod ) matrix_type = 'val_acc' if self.scoreParam.lower() == 'accuracy': matrix_type = 'val_acc' elif(self.scoreParam.lower() == 'roc_auc'): matrix_type = 'val_auc' elif(self.scoreParam.lower() == 'recall'): matrix_type = 'val_recall_m' elif(self.scoreParam.lower() == 'precision'): matrix_type = 'val_precision_m' elif(self.scoreParam.lower() == 'f1_score'): matrix_type = 'val_f1_m' analyze_objectCNN = talos.Analyze(scan_object) highValAccCNN = analyze_objectCNN.high(matrix_type) dfCNN = analyze_objectCNN.data newdfCNN = dfCNN.loc[dfCNN[matrix_type] == highValAccCNN] if(len(newdfCNN) > 1): lowLoss = analyze_objectCNN.low('val_loss') newdfCNN = newdfCNN.loc[newdfCNN['val_loss'] == lowLoss] best_paramsCNN["numConvLayers"] = list(newdfCNN["numConvLayers"]) best_paramsCNN["MaxPool"] = list(newdfCNN["MaxPool"]) best_paramsCNN["activation"] = list(newdfCNN["activation"]) best_paramsCNN["optimizer"] = list(newdfCNN["optimizer"]) best_paramsCNN["losses"] = list(newdfCNN["losses"]) best_paramsCNN["first_layer"] = list(newdfCNN["first_neuron"]) best_paramsCNN["shapes"] = list(newdfCNN["shapes"]) best_paramsCNN["hidden_layers"] = list(newdfCNN["hidden_layers"]) best_paramsCNN["dropout"] = list(newdfCNN["dropout"]) best_paramsCNN["batch_size"] = list(newdfCNN["batch_size"]) best_paramsCNN["epochs"] = list(newdfCNN["epochs"]) best_paramsCNN["lr"] = list(newdfCNN["lr"]) best_paramsCNN["last_activation"] = list(newdfCNN["last_activation"])[0] best_modelCNN = scan_object.best_model(metric='val_acc', asc=True) try: if(len(best_paramsCNN["losses"]) == 0): loss_matrix = 'binary_crossentropy' else: loss_matrix = best_paramsCNN["losses"][0] if(len(best_paramsCNN["optimizer"]) == 0): optimizer = 'Nadam' else: optimizer = best_paramsCNN["optimizer"][0] if(best_paramsCNN["batch_size"] == 0): batchsize = 32 else: batchsize = best_paramsCNN["batch_size"][0] except: loss_matrix = 'binary_crossentropy' optimizer = 'Nadam' batchsize = 32 if self.scoreParam == 'accuracy': best_modelCNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['accuracy']) elif self.scoreParam == 'recall': best_modelCNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[recall_m]) elif self.scoreParam == 'precision': best_modelCNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[precision_m]) elif self.scoreParam == 'roc_auc': best_modelCNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[tf.keras.metrics.AUC()]) elif self.scoreParam == 'f1_score': best_modelCNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[f1_m]) self.log.info("----------> Score Matrix: "+str(best_modelCNN.metrics_names)) scoreCNN = best_modelCNN.evaluate(X1,y, batch_size=batchsize) self.log.info("----------> Score: "+str(scoreCNN)) self.log.info("----------> Model Params: "+str(best_paramsCNN)) executionTime=time.time() - start self.log.info('----------> Total Execution: '+str(executionTime)+'\n') XSNN = np.expand_dims(self.testX, axis=2) #predictedData = best_modelCNN.predict_classes(XSNN) predictedData=np.argmax(best_modelCNN.predict(XSNN),axis=1) #predictedData = best_modelSNN.predict(self.testX) if 'accuracy' in str(self.scoreParam): score = accuracy_score(self.testY,predictedData) elif 'recall' in str(self.scoreParam): score = recall_score(self.testY,predictedData, average='macro') elif 'precision' in str(self.scoreParam): score = precision_score(self.testY,predictedData,average='macro') elif 'f1_score' in str(self.scoreParam): score = f1_score(self.testY,predictedData, average='macro') elif 'roc_auc' in str(self.scoreParam): score = roc_auc_score(self.testY,predictedData,average="macro") score = round((score*100),2) self.log.info("----------> Testing Score: "+str(score)) scoreCNN[1] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"Convolutional Neural Network (1D)","FeatureEngineering":"'+str(self.best_feature_model)+'","Score":'+str(scoreCNN[1])+'}' self.log.info('Status:- |... DL Algorithm applied: Convolutional Neural Network (1D)') self.log.info('Status:- |... Score after hyperparameter tuning: '+str(round(score,2))) modelScore = [] if len(scoreSNN) != 0: modelScore.append(scoreSNN[1]) if len(scoreRNN) != 0: modelScore.append(scoreRNN[1]) if len(scoreRNNGRU) != 0: modelScore.append(scoreRNNGRU[1]) if len(scoreRNNLSTM) != 0: modelScore.append(scoreRNNLSTM[1]) if len(scoreCNN) != 0: modelScore.append(scoreCNN[1]) selectedModel = "" best_params="" if len(scoreSNN) != 0 and max(modelScore) == scoreSNN[1]: selectedModel = "Neural Network" best_model = best_modelSNN best_params = best_paramsSNN elif len(scoreRNN) != 0 and max(modelScore) == scoreRNN[1]: selectedModel = "Recurrent Neural Network" best_model = best_modelRNN best_params = best_paramsRNN elif len(scoreRNNGRU) != 0 and max(modelScore) == scoreRNNGRU[1]: selectedModel = "Recurrent Neural Network (GRU)" best_model = best_modelRNNGRU best_params = best_paramsRNNGRU elif len(scoreRNNLSTM) != 0 and max(modelScore) == scoreRNNLSTM[1]: selectedModel = "Recurrent Neural Network (LSTM)" best_model = best_modelRNNLSTM best_params = best_paramsRNNLSTM elif len(scoreCNN) != 0 and max(modelScore) == scoreCNN[1]: selectedModel = "Convolutional Neural Network (1D)" best_model = best_modelCNN best_params = best_paramsCNN modelScore = max(modelScore) executionTime=time.time() - start self.log.info("-------> ExecutionTime(sec) :"+str(executionTime)+'\n') self.log.info('Status:- |... Best Algorithm selected: '+str(selectedModel)+' '+str(round(modelScore,2))) self.log.info('-------> Best Params: '+str(best_params)) return selectedModel,modelScore,best_model,best_params,X1,XSNN,scoredetails,loss_matrix,optimizer except Exception as inst: self.log.info( '\n-----> classificationModel failed!!!.'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import numpy as np import pandas as pd import talos from talos import Evaluate import json import sys import time import os import tensorflow.keras.utils as kutils from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense,Dropout,LSTM,GRU,SimpleRNN,Flatten,Input from sklearn.model_selection import train_test_split from tensorflow.keras.layers import Conv1D,MaxPooling1D from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold from sklearn.metrics import r2_score from sklearn.metrics import mean_absolute_error,make_scorer from sklearn.metrics import mean_squared_error import logging import tensorflow as tf import tensorflow.keras.backend as K def rmse_m(y_true, y_pred): return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1)) def r_square(y_true, y_pred): SS_res = K.sum(K.square(y_true-y_pred)) SS_tot = K.sum(K.square(y_true-K.mean(y_true))) return (1 - SS_res/(SS_tot+K.epsilon())) class DLRegressionModel: def __init__(self,modelList, modelParams, scoreParam, cvSplit, featuresData, targetData,testX,testY, method,randomMethod,roundLimit,best_feature_model): self.modelList =modelList self.modelParams =modelParams self.scoreParam = scoreParam self.cvSplit =cvSplit self.featuresData =featuresData self.targetData = targetData self.testX = testX self.testY = testY self.method =method #self.logFile = logFile self.randomMethod=randomMethod self.roundLimit=roundLimit self.log = logging.getLogger('eion') self.best_feature_model = best_feature_model def RNNRegression(self,x_train,y_train,x_val,y_val,params): tf.keras.backend.clear_session() x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) x_val = np.reshape(x_val, (x_val.shape[0], x_val.shape[1], 1)) model = Sequential() if params['RNNType'] == "LSTM" : if params['numRNNLayers'] > 1: model.add(LSTM(params['first_neuron'],return_sequences=True,input_shape=(x_train.shape[1],1))) for x in range(1,params['numRNNLayers']): model.add(LSTM(params['first_neuron'])) else: model.add(LSTM(params['first_neuron'],input_shape=(x_train.shape[1],1))) elif params['RNNType'] == "GRU" : if params['numRNNLayers'] > 1: model.add(GRU(params['first_neuron'],return_sequences=True,input_shape=(x_train.shape[1],1))) for x in range(1,params['numRNNLayers']): model.add(GRU(params['first_neuron'])) else: model.add(GRU(params['first_neuron'],input_shape=(x_train.shape[1],1))) elif params['RNNType'] == "SimpleRNN" : if params['numRNNLayers'] > 1: model.add(SimpleRNN(params['first_neuron'],return_sequences=True,input_shape=(x_train.shape[1],1))) for x in range(1,params['numRNNLayers']): model.add(SimpleRNN(params['first_neuron'])) else: model.add(SimpleRNN(params['first_neuron'],input_shape=(x_train.shape[1],1))) talos.utils.hidden_layers(model, params, 1) model.add(Dense(1,activation=params['activation'])) model.compile(loss=params['losses'],optimizer=params['optimizer'],metrics=['mae','mse',rmse_m,r_square]) out = model.fit(x_train, y_train, validation_data=(x_val, y_val), batch_size=params['batch_size'], epochs=params['epochs'],verbose=0,shuffle=True) return out, model def SNNRegression(self,x_train,y_train,x_val,y_val,params): tf.keras.backend.clear_session() model = Sequential() model.add(Dense(params['first_neuron'],input_dim=x_train.shape[1],activation=params['activation'])) talos.utils.hidden_layers(model, params, 1) model.add(Dense(1, activation=params['activation'])) model.compile(loss=params['losses'], optimizer=params['optimizer'], metrics=['mae','mse',rmse_m,r_square]) out = model.fit(x=x_train, y=y_train, validation_data=(x_val, y_val), epochs=params['epochs'], batch_size=params['batch_size'], verbose=0) return out, model def CNNRegression(self,x_train,y_train,x_val,y_val,params): tf.keras.backend.clear_session() x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) self.log.info(x_train.shape) x_val = np.reshape(x_val, (x_val.shape[0], x_val.shape[1], 1)) model = Sequential() self.log.info(params['kernel_size']) model.add(Conv1D(filters=params['first_neuron'], kernel_size=int(params['kernel_size']), activation=params['activation'], input_shape=(x_train.shape[1],1)) ) if params['numConvLayers'] > 1: for x in range(1,params['numConvLayers']): if params['MaxPool'] == "True": model.add(MaxPooling1D(pool_size=2)) model.add(Conv1D(filters=8, kernel_size=int(params['kernel_size']), activation=params['activation'])) talos.utils.hidden_layers(model, params, 1) model.add(Flatten()) model.add(Dense(1)) model.compile(loss=params['losses'],optimizer=params['optimizer'],metrics=['mae','mse',rmse_m,r_square]) out = model.fit(x_train, y_train, validation_data=(x_val, y_val), batch_size=params['batch_size'], epochs=params['epochs'],verbose=0,shuffle=True) return out, model def TalosScan(self,modelObj): try: #dataPath = pd.read_csv(self.dataLocation) #X = dataPath.drop(self.targetData, axis=1) X = self.featuresData x = X.values loss_matrix = 'mean_absolute_error' optimizer='Nadam' Y= self.targetData y = Y.values XSNN = X.values X1 = np.expand_dims(X, axis=2) scoredetails = '' kf = KFold(n_splits = self.cvSplit) for train_index, test_index in kf.split(X): X_train, X_test = x[train_index], x[test_index] y_train, y_test = y[train_index], y[test_index] data = self.modelParams models = data.keys() lstart = time.time() scoreSNN = [] scoreRNN = [] scoreCNN = [] scoreRNNGRU = [] scoreRNNLSTM = [] best_paramsSNN = {} best_paramsRNN = {} best_paramsRNNGRU = {} best_paramsRNNLSTM = {} best_paramsCNN = {} if "Neural Network"in self.modelList: self.log.info("-------> Model Name: Neural Network") start = time.time() data = self.modelParams["Neural Network"] p = {"activation":data["activation"].split(","), "optimizer":data["optimizer"].split(","), "losses":data["losses"].split(","), "first_neuron":[int(n) for n in data["first_layer"].split(",")], "shapes": data["shapes"].split(","), "hidden_layers":[int(n) for n in data["hidden_layers"].split(",")], "dropout": [float(n) for n in data["dropout"].split(",")], "lr": [float(n) for n in data["learning_rate"].split(",")], "batch_size": [int(n) for n in data["batch_size"].split(",")], "epochs": [int(n) for n in data["epochs"].split(",")]} scan_object = talos.Scan(x=X_train,y=y_train,x_val = X_test,y_val = y_test,model = modelObj.SNNRegression,experiment_name='SNN',params=p,round_limit=self.roundLimit,random_method=self.randomMethod) matrix_type = 'val_loss' if self.scoreParam.lower() == 'rmse': matrix_type = 'val_rmse_m' elif(self.scoreParam.lower() == 'r2'): matrix_type = 'val_r_square' elif(self.scoreParam.lower() == 'mae'): matrix_type = 'val_mae' elif(self.scoreParam.lower() == 'mse'): matrix_type = 'val_mse' analyze_objectSNN = talos.Analyze(scan_object) highValAccSNN = analyze_objectSNN.low(matrix_type) dfSNN = analyze_objectSNN.data newdfSNN = dfSNN.loc[dfSNN[matrix_type] == highValAccSNN] best_paramsSNN["activation"] = list(newdfSNN["activation"])[0] best_paramsSNN["optimizer"] = list(newdfSNN["optimizer"])[0] best_paramsSNN["losses"] = list(newdfSNN["losses"])[0] best_paramsSNN["first_layer"] = list(newdfSNN["first_neuron"])[0] best_paramsSNN["shapes"] = list(newdfSNN["shapes"])[0] best_paramsSNN["hidden_layers"] = list(newdfSNN["hidden_layers"])[0] best_paramsSNN["dropout"] = list(newdfSNN["dropout"])[0] best_paramsSNN["batch_size"] = list(newdfSNN["batch_size"])[0] best_paramsSNN["epochs"] = list(newdfSNN["epochs"])[0] best_paramsSNN["lr"] = list(newdfSNN["lr"])[0] best_modelSNN = scan_object.best_model(metric=matrix_type, asc=True) loss_matrix = best_paramsSNN["losses"] optimizer = best_paramsSNN["optimizer"] batchsize = best_paramsSNN["batch_size"] if self.scoreParam == 'rmse': best_modelSNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[rmse_m]) elif self.scoreParam == 'r2': best_modelSNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[r_square]) elif self.scoreParam == 'mae': best_modelSNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mae']) else: best_modelSNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mse']) scoreSNN = best_modelSNN.evaluate(XSNN,Y, batch_size=batchsize) self.log.info("----------> Score Matrix: "+str(best_modelSNN.metrics_names)) self.log.info("----------> Score: "+str(scoreSNN)) self.log.info("----------> Model Params: "+str(best_paramsSNN)) executionTime=time.time() - start self.log.info('----------> SNN Execution Time: '+str(executionTime)+'\n') XSNN = self.testX.values predictedData = best_modelSNN.predict(XSNN) if self.scoreParam.lower() == 'mse': score = mean_squared_error(self.testY,predictedData) elif self.scoreParam.lower() == 'rmse': score=mean_squared_error(self.testY,predictedData,squared=False) elif self.scoreParam.lower() == 'mae': score=mean_absolute_error(self.testY,predictedData) elif self.scoreParam.lower() == 'r2': score=r2_score(self.testY,predictedData) else: score = scoreSNN[1] self.log.info("----------> Testing Score: "+str(score)) scoreSNN[1] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"Neural Network","FeatureEngineering":"'+str(self.best_feature_model)+'","Score":'+str(scoreSNN[1])+'}' self.log.info('Status:- |... DL Algorithm applied: Neural Network') self.log.info('Status:- |... Score after hyperparameter tuning: '+str(round(score,2))) if "Recurrent Neural Network"in self.modelList: self.log.info("-------> Model Name: Recurrent Neural Network") start = time.time() data = self.modelParams["Recurrent Neural Network"] p = {"RNNType":["SimpleRNN"], "numRNNLayers":[int(n) for n in data["numRNNLayers"].split(",")], "activation":data["activation"].split(","), "optimizer":data["optimizer"].split(","), "losses":data["losses"].split(","), "first_neuron":[int(n) for n in data["first_layer"].split(",")], "shapes": data["shapes"].split(","), "hidden_layers":[int(n) for n in data["hidden_layers"].split(",")], "dropout": [float(n) for n in data["dropout"].split(",")], "lr": [float(n) for n in data["learning_rate"].split(",")], "batch_size": [int(n) for n in data["batch_size"].split(",")], "epochs": [int(n) for n in data["epochs"].split(",")]} scan_object = talos.Scan(x=X_train,y=y_train,x_val = X_test,y_val = y_test,model = modelObj.RNNRegression,experiment_name='RNN',params=p,round_limit=self.roundLimit,random_method=self.randomMethod) matrix_type = 'val_loss' if self.scoreParam.lower() == 'rmse': matrix_type = 'val_rmse_m' elif(self.scoreParam.lower() == 'r2'): matrix_type = 'val_r_square' elif(self.scoreParam.lower() == 'mae'): matrix_type = 'val_mae' elif(self.scoreParam.lower() == 'mse'): matrix_type = 'val_mse' analyze_objectRNN = talos.Analyze(scan_object) highValAccRNN = analyze_objectRNN.low(matrix_type) dfRNN = analyze_objectRNN.data newdfRNN = dfRNN.loc[dfRNN[matrix_type] == highValAccRNN] best_paramsRNN["RNNType"] = "SimpleRNN" best_paramsRNN["numRNNLayers"] = list(newdfRNN["numRNNLayers"])[0] best_paramsRNN["activation"] = list(newdfRNN["activation"])[0] best_paramsRNN["optimizer"] = list(newdfRNN["optimizer"])[0] best_paramsRNN["losses"] = list(newdfRNN["losses"])[0] best_paramsRNN["first_layer"] = list(newdfRNN["first_neuron"])[0] best_paramsRNN["shapes"] = list(newdfRNN["shapes"])[0] best_paramsRNN["hidden_layers"] = list(newdfRNN["hidden_layers"])[0] best_paramsRNN["dropout"] = list(newdfRNN["dropout"])[0] best_paramsRNN["batch_size"] = list(newdfRNN["batch_size"])[0] best_paramsRNN["epochs"] = list(newdfRNN["epochs"])[0] best_paramsRNN["lr"] = list(newdfRNN["lr"])[0] best_modelRNN = scan_object.best_model(metric=matrix_type, asc=True) loss_matrix = best_paramsRNN["losses"] optimizer = best_paramsRNN["optimizer"] batchsize = best_paramsRNN["batch_size"] if self.scoreParam == 'rmse': best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[rmse_m]) elif self.scoreParam == 'r2': best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[r_square]) elif self.scoreParam == 'mae': best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mae']) else: best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mse']) scoreRNN = best_modelRNN.evaluate(X1,Y, batch_size=batchsize) self.log.info("----------> Score Matrix: "+str(best_modelRNN.metrics_names)) self.log.info("----------> Score: "+str(scoreRNN)) self.log.info("----------> Model Params: "+str(best_paramsRNN)) executionTime=time.time() - start self.log.info('----------> RNN Execution Time: '+str(executionTime)+'\n') XSNN = np.expand_dims(self.testX, axis=2) predictedData = best_modelRNN.predict(XSNN) if self.scoreParam.lower() == 'mse': score = mean_squared_error(self.testY,predictedData) elif self.scoreParam.lower() == 'rmse': score=mean_squared_error(self.testY,predictedData,squared=False) elif self.scoreParam.lower() == 'mae': score=mean_absolute_error(self.testY,predictedData) elif self.scoreParam.lower() == 'r2': score=r2_score(self.testY,predictedData) else: score = scoreRNN[1] self.log.info("----------> Testing Score: "+str(score)) scoreRNN[1] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"Recurrent Neural Network","FeatureEngineering":"'+str(self.best_feature_model)+'","Score":'+str(scoreRNN[1])+'}' self.log.info('Status:- |... DL Algorithm applied: Recurrent Neural Network') self.log.info('Status:- |... Score after hyperparameter tuning: '+str(round(score,2))) if "Recurrent Neural Network (GRU)"in self.modelList: self.log.info("-------> Model Name: Recurrent Neural Network (GRU)") start = time.time() data = self.modelParams["Recurrent Neural Network (GRU)"] p = {"RNNType":["GRU"], "numRNNLayers":[int(n) for n in data["numRNNLayers"].split(",")], "activation":data["activation"].split(","), "optimizer":data["optimizer"].split(","), "losses":data["losses"].split(","), "first_neuron":[int(n) for n in data["first_layer"].split(",")], "shapes": data["shapes"].split(","), "hidden_layers":[int(n) for n in data["hidden_layers"].split(",")], "dropout": [float(n) for n in data["dropout"].split(",")], "lr": [float(n) for n in data["learning_rate"].split(",")], "batch_size": [int(n) for n in data["batch_size"].split(",")], "epochs": [int(n) for n in data["epochs"].split(",")]} scan_object = talos.Scan(x=X_train,y=y_train,x_val = X_test,y_val = y_test,model = modelObj.RNNRegression,experiment_name='RNNGRU',params=p,round_limit=self.roundLimit,random_method=self.randomMethod) matrix_type = 'val_loss' if self.scoreParam.lower() == 'rmse': matrix_type = 'val_rmse_m' elif(self.scoreParam.lower() == 'r2'): matrix_type = 'val_r_square' elif(self.scoreParam.lower() == 'mae'): matrix_type = 'val_mae' elif(self.scoreParam.lower() == 'mse'): matrix_type = 'val_mse' analyze_objectRNNGRU = talos.Analyze(scan_object) highValAccRNNGRU = analyze_objectRNNGRU.low(matrix_type) dfRNNGRU = analyze_objectRNNGRU.data newdfRNNGRU = dfRNNGRU.loc[dfRNNGRU[matrix_type] == highValAccRNNGRU] best_paramsRNNGRU["RNNType"] = "GRU" best_paramsRNNGRU["numRNNLayers"] = list(newdfRNNGRU["numRNNLayers"])[0] best_paramsRNNGRU["activation"] = list(newdfRNNGRU["activation"])[0] best_paramsRNNGRU["optimizer"] = list(newdfRNNGRU["optimizer"])[0] best_paramsRNNGRU["losses"] = list(newdfRNNGRU["losses"])[0] best_paramsRNNGRU["first_layer"] = list(newdfRNNGRU["first_neuron"])[0] best_paramsRNNGRU["shapes"] = list(newdfRNNGRU["shapes"])[0] best_paramsRNNGRU["hidden_layers"] = list(newdfRNNGRU["hidden_layers"])[0] best_paramsRNNGRU["dropout"] = list(newdfRNNGRU["dropout"])[0] best_paramsRNNGRU["batch_size"] = list(newdfRNNGRU["batch_size"])[0] best_paramsRNNGRU["epochs"] = list(newdfRNNGRU["epochs"])[0] best_paramsRNNGRU["lr"] = list(newdfRNNGRU["lr"])[0] best_modelRNNGRU = scan_object.best_model(metric=matrix_type, asc=True) loss_matrix = best_paramsRNNGRU["losses"] optimizer = best_paramsRNNGRU["optimizer"] batchsize = best_paramsRNNGRU["batch_size"] if self.scoreParam == 'rmse': best_modelRNNGRU.compile(loss=loss_matrix,optimizer=optimizer, metrics=[rmse_m]) elif self.scoreParam == 'r2': best_modelRNNGRU.compile(loss=loss_matrix,optimizer=optimizer, metrics=[r_square]) elif self.scoreParam == 'mae': best_modelRNNGRU.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mae']) else: best_modelRNNGRU.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mse']) scoreRNNGRU = best_modelRNNGRU.evaluate(X1,Y, batch_size=batchsize) self.log.info("----------> Score Matrix: "+str(best_modelRNNGRU.metrics_names)) self.log.info("----------> Score: "+str(scoreRNNGRU)) self.log.info("----------> Model Params: "+str(best_paramsRNNGRU)) executionTime=time.time() - start self.log.info('----------> RNN Execution Time: '+str(executionTime)+'\n') XSNN = np.expand_dims(self.testX, axis=2) predictedData = best_modelRNNGRU.predict(XSNN) if self.scoreParam.lower() == 'mse': score = mean_squared_error(self.testY,predictedData) elif self.scoreParam.lower() == 'rmse': score=mean_squared_error(self.testY,predictedData,squared=False) elif self.scoreParam.lower() == 'mae': score=mean_absolute_error(self.testY,predictedData) elif self.scoreParam.lower() == 'r2': score=r2_score(self.testY,predictedData) else: score = scoreRNNGRU[1] self.log.info("----------> Testing Score: "+str(score)) scoreRNNGRU[1] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"Recurrent Neural Network (GRU)","FeatureEngineering":"'+str(self.best_feature_model)+'","Score":'+str(scoreRNNGRU[1])+'}' self.log.info('Status:- |... DL Algorithm applied: Recurrent Neural Network (GRU)') self.log.info('Status:- |... Score after hyperparameter tuning: '+str(round(score,2))) if "Recurrent Neural Network (LSTM)"in self.modelList: self.log.info("-------> Model Name: Recurrent Neural Network (LSTM)") start = time.time() data = self.modelParams["Recurrent Neural Network (LSTM)"] p = {"RNNType":["LSTM"], "numRNNLayers":[int(n) for n in data["numRNNLayers"].split(",")], "activation":data["activation"].split(","), "optimizer":data["optimizer"].split(","), "losses":data["losses"].split(","), "first_neuron":[int(n) for n in data["first_layer"].split(",")], "shapes": data["shapes"].split(","), "hidden_layers":[int(n) for n in data["hidden_layers"].split(",")], "dropout": [float(n) for n in data["dropout"].split(",")], "lr": [float(n) for n in data["learning_rate"].split(",")], "batch_size": [int(n) for n in data["batch_size"].split(",")], "epochs": [int(n) for n in data["epochs"].split(",")]} scan_object = talos.Scan(x=X_train,y=y_train,x_val = X_test,y_val = y_test,model = modelObj.RNNRegression,experiment_name='RNNLSTM',params=p,round_limit=self.roundLimit,random_method=self.randomMethod) matrix_type = 'val_loss' if self.scoreParam.lower() == 'rmse': matrix_type = 'val_rmse_m' elif(self.scoreParam.lower() == 'r2'): matrix_type = 'val_r_square' elif(self.scoreParam.lower() == 'mae'): matrix_type = 'val_mae' elif(self.scoreParam.lower() == 'mse'): matrix_type = 'val_mse' analyze_objectRNNLSTM = talos.Analyze(scan_object) highValAccRNNLSTM = analyze_objectRNNLSTM.low(matrix_type) dfRNNLSTM = analyze_objectRNNLSTM.data newdfRNNLSTM = dfRNNLSTM.loc[dfRNNLSTM[matrix_type] == highValAccRNNLSTM] best_paramsRNNLSTM["RNNType"] = "GRU" best_paramsRNNLSTM["numRNNLayers"] = list(newdfRNNLSTM["numRNNLayers"])[0] best_paramsRNNLSTM["activation"] = list(newdfRNNLSTM["activation"])[0] best_paramsRNNLSTM["optimizer"] = list(newdfRNNLSTM["optimizer"])[0] best_paramsRNNLSTM["losses"] = list(newdfRNNLSTM["losses"])[0] best_paramsRNNLSTM["first_layer"] = list(newdfRNNLSTM["first_neuron"])[0] best_paramsRNNLSTM["shapes"] = list(newdfRNNLSTM["shapes"])[0] best_paramsRNNLSTM["hidden_layers"] = list(newdfRNNLSTM["hidden_layers"])[0] best_paramsRNNLSTM["dropout"] = list(newdfRNNLSTM["dropout"])[0] best_paramsRNNLSTM["batch_size"] = list(newdfRNNLSTM["batch_size"])[0] best_paramsRNNLSTM["epochs"] = list(newdfRNNLSTM["epochs"])[0] best_paramsRNNLSTM["lr"] = list(newdfRNNLSTM["lr"])[0] best_modelRNNLSTM = scan_object.best_model(metric=matrix_type, asc=True) loss_matrix = best_paramsRNNLSTM["losses"] optimizer = best_paramsRNNLSTM["optimizer"] batchsize = best_paramsRNNLSTM["batch_size"] if self.scoreParam == 'rmse': best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=[rmse_m]) elif self.scoreParam == 'r2': best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=[r_square]) elif self.scoreParam == 'mae': best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mae']) else: best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mse']) scoreRNNLSTM = best_modelRNNLSTM.evaluate(X1,Y, batch_size=batchsize) self.log.info("----------> Score Matrix: "+str(best_modelRNNLSTM.metrics_names)) self.log.info("----------> Score: "+str(scoreRNNLSTM)) self.log.info("----------> Model Params: "+str(best_paramsRNNLSTM)) executionTime=time.time() - start self.log.info('----------> RNN Execution Time: '+str(executionTime)+'\n') XSNN = np.expand_dims(self.testX, axis=2) predictedData = best_modelRNNLSTM.predict(XSNN) if self.scoreParam.lower() == 'mse': score = mean_squared_error(self.testY,predictedData) elif self.scoreParam.lower() == 'rmse': score=mean_squared_error(self.testY,predictedData,squared=False) elif self.scoreParam.lower() == 'mae': score=mean_absolute_error(self.testY,predictedData) elif self.scoreParam.lower() == 'r2': score=r2_score(self.testY,predictedData) else: score = scoreRNNLSTM[1] self.log.info("----------> Testing Score: "+str(score)) scoreRNNLSTM[1] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"Recurrent Neural Network (LSTM)","FeatureEngineering":"'+str(self.best_feature_model)+'","Score":'+str(scoreRNNLSTM[1])+'}' self.log.info('Status:- |... DL Algorithm applied: Recurrent Neural Network (LSTM)') self.log.info('Status:- |... Score after hyperparameter tuning: '+str(round(score,2))) if "Convolutional Neural Network (1D)"in self.modelList: self.log.info("-------> Model Name: CNN") start = time.time() data = self.modelParams["Convolutional Neural Network (1D)"] p = {"activation":data["activation"].split(","), "kernel_size":data["kernel_size"].split(","), "numConvLayers":[int(n) for n in data["numConvLayers"].split(",")], "MaxPool":data["activation"].split(","), "optimizer":data["optimizer"].split(","), "losses":data["losses"].split(","), "first_neuron":[int(n) for n in data["first_layer"].split(",")], "shapes": data["shapes"].split(","), "hidden_layers":[int(n) for n in data["hidden_layers"].split(",")], "dropout": [float(n) for n in data["dropout"].split(",")], "lr": [float(n) for n in data["learning_rate"].split(",")], "batch_size": [int(n) for n in data["batch_size"].split(",")], "epochs": [int(n) for n in data["epochs"].split(",")]} scan_object = talos.Scan(x=X_train,y=y_train, x_val = X_test, y_val = y_test, model = modelObj.CNNRegression,experiment_name='CNN', params=p,round_limit=self.roundLimit,random_method=self.randomMethod) matrix_type = 'val_loss' if self.scoreParam.lower() == 'rmse': matrix_type = 'val_rmse_m' elif(self.scoreParam.lower() == 'r2'): matrix_type = 'val_r_square' elif(self.scoreParam.lower() == 'mae'): matrix_type = 'val_mae' elif(self.scoreParam.lower() == 'mse'): matrix_type = 'val_mse' analyze_objectCNN = talos.Analyze(scan_object) highValAccCNN = analyze_objectCNN.low(matrix_type) dfCNN = analyze_objectCNN.data newdfCNN = dfCNN.loc[dfCNN[matrix_type] == highValAccCNN] best_paramsCNN["numConvLayers"] = list(newdfCNN["numConvLayers"])[0] best_paramsCNN["MaxPool"] = list(newdfCNN["MaxPool"])[0] best_paramsCNN["activation"] = list(newdfCNN["activation"])[0] best_paramsCNN["optimizer"] = list(newdfCNN["optimizer"])[0] best_paramsCNN["losses"] = list(newdfCNN["losses"])[0] best_paramsCNN["first_layer"] = list(newdfCNN["first_neuron"])[0] best_paramsCNN["shapes"] = list(newdfCNN["shapes"])[0] best_paramsCNN["hidden_layers"] = list(newdfCNN["hidden_layers"])[0] best_paramsCNN["dropout"] = list(newdfCNN["dropout"])[0] best_paramsCNN["batch_size"] = list(newdfCNN["batch_size"])[0] best_paramsCNN["epochs"] = list(newdfCNN["epochs"])[0] best_paramsCNN["lr"] = list(newdfCNN["lr"])[0] best_modelCNN = scan_object.best_model(metric=matrix_type, asc=True) loss_matrix = best_paramsCNN["losses"] optimizer = best_paramsCNN["optimizer"] batchsize = best_paramsCNN["batch_size"] if self.scoreParam == 'rmse': best_modelCNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[rmse_m]) elif self.scoreParam == 'r2': best_modelCNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=[r_square]) elif self.scoreParam == 'mae': best_modelCNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mae']) else: best_modelCNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mse']) scoreCNN = best_modelCNN.evaluate(X1,Y, batch_size=batchsize) self.log.info("----------> Score Matrix: "+str(best_modelCNN.metrics_names)) self.log.info("----------> Score: "+str(scoreCNN)) self.log.info("----------> Model Params: "+str(best_paramsCNN)) executionTime=time.time() - start self.log.info('----------> CNN Execution Time: '+str(executionTime)+'\n') XSNN = np.expand_dims(self.testX, axis=2) predictedData = best_modelCNN.predict(XSNN) if self.scoreParam.lower() == 'mse': score = mean_squared_error(self.testY,predictedData) elif self.scoreParam.lower() == 'rmse': score=mean_squared_error(self.testY,predictedData,squared=False) elif self.scoreParam.lower() == 'mae': score=mean_absolute_error(self.testY,predictedData) elif self.scoreParam.lower() == 'r2': score=r2_score(self.testY,predictedData) else: score = scoreCNN[1] self.log.info("----------> Testing Score: "+str(score)) scoreCNN[1] = score if(scoredetails != ''): scoredetails += ',' scoredetails += '{"Model":"CNN","FeatureEngineering":"'+str(self.best_feature_model)+'","Score":'+str(scoreCNN[1])+'}' self.log.info('Status:- |... DL Algorithm applied: CNN') self.log.info('Status:- |... Score after hyperparameter tuning: '+str(round(score,2))) modelScore = [] if len(scoreSNN) != 0: modelScore.append(scoreSNN[1]) if len(scoreRNN) != 0: modelScore.append(scoreRNN[1]) if len(scoreRNNGRU) != 0: modelScore.append(scoreRNNGRU[1]) if len(scoreRNNLSTM) != 0: modelScore.append(scoreRNNLSTM[1]) if len(scoreCNN) != 0: modelScore.append(scoreCNN[1]) selectedModel = "" best_model = "" if self.scoreParam == "r2": if len(scoreSNN) != 0 and max(modelScore) == scoreSNN[1]: selectedModel = "Neural Network" best_model = best_modelSNN best_params = best_paramsSNN elif len(scoreRNN) != 0 and max(modelScore) == scoreRNN[1]: selectedModel = "Recurrent Neural Network" best_model = best_modelRNN best_params = best_paramsRNN elif len(scoreRNNGRU) != 0 and max(modelScore) == scoreRNNGRU[1]: selectedModel = "Recurrent Neural Network (GRU)" best_model = best_modelRNNGRU best_params = best_paramsRNNGRU elif len(scoreRNNLSTM) != 0 and max(modelScore) == scoreRNNLSTM[1]: selectedModel = "Recurrent Neural Network (LSTM)" best_model = best_modelRNNLSTM best_params = best_paramsRNNLSTM elif len(scoreCNN) != 0 and max(modelScore) == scoreCNN[1]: selectedModel = "Convolutional Neural Network (1D)" best_model = best_modelCNN best_params = best_paramsCNN modelScore = max(modelScore) else: if len(scoreSNN) != 0 and min(modelScore) == scoreSNN[1]: selectedModel = "Neural Network" best_model = best_modelSNN best_params = best_paramsSNN elif len(scoreRNN) != 0 and min(modelScore) == scoreRNN[1]: selectedModel = "Recurrent Neural Network" best_model = best_modelRNN best_params = best_paramsRNN elif len(scoreRNNGRU) != 0 and min(modelScore) == scoreRNNGRU[1]: selectedModel = "Recurrent Neural Network (GRU)" best_model = best_modelRNNGRU best_params = best_paramsRNNGRU elif len(scoreRNNLSTM) != 0 and min(modelScore) == scoreRNNLSTM[1]: selectedModel = "Recurrent Neural Network (LSTM)" best_model = best_modelRNNLSTM best_params = best_paramsRNNLSTM elif len(scoreCNN) != 0 and min(modelScore) == scoreCNN[1]: selectedModel = "Convolutional Neural Network (1D)" best_model = best_modelCNN best_params = best_paramsCNN modelScore = min(modelScore) executionTime=time.time() - lstart self.log.info("-------> Total Execution Time(sec):"+str(executionTime)) self.log.info('Status:- |... Best Algorithm selected: '+str(selectedModel)+' '+str(round(modelScore,2))) return selectedModel,modelScore,best_model,best_params,X1,XSNN,scoredetails,loss_matrix,optimizer except Exception as inst: self.log.info( '\n-----> regressionModel failed!!!.'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import json import shutil import logging import sys from AionConfigManager import AionConfigManager from sklearn.externals import joblib class edgeformats: def __init__(self,deploy_path): self.deploy_path = deploy_path self.edge_deploy_path = os.path.join(deploy_path,"edge") os.mkdir(self.edge_deploy_path) def converttoedgedeployment(self,saved_model,edge_format,xtrain,model_type,iterName,iterVersion,features,profiled_data_file): if edge_format == 'onnx': from skl2onnx import convert_sklearn from skl2onnx.common.data_types import FloatTensorType xtrain = xtrain[features] initial_type = [('float_input', FloatTensorType([None, xtrain.shape[1]]))] filename = os.path.join(self.deploy_path,saved_model) loaded_model = joblib.load(filename) onx = convert_sklearn(loaded_model, initial_types=initial_type) onnx_filename = os.path.join(self.edge_deploy_path, model_type + '_' + iterName + '_' + iterVersion + '.onnx') with open(onnx_filename, "wb") as f: f.write(onx.SerializeToString()) self.createedgeruntimeFile(onnx_filename,profiled_data_file,features) def createedgeruntimeFile(self,onnx_filename,datafilepath,features): runtimefilecontent = '' runtimefilecontent += 'import pandas' runtimefilecontent += '\n' runtimefilecontent += 'import numpy' runtimefilecontent += '\n' runtimefilecontent += 'import sys' runtimefilecontent += '\n' runtimefilecontent += 'import onnxruntime as rt' runtimefilecontent += '\n' runtimefilecontent += 'def onnx_runtime_validation():' runtimefilecontent += '\n' runtimefilecontent += ' modelfile = r"'+str(onnx_filename)+'"' runtimefilecontent += '\n' runtimefilecontent += ' datafile = r"'+str(datafilepath)+'"' runtimefilecontent += '\n' runtimefilecontent += ' dataframe = pandas.read_csv(datafile)' runtimefilecontent += '\n' runtimefilecontent += ' dataframe = dataframe['+str(features)+']' runtimefilecontent += '\n' runtimefilecontent += ' df = dataframe.head(8)' runtimefilecontent += '\n' runtimefilecontent += ' dataset = df.values' runtimefilecontent += '\n' runtimefilecontent += ' sess = rt.InferenceSession(modelfile)' runtimefilecontent += '\n' runtimefilecontent += ' input_name = sess.get_inputs()[0].name' runtimefilecontent += '\n' runtimefilecontent += ' label_name = sess.get_outputs()[0].name' runtimefilecontent += '\n' runtimefilecontent += ' inputsize=sess.get_inputs()[0].shape' runtimefilecontent += '\n' runtimefilecontent += ' XYZ = dataset[:,0:inputsize[1]].astype(float)' runtimefilecontent += '\n' runtimefilecontent += ' pred_onx = sess.run([label_name], {input_name: XYZ.astype(numpy.float32)[0:8]})[0]' runtimefilecontent += '\n' runtimefilecontent += ' df[\'predictions\'] = pred_onx' runtimefilecontent += '\n' runtimefilecontent += ' result = df.to_json(orient="records")' runtimefilecontent += '\n' runtimefilecontent += ' return(result)' runtimefilecontent += '\n' runtimefilecontent += 'if __name__ == "__main__":' runtimefilecontent += '\n' runtimefilecontent += ' output = onnx_runtime_validation()' runtimefilecontent += '\n' runtimefilecontent += ' print("predictions:",output)' filename = os.path.join(self.edge_deploy_path,'onnxvalidation.py') f = open(filename, "w") f.write(str(runtimefilecontent)) f.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import json from pathlib import Path from AION.prediction_package.imports import importModule from AION.prediction_package import utility from AION.prediction_package.utility import TAB_CHAR from importlib.metadata import version """ This file provide the functionality which is common for most of the problem types deployment. """ def main_code(): return """ class predict(): def __init__(self): self.profiler = inputprofiler() self.selector = selector() self.trainer = trainer() self.formatter = output_format() def run(self, data): try: df = self._parse_data(data) raw_df = df.copy() df = self.profiler.run(df) df = self.selector.run(df) df = self.trainer.run(df) output = self.formatter.run(raw_df, df) print("predictions:",output) return (output) except Exception as e: output = {"status":"FAIL","message":str(e).strip('"')} print("predictions:",json.dumps(output)) return (json.dumps(output)) def _parse_data(self, data): file_path = Path(data) if file_path.suffix == ".tsv": df = pd.read_csv(data,encoding='utf-8',sep='\\t',skipinitialspace = True,na_values=['-','?']) elif file_path.suffix in [".csv", ".dat"]: df=pd.read_csv(data,encoding='utf-8',skipinitialspace = True,na_values=['-','?']) elif file_path.suffix in [".gz"] and file_path.stem.endswith('.csv'): df=pd.read_csv(data,encoding='utf-8',skipinitialspace = True,na_values=['-','?']) elif file_path.suffix == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) df = pd.json_normalize(jsonData) else: jsonData = json.loads(data) df = pd.json_normalize(jsonData) return df import sys if __name__ == "__main__": output = predict().run(sys.argv[1]) """ def profiler_code(params, indent=0): """ This will create the profiler file based on the config file. separated file is created as profiler is required for input drift also. """ imported_modules = [ {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'scipy', 'mod_from': None, 'mod_as': None}, {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None} ] importer = importModule() utility.import_modules(importer, imported_modules) code = """ class inputprofiler(): """ init_code = """ def __init__(self): """ if params.get('text_features'): imported_modules.append({'module':'importlib.util'}) init_code += """ # preprocessing preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if not preprocess_path.exists(): raise ValueError(f'Preprocess model file not found: {preprocess_path}') self.profiler = joblib.load(preprocess_path) """ run_code = """ def run(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) """ if params.get('input_features_type'): imported_modules.append({'module':'dtype','mod_from':'numpy'}) run_code += f""" df = df.astype({params.get('input_features_type')}) """ if params.get('word2num_features'): imported_modules.append({'module':'w2n','mod_from':'word2number'}) run_code += f""" def s2n(value): try: x=eval(value) return x except: try: return w2n.word_to_num(value) except: return np.nan df[{params['word2num_features']}] = df[{params['word2num_features']}].apply(lambda x: s2n(x))""" if params.get('unpreprocessed_columns'): run_code += f""" unpreprocessed_data = df['{params['unpreprocessed_columns'][0]}'] df.drop(['{params['unpreprocessed_columns'][0]}'], axis=1,inplace=True) """ if params.get('force_numeric_conv'): run_code += f""" df[{params['force_numeric_conv']}] = df[{params['force_numeric_conv']}].apply(pd.to_numeric,errors='coerce')""" if params.get('conversion_method','').lower() == 'glove': code_text, modules = __profiler_glove_code(params) imported_modules.extend( modules) init_code += code_text elif params.get('conversion_method','').lower() == 'fasttext': init_code += __profiler_fasttext_code(params) run_code += __profiler_main_code(params) if params.get('unpreprocessed_columns'): run_code += f""" df['{params.get('unpreprocessed_columns')[0]}'] = unpreprocessed_data """ utility.import_modules(importer, imported_modules) import_code = importer.getCode() return import_code + code + init_code + run_code def __profiler_glove_code(params, indent=2): modules = [] modules.append({'module':'load_pretrained','mod_from':'text.Embedding'}) modules.append({'module':'TextProcessing','mod_from':'text'}) code = """ model_path = TextProcessing.checkAndDownloadPretrainedModel('glove') embed_size, pretrained_model = load_pretrained(model_path) self.profiler.set_params(text_process__vectorizer__external_model = pretrained_model) """ return code.replace('\n', '\n'+(indent * TAB_CHAR)), modules def __profiler_fasttext_code(params, indent=2): code = """ def get_pretrained_model_path(): try: from AION.appbe.dataPath import DATA_DIR modelsPath = Path(DATA_DIR)/'PreTrainedModels'/'TextProcessing' except: modelsPath = Path('aion')/'PreTrainedModels'/'TextProcessing' if not modelsPath.exists(): modelsPath.mkdir(parents=True, exist_ok=True) return modelsPath if not importlib.util.find_spec('fasttext'): raise ValueError('fastText not installed') else: import os import fasttext import fasttext.util cwd = os.getcwd() os.chdir(get_pretrained_model_path()) fasttext.util.download_model('en', if_exists='ignore') pretrained_model = fasttext.load_model('cc.en.300.bin') os.chdir(cwd) self.profiler.set_params(text_process__vectorizer__external_model = pretrained_model) self.profiler.set_params(text_process__vectorizer__external_model_type = 'binary') """ return code.replace('\n', '\n'+(indent * TAB_CHAR)) def __profiler_main_code(params, indent=2): code = f""" df = self.profiler.transform(df) columns = {params['output_features']} if isinstance(df, scipy.sparse.spmatrix): df = pd.DataFrame(df.toarray(), columns=columns) else: df = pd.DataFrame(df, columns=columns) return df """ return code.replace('\n', '\n'+(indent * TAB_CHAR)) def feature_selector_code( params, indent=0): modules = [ {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'} ] code = """ class selector(): # this class def __init__(self): pass def run(self, df):""" code +=f""" return df[{params['output_features']}] """ return code, modules def feature_reducer_code( params, indent=0): modules = [ {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None} ] code = f""" class selector(): def __init__(self): reducer_file = (Path(__file__).parent/"model")/"{params['reducer_file']}" if not reducer_file.exists(): raise ValueError(f'Failed to load Feature Engineering model file: {{reducer_file}}') self.model = joblib.load(reducer_file) def run(self, df): reducer_input = {params['input_features']} reducer_output = {params['output_features']} df = self.model.transform(df[reducer_input]) return pd.DataFrame(df,columns=reducer_output) """ if indent: code = code.replace('\n', '\n'+(indent * TAB_CHAR)) return code, modules def create_feature_list(config=None, target_feature=None, deploy_path=None): featurelist = [] if 'profiler' in config: if 'input_features_type' in config['profiler']: input_features = config['profiler']['input_features_type'] for x in input_features: featurelt={} featurelt['feature'] = x if x == target_feature: featurelt['Type'] = 'Target' else: if input_features[x] in ['int','int64','float','float64']: featurelt['Type'] = 'Numeric' elif input_features[x] == 'object': featurelt['Type'] = 'Text' elif input_features[x] == 'category': featurelt['Type'] = 'Category' else: featurelt['Type'] = 'Unknown' featurelist.append(featurelt) featurefile = f""" import json def getfeatures(): try: features = {featurelist} outputjson = {{"status":"SUCCESS","features":features}} output = json.dumps(outputjson) print("Features:",output) return(output) except Exception as e: output = {{"status":"FAIL","message":str(e).strip(\'"\')}} print("Features:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = getfeatures() """ with open( deploy_path/'featureslist.py', 'wb') as f: f.write( str(featurefile).encode('utf8')) def requirement_file(deploy_path,model,textFeatures,learner_type='ML'): modules = ['pandas','numpy','alibi','matplotlib','joblib','shap','ipython','category_encoders','scikit-learn','word2number','flask_restful','evidently','Flask-Cors'] requires = '' for mod in modules: requires += f"{mod}=={version(mod)}\n" if len(textFeatures) > 0: tmodules = ['spacy','nltk','textblob','demoji','beautifulsoup4','text-unidecode','pyspellchecker','contractions','protobuf'] for mod in tmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Extreme Gradient Boosting (XGBoost)': mmodules = ['xgboost'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Light Gradient Boosting (LightGBM)': mmodules = ['lightgbm'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Categorical Boosting (CatBoost)': mmodules = ['catboost'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'arima': mmodules = ['pmdarima'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'fbprophet': mmodules = ['prophet'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'lstm' or model.lower() == 'mlp' or learner_type =='DL': mmodules = ['tensorflow'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() in ['cox', 'kaplanmeierfitter']: #bug 12833 mmodules = ['lifelines'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'sentencetransformer': #bug 12833 mmodules = ['sentence_transformers'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" with open( deploy_path/'requirements.txt', 'wb') as f: f.write(str(requires).encode('utf8')) def create_readme_file(deploy_path,modelfile,features): data = json.dumps([{x:x+'_value'} for x in features]) backslash_data = data.replace('"', '\\"') content = f""" ========== Files Structures ========== {modelfile} ------ Trained Model aion_prediction.py --> Python package entry point script/inputprofiler.py --> Profiling like FillNA and Category to Numeric ========== How to call the model ========== ============== From Windows Terminal ========== python aion_prediction.py "{backslash_data}" ============== From Linux Terminal ========== python aion_prediction.py "{data}" ============== Output ========== {{"status":"SUCCESS","data":[{{"Data1":"Value","prediction":"Value"}}]}} ## for single Row/Record {{"status":"SUCCESS","data":[{{"Data1":"Value","prediction":"Value"}},{{"Data1":"Value","prediction":"Value"}}]}} ## For Multiple Row/Record {{"status":"ERROR","message":"description"}} ## In Case Exception or Error """ filename = deploy_path/'readme.txt' with open(filename, 'w') as f: f.write(content) def create_util_folder(deploy_path): import tarfile ext_path = Path(__file__).parent.parent/'utilities' for x in ext_path.iterdir(): if x.suffix == '.tar': if x.name not in ['scikit_surprise-1.1.1.dist-info.tar','surprise.tar']: my_tar = tarfile.open(x) my_tar.extractall(deploy_path) my_tar.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os,sys import platform import json import shutil import logging from pathlib import Path from prediction_package import production from prediction_package import prediction_transformation as cs class DeploymentManager: def __init__(self): self.requirementfile='' self.modelfile='' self.s2i_environmentfile='' self.selectorfile='' self.profilerfile='' self.readmepackagename='' self.pythonpackage='' self.log = logging.getLogger('eion') def include_import_file(self,learner_type,method,scoreParam,model_type,model): if((learner_type == 'DL') or (learner_type == 'TextDL')): self.modelfile += 'from tensorflow.keras.models import load_model' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras import backend as K' self.modelfile += '\n' self.modelfile += 'import tensorflow as tf' self.modelfile += '\n' if (learner_type == 'ML' and model_type.lower()=='anomaly_detection' and model.lower() == 'autoencoder'): self.modelfile += 'import joblib' self.modelfile += '\n' self.modelfile += 'import os' self.modelfile += '\n' self.modelfile += 'import pandas as pd' self.modelfile += '\n' self.modelfile += 'import numpy as np' self.modelfile += '\n' self.modelfile += 'from pathlib import Path' self.modelfile += '\n' self.modelfile += 'import tensorflow as tf' self.modelfile += '\n' self.modelfile += 'from keras.models import load_model' self.modelfile += '\n' self.modelfile += 'import warnings' self.modelfile += '\n' self.modelfile += 'from sklearn.preprocessing import StandardScaler' self.modelfile += '\n' self.modelfile += 'warnings.filterwarnings("ignore")' self.modelfile += '\n' if(learner_type == 'ImageClassification'): self.modelfile += 'import os' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.models import Sequential' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.layers import Dense, Dropout, Flatten' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.preprocessing import image' self.modelfile += '\n' self.modelfile += 'import numpy as np' self.modelfile += '\n' self.modelfile += 'import tensorflow as tf' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.layers import Input' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.models import Model' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.optimizers import Adam' self.modelfile += '\n' self.modelfile += 'import cv2' self.modelfile += '\n' if(learner_type == 'objectDetection'): self.modelfile += 'import os\n' self.modelfile += 'from object_detection.utils import label_map_util\n' self.modelfile += 'from object_detection.utils import config_util\n' self.modelfile += 'from object_detection.utils import visualization_utils as viz_utils\n' self.modelfile += 'from object_detection.builders import model_builder\n' self.modelfile += 'import tensorflow as tf\n' self.modelfile += 'import numpy as np\n' self.modelfile += 'from PIL import Image\n' self.modelfile += 'import matplotlib.pyplot as plt\n' self.modelfile += 'import pandas as pd\n' self.modelfile += 'from pathlib import Path\n' if(learner_type == 'Text Similarity'): self.modelfile += 'from tensorflow.keras.models import load_model' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras import backend as K' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.preprocessing.sequence import pad_sequences' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras.preprocessing.text import Tokenizer' self.modelfile += '\n' self.modelfile += 'import tensorflow as tf' self.modelfile += '\n' if(model == 'Neural Architecture Search'): self.modelfile += 'from tensorflow.keras.models import load_model' self.modelfile += '\n' self.modelfile += 'from tensorflow.keras import backend as K' self.modelfile += '\n' self.modelfile += 'import tensorflow as tf' self.modelfile += '\n' self.modelfile += 'import joblib' self.modelfile += '\n' self.modelfile += 'import os' self.modelfile += '\n' self.modelfile += 'import pandas as pd' self.modelfile += '\n' self.modelfile += 'from sklearn.decomposition import LatentDirichletAllocation\n' self.modelfile += 'import numpy as np\n' self.modelfile += 'from pathlib import Path\n' if model.lower() == 'deep q network' or model.lower() == 'dueling deep q network': self.modelfile += 'from tensorflow import constant' self.modelfile += '\n' self.modelfile += 'from tf_agents.trajectories import time_step' self.modelfile += '\n' self.requirementfile += 'tensorflow==2.5.0' if model.lower() == 'lstm' or model.lower() == 'mlp': self.modelfile += 'from tensorflow.keras.models import load_model' self.modelfile += '\n' self.requirementfile += 'tensorflow==2.5.0' if(learner_type == 'Text Similarity'): self.modelfile += 'def cosine_distance(vests):' self.modelfile += '\n'; self.modelfile += ' x, y = vests' self.modelfile += '\n'; self.modelfile += ' x = K.l2_normalize(x, axis=-1)' self.modelfile += '\n'; self.modelfile += ' y = K.l2_normalize(y, axis=-1)' self.modelfile += '\n'; self.modelfile += ' return -K.mean(x * y, axis=-1, keepdims=True)' self.modelfile += '\n'; self.modelfile += 'def cos_dist_output_shape(shapes):' self.modelfile += '\n'; self.modelfile += ' shape1, shape2 = shapes' self.modelfile += '\n'; self.modelfile += ' return (shape1[0],1)' self.modelfile += '\n'; if(learner_type == 'TextDL' or learner_type == 'DL'): if(scoreParam.lower() == 'recall' or scoreParam.lower() == 'f1_score'): self.modelfile += 'def recall_m(y_true, y_pred):' self.modelfile += '\n'; self.modelfile += ' true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))' self.modelfile += '\n'; self.modelfile += ' possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))' self.modelfile += '\n'; self.modelfile += ' recall = true_positives / (possible_positives + K.epsilon())' self.modelfile += '\n'; self.modelfile += ' return recall' self.modelfile += '\n'; if(scoreParam.lower() == 'precision' or scoreParam.lower() == 'f1_score'): self.modelfile += 'def precision_m(y_true, y_pred):' self.modelfile += '\n'; self.modelfile += ' true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))' self.modelfile += '\n'; self.modelfile += ' predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))' self.modelfile += '\n'; self.modelfile += ' precision = true_positives / (predicted_positives + K.epsilon())' self.modelfile += '\n'; self.modelfile += ' return precision' self.modelfile += '\n'; if(scoreParam.lower() == 'f1_score'): self.modelfile += 'def f1_m(y_true, y_pred):' self.modelfile += '\n'; self.modelfile += ' precision = precision_m(y_true, y_pred)' self.modelfile += '\n'; self.modelfile += ' recall = recall_m(y_true, y_pred)' self.modelfile += '\n'; self.modelfile += ' return 2*((precision*recall)/(precision+recall+K.epsilon()))' self.modelfile += '\n'; if(scoreParam.lower() == 'rmse'): self.modelfile += 'def rmse_m(y_true, y_pred):' self.modelfile += '\n'; self.modelfile += ' return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))' self.modelfile += '\n'; if(scoreParam.lower() =='r2'): self.modelfile += 'def r_square(y_true, y_pred):' self.modelfile += '\n'; self.modelfile += ' SS_res = K.sum(K.square(y_true-y_pred))' self.modelfile += '\n'; self.modelfile += ' SS_tot = K.sum(K.square(y_true-K.mean(y_true)))' self.modelfile += '\n'; self.modelfile += ' return (1 - SS_res/(SS_tot+K.epsilon()))' self.modelfile += '\n'; if(learner_type.lower() in ['similarityidentification','contextualsearch']): self.modelfile += 'from pathlib import Path\n' if model_type == 'BM25': self.modelfile += 'from rank_bm25 import BM25Okapi\n' elif scoreParam == 'VectorDB Cosine': self.modelfile += 'import chromadb\n' else: self.modelfile += 'from sklearn.metrics.pairwise import cosine_similarity\n' self.pythonpackage += '========== Python Packags Requires =========' self.pythonpackage += '\n' self.pythonpackage += 'scikit-learn' self.pythonpackage += '\n' self.pythonpackage += 'scipy' self.pythonpackage += '\n' self.pythonpackage += 'numpy' self.pythonpackage += '\n' if((learner_type == 'DL') or (learner_type =='TextDL')): self.modelfile += 'import numpy as np' self.modelfile += '\n' self.requirementfile += 'scikit-learn==0.21.3' self.requirementfile += '\n' self.requirementfile += 'scipy==1.3.3' self.requirementfile += '\n' self.requirementfile += 'numpy==1.17.4' self.requirementfile += '\n' if(learner_type == 'TextML'): self.requirementfile += 'spacy==2.2.3' self.requirementfile += '\n' self.requirementfile += 'https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz' self.requirementfile += '\n' if(learner_type == 'DL' or learner_type == 'TextDL'): self.requirementfile += 'keras==2.3.1' self.requirementfile += '\n' self.requirementfile += 'tensorflow==2.0.0b1' self.requirementfile += '\n' if(learner_type == 'RecommenderSystem'): self.requirementfile += 'surprise' self.requirementfile += '\n' if(method == 'package'): self.modelfile += 'import surprise' self.modelfile += '\n' self.modelfile += 'import statsmodels' self.modelfile += '\n' self.requirementfile += 'statsmodels==0.10.2' self.requirementfile += '\n' def crate_readme_file(self,deploy_path,modelfile,features,method,single_file=False): self.readme='========== Files Structures ==========' self.readme+='\n' self.readme+=modelfile+' ------ Trained Model' self.readme+='\n' self.readme+='aion_prediction.py --> Python package entry point' self.readme+='\n' if not single_file: self.readme+='script/inputprofiler.py --> Profiling like FillNA and Category to Numeric' self.readme+='\n' self.readme+='script/selector.py --> Feature Selection' self.readme+='\n' self.readme+='script/trained_model.py --> Read the model file and call the prediction' self.readme+='\n' self.readme+='script/output_format.py --> Output formatter file' self.readme+='\n' self.readme+= self.pythonpackage self.readme+= '========== How to call the model ==========' self.readme+='\n' self.readme+= '============== From Windows Terminal ==========' self.readme+='\n' if method == 'optimus_package': self.readme += 'python aion_prediction.py filename.json' self.readme +='\n' self.readme += '========== Embedded Methods ==========' self.readme +='\n' self.readme += 'Function Name: predict_from_json - When input is Json Data' self.readme +='\n' self.readme += 'Function Name: predict_from_file - When input is Json File' self.readme +='\n' else: callpython = 'python aion_prediction.py "[{' for x in features: if(callpython != 'python prediction.py "[{'): callpython += ',' callpython += '\\\"'+str(x)+'\\\"'+':'+'\\\"'+str(x)+'_value'+'\\\"' callpython += '}]"' self.readme += callpython self.readme+='\n' self.readme+= '============== From Linux Terminal ==========' self.readme+='\n' callpython = 'python aion_prediction.py \'[{' temp =callpython for x in features: if(callpython != temp): callpython += ',' callpython += '"'+str(x)+'"'+':'+'"'+str(x)+'_value'+'"' callpython += '}]\'' self.readme += callpython self.readme+='\n' self.readme+= '============== Output ==========' self.readme+='\n' self.readme+= '{"status":"SUCCESS","data":[{"Data1":"Value","prediction":"Value"}]}' ## For Single Row/Record' self.readme+='\n' self.readme+= '{"status":"SUCCESS","data":[{"Data1":"Value","prediction":"Value"},{"Data1":"Value","prediction":"Value"}]} ## For Multiple Row/Record' self.readme+='\n' self.readme+= '{"status":"ERROR","message":"description"} ## In Case Exception or Error' self.readme+='\n' #print(self.readme) filename = os.path.join(deploy_path,'readme.txt') self.log.info('-------> Readme File Location: '+filename) f = open(filename, "wb") f.write(str(self.readme).encode('utf8')) f.close() def create_class(self,classname): #self.modelfile += 'class '+classname+'(object):' self.modelfile += 'class trained_model(object):' self.modelfile += '\n' def profiler_code(self,model_type,model,output_columns, features, text_feature,wordToNumericFeatures=[], deploy={},datetimeFeature=''): profiler = deploy.get('profiler',{}) if isinstance(features, str): features = features.split(',') code = f""" import scipy import joblib import numpy as np import pandas as pd from pathlib import Path """ if text_feature: code += """ import importlib.util\n""" if wordToNumericFeatures: code += """ from word2number import w2n def s2n(value): try: x=eval(value) return x except: try: return w2n.word_to_num(value) except: return np.nan """ if 'code' in deploy.get('preprocess',{}).keys(): code += deploy['preprocess']['code'] if profiler.get('conversion_method','').lower() == 'glove': code += """ class inputprofiler(object): def __init__(self): self.model = None preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if preprocess_path.exists(): self.model = joblib.load(preprocess_path) from text.Embedding import load_pretrained from text import TextProcessing model_path = TextProcessing.checkAndDownloadPretrainedModel('glove') embed_size, loaded_model = load_pretrained(model_path) self.model.set_params(text_process__vectorizer__external_model = loaded_model) else: raise ValueError('Preprocess model not found') def apply_profiler(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) """ elif profiler.get('conversion_method','').lower() == 'fasttext': code += """ def get_pretrained_model_path(): try: from AION.appbe.dataPath import DATA_DIR modelsPath = Path(DATA_DIR)/'PreTrainedModels'/'TextProcessing' except: modelsPath = Path('aion')/'PreTrainedModels'/'TextProcessing' if not modelsPath.exists(): modelsPath.mkdir(parents=True, exist_ok=True) return modelsPath class inputprofiler(object): def __init__(self): self.model = None preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if preprocess_path.exists(): self.model = joblib.load(preprocess_path) if not importlib.util.find_spec('fasttext'): raise ValueError('fastText not installed') else: import os import fasttext import fasttext.util cwd = os.getcwd() os.chdir(get_pretrained_model_path()) fasttext.util.download_model('en', if_exists='ignore') loaded_model = fasttext.load_model('cc.en.300.bin') os.chdir(cwd) self.model.set_params(text_process__vectorizer__external_model = loaded_model) self.model.set_params(text_process__vectorizer__external_model_type = 'binary') else: raise ValueError('Preprocess model not found') def apply_profiler(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) """ else: code += """ class inputprofiler(object): def __init__(self): self.model = None preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if preprocess_path.exists(): self.model = joblib.load(preprocess_path) else: raise ValueError('Preprocess model not found') def apply_profiler(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) """ if 'code' in deploy.get('preprocess',{}).keys(): code += " df = preprocess( df)\n" if wordToNumericFeatures: code += f""" df[{wordToNumericFeatures}] = df[{wordToNumericFeatures}].apply(lambda x: s2n(x))""" if profiler.get('unpreprocessed_columns'): code += f""" unpreprocessed_data = df['{profiler['unpreprocessed_columns'][0]}'] df.drop(['{profiler['unpreprocessed_columns'][0]}'], axis=1,inplace=True) """ if profiler.get('force_numeric_conv'): code += f""" df[{profiler['force_numeric_conv']}] = df[{profiler['force_numeric_conv']}].apply(pd.to_numeric,errors='coerce') """ code += f""" if self.model: df = self.model.transform(df)""" code += f""" columns = {output_columns} if isinstance(df, scipy.sparse.spmatrix): df = pd.DataFrame(df.toarray(), columns=columns) else: df = pd.DataFrame(df, columns=columns) """ ##The below if loop for avoiding unpreprocessed column variable storing which is not used for anomaly detection if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na': pass else: if profiler.get('unpreprocessed_columns'): code += f""" df['{profiler.get('unpreprocessed_columns')[0]}'] = unpreprocessed_data """ if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na': ##This below set_index is wrong, because we drop datetimefeature before profiling and doing set_index. So commented now. # code += f""" # df.set_index('{datetimeFeature}', inplace=True)""" code += f""" return(df,'{datetimeFeature}')\n""" else: code += f""" return(df)""" return code def no_profiling_code(self, features): if isinstance(features, str): features = features.split(',') return f""" import pandas as pd import numpy as np class inputprofiler(object): def apply_profiler(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) return df[{features}] """ def create_profiler_file(self,learner_type,deploy_path,profiler,features,numericToLabel_json,column_merge_flag,text_features,preprocessing_pipe,firstDocFeature,secondDocFeature,normalizer,normFeatures,wordToNumericFeatures,conversion_method,model_type,preprocess_pipe,preprocess_out_columns, label_encoder,model, config=None,datetimeFeature=''): filename = str(Path(deploy_path)/'script'/'inputprofiler.py') if 'profiler' in config: if model_type == 'BM25': code = self.profiler_code(model_type,model,['tokenize'],features, text_features,config['profiler']['word2num_features']) elif model == 'KaplanMeierFitter': code = self.no_profiling_code(features) elif model.lower() in ['arima', 'fbprophet']: #task 12627 code = self.no_profiling_code('noofforecasts') else: code = self.profiler_code(model_type,model,config['profiler']['output_features'],features, text_features,config['profiler']['word2num_features'],config,datetimeFeature) if code: with open(filename,'w',encoding="utf-8") as f: f.write(code) self.log.info('-------> Profiler File Location :'+filename) return self.profilerfile += 'import pandas as pd' self.profilerfile += '\n' self.profilerfile += 'import joblib' self.profilerfile += '\n' self.profilerfile += 'import os' self.profilerfile += '\n' self.profilerfile += 'from word2number import w2n' self.profilerfile += '\n' self.profilerfile += 'import numpy as np' self.profilerfile += '\nfrom pathlib import Path\n' #print("1") #print(profiler) if(learner_type == 'Text Similarity' or len(text_features) > 0): self.profilerfile += 'from text import TextProcessing' self.profilerfile += '\n' self.profilerfile += 'def textCleaning(textCorpus):' self.profilerfile += '\n' self.profilerfile += ' textProcessor = TextProcessing.TextProcessing()' self.profilerfile += '\n' self.profilerfile += ' textCorpus = textProcessor.transform(textCorpus)' self.profilerfile += '\n' self.profilerfile += ' return(textCorpus)' self.profilerfile += '\n' self.profilerfile += 'class inputprofiler(object):' self.profilerfile += '\n' self.profilerfile += ' def s2n(self,value):' self.profilerfile += '\n' self.profilerfile += ' try:' self.profilerfile += '\n' self.profilerfile += ' x=eval(value)' self.profilerfile += '\n' self.profilerfile += ' return x' self.profilerfile += '\n' self.profilerfile += ' except:' self.profilerfile += '\n' self.profilerfile += ' try:' self.profilerfile += '\n' self.profilerfile += ' return w2n.word_to_num(value)' self.profilerfile += '\n' self.profilerfile += ' except:' self.profilerfile += '\n' self.profilerfile += ' return np.nan ' self.profilerfile += '\n' self.profilerfile += ' def apply_profiler(self,df):' self.profilerfile += '\n' if(len(wordToNumericFeatures) > 0): for w2nFeature in wordToNumericFeatures: if w2nFeature not in features: continue self.profilerfile += " df['"+w2nFeature+"']=df['"+w2nFeature+"'].apply(lambda x: self.s2n(x))" self.profilerfile += '\n' self.profilerfile += " df = df.replace(r'^\s*$', np.NaN, regex=True)" self.profilerfile += '\n' self.profilerfile += ' try:' self.profilerfile += '\n' self.profilerfile += ' df.dropna(how="all",axis=1,inplace=True)' self.profilerfile += '\n' self.profilerfile += ' except:' self.profilerfile += '\n' self.profilerfile += ' df.fillna(0)' self.profilerfile += '\n' if model_type.lower() != 'timeseriesforecasting': #task 11997 self.profilerfile += ' preprocess_path = Path(__file__).parent.parent/"model"/"preprocess_pipe.pkl"\n' self.profilerfile += ' if preprocess_path.exists():\n' self.profilerfile += ' model = joblib.load(preprocess_path)\n' if model_type.lower()=='anomaly_detection' and model.lower() == 'autoencoder': self.profilerfile += f" df[{features}] = model.transform(df[{features}])\n" else: self.profilerfile += f" df = model.transform(df)\n" if 'operation' in profiler: y = profiler['operation'] for action in y: feature = action['feature'] #if feature not in features: # continue operation = action['Action'] if(operation == 'Drop'): self.profilerfile += " if '"+feature+"' in df.columns:" self.profilerfile += '\n' self.profilerfile += " df.drop(columns=['"+feature+"'],inplace = True)" self.profilerfile += '\n' if(operation == 'FillValue'): self.profilerfile += " if '"+feature+"' in df.columns:" self.profilerfile += '\n' fvalue = action['value'] self.profilerfile += " df['"+feature+"'] = df['"+feature+"'].fillna(value='"+fvalue+"')" self.profilerfile += '\n' if(operation == 'Encoder'): value = action['value'] value = value.replace("\n", "\\n") self.profilerfile += " if '"+feature+"' in df.columns:" self.profilerfile += '\n' self.profilerfile += " le_dict="+str(value) self.profilerfile += '\n' self.profilerfile += " df['"+feature+"'] = df['"+feature+"'].apply(lambda x: le_dict.get(x,-1))" self.profilerfile += '\n' self.profilerfile += " if -1 in df['"+feature+"'].values:" self.profilerfile += '\n' self.profilerfile += " raise Exception('Category value of "+feature+" not present in training data')" self.profilerfile += '\n' if 'conversion' in profiler: catergoryConverton = profiler['conversion'] #print(catergoryConverton) if (catergoryConverton['categoryEncoding'].lower() in ['targetencoding','onehotencoding']) and ('features' in catergoryConverton): self.profilerfile += " encoder = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','categoryEncoder.pkl'))" self.profilerfile += '\n' self.profilerfile += " CategoryFeatures = "+str(catergoryConverton['features']) self.profilerfile += '\n' if catergoryConverton['categoryEncoding'].lower() == 'onehotencoding': self.profilerfile += " transformed_data = encoder.transform(df[CategoryFeatures]).toarray()" self.profilerfile += '\n' self.profilerfile += " feature_labels = encoder.get_feature_names(CategoryFeatures)" self.profilerfile += '\n' self.profilerfile += " transformed_data = pd.DataFrame(transformed_data,columns=feature_labels) " self.profilerfile += '\n' else: self.profilerfile += " transformed_data = encoder.transform(df[CategoryFeatures])" self.profilerfile += '\n' self.profilerfile += " dataColumns=list(df.columns)" self.profilerfile += '\n' self.profilerfile += " nonNormFeatures=list(set(dataColumns) - set(CategoryFeatures))" self.profilerfile += '\n' self.profilerfile += " dataArray=df[nonNormFeatures]" self.profilerfile += '\n' self.profilerfile += " df = pd.concat([dataArray, transformed_data],axis=1)" self.profilerfile += '\n' y = json.loads(numericToLabel_json) for feature_details in y: feature = feature_details['feature'] if feature not in features: continue label = feature_details['Labels'] bins = feature_details['Bins'] self.profilerfile += " if '"+feature+"' in df.columns:" self.profilerfile += '\n' self.profilerfile += " cut_bins="+str(bins) self.profilerfile += '\n' self.profilerfile += " cut_labels="+str(label) self.profilerfile += '\n' self.profilerfile += " df['"+feature+"'] = pd.cut(df['"+feature+"'],bins=cut_bins,labels=cut_labels)" self.profilerfile += '\n' self.profilerfile += " df['"+feature+"'] = df['"+feature+"'].fillna(value=0)" self.profilerfile += '\n' if(len(text_features) > 0): if(len(text_features) > 1): self.profilerfile += ' merge_features = '+str(text_features) self.profilerfile += '\n' self.profilerfile += ' df[\'combined\'] = df[merge_features].apply(lambda row: \' \'.join(row.values.astype(str)), axis=1)' self.profilerfile += '\n' self.profilerfile += ' features = [\'combined\']' self.profilerfile += '\n' else: self.profilerfile += " features = "+str(text_features) self.profilerfile += '\n' if model_type == 'BM25': self.profilerfile += """\ df_text = df[features[0]] pipe = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','{preprocessing_pipe}')) df['tokenize'] = pipe.transform(df_text)\n""".format(preprocessing_pipe=preprocessing_pipe) elif conversion_method == 'sentenceTransformer': self.profilerfile += """\ df_text = df[features[0]] from sentence_transformers import SentenceTransformer model = SentenceTransformer(\'sentence-transformers/msmarco-distilroberta-base-v2\') df_vect = model.encode(df_text) for empCol in {text_features}: df = df.drop(columns=[empCol]) if isinstance(df_vect, np.ndarray): df1 = pd.DataFrame(df_vect) else: df1 = pd.DataFrame(df_vect.toarray(),columns = pipe.named_steps[\'vectorizer\'].get_feature_names()) df1 = df1.add_suffix(\'_vect\') df = pd.concat([df, df1],axis=1)\n""".format(preprocessing_pipe=preprocessing_pipe, text_features=text_features) else: self.profilerfile += """\ df_text = df[features[0]] pipe = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','{preprocessing_pipe}')) df_vect=pipe.transform(df_text) for empCol in {text_features}: df = df.drop(columns=[empCol]) if isinstance(df_vect, np.ndarray): df1 = pd.DataFrame(df_vect) else: df1 = pd.DataFrame(df_vect.toarray(),columns = pipe.named_steps[\'vectorizer\'].get_feature_names()) df1 = df1.add_suffix(\'_vect\') df = pd.concat([df, df1],axis=1)\n""".format(preprocessing_pipe=preprocessing_pipe, text_features=text_features) if(learner_type == 'Text Similarity'): self.profilerfile += ' df[\''+firstDocFeature+'\'] = textCleaning(df[\''+firstDocFeature+'\'])' self.profilerfile += '\n' self.profilerfile += ' df[\''+secondDocFeature+'\'] = textCleaning(df[\''+secondDocFeature+'\'])' self.profilerfile += '\n' if len(normFeatures) > 0 and normalizer != '': self.profilerfile += " normFeatures = "+str(normFeatures) self.profilerfile += '\n' self.profilerfile += ' normalizepipe = joblib.load(os.path.join(os.path.dirname(os.path.abspath(__file__)),\'..\',\'model\',\''+normalizer+'\'))' self.profilerfile += '\n' self.profilerfile += ' dataColumns=list(df.columns)' self.profilerfile += '\n' self.profilerfile += ' nonNormFeatures=list(set(dataColumns) - set(normFeatures))' self.profilerfile += '\n' self.profilerfile += ' dataframe=df[normFeatures]' self.profilerfile += '\n' self.profilerfile += ' transDf = normalizepipe.transform(dataframe)' self.profilerfile += '\n' self.profilerfile += ' nontransDF=df[nonNormFeatures].values' self.profilerfile += '\n' self.profilerfile += ' dataColumns=normFeatures+nonNormFeatures' self.profilerfile += '\n' self.profilerfile += ' scaledDf = pd.DataFrame(np.hstack((transDf, nontransDF)),columns=dataColumns)' self.profilerfile += '\n' self.profilerfile += ' df=scaledDf' self.profilerfile += '\n' else: self.profilerfile += ' df=df.dropna()\n' self.profilerfile += ' return(df)' filename = os.path.join(deploy_path,'script','inputprofiler.py') self.log.info('-------> Profiler File Location :'+filename) f = open(filename, "w",encoding="utf-8") f.write(str(self.profilerfile)) f.close() def isEnglish(self, s): try: s.encode(encoding='utf-8').decode('ascii') except UnicodeDecodeError: return False else: return True def create_selector_file(self,deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature, model_type,model,config=None): cs.create_selector_file(self,deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature, model_type,model,config) def create_init_function_for_regression(self,modelfile): self.modelfile += ' def __init__(self):' self.modelfile += '\n' self.modelfile += " self.model = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' def create_init_function_for_classification(self,modelfile,classes,learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,modelName,model_type,imageconfig): cs.create_init_function_for_classification(self,modelfile,classes,learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,modelName,model_type,imageconfig) def create_predict_proba(self,learner_type,method): self.modelfile += ' def predict(self,X,features_names):' self.modelfile += '\n' self.modelfile += ' return self.model.predict_proba(X)' def create_forcast(self,method,no_of_prediction): self.modelfile += ' def predict(self,X,features_names):' self.modelfile += '\n' self.modelfile += ' no_of_prediction = '+str(no_of_prediction) self.modelfile += '\n' self.modelfile += ' lag_order = self.model.k_ar' self.modelfile += '\n' self.modelfile += ' return self.model.forecast(X.values[-lag_order:],steps=no_of_prediction)' def create_predict(self,learner_type,method,model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,feature,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scoreParam=None): scorePrm = scoreParam cs.create_predict(self,learner_type,method,model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,feature,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scorePrm) def save_model_deploy(self,outputfolder,modelname): #filename = outputfolder+modelname+'.py' filename = os.path.join(outputfolder,'script','trained_model.py') self.log.info('-------> Model File Location :'+filename) f = open(filename, "w",encoding="utf-8") f.write(str(self.modelfile)) f.close() def create_TextCleaner(self,outputfolder): profilerPath = os.path.join(outputfolder,'profiler') try: os.makedirs(profilerPath) except OSError: self.log.info("ProfilePath Folder Already Exists") try: textprofileFileLocation = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','profiler','textDataProfiler.py') initFileLocation = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','profiler','__init__.py') shutil.copy2(textprofileFileLocation,profilerPath) shutil.copy2(initFileLocation,profilerPath) ''' if(platform.system() == 'Windows'): shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'\\..\\profiler\\textDataProfiler.py',profilerPath) shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'\\..\\profiler\\__init__.py',profilerPath) else: shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'/../profiler/textDataProfiler.py',profilerPath) shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'/../profiler/__init__.py',profilerPath) ''' except OSError: self.log.info("Copy to Profiler Path Failed") def listToString(self,s): str1='[' for feature in s: if(str1 != '['): str1 += ',' str1 += '"'+feature+'"' str1+=']' return str1 def print_files(self): self.log.info(self.modelfile) def create_util_folder(self, deploy_path,learner_type): import tarfile ext_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..','utilities')) for x in os.listdir(ext_path): if x.endswith('.tar'): if x not in ['scikit_surprise-1.1.1.dist-info.tar','surprise.tar']: tarPackage = os.path.join(ext_path, x) my_tar = tarfile.open(tarPackage) my_tar.extractall(deploy_path) my_tar.close() else: if learner_type == 'RecommenderSystem': tarPackage = os.path.join(ext_path, x) my_tar = tarfile.open(tarPackage) my_tar.extractall(deploy_path) my_tar.close() def deploy_model(self,deploy_name,deployJson,learner_type,model_type,model,scoreParam,saved_model,deploy_path,features,profiler,datalocation,output_label,column_merge_flag,textFeatures,numericalFeatures,nonNumericFeatures,preprocessing_pipe,numericToLabel_json,threshold,loss_matrix,optimizer,firstDocFeature,secondDocFeature,padding_length,trained_data_file,dictDiffCount,targetFeature,normalizer,normFeatures,pcaModel_pickle_file,bpca_features,apca_features,optimizationmethod,deployFolder,iterName,iterVersion,wordToNumericFeatures,imageconfig,sessonal_freq,additional_regressors,grouperbyjson,rowfilterexpression,xtrain,profiled_data_file,conversion_method,modelFeatures,indexFeature,lag_order,scalertransformationFile,no_of_prediction,preprocess_pipe,preprocess_out_columns, label_encoder,datetimeFeature,usecaseLocation,config=None): try: serviceName = '{}{}{}'.format(iterName, '_' if iterVersion != '' else '', iterVersion) self.log.info('-------> Deploy Location :'+deploy_path) if production.is_supported(model_type.lower()): if learner_type == 'Text Similarity': coder = production.get_deployer(learner_type) coder.create_file(deploy_path, preprocessing_pipe, saved_model, firstDocFeature, secondDocFeature) elif model_type.lower() in ['classification', 'regression','clustering','timeseriesforecasting']: params = {} params['usecase_name']= iterName params['usecase_ver']= iterVersion params['features']={} params['features']['input_feat'] = config['profiler']['input_features'] params['features']['target_feat'] = targetFeature params['features']['text_feat'] = textFeatures params['paths']={} params['paths']['deploy'] = Path(deploy_path) params['paths']['usecase'] = params['paths']['deploy'].parent params['profiler']=config['profiler'] if 'code' in config.get('preprocess',{}).keys(): params['profiler']['preprocess']=config['preprocess'] params['selector']={} params['selector']['reducer']=True if pcaModel_pickle_file else False params['selector']['reducer_file']=pcaModel_pickle_file if pcaModel_pickle_file: params['selector']['input_features']=bpca_features params['selector']['output_features']=apca_features else: params['selector']['input_features']=config['profiler']['input_features'] params['selector']['output_features']=features params['training']={} params['training']['algo']= model params['training']['model_file']=saved_model if model_type.lower() == 'timeseriesforecasting': if params['training']['algo'] in ['LSTM','MLP','ENCODER_DECODER_LSTM_MVI_UVO']: params['training']['lag_order'] = int(lag_order) params['training']['scaler_file'] = Path(scalertransformationFile).name elif params['training']['algo'] == 'VAR': params['training']['dictDiffCount'] = dictDiffCount params['training']['no_of_prediction'] = no_of_prediction elif params['training']['algo'] == 'FBPROPHET': params['training']['sessonal_freq'] = sessonal_freq params['training']['additional_regressors'] = additional_regressors self.log.info(params) deployer = production.get_deployer(model_type.lower(), params=params) deployer.run( ) self.log.info('Status:- |... Model deployment files created') self.log.info('Status:- |... Model deployment completed') return else: # for output_formatter.py from prediction_package.output_formatter import outputFormatter outputObj = outputFormatter() outputObj.crate_output_format_file(deploy_path, learner_type, model_type, model, output_label, threshold, trained_data_file, dictDiffCount, targetFeature, features,datetimeFeature) #for aion_predict.py from prediction_package.aion_prediction import aionPrediction predictionObj = aionPrediction() # print(deploy_path) predictionObj.create_prediction_file(deploy_name, deploy_path, learner_type, grouperbyjson,rowfilterexpression,model_type,datetimeFeature) # for aion_service.py predictionObj.create_model_service(deploy_path, serviceName, model_type) # for aion_publish.py predictionObj.create_publish_service(usecaseLocation, iterName, iterVersion, model_type) if learner_type.lower()=="recommendersystem": # Task 11190--- #For recommender system from prediction_package.recommender_code import generate_recommender_code generate_recommender_code(deploy_path) return #self.create_TextCleaner(deploy_path) if(len(textFeatures) > 0): self.create_TextCleaner(deploy_path) self.include_import_file(learner_type,deployJson['method'],scoreParam, model_type,model) if((learner_type == 'TS' and model.lower() not in ['lstm','mlp','var']) or learner_type == 'RecommenderSystem'): features=[] self.create_class(deploy_name) if len(bpca_features) != 0: self.create_profiler_file(learner_type,deploy_path,profiler,bpca_features,numericToLabel_json,column_merge_flag,textFeatures,preprocessing_pipe,firstDocFeature,secondDocFeature,normalizer,normFeatures,wordToNumericFeatures,conversion_method,model_type,preprocess_pipe,preprocess_out_columns, label_encoder, model, config,datetimeFeature) else: self.create_profiler_file(learner_type,deploy_path,profiler,features,numericToLabel_json,column_merge_flag,textFeatures,preprocessing_pipe,firstDocFeature,secondDocFeature,normalizer,normFeatures,wordToNumericFeatures,conversion_method,model_type,preprocess_pipe,preprocess_out_columns, label_encoder, model, config,datetimeFeature) self.create_selector_file(deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature,model_type, model,config) self.create_init_function_for_classification(saved_model,'classes',learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,model,model_type,imageconfig) except Exception as e: print(e) import traceback exception_type, exception_object, exception_traceback = sys.exc_info() filename = exception_traceback.tb_frame.f_code.co_filename line_number = exception_traceback.tb_lineno self.log.info("Exception type: ", exception_type) self.log.info("File name: ", filename) self.log.info("Line number: ", line_number) self.log.info("multivariate model build error traceback: \n"+str(traceback.print_exc())) raise Exception(e) #print(model) if(model.lower() == 'var'): self.log.info("Create Forecast Function") self.create_forcast(deployJson['method'],no_of_prediction) else: self.create_predict(learner_type,deployJson['method'],model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,features,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scoreParam) self.save_model_deploy(deploy_path,deploy_name) if(len(textFeatures) > 0): if model_type.lower() == 'classification' or model_type.lower() == 'regression' or model_type.lower() == 'timeseriesforecasting': predictionObj.create_text_drift_file(deploy_path,textFeatures,targetFeature,model_type) if model_type.lower() == 'classification': predictionObj.create_classification_text_performance_file(deploy_path,textFeatures,targetFeature) elif model_type.lower() == 'regression': predictionObj.create_regression_text_performance_file(deploy_path,textFeatures,targetFeature) else: if model_type.lower() == 'classification' or model_type.lower() == 'regression' or model_type.lower() == 'timeseriesforecasting': #task 11997 predictionObj.create_drift_file(deploy_path,features,targetFeature,model_type) if model_type.lower() == 'classification': predictionObj.create_classification_performance_file(deploy_path,features,targetFeature) elif model_type.lower() == 'regression': predictionObj.create_regression_performance_file(deploy_path,features,targetFeature) self.log.info('Status:- |... Model deployment files created') self.crate_readme_file(deploy_path,saved_model,features,deployJson['method']) from prediction_package.requirements import requirementfile requirementfile(deploy_path,model,textFeatures,learner_type) os.chdir(deploy_path) textdata = False if(learner_type == 'Text Similarity' or len(textFeatures) > 0): textdata = True self.create_util_folder(deploy_path,learner_type) self.log.info('Status:- |... Model deployment completed') def deployTSum(self,deploy_path,preTrainedModellocation): def create_predict(preTrainedModellocation): text = f""" import sys import json def predict(data): try: import pandas as pd import numpy as np from pathlib import Path keywordsFile =Path(__file__).parent/'data'/'keywordDataBase.csv' outputSumFile =Path(__file__).parent/'data'/'summarizedOutput.csv' fileName=data #print("fileName---",fileName) inputDataFileFrame = pd.DataFrame() inputDataFileFrame['Sentences']="" rowIndex=0 if fileName.endswith(".pdf"): from pypdf import PdfReader reader = PdfReader(fileName) number_of_pages = len(reader.pages) text="" textOutputForFile="" OrgTextOutputForFile="" for i in range(number_of_pages) : page = reader.pages[i] text1 = page.extract_text() text=text+text1 import nltk tokens = nltk.sent_tokenize(text) for sentence in tokens: sentence=sentence.replace("\\n", " ") if (len(sentence.split()) < 4 ) or (len(str(sentence.split(',')).split()) < 8)or (any(chr.isdigit() for chr in sentence)) : continue inputDataFileFrame.at[rowIndex,'Sentences']=str(sentence.strip()) rowIndex=rowIndex+1 if fileName.endswith(".txt"): data=[] with open(fileName, "r",encoding="utf-8") as f: data.append(f.read()) str1 = "" for ele in data: str1 += ele sentences=str1.split(".") count=0 for sentence in sentences: count += 1 inputDataFileFrame.at[rowIndex,'Sentences']=str(sentence.strip()) rowIndex=rowIndex+1 inputDataFileFrame['LabelByKw']=0 #print(inputDataFileFrame) keywordsFileFrame=pd.read_csv(keywordsFile,encoding='utf-8') Keyword_list = keywordsFileFrame['Keyword'].tolist() for i in inputDataFileFrame.index: for x in Keyword_list: if (str(inputDataFileFrame["Sentences"][i])).lower().find(x) != -1: inputDataFileFrame['LabelByKw'][i]=1 break import pickle from sklearn.preprocessing import LabelEncoder pkl_filename='classificationModel.sav' pkl_filename =Path(__file__).parent/'model'/'classificationModel.sav' with open(pkl_filename, 'rb') as file: pickle_model = pickle.load(file) testsample=inputDataFileFrame[["Sentences"]] labelencoder = LabelEncoder() testsample["Sentences"] = labelencoder.fit_transform(testsample["Sentences"]) y_predicted = pickle_model.predict_proba(testsample) df=pd.DataFrame({{"SectionName":np.nan,"Sentences":np.nan, "Predicted_Prob":y_predicted[:,1]}}) df['LabelByModel']=df['Predicted_Prob'].apply(lambda x: 0 if x <= 0.5 else 1 ) inputDataFileFrame['LabelByModel']= df['LabelByModel'] textToSum="" for i in inputDataFileFrame.index: if (inputDataFileFrame['LabelByModel'][i] or inputDataFileFrame['LabelByKw'][i]) : textToSum=textToSum+" "+inputDataFileFrame["Sentences"][i] stdir=r"{preTrainedModellocation}" stdir = stdir.replace('\\\\', '\\\\\\\\') from transformers import AutoTokenizer, AutoModelForSeq2SeqLM modelbert = AutoModelForSeq2SeqLM.from_pretrained(stdir,local_files_only=True) tokenizer = AutoTokenizer.from_pretrained(stdir,local_files_only=True) inputs = tokenizer("summarize: " + textToSum, return_tensors="pt", max_length=512, truncation=True) outputs = modelbert.generate(inputs["input_ids"], max_length=512, min_length=140, length_penalty=2.0, num_beams=4, early_stopping=True) summarizedOutputOfSection= tokenizer.decode(outputs[0]) summarizedOutputOfSection=summarizedOutputOfSection.replace("</s>","") summarizedOutputOfSection=summarizedOutputOfSection.replace("<s>","") sumDatadata = [summarizedOutputOfSection] df = pd.DataFrame(sumDatadata, columns=['textSum']) df.to_csv(outputSumFile,encoding='utf-8') outputjson = {{"status":"SUCCESS","msg":"Press Download button to download summarized output","data":summarizedOutputOfSection}} print("predictions:",json.dumps(outputjson)) return (json.dumps(outputjson)) except KeyError as e: output = {{"status":"FAIL","message":str(e).strip('"')}} print("predictions:",json.dumps(output)) return (json.dumps(output)) except Exception as e: output = {{"status":"FAIL","message":str(e).strip('"')}} print("predictions:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = predict(sys.argv[1]) """ return text deploy_path = Path(deploy_path) aion_prediction = deploy_path/'aion_predict.py' with open(aion_prediction, 'w') as f: f.write(create_predict(preTrainedModellocation)) #task 11190: Item based Recommender system---Usnish import os def generate_recommender_code(deployPath): code = """ import pandas as pd import numpy as np import os ITEMID = 'itemId' DATA_FOLDER = 'data' USER_ITEM_MATRIX = 'user_item_matrix.csv' ITEM_SIMILARITY_MATRIX = 'item_similarity_matrix.csv' RATING = 'rating' SIMILARITY_SCORE = 'similarity_score' class collaborative_filter(object): def __init__(self): self.matrix = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', DATA_FOLDER, USER_ITEM_MATRIX),index_col=0) self.matrix.index.name = ITEMID self.item_similarity_cosine = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', DATA_FOLDER, ITEM_SIMILARITY_MATRIX)) self.item_similarity_cosine.index.name = ITEMID self.item_similarity_cosine.columns.name = ITEMID def item_based_rec(self,picked_userid, number_of_recommendations,number_of_similar_items=5): import operator if not isinstance(picked_userid,str): picked_userid = str(picked_userid) if picked_userid not in self.matrix.columns: raise KeyError("UserID Does Not Exist") # Movies that the target user has not watched try: picked_userid_unwatched = pd.DataFrame(self.matrix[picked_userid].isna()).reset_index() picked_userid_unwatched = picked_userid_unwatched[picked_userid_unwatched[picked_userid] == True][ITEMID].values.tolist() # Movies that the target user has watched picked_userid_watched = pd.DataFrame(self.matrix[picked_userid].dropna(axis=0, how='all') \ .sort_values(ascending=False)) \ .reset_index() \ .rename(columns={picked_userid: 'rating'}) # Dictionary to save the unwatched movie and predicted rating pair rating_prediction = {} # Loop through unwatched movies for picked_movie in picked_userid_unwatched: if not isinstance(picked_movie,str): picked_movie = str(picked_movie) # Calculate the similarity score of the picked movie with other movies try: picked_movie_similarity_score = self.item_similarity_cosine[[picked_movie]].reset_index().rename( columns={picked_movie: SIMILARITY_SCORE}) # Rank the similarities between the picked user watched movie and the picked unwatched movie. picked_userid_watched_similarity = pd.merge(left=picked_userid_watched, right=picked_movie_similarity_score, on=ITEMID, how='inner') \ .sort_values(SIMILARITY_SCORE, ascending=False)[ :number_of_similar_items] # Calculate the predicted rating using weighted average of similarity scores and the ratings from picked user try: predicted_rating = round(np.average(picked_userid_watched_similarity[RATING],weights=picked_userid_watched_similarity[SIMILARITY_SCORE]), 6) except Exception as e: predicted_rating = 0 # Save the predicted rating in the dictionary rating_prediction[picked_movie] = predicted_rating except Exception as e: rating_prediction[picked_movie] = 0 # Return the top recommended movies return sorted(rating_prediction.items(), key=operator.itemgetter(1), reverse=True)[:number_of_recommendations] except Exception as e: print(e) raise KeyError(str(e)) def predict(self,X): predictions = [] for index,row in X.iterrows(): score = self.item_based_rec(int(row["uid"]),int(row["numberOfRecommendation"])) df = pd.DataFrame(score,columns=['ItemId','Ratings']) predictions.append(df) return predictions""" filename = os.path.join(deployPath, 'script', 'item_recommendation.py') # print(deploy_path) f = open(filename, "wb") f.write(str(code).encode('utf8')) f.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import json import shutil import logging class aionPrediction: def __init__(self): self.log = logging.getLogger('eion') def create_optimus_prediction_file (self,classname,deploy_path,learner_type): self.predictionFile = 'import warnings' self.predictionFile += '\n' self.predictionFile += 'warnings.filterwarnings("ignore")' self.predictionFile += '\n' self.predictionFile += 'import json' self.predictionFile += '\n' self.predictionFile += 'import os' self.predictionFile += '\n' self.predictionFile += 'import sys' self.predictionFile += '\n' self.predictionFile += 'import pandas as pd' self.predictionFile += '\n' self.predictionFile += 'from pandas import json_normalize' self.predictionFile += '\n' self.predictionFile += 'from importlib import import_module' self.predictionFile += '\n' self.predictionFile += 'import importlib.util' self.predictionFile += '\n' self.predictionFile += 'class prediction:' self.predictionFile += '\n' self.predictionFile += ' def predict_from_json(self,json_data):' self.predictionFile += '\n' self.predictionFile += ' data = json.loads(json_data)' self.predictionFile += '\n' self.predictionFile += ' output=self.predict(data)' self.predictionFile += '\n' self.predictionFile += ' print("predictions:",output)' self.predictionFile += '\n' self.predictionFile += '\n' self.predictionFile += ' def predict_from_file(self,filename):' self.predictionFile += '\n' self.predictionFile += ' with open(filename,\'r\',encoding=\'utf-8\') as f:' self.predictionFile += '\n' self.predictionFile += ' data = json.load(f)' self.predictionFile += '\n' self.predictionFile += ' output=self.predict(data)' self.predictionFile += '\n' self.predictionFile += ' print("predictions:",output)' self.predictionFile += '\n' self.predictionFile += '\n' self.predictionFile += ' def predict(self,json_data):' self.predictionFile += '\n' self.predictionFile += ' try:' self.predictionFile += '\n' #self.predictionFile += ' jsonData = json.loads(json_data)' self.predictionFile += ' jsonData=json_data' self.predictionFile += '\n' self.predictionFile += ' model_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/trained_model.py")' self.predictionFile += '\n' self.predictionFile += ' model = importlib.util.module_from_spec(model_obj)' self.predictionFile += '\n' self.predictionFile += ' model_obj.loader.exec_module(model)' self.predictionFile += '\n' #if(learner_type != 'TextML'): self.predictionFile += ' profiler_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/inputprofiler.py")' self.predictionFile += '\n' self.predictionFile += ' inputprofiler = importlib.util.module_from_spec(profiler_obj)' self.predictionFile += '\n' self.predictionFile += ' profiler_obj.loader.exec_module(inputprofiler)' self.predictionFile += '\n' self.predictionFile += ' selector_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/selector.py")' self.predictionFile += '\n' self.predictionFile += ' selector = importlib.util.module_from_spec(selector_obj)' self.predictionFile += '\n' self.predictionFile += ' selector_obj.loader.exec_module(selector)' self.predictionFile += '\n' self.predictionFile += ' output_format_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/output_format.py")' self.predictionFile += '\n' self.predictionFile += ' output_format = importlib.util.module_from_spec(output_format_obj)' self.predictionFile += '\n' self.predictionFile += ' output_format_obj.loader.exec_module(output_format)' self.predictionFile += '\n' self.predictionFile += ' df = json_normalize(jsonData)' self.predictionFile += '\n' self.predictionFile += ' df0 = df.copy()' self.predictionFile += '\n' #if(learner_type != 'TextML'): self.predictionFile += ' profilerobj = inputprofiler.inputprofiler()' self.predictionFile += '\n' self.predictionFile += ' df = profilerobj.apply_profiler(df)' self.predictionFile += '\n' self.predictionFile += ' selectobj = selector.selector()' self.predictionFile += '\n' self.predictionFile += ' df = selectobj.apply_selector(df)' self.predictionFile += '\n' self.predictionFile += ' output = model.trained_model().predict(df,"")' self.predictionFile += '\n' self.predictionFile += ' outputobj = output_format.output_format()' self.predictionFile += '\n' self.predictionFile += ' output = outputobj.apply_output_format(df0,output)' #self.predictionFile += '\n' #self.predictionFile += ' print(output)' self.predictionFile += '\n' self.predictionFile += ' return output' self.predictionFile += '\n' self.predictionFile += ' except KeyError as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' return json.dumps(output)' self.predictionFile += '\n' self.predictionFile += ' except Exception as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' return json.dumps(output)' self.predictionFile += '\n' self.predictionFile += '\n' self.predictionFile += 'if __name__ == "__main__":' self.predictionFile += '\n' self.predictionFile += ' predictobj = prediction()' self.predictionFile += '\n' self.predictionFile += ' predictobj.predict_from_file(sys.argv[1])' self.predictionFile += '\n' filename = os.path.join(deploy_path,'prediction.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_text_drift_file(self,deploy_path,features,target,model_type): #task-14549 self.predictionFile = 'import warnings' self.predictionFile += '\n' self.predictionFile += 'warnings.filterwarnings("ignore")' self.predictionFile += '\n' self.predictionFile += 'import json' self.predictionFile += '\n' self.predictionFile += 'import os' self.predictionFile += '\n' self.predictionFile += 'import sys' self.predictionFile += '\n' self.predictionFile += 'import pandas as pd' self.predictionFile += '\n' self.predictionFile += 'from monitoring import check_drift' self.predictionFile += '\n' self.predictionFile += 'def drift(data):' self.predictionFile += '\n' self.predictionFile += ' try:' self.predictionFile += '\n' self.predictionFile += ' if os.path.splitext(data)[1] == ".json":' self.predictionFile += '\n' self.predictionFile += ' with open(data,\'r\',encoding=\'utf-8\') as f:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.load(f)' self.predictionFile += '\n' self.predictionFile += ' else:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.loads(data)' self.predictionFile += '\n' self.predictionFile += ' jsonData[\'features\'] = \''+",".join([feature for feature in features])+'\'' self.predictionFile += '\n' self.predictionFile += ' jsonData[\'target\'] = \''+target+'\'' self.predictionFile += '\n' if model_type.lower() != 'timeseriesforecasting': #task 11997 self.predictionFile += ' htmlfilepath=evidently_details(jsonData)' self.predictionFile += '\n' else: self.predictionFile += ' htmlfilepath=\'\'' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.dumps(jsonData)' self.predictionFile += '\n' self.predictionFile += ' output = check_drift(jsonData)' self.predictionFile += '\n' self.predictionFile += ' output = json.loads(output)' self.predictionFile += '\n' self.predictionFile += ' output[\'htmlPath\'] = str(htmlfilepath)' self.predictionFile += '\n' self.predictionFile += ' print("drift:", json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return(output)' self.predictionFile += '\n' self.predictionFile += ' except KeyError as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("drift:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' except Exception as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("drift:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' if model_type.lower() != 'timeseriesforecasting': #task 11997 self.predictionFile += 'def evidently_details(deployJson):' self.predictionFile += '\n' self.predictionFile += ' features = deployJson[\'features\'].split(\',\')' self.predictionFile += '\n' self.predictionFile += ' target = deployJson[\'target\']' self.predictionFile += '\n' self.predictionFile += """\ try: from evidently.report import Report from evidently.metrics import TextDescriptorsDriftMetric, ColumnDriftMetric from evidently.pipeline.column_mapping import ColumnMapping from sklearn.preprocessing import LabelEncoder historicaldataFrame=pd.read_csv(deployJson['trainingDataLocation'],skipinitialspace = True,na_values=['-','?']) currentdataFrame=pd.read_csv(deployJson['currentDataLocation'],skipinitialspace = True,na_values=['-','?']) historicaldataFrame.columns = historicaldataFrame.columns.str.strip() currentdataFrame.columns = currentdataFrame.columns.str.strip() hdf = historicaldataFrame.dropna(subset=features) cdf = currentdataFrame.dropna(subset=features) hdf['Text_Features'] = hdf[features].apply("-".join, axis=1) cdf['Text_Features'] = cdf[features].apply("-".join, axis=1) hdf['target'] = historicaldataFrame[target] cdf['target'] = currentdataFrame[target] le = LabelEncoder() le.fit(hdf['target']) hdf['target'] = le.transform(hdf['target']) le.fit(cdf['target']) cdf['target'] = le.transform(cdf['target']) hd = hdf[['Text_Features', 'target']] cd = cdf[['Text_Features', 'target']] column_mapping = ColumnMapping() column_mapping.target = 'target' column_mapping.prediction = 'target' column_mapping.text_features = ['Text_Features'] column_mapping.numerical_features = [] column_mapping.categorical_features = [] performance_report = Report(metrics=[ColumnDriftMetric('target'),TextDescriptorsDriftMetric(column_name='Text_Features')]) performance_report.run(reference_data=hd, current_data=cd,column_mapping=column_mapping) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),"log","My_report.html") performance_report.save_html(report) return(report) except Exception as e: print('Error: ', e) return('NA')""" self.predictionFile += '\n' self.predictionFile += 'if __name__ == "__main__":' self.predictionFile += '\n' self.predictionFile += ' output = drift(sys.argv[1])' filename = os.path.join(deploy_path,'aion_ipdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_drift_file(self,deploy_path,features,target,model_type): self.predictionFile = 'import warnings' self.predictionFile += '\n' self.predictionFile += 'warnings.filterwarnings("ignore")' self.predictionFile += '\n' self.predictionFile += 'import json' self.predictionFile += '\n' self.predictionFile += 'import os' self.predictionFile += '\n' self.predictionFile += 'import sys' self.predictionFile += '\n' self.predictionFile += 'import pandas as pd' self.predictionFile += '\n' self.predictionFile += 'from monitoring import check_drift' self.predictionFile += '\n' self.predictionFile += 'from pandas import json_normalize' self.predictionFile += '\n' self.predictionFile += 'from script.inputprofiler import inputprofiler' self.predictionFile += '\n' self.predictionFile += 'def drift(data):' self.predictionFile += '\n' self.predictionFile += ' try:' self.predictionFile += '\n' self.predictionFile += ' if os.path.splitext(data)[1] == ".json":' self.predictionFile += '\n' self.predictionFile += ' with open(data,\'r\',encoding=\'utf-8\') as f:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.load(f)' self.predictionFile += '\n' self.predictionFile += ' else:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.loads(data)' self.predictionFile += '\n' self.predictionFile += ' jsonData[\'features\'] = \''+",".join([feature for feature in features])+'\'' self.predictionFile += '\n' self.predictionFile += ' jsonData[\'target\'] = \''+target+'\'' self.predictionFile += '\n' if model_type.lower() != 'timeseriesforecasting': #task 11997 self.predictionFile += ' htmlfilepath=evidently_details(jsonData)' self.predictionFile += '\n' else: self.predictionFile += ' htmlfilepath=\'\'' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.dumps(jsonData)' self.predictionFile += '\n' self.predictionFile += ' output = check_drift(jsonData)' self.predictionFile += '\n' self.predictionFile += ' output = json.loads(output)' self.predictionFile += '\n' self.predictionFile += ' output[\'htmlPath\'] = str(htmlfilepath)' self.predictionFile += '\n' self.predictionFile += ' output = json.dumps(output)' self.predictionFile += '\n' self.predictionFile += ' print("drift:",output)' self.predictionFile += '\n' self.predictionFile += ' return(output)' self.predictionFile += '\n' self.predictionFile += ' except KeyError as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("drift:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' except Exception as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("drift:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' if model_type.lower() != 'timeseriesforecasting': #task 11997 self.predictionFile += 'def evidently_details(deployJson):' self.predictionFile += '\n' self.predictionFile += ' features = deployJson[\'features\'].split(\',\')' self.predictionFile += '\n' self.predictionFile += ' target = deployJson[\'target\']' self.predictionFile += '\n' self.predictionFile += """\ try: from evidently.report import Report from evidently.metric_preset import DataDriftPreset historicaldataFrame=pd.read_csv(deployJson['trainingDataLocation'],skipinitialspace = True,na_values=['-','?']) currentdataFrame=pd.read_csv(deployJson['currentDataLocation'],skipinitialspace = True,na_values=['-','?']) historicaldataFrame.columns = historicaldataFrame.columns.str.strip() currentdataFrame.columns = currentdataFrame.columns.str.strip() profilerobj = inputprofiler() historicaldataFramep = profilerobj.run(historicaldataFrame) currentdataFramep = profilerobj.run(currentdataFrame) hdf = historicaldataFramep[features] cdf = currentdataFramep[features] hdf['target'] = historicaldataFrame[target] cdf['target'] = currentdataFrame[target] data_drift_report = Report(metrics = [DataDriftPreset()]) data_drift_report.run(reference_data=hdf,current_data=cdf,column_mapping = None) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','my_report.html') data_drift_report.save_html(report) return(report) except Exception as e: print('Error') return('NA')""" self.predictionFile += '\n' self.predictionFile += 'if __name__ == "__main__":' self.predictionFile += '\n' self.predictionFile += ' output = drift(sys.argv[1])' filename = os.path.join(deploy_path,'aion_ipdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_prediction_file(self,classname,deploy_path,learner_type,grouperbyjson,rowfilterexpression,model_type,datetimeFeature): self.predictionFile = 'import warnings' self.predictionFile += '\n' self.predictionFile += 'warnings.filterwarnings("ignore")' self.predictionFile += '\n' self.predictionFile += 'import json' self.predictionFile += '\n' self.predictionFile += 'import os' self.predictionFile += '\n' self.predictionFile += 'import sys' self.predictionFile += '\n' self.predictionFile += 'import pandas as pd' self.predictionFile += '\n' self.predictionFile += 'from pandas import json_normalize' self.predictionFile += '\n' if(learner_type.lower() != 'recommendersystem'): #task 11190 self.predictionFile += 'from script.selector import selector' self.predictionFile += '\n' self.predictionFile += 'from script.inputprofiler import inputprofiler' self.predictionFile += '\n' #self.predictionFile += 'from '+classname+' import '+classname self.predictionFile += 'from script.trained_model import trained_model' self.predictionFile += '\n' else: self.predictionFile += 'from script.item_recommendation import collaborative_filter' self.predictionFile += '\n' self.predictionFile += 'from script.output_format import output_format' self.predictionFile += '\n' if (learner_type != 'RecommenderSystem'): #task 11190 self.predictionFile += 'profilerobj = inputprofiler()' self.predictionFile += '\n' self.predictionFile += 'selectobj = selector()' self.predictionFile += '\n' self.predictionFile += 'modelobj = trained_model()' self.predictionFile += '\n' else: self.predictionFile += 'colabobj = collaborative_filter()' self.predictionFile += '\n' self.predictionFile += 'outputobj = output_format()' self.predictionFile += '\n' self.predictionFile += 'def predict(data):' self.predictionFile += '\n' self.predictionFile += ' try:' self.predictionFile += '\n' self.predictionFile += ' if os.path.splitext(data)[1] == ".tsv":' self.predictionFile += '\n' self.predictionFile += ' df=pd.read_csv(data,encoding=\'utf-8\',sep=\'\\t\',skipinitialspace = True,na_values=[\'-\',\'?\'])' self.predictionFile += '\n' self.predictionFile += ' elif os.path.splitext(data)[1] == ".csv":' self.predictionFile += '\n' self.predictionFile += ' df=pd.read_csv(data,encoding=\'utf-8\',skipinitialspace = True,na_values=[\'-\',\'?\'])' self.predictionFile += '\n' self.predictionFile += ' elif os.path.splitext(data)[1] == ".dat":' self.predictionFile += '\n' self.predictionFile += ' df=pd.read_csv(data,encoding=\'utf-8\',skipinitialspace = True,na_values=[\'-\',\'?\'])' self.predictionFile += '\n' self.predictionFile += ' else:' self.predictionFile += '\n' self.predictionFile += ' if os.path.splitext(data)[1] == ".json":' self.predictionFile += '\n' self.predictionFile += ' with open(data,\'r\',encoding=\'utf-8\') as f:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.load(f)' self.predictionFile += '\n' self.predictionFile += ' else:' self.predictionFile += '\n' self.predictionFile += ' jsonData = json.loads(data)' self.predictionFile += '\n' self.predictionFile += ' df = json_normalize(jsonData)' self.predictionFile += '\n' self.predictionFile += ' df.rename(columns=lambda x: x.strip(), inplace=True)' self.predictionFile += '\n' if str(rowfilterexpression) != '': self.predictionFile += ' filterexpression = "'+rowfilterexpression+'"' self.predictionFile += '\n' self.predictionFile += ' df = df.query(filterexpression)' self.predictionFile += '\n' #print(grouperbyjson) if str(grouperbyjson) != '': datetime = grouperbyjson['datetime'] unit = grouperbyjson['unit'] if unit == '': self.predictionFile += ' df[\'date\'] = pd.to_datetime(df[\''+datetime+'\'])' self.predictionFile += '\n' else: self.predictionFile += ' df[\'date\'] = pd.to_datetime(df[\''+datetime+'\'],unit=\''+unit+'\')' self.predictionFile += '\n' self.predictionFile += ' df = df.reset_index()' self.predictionFile += '\n' self.predictionFile += ' df.set_index(\'date\',inplace=True)' self.predictionFile += '\n' self.predictionFile += ' df = df.'+grouperbyjson['groupbystring'] self.predictionFile += '\n' self.predictionFile += ' df.columns = df.columns.droplevel(0)' self.predictionFile += '\n' self.predictionFile += ' df = df.reset_index()' self.predictionFile += '\n' self.predictionFile += ' df0 = df.copy()' self.predictionFile += '\n' if(learner_type != 'RecommenderSystem'): #task 11190 if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na': self.predictionFile += ' df,datetimeFeature = profilerobj.apply_profiler(df)' self.predictionFile += '\n' else: self.predictionFile += ' df = profilerobj.apply_profiler(df)' self.predictionFile += '\n' self.predictionFile += ' df = selectobj.apply_selector(df)' self.predictionFile += '\n' #self.predictionFile += ' modelobj = '+classname+'()' self.predictionFile += ' output = modelobj.predict(df,"")' self.predictionFile += '\n' else: self.predictionFile += ' output = colabobj.predict(df)' self.predictionFile += '\n' if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na': self.predictionFile += ' output = outputobj.apply_output_format(df0,output,datetimeFeature)' self.predictionFile += '\n' else: self.predictionFile += ' output = outputobj.apply_output_format(df0,output)' self.predictionFile += '\n' self.predictionFile += ' print("predictions:",output)' self.predictionFile += '\n' self.predictionFile += ' return(output)' self.predictionFile += '\n' self.predictionFile += ' except KeyError as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("predictions:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' except Exception as e:' self.predictionFile += '\n' self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}' self.predictionFile += '\n' self.predictionFile += ' print("predictions:",json.dumps(output))' self.predictionFile += '\n' self.predictionFile += ' return (json.dumps(output))' self.predictionFile += '\n' self.predictionFile += 'if __name__ == "__main__":' self.predictionFile += '\n' self.predictionFile += ' output = predict(sys.argv[1])' filename = os.path.join(deploy_path,'aion_predict.py') f = open(filename, "w") f.write(str(self.predictionFile)) f.close() def create_classification_text_performance_file(self,deploy_path,features,target): features = ",".join([feature for feature in features]) self.predictionFile = """\ import pandas as pd import warnings warnings.filterwarnings("ignore") import json import os import sys from pandas import json_normalize # from evidently.dashboard import Dashboard # from evidently.tabs import ClassificationPerformanceTab from evidently.pipeline.column_mapping import ColumnMapping from aion_predict import predict from evidently.report import Report from evidently.pipeline.column_mapping import ColumnMapping from evidently.metric_preset import ClassificationPreset def odrift(data): try: """ self.predictionFile += ' features = \''+features+'\'' self.predictionFile += '\n' self.predictionFile += ' target = \''+target+'\'' self.predictionFile += '\n' self.predictionFile +="""\ if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) production = predict().run(jsonData['currentDataLocation']) reference = predict().run(jsonData['trainingDataLocation']) production = json.loads(production) reference = json.loads(reference) if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'): production = production['data'] production = json_normalize(production) reference = reference['data'] reference = json_normalize(reference) production['target'] = production[target] reference['target'] = reference[target] column_mapping = ColumnMapping() column_mapping.target = target column_mapping.prediction = 'prediction' column_mapping.datetime = None column_mapping.text_features = features.split(',') iris_model_performance_dashboard = Report(metrics=[ClassificationPreset()]) iris_model_performance_dashboard.run(reference_data=reference, current_data=production,column_mapping=column_mapping) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html') iris_model_performance_dashboard.save_html(report) metrics_output = iris_model_performance_dashboard.as_dict() output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']} print("drift:",json.dumps(output)) return (json.dumps(output)) except KeyError as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) except Exception as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = odrift(sys.argv[1])""" filename = os.path.join(deploy_path,'aion_opdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_classification_performance_file(self,deploy_path,features,target): features = ",".join([feature for feature in features]) self.predictionFile = """\ import pandas as pd import warnings warnings.filterwarnings("ignore") import json import os import sys from pandas import json_normalize from evidently.report import Report from evidently.metric_preset import ClassificationPreset from evidently.pipeline.column_mapping import ColumnMapping from aion_predict import predict def odrift(data): try: """ self.predictionFile += ' features = \''+features+'\'' self.predictionFile += '\n' self.predictionFile += ' target = \''+target+'\'' self.predictionFile += '\n' self.predictionFile +="""\ if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) production = predict().run(jsonData['currentDataLocation']) reference = predict().run(jsonData['trainingDataLocation']) production = json.loads(production) reference = json.loads(reference) if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'): production = production['data'] production = json_normalize(production) reference = reference['data'] reference = json_normalize(reference) production['target'] = production[target] reference['target'] = reference[target] column_mapping = ColumnMapping() column_mapping.target = target column_mapping.prediction = 'prediction' column_mapping.datetime = None column_mapping.numerical_features = features.split(',') model_performance_dashboard = Report(metrics = [ClassificationPreset()]) model_performance_dashboard.run(reference_data =reference, current_data =production, column_mapping = column_mapping) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html') model_performance_dashboard.save_html(report) metrics_output = model_performance_dashboard.as_dict() output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']} print("drift:",json.dumps(output)) return (json.dumps(output)) else: output = {"status":"SUCCESS","htmlPath":'NA'} print("drift:",json.dumps(output)) return (json.dumps(output)) except KeyError as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) except Exception as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = odrift(sys.argv[1])""" filename = os.path.join(deploy_path,'aion_opdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_model_service(self,deploy_path,serviceName,problemType): filedata = """ from flask import Flask, jsonify, request from flask_restful import Resource, Api from aion_predict import predict""" if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ from aion_xai import local_analysis from aion_ipdrift import drift from aion_opdrift import odrift""" filedata += """ import json import os import pandas as pd import io import argparse from pathlib import Path from flask_cors import CORS, cross_origin app = Flask(__name__) #cross origin resource from system arguments parser = argparse.ArgumentParser() parser.add_argument('-ip', '--ipaddress', help='IP Address') parser.add_argument('-p', '--port', help='Port Number') parser.add_argument("-cors", type=str, required=False) d = vars(parser.parse_args()) modelPath = Path(__file__).parent try: with open( (modelPath/'etc')/'display.json', 'r') as f: disp_data = json.load(f) is_explainable = not disp_data.get('textFeatures') except: disp_data = {} is_explainable = True if "cors" in d.keys(): if d["cors"] != '' and d["cors"] != None: d["cors"] = [s.strip() for s in d["cors"].split(",")] #cors = CORS(app, resources={r"/AION/*": {"origins": ["http://localhost", "http://localhost:5000"]}}) cors = CORS(app, resources={r"/AION/*": {"origins": d["cors"]}}) api = Api(app) class predictapi(Resource): def get(self): features = disp_data.get('modelFeatures') if features: msg=\""" RequestType: POST Content-Type=application/json Body: {displaymsg} \""".format(displaymsg={ x:'Value' for x in features}) else: displaymsg='Data in JSON Format' return jsonify(displaymsg) def post(self): data = request.get_json() output = predict().run(json.dumps(data)) return jsonify(json.loads(output)) class predictfileapi(Resource): def post(self): if 'file' in request.files: file = request.files['file'] urlData = file.read() rawData = pd.read_csv(io.StringIO(urlData.decode('utf-8'))) data = rawData.to_json(orient='records') output = predict().run(data) return jsonify(json.loads(output)) else: displaymsg='File is mising' return jsonify(displaymsg) def get(self): msg=\""" RequestType: POST Body:send file content in body\""" return jsonify(msg) """ if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ class explainapi(Resource): def get(self): features = disp_data.get('modelFeatures') if features: msg=\""" RequestType: POST Content-Type=application/json Body: {displaymsg} \""".format(displaymsg={ x:'Value' for x in features}) else: displaymsg='Data in JSON Format' return jsonify(displaymsg) def post(self): data = request.get_json() if is_explainable: output = local_analysis(json.dumps(data)) else: output = json.dumps({"status":"FAIL","data":"explain api is not supported when text features are used for training"}) return jsonify(json.loads(output)) class monitoringapi(Resource): def get(self): return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'}) def post(self): data = request.get_json() output = drift(json.dumps(data)) return jsonify(json.loads(output)) class performanceapi(Resource): def get(self): return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'}) def post(self): data = request.get_json() output = odrift(json.dumps(data)) return jsonify(json.loads(output)) """ filedata += """ api.add_resource(predictapi, '/AION/{serviceName}/predict')""".format(serviceName=serviceName) filedata += """ api.add_resource(predictfileapi, '/AION/{serviceName}/predict_file')""".format(serviceName=serviceName) if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ api.add_resource(explainapi, '/AION/{serviceName}/explain') api.add_resource(monitoringapi, '/AION/{serviceName}/monitoring') api.add_resource(performanceapi, '/AION/{serviceName}/performance')""".format(serviceName=serviceName) filedata += """ if __name__ == '__main__': args = parser.parse_args() app.run(args.ipaddress,port = args.port,debug = True)""" filename = os.path.join(deploy_path,'aion_service.py') f = open(filename, "wb") f.write(str(filedata).encode('utf8')) f.close() def create_regression_performance_file(self,deploy_path,features,target): features = ",".join([feature for feature in features]) self.predictionFile = """\ import pandas as pd import warnings warnings.filterwarnings("ignore") import json import os import sys from pandas import json_normalize from evidently.report import Report from evidently.metric_preset import RegressionPreset from evidently.pipeline.column_mapping import ColumnMapping from aion_predict import predict def odrift(data): try: """ self.predictionFile += ' features = \''+features+'\'' self.predictionFile += '\n' self.predictionFile += ' target = \''+target+'\'' self.predictionFile += '\n' self.predictionFile +="""\ if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) production = predict().run(jsonData['currentDataLocation']) reference = predict().run(jsonData['trainingDataLocation']) production = json.loads(production) reference = json.loads(reference) if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'): production = production['data'] production = json_normalize(production) reference = reference['data'] reference = json_normalize(reference) production['target'] = production[target] reference['target'] = reference[target] column_mapping = ColumnMapping() column_mapping.target = target column_mapping.prediction = 'prediction' column_mapping.datetime = None column_mapping.numerical_features = features.split(',') iris_model_performance_dashboard = Report(metrics=[RegressionPreset()]) iris_model_performance_dashboard.run(reference_data = reference, current_data = production, column_mapping = column_mapping) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html') iris_model_performance_dashboard.save_html(report) metrics_output = iris_model_performance_dashboard.as_dict() output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']} print("drift:",json.dumps(output)) return (json.dumps(output)) else: output = {"status":"SUCCESS","htmlPath":'NA'} print("drift:",json.dumps(output)) return (json.dumps(output)) except KeyError as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) except Exception as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = odrift(sys.argv[1])""" filename = os.path.join(deploy_path,'aion_opdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_regression_text_performance_file(self,deploy_path,features,target): features = ",".join([feature for feature in features]) self.predictionFile = """\ import pandas as pd import warnings warnings.filterwarnings("ignore") import json import os import sys from pandas import json_normalize from aion_predict import predict from evidently.report import Report from evidently.pipeline.column_mapping import ColumnMapping from evidently.metric_preset import RegressionPreset def odrift(data): try: """ self.predictionFile += ' features = \''+features+'\'' self.predictionFile += '\n' self.predictionFile += ' target = \''+target+'\'' self.predictionFile += '\n' self.predictionFile +="""\ if os.path.splitext(data)[1] == ".json": with open(data,'r',encoding='utf-8') as f: jsonData = json.load(f) else: jsonData = json.loads(data) production = predict().run(jsonData['currentDataLocation']) reference = predict().run(jsonData['trainingDataLocation']) production = json.loads(production) reference = json.loads(reference) if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'): production = production['data'] production = json_normalize(production) reference = reference['data'] reference = json_normalize(reference) production['target'] = production[target] reference['target'] = reference[target] column_mapping = ColumnMapping() column_mapping.target = target column_mapping.prediction = 'prediction' column_mapping.datetime = None column_mapping.numerical_features = features.split(',') iris_model_performance_dashboard = Report(metrics=[RegressionPreset()]) iris_model_performance_dashboard.run(reference_data=reference, current_data=production,column_mapping=column_mapping) report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html') iris_model_performance_dashboard.save_html(report) metrics_output = iris_model_performance_dashboard.as_dict() output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']} print("drift:",json.dumps(output)) return (json.dumps(output)) else: output = {"status":"SUCCESS","htmlPath":'NA'} print("drift:",json.dumps(output)) return (json.dumps(output)) except KeyError as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) except Exception as e: print(e) output = {"status":"FAIL","message":str(e).strip('"')} print("drift:",json.dumps(output)) return (json.dumps(output)) if __name__ == "__main__": output = odrift(sys.argv[1])""" filename = os.path.join(deploy_path,'aion_opdrift.py') f = open(filename, "wb") f.write(str(self.predictionFile).encode('utf8')) f.close() def create_publish_service(self,datalocation,usecaseid,version,problemType): filename = os.path.join(datalocation,'aion_publish_service.py') if not os.path.exists(filename): filedata = """ import sys import json import time import sqlite3 import argparse import pandas as pd import io from pathlib import Path from datetime import datetime filename = Path(__file__).parent/'config.json' with open (filename, "r") as f: data = json.loads(f.read()) modelVersion = str(data['version']) modelPath = Path(__file__).parent/modelVersion sys.path.append(str(modelPath)) try: with open( (modelPath/'etc')/'display.json', 'r') as f: disp_data = json.load(f) is_explainable = not disp_data.get('textFeatures') except: disp_data = {} is_explainable = True from flask import Flask, jsonify, request from flask_restful import Resource, Api from flask_cors import CORS, cross_origin from flask import Response from aion_predict import predict """ if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ from aion_ipdrift import drift from aion_opdrift import odrift if is_explainable: from aion_xai import local_analysis """ filedata += """ dataPath = Path(__file__).parent/'data' dataPath.mkdir(parents=True, exist_ok=True) app = Flask(__name__) #cross origin resource from system arguments parser = argparse.ArgumentParser() parser.add_argument('-ip', '--ipaddress', help='IP Address') parser.add_argument('-p', '--port', help='Port Number') parser.add_argument("-cors", type=str, required=False) d = vars(parser.parse_args()) if "cors" in d.keys(): if d["cors"] != '' and d["cors"] != None: d["cors"] = [s.strip() for s in d["cors"].split(",")] #cors = CORS(app, resources={r"/AION/*": {"origins": ["http://localhost", "http://localhost:5000"]}}) cors = CORS(app, resources={r"/AION/*": {"origins": d["cors"]}}) api = Api(app) class sqlite_db(): def __init__(self, location, database_file=None): if not isinstance(location, Path): location = Path(location) if database_file: self.database_name = database_file else: self.database_name = location.stem + '.db' db_file = str(location/self.database_name) self.conn = sqlite3.connect(db_file) self.cursor = self.conn.cursor() self.tables = [] def table_exists(self, name): if name in self.tables: return True elif name: query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';" listOfTables = self.cursor.execute(query).fetchall() if len(listOfTables) > 0 : self.tables.append(name) return True return False def read(self, table_name,condition=''): if condition == '': return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn) else: return pd.read_sql_query(f"SELECT * FROM {table_name} WHERE {condition}", self.conn) def create_table(self,name, columns, dtypes): query = f'CREATE TABLE IF NOT EXISTS {name} (' for column, data_type in zip(columns, dtypes): query += f"'{column}' TEXT," query = query[:-1] query += ');' self.conn.execute(query) return True def update(self,table_name,updates,condition): update_query = f'UPDATE {table_name} SET {updates} WHERE {condition}' self.cursor.execute(update_query) self.conn.commit() return True def write(self,data, table_name): if not self.table_exists(table_name): self.create_table(table_name, data.columns, data.dtypes) tuple_data = list(data.itertuples(index=False, name=None)) insert_query = f'INSERT INTO {table_name} VALUES(' for i in range(len(data.columns)): insert_query += '?,' insert_query = insert_query[:-1] + ')' self.cursor.executemany(insert_query, tuple_data) self.conn.commit() return True def delete(self, name): pass def close(self): self.conn.close()""" filedata += """ app = Flask(__name__) api = Api(app) class predictapi(Resource): def get(self): features = disp_data.get('modelFeatures') if features: msg=\""" RequestType: POST Content-Type=application/json Body: {displaymsg} \""".format(displaymsg={ x:'Value' for x in features}) else: displaymsg='Data in JSON Format' return jsonify(displaymsg) def post(self): sqlite_dbObj = sqlite_db(dataPath,'data.db') if not sqlite_dbObj.table_exists('metrices'): data = {'noOfPredictCalls':'0','noOfDriftCalls':'0',"noOfActualCalls":'0',"mid":'0'} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.create_table('metrices',data.columns, data.dtypes) data = request.get_json() output = predict().run(json.dumps(data)) outputobj = json.loads(output) if outputobj['status'] == 'SUCCESS': try: df2 = pd.read_json(json.dumps(outputobj['data']), orient ='records') if not sqlite_dbObj.table_exists('prodData'): sqlite_dbObj.create_table('prodData',df2.columns, df2.dtypes) sqlite_dbObj.write(df2,'prodData') except: pass try: data = sqlite_dbObj.read('metrices') #print(data) if len(data) == 0: data = [{'mid':'0','noOfPredictCalls':'1','noOfDriftCalls':'0',"noOfActualCalls":'0'}] data = pd.read_json(json.dumps(data), orient ='records') sqlite_dbObj.write(data,'metrices') else: noofPredictCalls = int(data['noOfPredictCalls'].iloc[0])+1 sqlite_dbObj.update('metrices',"noOfPredictCalls = '"+str(noofPredictCalls)+"'","mid = 0") except Exception as e: print(e) pass return jsonify(json.loads(output)) class predictfileapi(Resource): def post(self): sqlite_dbObj = sqlite_db(dataPath,'data.db') if not sqlite_dbObj.table_exists('metrices'): data = {'noOfPredictCalls':'0','noOfDriftCalls':'0',"noOfActualCalls":'0',"mid":'0'} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.create_table('metrices',data.columns, data.dtypes) if 'file' in request.files: file = request.files['file'] urlData = file.read() rawData = pd.read_csv(io.StringIO(urlData.decode('utf-8'))) data = rawData.to_json(orient='records') output = predict().run(data) outputobj = json.loads(output) if outputobj['status'] == 'SUCCESS': try: df2 = pd.read_json(json.dumps(outputobj['data']), orient ='records') if not sqlite_dbObj.table_exists('prodData'): sqlite_dbObj.create_table('prodData',df2.columns, df2.dtypes) sqlite_dbObj.write(df2,'prodData') except: pass try: data = sqlite_dbObj.read('metrices') #print(data) if len(data) == 0: data = [{'mid':'0','noOfPredictCalls':'1','noOfDriftCalls':'0',"noOfActualCalls":'0'}] data = pd.read_json(json.dumps(data), orient ='records') sqlite_dbObj.write(data,'metrices') else: noofPredictCalls = int(data['noOfPredictCalls'].iloc[0])+1 sqlite_dbObj.update('metrices',"noOfPredictCalls = '"+str(noofPredictCalls)+"'","mid = 0") except Exception as e: print(e) pass return jsonify(json.loads(output)) else: output = {'status':'error','msg':'File is missing'} return jsonify(output) """ if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ class explainapi(Resource): def get(self): features = disp_data.get('modelFeatures') if features: msg=\""" RequestType: POST Content-Type=application/json Body: {displaymsg} \""".format(displaymsg={ x:'Value' for x in features}) else: displaymsg='Data in JSON Format' return jsonify(displaymsg) def post(self): data = request.get_json() if is_explainable: output = local_analysis(json.dumps(data)) else: output = json.dumps({"status":"FAIL","data":"explain api is not supported when text features are used for training"}) return jsonify(json.loads(output)) class monitoringapi(Resource): def get(self): return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'}) def post(self): sqlite_dbObj = sqlite_db(dataPath,'data.db') if not sqlite_dbObj.table_exists('monitoring'): data = {'status':'No Drift','Msg':'No Input Drift Found','RecordTime':'Time','version':'1'} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.create_table('monitoring',data.columns, data.dtypes) trainingDataPath = (modelPath/'data')/'preprocesseddata.csv.gz' if not sqlite_dbObj.table_exists('prodData'): return jsonify({'status':'Error','msg':'Prod data not available'}) data = sqlite_dbObj.read('prodData') filetimestamp = str(int(time.time())) dataFile = dataPath/('AION_' + filetimestamp+'.csv') data.to_csv(dataFile, index=False) data = request.get_json() data={'trainingDataLocation':trainingDataPath,'currentDataLocation':dataFile} output = drift(json.dumps(data)) outputData = json.loads(output) status = outputData['status'] if status == 'SUCCESS': Msg = str(outputData['data']) else: Msg = 'Error during drift analysis' now = datetime.now() # current date and time date_time = now.strftime("%m/%d/%Y, %H:%M:%S") data = {'status':status,'Msg':Msg,'RecordTime':date_time,'version':modelVersion} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.write(data,'monitoring') return jsonify(json.loads(output))""" filedata += """ class matricesapi(Resource): def get(self): sqlite_dbObj = sqlite_db(dataPath,'data.db') if sqlite_dbObj.table_exists('metrices'): df1 = sqlite_dbObj.read('metrices') else: df1 = pd.DataFrame() #print(df1) if sqlite_dbObj.table_exists('monitoring'): df2 = sqlite_dbObj.read('monitoring') else: df2 = pd.DataFrame() msg = {'Deployed Version':str(modelVersion)} if df1.shape[0] > 0: msg.update({'noOfPredictCalls':str(df1['noOfPredictCalls'].iloc[0])}) else: msg.update({'noOfPredictCalls':'0'}) driftDetails = [] for idx in reversed(df2.index): driftd = {'version':str(df2.version[idx]),'status':str(df2.status[idx]),'recordTime':str(df2.RecordTime[idx]),'msg':str(df2.Msg[idx])} driftDetails.append(driftd) msg.update({'driftDetails':driftDetails}) return jsonify(msg) class performanceapi(Resource): def get(self): return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'}) def post(self): sqlite_dbObj = sqlite_db(dataPath,'data.db') if not sqlite_dbObj.table_exists('monitoring'): data = {'status':'No Drift','Msg':'No Input Drift Found','RecordTime':'Time','version':'1'} data = pd.DataFrame(data, index=[0]) sqlite_dbObj.create_table('monitoring',data.columns, data.dtypes) trainingDataPath = (modelPath/'data')/'preprocesseddata.csv.gz' if not sqlite_dbObj.table_exists('prodData'): return jsonify({'status':'Error','msg':'Prod data not available'}) data = sqlite_dbObj.read('prodData') filetimestamp = str(int(time.time())) dataFile = dataPath/('AION_' + filetimestamp+'.csv') data.to_csv(dataFile, index=False) data = request.get_json() data={'trainingDataLocation':trainingDataPath,'currentDataLocation':dataFile} output = odrift(json.dumps(data)) return jsonify(json.loads(output)) """ filedata += """ api.add_resource(predictapi, '/AION/{serviceName}/predict') api.add_resource(predictfileapi, '/AION/{serviceName}/predict_file') api.add_resource(matricesapi, '/AION/{serviceName}/metrices')""".format(serviceName=usecaseid) if problemType.lower() == 'classification' or problemType.lower() == 'regression': filedata += """ api.add_resource(explainapi, '/AION/{serviceName}/explain') api.add_resource(monitoringapi, '/AION/{serviceName}/monitoring') api.add_resource(performanceapi, '/AION/{serviceName}/performance') """.format(serviceName=usecaseid) filedata += """ if __name__ == '__main__': args = parser.parse_args() app.run(args.ipaddress,port = args.port,debug = True)""" f = open(filename, "wb") f.write(str(filedata).encode('utf8')) f.close() data = {'version':version} filename = os.path.join(datalocation,'config.json') with open(filename, "w") as outfile: json.dump(data, outfile) outfile.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' TAB_CHAR = ' ' * 4 def import_modules(importer, modules_list): for module in modules_list: mod_from = module.get('mod_from',None) mod_as = module.get('mod_as',None) importer.addModule(module['module'], mod_from=mod_from, mod_as=mod_as) """ /** * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * © Copyright HCL Technologies Ltd. 2021, 2022 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. */ """ from importlib.metadata import version import sys class importModule(): def __init__(self): self.importModule = {} self.stdlibModule = [] self.localModule = {} def addLocalModule(self,module, mod_from=None, mod_as=None): if module == '*': if module not in self.localModule.keys(): self.localModule[module]= [mod_from] else: self.localModule[module].append(mod_from) elif module not in self.localModule.keys(): self.localModule[module] = {'from':mod_from, 'as':mod_as} def addModule(self, module, mod_from=None, mod_as=None): if module not in self.importModule.keys(): self.importModule[module] = {'from':mod_from, 'as':mod_as} if module in sys.stdlib_module_names: self.stdlibModule.append(module) elif isinstance(self.importModule[module], list): if mod_as not in [x['as'] for x in self.importModule[module]]: self.importModule[module].append({'from':mod_from, 'as':mod_as}) elif mod_as not in [x['from'] for x in self.importModule[module]]: self.importModule[module].append({'from':mod_from, 'as':mod_as}) elif mod_as != self.importModule[module]['as']: as_list = [self.importModule[module]] as_list.append({'from':mod_from, 'as':mod_as}) self.importModule[module] = as_list elif mod_from != self.importModule[module]['from']: as_list = [self.importModule[module]] as_list.append({'from':mod_from, 'as':mod_as}) self.importModule[module] = as_list def getModules(self): return (self.importModule, self.stdlibModule) def getBaseModule(self, extra_importers=[]): modules_alias = { 'sklearn':'scikit-learn', 'genetic_selection':'sklearn-genetic', 'google': 'google-cloud-storage', 'azure':'azure-storage-file-datalake'} local_modules = {'AIX':'/app/AIX-0.1-py3-none-any.whl'} modules = [] require = "" if extra_importers: extra_importers = [importer.importModule for importer in extra_importers if isinstance(importer, importModule)] importers_module = [self.importModule] + extra_importers for importer_module in importers_module: for k,v in importer_module.items(): if v['from']: mod = v['from'].split('.')[0] else: mod = k if mod in modules_alias.keys(): mod = modules_alias[mod] modules.append(mod) modules = list(set(modules)) for mod in modules: try: if mod in local_modules.keys(): require += f"{local_modules[mod]}\n" else: require += f"{mod}=={version(mod)}\n" except : if mod not in sys.stdlib_module_names: raise return require def getCode(self): def to_string(k, v): mod = '' if v['from']: mod += 'from {} '.format(v['from']) mod += 'import {}'.format(k) if v['as']: mod += ' as {} '.format(v['as']) return mod modules = "" local_modules = "" std_lib_modules = "" third_party_modules = "" for k,v in self.importModule.items(): if k in self.stdlibModule: std_lib_modules = std_lib_modules + '\n' + to_string(k, v) elif isinstance(v, dict): third_party_modules = third_party_modules + '\n' + to_string(k, v) elif isinstance(v, list): for alias in v: third_party_modules = third_party_modules + '\n' + to_string(k, alias) for k,v in self.localModule.items(): if k != '*': local_modules = local_modules + '\n' + to_string(k, v) else: for mod_from in v: local_modules = local_modules + '\n' + f'from {mod_from} import {k}' if std_lib_modules: modules = modules + "\n#Standard Library modules" + std_lib_modules if third_party_modules: modules = modules + "\n\n#Third Party modules" + third_party_modules if local_modules: modules = modules + "\n\n#local modules" + local_modules + '\n' return modules def copyCode(self, importer): self.importModule, self.stdlibModule = importer.getModules() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import subprocess import os import glob import sys import python_minifier def encrypt_files(path): cwd = os.getcwd() secure_path = os.path.join(path,'SecuredScripts') try: if not os.path.exists(secure_path): os.mkdir(secure_path) files = [f for f in glob.glob(path + "/*.py")] for file in files: #encrypted_file_details[0] = file #file = files[0] #print(file) #filename_w_dir = os.path.splitext(file) filename_w_ext = os.path.basename(file) filename, file_extension = os.path.splitext(filename_w_ext) file_folder_path = os.path.join(secure_path,filename) #print(file_folder_path) if not os.path.exists(file_folder_path): os.mkdir(file_folder_path) # Minify python source code minify_file = os.path.join(file_folder_path,filename+'_minify.py') pythonfolder,_ = os.path.split(sys.executable) pyminify_script = os.path.join(pythonfolder,'Scripts','pyminify.exe') minify_command = "\""+sys.executable+"\" \""+pyminify_script+ "\" \"" + file + "\" > \"" + minify_file+"\"" subprocess.call(minify_command, shell=True) # Change directory to folder path os.chdir(file_folder_path) # Obfuscate minified file pyarmor_script = os.path.join(pythonfolder,'Scripts','pyarmor.exe') obfusc_commmand = "\""+sys.executable+"\" \""+pyarmor_script+"\" obfuscate \"" + minify_file+"\"" #print(obfusc_commmand) subprocess.call(obfusc_commmand, shell=True) # Change directory to dist path obfusc_file = os.path.join(file_folder_path,'dist',filename+'_minify.py') #print(obfusc_file) chdirpath = os.path.join(file_folder_path,'dist') os.chdir(chdirpath) # Compress obfuscated file compressed_file = os.path.join(file_folder_path,'dist',filename+'_compressed.py') #print(compressed_file) pyminifier_script = os.path.join(pythonfolder,'Scripts','pyminifier.exe') compress_command = "\""+sys.executable+"\" \""+pyminifier_script+"\" --gzip -o \"" +compressed_file + "\" \"" + obfusc_file+"\"" #print(compress_command) subprocess.call(compress_command, shell=True) #compile_command = sys.executable+'-m py_compile "' + compressed_file+'"' #print(compile_command) #subprocess.call(compile_command , shell=True) #encrypted_file_details['compiled_file'] = file #compiled_file = os.path.join(file_folder_path,'dist','__pycache__',filename+'_compressed.cpython-37.pyc') #encrypted_file_details[1] = compiled_file #encrypted_file_list.append(encrypted_file_details) #encrypted_file = filename + '_compressed.cpython-37_encrypted.pyc' #encrypt_command = "python " + cwd + "\\Encrypt_Key_Dcrypt.py " + compiled_file + ' ' + encrypted_file + " --g -e" #print(encrypt_command) #subprocess.call(encrypt_command, shell=True) #encrypted_file_list += ']' #return(encrypted_file_list) os.chdir(path) except OSError as err: print ("Creation of the directory %s failed "+str(err)) # Driver function if __name__=="__main__": path = sys.argv[1] encrypt_files(path) #(base) C:\Himanshu\DataPreprocessing>pyminify DataPreprocessing.py > DataPreprocessing_minify.py #Obfuscate #(base) C:\Himanshu\DataPreprocessing>pyarmor obfuscate C:\Himanshu\DataPreprocessing\DataPreprocessing_minify.py #Compression #(base) C:\Himanshu\DataPreprocessing>pyminifier --gzip -o C:\Himanshu\DataPreprocessing\dist\DataPreprocessing_compressed.py C:\Himanshu\DataPreprocessing\dist\DataPreprocessing_minify.py #(base) C:\Himanshu\DataPreprocessing>cd dist #(base) C:\Himanshu\DataPreprocessing\dist>python DataPreprocessing_compressed.py "DocumentText" "Label" 90 ".csv" "C:\Himanshu\DataAcquisition\ClassificationDataNewBalanced.csv" #Compiling compressed .py to .pyc file #(base) C:\Himanshu\DataPreprocessing\dist>python -m py_compile DataPreprocessing_compressed.py #Encrypt .pyc file #(base) C:\Himanshu\DataPreprocessing\dist>python C:\Himanshu\Encrypt_Key_Dcrypt.py C:\Himanshu\DataPreprocessing\dist\__pycache__\DataPreprocessing_compressed.cpython-36.pyc DataPreprocessing_compressed.cpython-36_encrypted.pyc --g -e #Decrypt file #(base) C:\Himanshu\DataPreprocessing\dist>python C:\Himanshu\Encrypt_Key_Dcrypt.py DataPreprocessing_compressed.cpython-36_encrypted.pyc DataPreprocessing_compressed.cpython-36_decrypted.pyc --d #Run decrypted file #(base) C:\Himanshu\DataPreprocessing\dist>python DataPreprocessing_compressed.cpython-36_decrypted.pyc "DocumentText" "Label" 90 ".csv" "C:\Himanshu\DataAcquisition\ClassificationDataNewBalanced.csv" ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import shutil import subprocess from os.path import expanduser import platform deploymentfolder = os.path.join(os.path.dirname(os.path.abspath(__file__)),'HCLT','AION','target') modelname='AION_12' version='1' def createDockerImage(deploymentfolder,modelname,version,learner_type,textdata): modelPath = os.path.join(deploymentfolder) filename = os.path.join(deploymentfolder,'docker_image') modelservice = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','run_modelService.py') shellscript = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','start_modelservice.sh') aix = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','AIX-0.1-py3-none-any.whl') drift = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','Drift-0.1-py3-none-any.whl') sitepackage = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','site-packages') model_dockerSetup = os.path.join(os.path.dirname(os.path.abspath(__file__)),'dockersetup','docker_'+modelname + '_' + version) docker_setup = os.path.join(model_dockerSetup,modelname + '_' + version) model_sitepackage = os.path.join(model_dockerSetup,'site-packages') model_dockerSetupservicefile = os.path.join(model_dockerSetup,'run_modelService.py') model_dockershellscript = os.path.join(model_dockerSetup,'start_modelservice.sh') model_aix = os.path.join(model_dockerSetup,'AIX-0.1-py3-none-any.whl') model_drift = os.path.join(model_dockerSetup,'Drift-0.1-py3-none-any.whl') try: os.mkdir(model_dockerSetup) except Exception as e: print("Error in creating Setup directpry "+str(e)) pass shutil.copytree(modelPath, docker_setup) if textdata: shutil.copytree(sitepackage, model_sitepackage) modelpretrainpath=os.path.join(model_dockerSetup,'HCLT','AION','PreTrainedModels','TextProcessing') ''' try: os.makedirs(modelpretrainpath, exist_ok=True) except Exception as e: print("Error in creating Setup directpry "+str(e)) pass ''' home = expanduser("~") if platform.system() == 'Windows': hostpretrainpath = os.path.join(home,'AppData','Local','HCLT','AION','PreTrainedModels','TextProcessing') else: hostpretrainpath = os.path.join(home,'HCLT','AION','PreTrainedModels','TextProcessing') shutil.copytree(hostpretrainpath, modelpretrainpath) shutil.copyfile(modelservice, model_dockerSetupservicefile) shutil.copyfile(shellscript, model_dockershellscript) shutil.copyfile(aix, model_aix) shutil.copyfile(drift,model_drift) try: os.mkdir(filename) except: pass requirementfilename = os.path.join(model_dockerSetup,'requirements.txt') installfilename = os.path.join(model_dockerSetup,'install.py') dockerfile = os.path.join(model_dockerSetup,'Dockerfile') dockerdata='FROM python:3.8-slim-buster' dockerdata+='\n' if textdata: dockerdata+='WORKDIR /root' dockerdata+='\n' dockerdata+='COPY HCLT HCLT' dockerdata+='\n' dockerdata+='WORKDIR /app' dockerdata+='\n' dockerdata+='COPY requirements.txt requirements.txt' dockerdata+='\n' dockerdata+='COPY '+modelname+'_'+version+' '+modelname+'_'+version dockerdata+='\n' if textdata: dockerdata+='COPY site-packages site-packages' dockerdata+='\n' dockerdata+='COPY install.py install.py' dockerdata+='\n' dockerdata+='COPY run_modelService.py run_modelService.py' dockerdata+='\n' dockerdata+='COPY AIX-0.1-py3-none-any.whl AIX-0.1-py3-none-any.whl' dockerdata+='\n' dockerdata+='COPY Drift-0.1-py3-none-any.whl Drift-0.1-py3-none-any.whl' dockerdata+='\n' dockerdata+='COPY start_modelservice.sh start_modelservice.sh' dockerdata+='\n' if textdata: dockerdata+='''RUN apt-get update \ && apt-get install -y build-essential manpages-dev \ && python -m pip install --no-cache-dir --upgrade pip \ && python -m pip install --no-cache-dir pandas==1.2.4 \ && python -m pip install --no-cache-dir numpy==1.19.5 \ && python -m pip install --no-cache-dir joblib==1.0.1 \ && python -m pip install --no-cache-dir Cython==0.29.23 \ && mv site-packages/* /usr/local/lib/python3.8/site-packages \ && python -m pip install --no-cache-dir scipy==1.6.3 \ && python -m pip install --no-cache-dir AIX-0.1-py3-none-any.whl \ && python -m pip install --no-cache-dir Drift-0.1-py3-none-any.whl \ && python -m pip install --no-cache-dir scikit-learn==0.24.2 \ && python -m pip install --no-cache-dir spacy==2.2.3 \ && python -m pip install --no-cache-dir nltk==3.6.2 \ && python -m pip install --no-cache-dir textblob==0.15.3 \ && python -m pip install --no-cache-dir gensim==3.8.3 \ && python -m pip install --no-cache-dir demoji==1.1.0 \ && python -m pip install --no-cache-dir lxml==4.6.3 \ && python -m pip install --no-cache-dir Beautifulsoup4==4.9.3 \ && python -m pip install --no-cache-dir Unidecode==1.2.0 \ && python -m pip install --no-cache-dir pyspellchecker==0.6.2 \ && python -m pip install --no-cache-dir pycontractions==2.0.1 \ && python -m pip install --no-cache-dir tensorflow==2.4.1 \ && python -m pip install --no-cache-dir nltk==3.6.2 \ && python -m pip install --no-cache-dir -r requirements.txt \ && python install.py \ && chmod +x start_modelservice.sh ENTRYPOINT ["./start_modelservice.sh"] ''' else: dockerdata+='''RUN apt-get update \ && apt-get install -y build-essential manpages-dev \ && python -m pip install --no-cache-dir --upgrade pip \ && python -m pip install --no-cache-dir pandas==1.2.4 \ && python -m pip install --no-cache-dir numpy==1.19.5 \ && python -m pip install --no-cache-dir joblib==1.0.1 \ && python -m pip install --no-cache-dir Cython==0.29.23 \ && python -m pip install --no-cache-dir scipy==1.6.3 \ && python -m pip install --no-cache-dir AIX-0.1-py3-none-any.whl \ && python -m pip install --no-cache-dir Drift-0.1-py3-none-any.whl \ && python -m pip install --no-cache-dir scikit-learn==0.24.2 \ && python -m pip install --no-cache-dir -r requirements.txt \ && chmod +x start_modelservice.sh ENTRYPOINT ["./start_modelservice.sh"] ''' f = open(dockerfile, "w") f.write(str(dockerdata)) f.close() requirementdata='' requirementdata+='word2number==1.1' if learner_type == 'DL': requirementdata+='\n' requirementdata+='tensorflow==2.5.0' f = open(requirementfilename, "w") f.write(str(requirementdata)) f.close() if textdata: installfile=''' import nltk import ssl try: _create_unverified_https_context = ssl._create_unverified_context except AttributeError: pass else: ssl._create_default_https_context = _create_unverified_https_context nltk.download('punkt') nltk.download('wordnet') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger')''' f = open(installfilename, "w") f.write(str(installfile)) f.close() try: command = 'docker pull python:3.8-slim-buster' os.system(command); #subprocess.check_call(["chmod", "+x", "start_modelservice.sh"], cwd=model_dockerSetup) subprocess.check_call(["docker", "build", "-t",modelname.lower()+":"+version,"."], cwd=model_dockerSetup) subprocess.check_call(["docker", "save", "-o",modelname.lower()+"_"+version+".tar",modelname.lower()+":"+version], cwd=model_dockerSetup) dockerfilepath = os.path.join(model_dockerSetup,modelname.lower()+"_"+version+".tar") shutil.copyfile(dockerfilepath, os.path.join(filename,modelname.lower()+"_"+version+".tar")) shutil.rmtree(model_dockerSetup) return 'Success','SUCCESSFULLY' except Exception as e: print("Error: "+str(e)) shutil.rmtree(model_dockerSetup) return 'Error',str(e) #createDockerImage(deploymentfolder,modelname,version) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from importlib.metadata import version import sys import os def requirementfile(deploy_path,model,textFeatures,learner_type): print('hola', model) modules = ['pandas','numpy','alibi','matplotlib','joblib','shap','ipython','category_encoders','scikit-learn','word2number','flask_restful','evidently','Flask-Cors'] requires = '' for mod in modules: requires += f"{mod}=={version(mod)}\n" if len(textFeatures) > 0: tmodules = ['spacy','nltk','textblob','demoji','beautifulsoup4','text-unidecode','pyspellchecker','contractions','protobuf'] for mod in tmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Extreme Gradient Boosting (XGBoost)': mmodules = ['xgboost'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Light Gradient Boosting (LightGBM)': mmodules = ['lightgbm'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model == 'Categorical Boosting (CatBoost)': mmodules = ['catboost'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'arima': mmodules = ['pmdarima'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'fbprophet': mmodules = ['prophet'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'lstm' or model.lower() == 'mlp' or learner_type =='DL': mmodules = ['tensorflow'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() in ['cox', 'kaplanmeierfitter']: #bug 12833 mmodules = ['lifelines'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" if model.lower() == 'sentencetransformer': #bug 12833 mmodules = ['sentence_transformers'] for mod in mmodules: requires += f"{mod}=={version(mod)}\n" filename = os.path.join(deploy_path,'requirements.txt') f = open(filename, "wb") f.write(str(requires).encode('utf8')) f.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import sys import subprocess import glob import shutil import time from aion_deployment.EncryptPythonSourceCode import encrypt_files import json def encrypt(alldirs): for dir in alldirs: try: encrypt_files(dir) except Exception as error_obj: print("Exception in encrypting", error_obj) print("-"*50) def replace_by_compressed(alldirs): for dir in alldirs: try: #print("Processing dir", dir) files = [f for f in glob.glob(dir + "/*.py")] secure_path = os.path.join(dir, 'SecuredScripts') time.sleep(6) for file in files: try: filename_w_ext = os.path.basename(file) filename, file_extension = os.path.splitext(filename_w_ext) if filename == "__init__": continue #print("Processing file", file) file_folder_path = os.path.join(secure_path, filename, 'dist') compressed_file_path = os.path.join(file_folder_path, filename+'_compressed.py') shutil.copy(compressed_file_path, dir) os.remove(file) new_compressed_file_path = os.path.join(dir, filename+'_compressed.py') target_file_path = os.path.join(dir, filename_w_ext) os.rename(new_compressed_file_path, target_file_path) if filename == 'aion_prediction': shutil.copytree(os.path.join(file_folder_path, 'pytransform'), os.path.join(dir, 'pytransform')) except Exception as error_obj: print("Exception in file ", error_obj) shutil.rmtree(secure_path) except Exception as error_obj: print("Exception in dir ", error_obj) def start_Obfuscate(path): project_path = path subdirs = [dI for dI in os.listdir(project_path) if os.path.isdir(os.path.join(project_path,dI))] alldirs = [ project_path, ] for subdir in subdirs: if(subdir != 'pytransform'): alldirs.append(os.path.join(project_path, subdir)) encrypt(alldirs) replace_by_compressed(alldirs) if __name__=="__main__": project_path = sys.argv[1] print("project_path", project_path) subdirs = [dI for dI in os.listdir(project_path) if os.path.isdir(os.path.join(project_path,dI))] alldirs = [ project_path, ] for subdir in subdirs: alldirs.append(os.path.join(project_path, subdir)) encrypt(alldirs) print("*"*50) replace_by_compressed(alldirs) # python eion_compress.py "C:\Users\ashwani.s\Desktop\22April\22April\Mohita" "C:\Users\ashwani.s\Desktop\eion\eion" > logfile.log ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from pathlib import Path from AION.prediction_package.imports import importModule from AION.prediction_package.aion_prediction import aionPrediction from AION.prediction_package.utility import TAB_CHAR from AION.prediction_package import utility from AION.prediction_package import common from AION.prediction_package.base import deployer def is_supported(problem_type, algo=None): """ Return True if problem_type supported otherwise False """ supported = ['classification','regression','clustering','timeseriesforecasting','Text Similarity'] return problem_type in supported def get_deployer(problem_type, algo=None, params={}): """ Return deployer class object based on problem type Raise error if no class is associated with problem type """ params['problem_type'] = problem_type if problem_type == 'classification': return classification( params) elif problem_type == 'regression': return regression( params) elif problem_type == 'clustering': return clustering( params) elif problem_type == 'timeseriesforecasting': from AION.prediction_package.time_series import forecasting return forecasting.get_deployer( params) elif problem_type == 'Text Similarity': return textSimilarity( params) else: raise ValueError('deployment is not supported') class classification( deployer): def __init__(self, params={}): super().__init__( params) self.feature_reducer = False if not self.name: self.name = 'classification' def create_idrift(self): obj = aionPrediction() if self.params['features']['text_feat']: obj.create_text_drift_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat'],self.name) else: obj.create_drift_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat'],self.name) def create_odrift(self): obj = aionPrediction() if self.params['features']['text_feat']: obj.create_classification_text_performance_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat']) else: obj.create_classification_performance_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat']) def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') code = f""" class trainer(): """ init_code, run_code = self._get_train_code() return code + init_code + run_code def _get_train_code(self): init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}')""" run_code = f""" def run(self, df):\ """ if self.params['training']['algo'] in ['Neural Network']: self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code += f""" self.model = load_model(model_file) """ run_code += """ df = df.astype(np.float32) return pd.DataFrame(np.argmax(self.model.predict(df),axis=1)) """ elif self.params['training']['algo'] in ['Neural Architecture Search']: self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') self.importer.addModule(module='autokeras',mod_as='ak') init_code += f""" self.model = load_model(model_file,custom_objects=ak.CUSTOM_OBJECTS) """ run_code += """ df = df.astype(np.float32) return pd.DataFrame(self.model.predict(df)) """ elif self.params['training']['algo'] in ['Deep Q Network','Dueling Deep Q Network']: self.importer.addModule('joblib') self.importer.addModule(module='numpy',mod_as='np') self.importer.addModule(module='constant',mod_from='tensorflow') self.importer.addModule(module='time_step',mod_from='tf_agents.trajectories') init_code += f""" self.model = joblib.load(model_file) """ run_code += """ df = df.astype(np.float32) q, _ = self.model(np.array(df), step_type=constant([time_step.StepType.FIRST] * np.array(df).shape[0]), training=False) return pd.DataFrame(q.numpy()) """ elif self.params['training']['algo'] in ['Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)']: self.importer.addModule(module='numpy',mod_as='np') self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code += f""" self.model = load_model(model_file) """ run_code += """ df = np.expand_dims(df, axis=2) df = df.astype(np.float32) return pd.DataFrame(np.argmax(self.model.predict(df),axis=1)) """ else: self.importer.addModule(module='joblib') self.importer.addModule(module='numpy',mod_as='np') init_code += f""" self.model = joblib.load(model_file) """ run_code += """ df = df.astype(np.float32) return pd.DataFrame(self.model.predict_proba(df), columns=self.model.classes_) """ return init_code, run_code def formatter_code(self): self.importer.addModule('json') self.importer.addModule('joblib') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__(self): pass def run(self, raw_df, output): output = round(output,2) encoder_file = (Path(__file__).parent/"model")/"label_encoder.pkl" if encoder_file.exists(): encoder = joblib.load(encoder_file) output.rename(columns=dict(zip(output.columns, encoder.inverse_transform(list(output.columns)))), inplace=True) raw_df['prediction'] = output.idxmax(axis=1) raw_df['probability'] = output.max(axis=1).round(2) raw_df['remarks'] = output.apply(lambda x: x.to_json(double_precision=2), axis=1) outputjson = raw_df.to_json(orient='records',double_precision=5) outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} return(json.dumps(outputjson)) """ class regression( deployer): def __init__(self, params={}): super().__init__( params) self.feature_reducer = False if not self.name: self.name = 'regression' def create_idrift(self): obj = aionPrediction() if self.params['features']['text_feat']: obj.create_text_drift_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat'],self.name) else: obj.create_drift_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat'],self.name) def create_odrift(self): obj = aionPrediction() if self.params['features']['text_feat']: obj.create_regression_text_performance_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat']) else: obj.create_regression_performance_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat']) def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') code = f""" class trainer(): """ init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') """ run_code = f""" def run(self, df):\ """ if self.params['training']['algo'] in ['Neural Architecture Search']: self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') self.importer.addModule(module='autokeras',mod_as='ak') init_code += f""" self.model = load_model(model_file,custom_objects=ak.CUSTOM_OBJECTS) """ run_code += """ df = df.astype(np.float32) return self.model.predict(df).reshape(1, -1) """ elif self.params['training']['algo'] in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)']: self.importer.addModule(module='numpy',mod_as='np') self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code += f""" self.model = load_model(model_file) """ run_code += """ df = np.expand_dims(df, axis=2) df = df.astype(np.float32) return self.model.predict(df).reshape(1, -1) """ else: self.importer.addModule('joblib') init_code += f""" self.model = joblib.load(model_file) """ run_code += """ df = df.astype(np.float32) return self.model.predict(df).reshape(1, -1) """ return code + init_code + run_code def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__(self): pass def run(self, raw_df, output): raw_df['prediction'] = output[0] raw_df['prediction'] = raw_df['prediction'].round(2) outputjson = raw_df.to_json(orient='records',double_precision=5) outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} return(json.dumps(outputjson)) """ class clustering( deployer): def __init__(self, params={}): super().__init__( params) self.feature_reducer = False if not self.name: self.name = 'clustering' def training_code( self): self.importer.addModule('joblib') self.importer.addModule(module='pandas',mod_as='pd') code = f""" class trainer(): """ init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') """ run_code = f""" def run(self, df):\ """ if self.params['training']['algo'] == 'DBSCAN': init_code += f""" self.model = joblib.load(model_file) """ run_code += """ return self.model.fit_predict(df) """ else: init_code += f""" self.model = joblib.load(model_file) """ run_code += """ return self.model.predict(df).reshape(1, -1) """ return code + init_code + run_code def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__(self): pass def run(self, raw_df, output): raw_df['prediction'] = output[0] raw_df['prediction'] = raw_df['prediction'].round(2) outputjson = raw_df.to_json(orient='records',double_precision=2) outputjson = {"status":"SUCCESS","data":json.loads(outputjson)} return(json.dumps(outputjson)) """ return code if __name__ == '__main__': config = {'usecase_name': 'AI0110', 'usecase_ver': '1', 'features': {'input_feat': ['v2'], 'target_feat': 'v1', 'text_feat': ['v2']}, 'paths': {'deploy': r'C:/Users/vashistah/AppData/Local/Programs/HCLTech/AION/data/target/AI0110/1', 'usecase': r'C:/Users/vashistah/AppData/Local/Programs/HCLTech/AION/data/target/AI0110'}, 'profiler': {'input_features': ['v2'], 'output_features': ['07xxxxxxxxx_vect', '08700621170150p_vect', '08702840625comuk_vect', '08718726270150gbpmtmsg18_vect', '1000s_vect', '10am7pm_vect', '10k_vect', '10p_vect', '10pmin_vect', '10ppm_vect', '11mths_vect', '125gift_vect', '12hrs_vect', '12mths_vect', '150p_vect', '150perwksub_vect', '150pm_vect', '150pmin_vect', '150pmsg_vect', '150pmsgrcvdhgsuite3422landsroww1j6hl_vect', '150pmtmsgrcvd18_vect', '150ppm_vect', '150ptone_vect', '150pwk_vect', '150week_vect', '16only_vect', '18only_vect', '1hr_vect', '1minmobsmorelkpobox177hp51fl_vect', '1st_vect', '1x150pwk_vect', '20p_vect', '20pmin_vect', '21st_vect', '220cm2_vect', '24hrs_vect', '25p_vect', '26th_vect', '2day_vect', '2find_vect', '2geva_vect', '2go_vect', '2marrow_vect', '2mrw_vect', '2nd_vect', '2nite_vect', '2optout_vect', '2p_vect', '2u_vect', '2waxsto_vect', '2wks_vect', '300p_vect', '31pmsg_vect', '3510i_vect', '3d_vect', '3g_vect', '3gbp_vect', '3hrs_vect', '3mins_vect', '3qxj9_vect', '3rd_vect', '3ss_vect', '3u_vect', '3uz_vect', '3wk_vect', '40gb_vect', '4a_vect', '4d_vect', '4eva_vect', '4get_vect', '4info_vect', '4mths_vect', '4th_vect', '4u_vect', '50p_vect', '5min_vect', '5pm_vect', '5wb_vect', '5we_vect', '60pmin_vect', '6hrs_vect', '6months_vect', '6pm_vect', '7250i_vect', '7ish_vect', '8am_vect', '8pm_vect', '8th_vect', '8wp_vect', '9ae_vect', '9ja_vect', '9pm_vect', '9t_vect', 'aathi_vect', 'abi_vect', 'ability_vect', 'abiola_vect', 'able_vect', 'abt_vect', 'abta_vect', 'aburo_vect', 'ac_vect', 'academic_vect', 'acc_vect', 'accept_vect', 'access_vect', 'accident_vect', 'accidentally_vect', 'accordingly_vect', 'account_vect', 'ache_vect', 'across_vect', 'acted_vect', 'action_vect', 'activate_vect', 'activities_vect', 'actor_vect', 'actual_vect', 'actually_vect', 'ad_vect', 'adam_vect', 'add_vect', 'added_vect', 'addicted_vect', 'addie_vect', 'address_vect', 'admin_vect', 'administrator_vect', 'admirer_vect', 'admit_vect', 'adore_vect', 'adoring_vect', 'ads_vect', 'adult_vect', 'advance_vect', 'adventure_vect', 'advice_vect', 'advise_vect', 'affair_vect', 'affairs_vect', 'affectionate_vect', 'afraid_vect', 'aft_vect', 'afternoon_vect', 'aftr_vect', 'agalla_vect', 'age_vect', 'age16_vect', 'ages_vect', 'ago_vect', 'agree_vect', 'ah_vect', 'aha_vect', 'ahead_vect', 'ahmad_vect', 'ai_vect', 'aight_vect', 'aint_vect', 'air_vect', 'airport_vect', 'airtel_vect', 'aiya_vect', 'aiyah_vect', 'aiyar_vect', 'aiyo_vect', 'al_vect', 'album_vect', 'alert_vect', 'alex_vect', 'alfie_vect', 'ali_vect', 'allah_vect', 'allow_vect', 'allowed_vect', 'almost_vect', 'alone_vect', 'along_vect', 'already_vect', 'alright_vect', 'alrite_vect', 'also_vect', 'always_vect', 'alwys_vect', 'amazing_vect', 'american_vect', 'among_vect', 'amount_vect', 'amp_vect', 'amt_vect', 'andros_vect', 'angry_vect', 'annie_vect', 'anniversary_vect', 'announcement_vect', 'anot_vect', 'another_vect', 'ans_vect', 'ansr_vect', 'answer_vect', 'answered_vect', 'answering_vect', 'answers_vect', 'anthony_vect', 'anti_vect', 'anybody_vect', 'anymore_vect', 'anyone_vect', 'anything_vect', 'anytime_vect', 'anyway_vect', 'anyways_vect', 'apartment_vect', 'app_vect', 'apparently_vect', 'applebees_vect', 'apply_vect', 'appointment_vect', 'appreciate_vect', 'appreciated_vect', 'approx_vect', 'apps_vect', 'appt_vect', 'april_vect', 'ar_vect', 'arcade_vect', 'ard_vect', 'area_vect', 'argh_vect', 'argument_vect', 'arm_vect', 'armand_vect', 'arms_vect', 'around_vect', 'arrange_vect', 'arrested_vect', 'arrive_vect', 'arsenal_vect', 'art_vect', 'arun_vect', 'asap_vect', 'ashley_vect', 'ask_vect', 'askd_vect', 'asked_vect', 'askin_vect', 'asking_vect', 'asks_vect', 'asleep_vect', 'ass_vect', 'assume_vect', 'ate_vect', 'atlanta_vect', 'atlast_vect', 'atm_vect', 'attached_vect', 'attempt_vect', 'attend_vect', 'auction_vect', 'august_vect', 'aunt_vect', 'aunty_vect', 'auto_vect', 'av_vect', 'available_vect', 'avatar_vect', 'ave_vect', 'avent_vect', 'avoid_vect', 'await_vect', 'awaiting_vect', 'awake_vect', 'award_vect', 'awarded_vect', 'away_vect', 'awesome_vect', 'aww_vect', 'b4_vect', 'ba_vect', 'babe_vect', 'babes_vect', 'babies_vect', 'baby_vect', 'back_vect', 'bad_vect', 'bag_vect', 'bags_vect', 'bahamas_vect', 'bak_vect', 'balance_vect', 'bank_vect', 'banks_vect', 'bar_vect', 'barely_vect', 'basic_vect', 'basically_vect', 'bat_vect', 'bath_vect', 'bathe_vect', 'bathing_vect', 'battery_vect', 'bay_vect', 'bb_vect', 'bc_vect', 'bck_vect', 'bcoz_vect', 'bday_vect', 'be_vect', 'bears_vect', 'beautiful_vect', 'beauty_vect', 'bec_vect', 'become_vect', 'becoz_vect', 'bed_vect', 'bedrm_vect', 'bedroom_vect', 'beer_vect', 'befor_vect', 'beg_vect', 'begin_vect', 'behave_vect', 'behind_vect', 'bein_vect', 'believe_vect', 'bell_vect', 'belly_vect', 'belovd_vect', 'best_vect', 'bet_vect', 'better_vect', 'beyond_vect', 'bf_vect', 'bid_vect', 'bids_vect', 'big_vect', 'bigger_vect', 'biggest_vect', 'bill_vect', 'billed_vect', 'billion_vect', 'bills_vect', 'bin_vect', 'biola_vect', 'birds_vect', 'birla_vect', 'birth_vect', 'birthdate_vect', 'birthday_vect', 'bishan_vect', 'bit_vect', 'bitch_vect', 'bite_vect', 'black_vect', 'blackberry_vect', 'blah_vect', 'blake_vect', 'blank_vect', 'bleh_vect', 'bless_vect', 'blessing_vect', 'bloo_vect', 'blood_vect', 'bloody_vect', 'blue_vect', 'bluetooth_vect', 'bluff_vect', 'boat_vect', 'body_vect', 'bold_vect', 'bone_vect', 'bonus_vect', 'boo_vect', 'book_vect', 'booked_vect', 'booking_vect', 'books_vect', 'boost_vect', 'booty_vect', 'bored_vect', 'boring_vect', 'born_vect', 'boss_vect', 'boston_vect', 'bother_vect', 'bottom_vect', 'bought_vect', 'bout_vect', 'bowl_vect', 'box_vect', 'box326_vect', 'box334sk38ch_vect', 'box97n7qp_vect', 'boy_vect', 'boye_vect', 'boyfriend_vect', 'boys_vect', 'boytoy_vect', 'brah_vect', 'brand_vect', 'bread_vect', 'break_vect', 'breathe_vect', 'bright_vect', 'brilliant_vect', 'bring_vect', 'bringing_vect', 'brings_vect', 'british_vect', 'bro_vect', 'broad_vect', 'broke_vect', 'broken_vect', 'bros_vect', 'brothas_vect', 'brother_vect', 'brought_vect', 'bruv_vect', 'bslvyl_vect', 'bt_vect', 'btnationalrate_vect', 'btw_vect', 'bucks_vect', 'bud_vect', 'budget_vect', 'buff_vect', 'buffet_vect', 'bugis_vect', 'building_vect', 'buns_vect', 'burger_vect', 'burns_vect', 'bus_vect', 'buses_vect', 'business_vect', 'busy_vect', 'butt_vect', 'buy_vect', 'buying_vect', 'buzz_vect', 'bx420_vect', 'bx420ip45we_vect', 'bye_vect', 'ca_vect', 'cabin_vect', 'cafe_vect', 'cake_vect', 'cal_vect', 'calculation_vect', 'calicut_vect', 'california_vect', 'call_vect', 'call2optout674_vect', 'callback_vect', 'callcost_vect', 'called_vect', 'caller_vect', 'callers_vect', 'callertune_vect', 'callin_vect', 'calling_vect', 'calls_vect', 'callså_vect', 'cam_vect', 'camcorder_vect', 'came_vect', 'camera_vect', 'cameravideo_vect', 'campus_vect', 'can_vect', 'canada_vect', 'canal_vect', 'canary_vect', 'cancel_vect', 'cancelled_vect', 'cancer_vect', 'cant_vect', 'captain_vect', 'car_vect', 'card_vect', 'cardiff_vect', 'care_vect', 'cared_vect', 'career_vect', 'careful_vect', 'carefully_vect', 'caring_vect', 'carlos_vect', 'caroline_vect', 'cars_vect', 'cartoon_vect', 'case_vect', 'cash_vect', 'cashbalance_vect', 'cashin_vect', 'castor_vect', 'cat_vect', 'catch_vect', 'catching_vect', 'caught_vect', 'cause_vect', 'cbe_vect', 'cc_vect', 'cd_vect', 'cdgt_vect', 'cds_vect', 'celebrate_vect', 'celebration_vect', 'cell_vect', 'center_vect', 'centre_vect', 'certainly_vect', 'cha_vect', 'chain_vect', 'challenge_vect', 'chance_vect', 'change_vect', 'changed_vect', 'changes_vect', 'channel_vect', 'character_vect', 'charge_vect', 'charged_vect', 'charges_vect', 'charity_vect', 'charles_vect', 'chase_vect', 'chasing_vect', 'chat_vect', 'chatting_vect', 'cheap_vect', 'cheaper_vect', 'cheat_vect', 'chechi_vect', 'check_vect', 'checked_vect', 'checking_vect', 'cheers_vect', 'chennai_vect', 'cherish_vect', 'chest_vect', 'chicken_vect', 'chikku_vect', 'child_vect', 'childish_vect', 'children_vect', 'chill_vect', 'chillin_vect', 'china_vect', 'chinese_vect', 'chip_vect', 'chocolate_vect', 'choice_vect', 'choose_vect', 'chosen_vect', 'christ_vect', 'christmas_vect', 'church_vect', 'cine_vect', 'cinema_vect', 'citizen_vect', 'city_vect', 'claim_vect', 'claims_vect', 'claire_vect', 'class_vect', 'classes_vect', 'clean_vect', 'cleaning_vect', 'clear_vect', 'clearly_vect', 'click_vect', 'clock_vect', 'close_vect', 'closed_vect', 'closer_vect', 'closes_vect', 'clothes_vect', 'club_vect', 'cn_vect', 'co_vect', 'coast_vect', 'coat_vect', 'cochin_vect', 'code_vect', 'coffee_vect', 'coin_vect', 'coins_vect', 'cold_vect', 'colleagues_vect', 'collect_vect', 'collected_vect', 'collecting_vect', 'collection_vect', 'college_vect', 'colour_vect', 'come_vect', 'comedy_vect', 'comes_vect', 'comin_vect', 'coming_vect', 'commercial_vect', 'common_vect', 'community_vect', 'comp_vect', 'company_vect', 'competition_vect', 'complete_vect', 'completed_vect', 'completely_vect', 'complimentary_vect', 'computer_vect', 'concentrate_vect', 'concert_vect', 'conditions_vect', 'conducts_vect', 'confidence_vect', 'confirm_vect', 'congrats_vect', 'congratulations_vect', 'connection_vect', 'consider_vect', 'considering_vect', 'constant_vect', 'constantly_vect', 'contact_vect', 'contacted_vect', 'contacts_vect', 'content_vect', 'contents_vect', 'continue_vect', 'contract_vect', 'control_vect', 'convey_vect', 'convinced_vect', 'cool_vect', 'coping_vect', 'copy_vect', 'cornwall_vect', 'correct_vect', 'cos_vect', 'cost_vect', 'costa_vect', 'costs_vect', 'costå_vect', 'could_vect', 'count_vect', 'countin_vect', 'country_vect', 'couple_vect', 'course_vect', 'cover_vect', 'coz_vect', 'cr9_vect', 'cramps_vect', 'crave_vect', 'crazy_vect', 'created_vect', 'credit_vect', 'credited_vect', 'credits_vect', 'creepy_vect', 'crisis_vect', 'crore_vect', 'cross_vect', 'croydon_vect', 'cruise_vect', 'cry_vect', 'cs_vect', 'csbcm4235wc1n3xx_vect', 'csstop_vect', 'cud_vect', 'cuddling_vect', 'cum_vect', 'cup_vect', 'curious_vect', 'current_vect', 'currently_vect', 'cust_vect', 'custcare_vect', 'custcare08718720201_vect', 'custom_vect', 'customer_vect', 'customers_vect', 'cut_vect', 'cute_vect', 'cutting_vect', 'cuz_vect', 'cw25wx_vect', 'da_vect', 'dad_vect', 'daddy_vect', 'daily_vect', 'damn_vect', 'dance_vect', 'dancing_vect', 'dare_vect', 'dark_vect', 'darlin_vect', 'darling_vect', 'darren_vect', 'dat_vect', 'date_vect', 'dates_vect', 'dating_vect', 'dave_vect', 'day_vect', 'days_vect', 'de_vect', 'dead_vect', 'deal_vect', 'dealer_vect', 'dealing_vect', 'dear_vect', 'dearly_vect', 'death_vect', 'decide_vect', 'decided_vect', 'decimal_vect', 'decision_vect', 'deep_vect', 'def_vect', 'definite_vect', 'definitely_vect', 'del_vect', 'delete_vect', 'deleted_vect', 'delhi_vect', 'deliver_vect', 'delivered_vect', 'deliveredtomorrow_vect', 'delivery_vect', 'dem_vect', 'demand_vect', 'den_vect', 'denis_vect', 'department_vect', 'depends_vect', 'depressed_vect', 'derek_vect', 'desires_vect', 'desperate_vect', 'details_vect', 'dey_vect', 'dhoni_vect', 'dial_vect', 'dick_vect', 'dictionary_vect', 'didn_vect', 'didnt_vect', 'didt_vect', 'die_vect', 'died_vect', 'diet_vect', 'different_vect', 'difficult_vect', 'digital_vect', 'dignity_vect', 'din_vect', 'dinner_vect', 'dint_vect', 'direct_vect', 'directly_vect', 'dirty_vect', 'dis_vect', 'discount_vect', 'discuss_vect', 'dislikes_vect', 'display_vect', 'distance_vect', 'distract_vect', 'disturb_vect', 'division_vect', 'dload_vect', 'dnt_vect', 'doc_vect', 'docs_vect', 'doctor_vect', 'doesnt_vect', 'dog_vect', 'dogging_vect', 'doggy_vect', 'doin_vect', 'dollars_vect', 'don_vect', 'done_vect', 'dont_vect', 'donåõt_vect', 'door_vect', 'dorm_vect', 'double_vect', 'dough_vect', 'download_vect', 'downloads_vect', 'draw_vect', 'dream_vect', 'dreams_vect', 'dress_vect', 'dressed_vect', 'dresser_vect', 'drink_vect', 'drinking_vect', 'drinks_vect', 'drive_vect', 'driver_vect', 'drivin_vect', 'driving_vect', 'drop_vect', 'dropped_vect', 'drug_vect', 'drugs_vect', 'drunk_vect', 'dry_vect', 'ds_vect', 'dubsack_vect', 'dude_vect', 'due_vect', 'dun_vect', 'dunno_vect', 'durban_vect', 'dvd_vect', 'earlier_vect', 'early_vect', 'earth_vect', 'easier_vect', 'easily_vect', 'east_vect', 'easter_vect', 'easy_vect', 'eat_vect', 'eaten_vect', 'eatin_vect', 'eating_vect', 'ebay_vect', 'ec2a_vect', 'ee_vect', 'eek_vect', 'eerie_vect', 'effects_vect', 'eg_vect', 'egg_vect', 'eggs_vect', 'eh_vect', 'eight_vect', 'either_vect', 'ela_vect', 'electricity_vect', 'else_vect', 'elsewhere_vect', 'em_vect', 'email_vect', 'embarassed_vect', 'empty_vect', 'end_vect', 'ended_vect', 'ending_vect', 'ends_vect', 'enemy_vect', 'energy_vect', 'eng_vect', 'engin_vect', 'england_vect', 'english_vect', 'enjoy_vect', 'enjoyed_vect', 'enough_vect', 'enter_vect', 'entered_vect', 'entitled_vect', 'entry_vect', 'enuff_vect', 'envelope_vect', 'er_vect', 'erm_vect', 'escape_vect', 'especially_vect', 'esplanade_vect', 'eta_vect', 'etc_vect', 'euro_vect', 'euro2004_vect', 'europe_vect', 'eve_vect', 'eveb_vect', 'even_vect', 'evening_vect', 'event_vect', 'ever_vect', 'every_vect', 'everybody_vect', 'everyday_vect', 'everyone_vect', 'everything_vect', 'everywhere_vect', 'evn_vect', 'evng_vect', 'ex_vect', 'exact_vect', 'exactly_vect', 'exam_vect', 'exams_vect', 'excellent_vect', 'except_vect', 'exciting_vect', 'excuse_vect', 'excuses_vect', 'executive_vect', 'exeter_vect', 'exhausted_vect', 'expect_vect', 'expecting_vect', 'expensive_vect', 'experience_vect', 'expired_vect', 'expires_vect', 'explain_vect', 'explicit_vect', 'explosive_vect', 'express_vect', 'extra_vect', 'eye_vect', 'eyes_vect', 'fa_vect', 'fab_vect', 'face_vect', 'facebook_vect', 'fact_vect', 'faggy_vect', 'failed_vect', 'fair_vect', 'faith_vect', 'fall_vect', 'falls_vect', 'family_vect', 'fan_vect', 'fancies_vect', 'fancy_vect', 'fantasies_vect', 'fantastic_vect', 'fantasy_vect', 'far_vect', 'farm_vect', 'fast_vect', 'faster_vect', 'fat_vect', 'father_vect', 'fathima_vect', 'fault_vect', 'fave_vect', 'favorite_vect', 'favour_vect', 'favourite_vect', 'fb_vect', 'feb_vect', 'february_vect', 'feel_vect', 'feelin_vect', 'feeling_vect', 'feels_vect', 'fees_vect', 'feet_vect', 'fell_vect', 'felt_vect', 'fetch_vect', 'fever_vect', 'field_vect', 'fifteen_vect', 'fight_vect', 'fighting_vect', 'figure_vect', 'file_vect', 'files_vect', 'fill_vect', 'filling_vect', 'fills_vect', 'film_vect', 'final_vect', 'finally_vect', 'find_vect', 'fine_vect', 'fingers_vect', 'finish_vect', 'finished_vect', 'first_vect', 'fit_vect', 'fix_vect', 'fixed_vect', 'flag_vect', 'flaked_vect', 'flash_vect', 'flat_vect', 'flight_vect', 'flights_vect', 'flirt_vect', 'floor_vect', 'flower_vect', 'fml_vect', 'fo_vect', 'follow_vect', 'followed_vect', 'following_vect', 'fone_vect', 'food_vect', 'fool_vect', 'football_vect', 'force_vect', 'foreign_vect', 'forever_vect', 'forevr_vect', 'forget_vect', 'forgets_vect', 'forgiven_vect', 'forgot_vect', 'format_vect', 'forums_vect', 'forward_vect', 'forwarded_vect', 'found_vect', 'four_vect', 'fr_vect', 'fran_vect', 'freak_vect', 'free_vect', 'freefone_vect', 'freemsg_vect', 'freephone_vect', 'freezing_vect', 'fren_vect', 'frens_vect', 'fret_vect', 'fri_vect', 'friday_vect', 'friend_vect', 'friends_vect', 'friendship_vect', 'fringe_vect', 'frm_vect', 'frnd_vect', 'frnds_vect', 'frndship_vect', 'fuck_vect', 'fuckin_vect', 'fucking_vect', 'ful_vect', 'full_vect', 'fullonsmscom_vect', 'fun_vect', 'funny_vect', 'future_vect', 'fyi_vect', 'gal_vect', 'gals_vect', 'game_vect', 'games_vect', 'gang_vect', 'gap_vect', 'gaps_vect', 'garage_vect', 'garbage_vect', 'gary_vect', 'gas_vect', 'gautham_vect', 'gave_vect', 'gay_vect', 'gd_vect', 'ge_vect', 'gee_vect', 'geeee_vect', 'geeeee_vect', 'gender_vect', 'generally_vect', 'genius_vect', 'gentle_vect', 'gentleman_vect', 'gently_vect', 'germany_vect', 'get_vect', 'gets_vect', 'gettin_vect', 'getting_vect', 'gf_vect', 'gibbs_vect', 'gift_vect', 'gim_vect', 'girl_vect', 'girls_vect', 'gist_vect', 'giv_vect', 'give_vect', 'given_vect', 'gives_vect', 'giving_vect', 'glad_vect', 'gn_vect', 'go_vect', 'goal_vect', 'god_vect', 'goes_vect', 'goin_vect', 'going_vect', 'gold_vect', 'gon_vect', 'gona_vect', 'gone_vect', 'good_vect', 'goodmorning_vect', 'goodnight_vect', 'goodnite_vect', 'google_vect', 'gorgeous_vect', 'gossip_vect', 'got_vect', 'goto_vect', 'gotten_vect', 'govtinstituitions_vect', 'gr8_vect', 'grace_vect', 'gram_vect', 'grand_vect', 'granite_vect', 'gravity_vect', 'great_vect', 'green_vect', 'greet_vect', 'greetings_vect', 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'real_vect', 'realise_vect', 'reality_vect', 'realized_vect', 'really_vect', 'realy_vect', 'reason_vect', 'reasonable_vect', 'reasons_vect', 'reboot_vect', 'recd_vect', 'receipt_vect', 'receive_vect', 'received_vect', 'receiving_vect', 'recent_vect', 'recently_vect', 'recession_vect', 'record_vect', 'records_vect', 'recovery_vect', 'red_vect', 'ref_vect', 'reference_vect', 'reg_vect', 'regards_vect', 'register_vect', 'registered_vect', 'regret_vect', 'regular_vect', 'relation_vect', 'relax_vect', 'released_vect', 'rem_vect', 'remain_vect', 'remains_vect', 'remember_vect', 'remembered_vect', 'remembr_vect', 'remind_vect', 'reminder_vect', 'remove_vect', 'rent_vect', 'rental_vect', 'rentl_vect', 'repair_vect', 'repeat_vect', 'replied_vect', 'reply_vect', 'replying_vect', 'report_vect', 'representative_vect', 'request_vect', 'requests_vect', 'research_vect', 'resend_vect', 'respect_vect', 'respond_vect', 'responding_vect', 'response_vect', 'responsibility_vect', 'rest_vect', 'restaurant_vect', 'result_vect', 'results_vect', 'retrieve_vect', 'return_vect', 'returned_vect', 'returns_vect', 'reveal_vect', 'revealed_vect', 'review_vect', 'revision_vect', 'reward_vect', 'rhythm_vect', 'rice_vect', 'rich_vect', 'ride_vect', 'right_vect', 'rights_vect', 'ring_vect', 'ringtone_vect', 'ringtones_vect', 'rite_vect', 'river_vect', 'road_vect', 'roads_vect', 'roast_vect', 'rock_vect', 'rocks_vect', 'rofl_vect', 'roger_vect', 'role_vect', 'ron_vect', 'room_vect', 'roommate_vect', 'roommates_vect', 'rooms_vect', 'rose_vect', 'round_vect', 'row_vect', 'roww1j6hl_vect', 'roww1jhl_vect', 'royal_vect', 'rply_vect', 'rs_vect', 'rstm_vect', 'ru_vect', 'rub_vect', 'rude_vect', 'rule_vect', 'run_vect', 'running_vect', 'runs_vect', 'rush_vect', 'sacrifice_vect', 'sad_vect', 'sae_vect', 'safe_vect', 'said_vect', 'salam_vect', 'salary_vect', 'sale_vect', 'salon_vect', 'sam_vect', 'santa_vect', 'sar_vect', 'sarasota_vect', 'sarcastic_vect', 'sary_vect', 'sat_vect', 'sathya_vect', 'saturday_vect', 'savamob_vect', 'save_vect', 'saved_vect', 'saw_vect', 'say_vect', 'saying_vect', 'says_vect', 'scared_vect', 'scary_vect', 'sch_vect', 'schedule_vect', 'school_vect', 'schools_vect', 'science_vect', 'scold_vect', 'score_vect', 'scores_vect', 'scotland_vect', 'scream_vect', 'screaming_vect', 'scrounge_vect', 'se_vect', 'sea_vect', 'search_vect', 'searching_vect', 'season_vect', 'seat_vect', 'sec_vect', 'second_vect', 'seconds_vect', 'secret_vect', 'secretary_vect', 'secs_vect', 'sed_vect', 'see_vect', 'seeing_vect', 'seem_vect', 'seemed_vect', 'seems_vect', 'seen_vect', 'selected_vect', 'selection_vect', 'self_vect', 'sell_vect', 'selling_vect', 'sells_vect', 'sem_vect', 'semester_vect', 'sen_vect', 'send_vect', 'sender_vect', 'sending_vect', 'sense_vect', 'sent_vect', 'sentence_vect', 'sept_vect', 'series_vect', 'serious_vect', 'seriously_vect', 'service_vect', 'services_vect', 'serving_vect', 'set_vect', 'setting_vect', 'settings_vect', 'settle_vect', 'settled_vect', 'seven_vect', 'several_vect', 'sex_vect', 'sexy_vect', 'sh_vect', 'sha_vect', 'shall_vect', 'share_vect', 'shd_vect', 'sheets_vect', 'shes_vect', 'shesil_vect', 'shining_vect', 'ship_vect', 'shipping_vect', 'shirt_vect', 'shirts_vect', 'shit_vect', 'shld_vect', 'shocking_vect', 'shoot_vect', 'shop_vect', 'shoppin_vect', 'shopping_vect', 'short_vect', 'shorter_vect', 'shortly_vect', 'shot_vect', 'shoving_vect', 'show_vect', 'shower_vect', 'showing_vect', 'shows_vect', 'shu_vect', 'shuhui_vect', 'shy_vect', 'si_vect', 'sick_vect', 'side_vect', 'sighs_vect', 'sight_vect', 'sign_vect', 'signing_vect', 'silence_vect', 'silent_vect', 'silver_vect', 'sim_vect', 'simple_vect', 'simply_vect', 'since_vect', 'sing_vect', 'singing_vect', 'single_vect', 'singles_vect', 'sipix_vect', 'sir_vect', 'sis_vect', 'sister_vect', 'sit_vect', 'site_vect', 'sitting_vect', 'situation_vect', 'siva_vect', 'six_vect', 'size_vect', 'sk3_vect', 'sk38xh_vect', 'skilgme_vect', 'skip_vect', 'sky_vect', 'skype_vect', 'skyped_vect', 'slap_vect', 'slave_vect', 'sleep_vect', 'sleepin_vect', 'sleeping_vect', 'sleepy_vect', 'slept_vect', 'slice_vect', 'slide_vect', 'slightly_vect', 'slip_vect', 'slippers_vect', 'slo_vect', 'slots_vect', 'slow_vect', 'slowly_vect', 'small_vect', 'smashed_vect', 'smile_vect', 'smiles_vect', 'smiling_vect', 'smoke_vect', 'smoking_vect', 'sms_vect', 'smth_vect', 'sn_vect', 'snake_vect', 'snow_vect', 'social_vect', 'sofa_vect', 'soft_vect', 'software_vect', 'sol_vect', 'some1_vect', 'somebody_vect', 'someone_vect', 'somethin_vect', 'something_vect', 'sometimes_vect', 'somewhere_vect', 'song_vect', 'songs_vect', 'sony_vect', 'sonyericsson_vect', 'soon_vect', 'sooner_vect', 'sore_vect', 'sorry_vect', 'sort_vect', 'sorting_vect', 'sound_vect', 'sounds_vect', 'south_vect', 'sp_vect', 'space_vect', 'spanish_vect', 'speak_vect', 'speaking_vect', 'special_vect', 'specialcall_vect', 'specially_vect', 'speed_vect', 'spend_vect', 'spending_vect', 'spent_vect', 'spk_vect', 'spoke_vect', 'spoken_vect', 'spook_vect', 'sport_vect', 'sports_vect', 'spree_vect', 'spring_vect', 'sptv_vect', 'sry_vect', 'st_vect', 'staff_vect', 'stamps_vect', 'stand_vect', 'standard_vect', 'standing_vect', 'star_vect', 'staring_vect', 'start_vect', 'started_vect', 'starting_vect', 'starts_vect', 'starwars3_vect', 'statement_vect', 'station_vect', 'stay_vect', 'stayed_vect', 'staying_vect', 'std_vect', 'steam_vect', 'step_vect', 'steve_vect', 'stick_vect', 'sticky_vect', 'still_vect', 'stock_vect', 'stockport_vect', 'stomach_vect', 'stomps_vect', 'stones_vect', 'stop_vect', 'stopped_vect', 'stops_vect', 'store_vect', 'stores_vect', 'story_vect', 'str_vect', 'straight_vect', 'stranger_vect', 'street_vect', 'stress_vect', 'strike_vect', 'strong_vect', 'strongbuy_vect', 'stuck_vect', 'student_vect', 'study_vect', 'studying_vect', 'stuff_vect', 'stupid_vect', 'style_vect', 'stylish_vect', 'sub_vect', 'subpoly_vect', 'subs_vect', 'subscribe6gbpmnth_vect', 'subscribed_vect', 'subscriber_vect', 'subscription_vect', 'success_vect', 'successful_vect', 'successfully_vect', 'sucks_vect', 'sue_vect', 'sufficient_vect', 'suggest_vect', 'suite_vect', 'suits_vect', 'sum1_vect', 'summer_vect', 'sun_vect', 'sunday_vect', 'sunlight_vect', 'sunny_vect', 'sunshine_vect', 'suntec_vect', 'sup_vect', 'super_vect', 'superb_vect', 'superior_vect', 'supervisor_vect', 'supply_vect', 'support_vect', 'suppose_vect', 'supposed_vect', 'suprman_vect', 'sura_vect', 'sure_vect', 'surely_vect', 'surfing_vect', 'surprise_vect', 'surprised_vect', 'survey_vect', 'sux_vect', 'suzy_vect', 'sw7_vect', 'sw73ss_vect', 'sweet_vect', 'swing_vect', 'system_vect', 'ta_vect', 'tablets_vect', 'tahan_vect', 'take_vect', 'taken_vect', 'takes_vect', 'takin_vect', 'taking_vect', 'talent_vect', 'talk_vect', 'talking_vect', 'tampa_vect', 'tape_vect', 'tariffs_vect', 'tat_vect', 'taunton_vect', 'taylor_vect', 'tb_vect', 'tc_vect', 'tcrw1_vect', 'tcs_vect', 'tea_vect', 'teach_vect', 'teacher_vect', 'teaches_vect', 'team_vect', 'tear_vect', 'tease_vect', 'teasing_vect', 'tech_vect', 'technical_vect', 'tee_vect', 'teeth_vect', 'tel_vect', 'telephone_vect', 'tell_vect', 'telling_vect', 'tells_vect', 'telugu_vect', 'temple_vect', 'ten_vect', 'tenants_vect', 'tenerife_vect', 'tension_vect', 'term_vect', 'terms_vect', 'terrible_vect', 'test_vect', 'testing_vect', 'text_vect', 'texted_vect', 'texting_vect', 'textoperator_vect', 'texts_vect', 'th_vect', 'thangam_vect', 'thank_vect', 'thanks_vect', 'thanksgiving_vect', 'thanx_vect', 'that_vect', 'thats_vect', 'thatåõs_vect', 'the_vect', 'theatre_vect', 'themob_vect', 'theory_vect', 'thesis_vect', 'thgt_vect', 'thing_vect', 'things_vect', 'think_vect', 'thinkin_vect', 'thinking_vect', 'thinks_vect', 'thk_vect', 'thnk_vect', 'tho_vect', 'though_vect', 'thought_vect', 'three_vect', 'throat_vect', 'throw_vect', 'thru_vect', 'tht_vect', 'thts_vect', 'thurs_vect', 'thursday_vect', 'tick_vect', 'ticket_vect', 'tickets_vect', 'tight_vect', 'tihs_vect', 'til_vect', 'till_vect', 'time_vect', 'times_vect', 'timing_vect', 'tired_vect', 'tirunelvali_vect', 'tirupur_vect', 'tissco_vect', 'tkts_vect', 'tm_vect', 'tming_vect', 'tmobile_vect', 'tmr_vect', 'tncs_vect', 'toa_vect', 'toclaim_vect', 'today_vect', 'todays_vect', 'tog_vect', 'together_vect', 'tok_vect', 'told_vect', 'tomarrow_vect', 'tomo_vect', 'tomorrow_vect', 'tone_vect', 'tones_vect', 'tones2youcouk_vect', 'tonight_vect', 'tonite_vect', 'took_vect', 'tool_vect', 'tooo_vect', 'toot_vect', 'top_vect', 'topic_vect', 'torch_vect', 'toshiba_vect', 'tot_vect', 'total_vect', 'totally_vect', 'touch_vect', 'tough_vect', 'tour_vect', 'towards_vect', 'town_vect', 'track_vect', 'trade_vect', 'traffic_vect', 'train_vect', 'training_vect', 'transaction_vect', 'transfer_vect', 'transport_vect', 'travel_vect', 'treat_vect', 'treated_vect', 'tried_vect', 'trip_vect', 'trips_vect', 'trouble_vect', 'true_vect', 'truffles_vect', 'truly_vect', 'trust_vect', 'truth_vect', 'try_vect', 'trying_vect', 'ts_vect', 'tscs_vect', 'tscs087147403231winawk_vect', 'tt_vect', 'ttyl_vect', 'tues_vect', 'tuesday_vect', 'tuition_vect', 'turn_vect', 'turning_vect', 'turns_vect', 'tv_vect', 'twelve_vect', 'twice_vect', 'two_vect', 'txt_vect', 'txtauction_vect', 'txtin_vect', 'txting_vect', 'txtno_vect', 'txts_vect', 'txtstop_vect', 'tyler_vect', 'type_vect', 'tyrone_vect', 'u4_vect', 'ubi_vect', 'ufind_vect', 'ugh_vect', 'uh_vect', 'uk_vect', 'uks_vect', 'ultimatum_vect', 'umma_vect', 'unable_vect', 'uncle_vect', 'understand_vect', 'understanding_vect', 'understood_vect', 'underwear_vect', 'unemployed_vect', 'uni_vect', 'unique_vect', 'university_vect', 'unless_vect', 'unlimited_vect', 'unnecessarily_vect', 'unredeemed_vect', 'unsold_vect', 'unsub_vect', 'unsubscribe_vect', 'upd8_vect', 'update_vect', 'updatenow_vect', 'upgrade_vect', 'upload_vect', 'upset_vect', 'upstairs_vect', 'ur_vect', 'ure_vect', 'urgent_vect', 'urgnt_vect', 'url_vect', 'urn_vect', 'urself_vect', 'us_vect', 'usb_vect', 'use_vect', 'used_vect', 'user_vect', 'usf_vect', 'using_vect', 'usual_vect', 'usually_vect', 'vale_vect', 'valentine_vect', 'valentines_vect', 'valid_vect', 'valid12hrs_vect', 'valuable_vect', 'value_vect', 'valued_vect', 'vary_vect', 've_vect', 'vegas_vect', 'verify_vect', 'version_vect', 'via_vect', 'vid_vect', 'video_vect', 'videochat_vect', 'videophones_vect', 'vijay_vect', 'vikky_vect', 'village_vect', 'violated_vect', 'violence_vect', 'vip_vect', 'virgin_vect', 'visit_vect', 'vivek_vect', 'vl_vect', 'voda_vect', 'vodafone_vect', 'vodka_vect', 'voice_vect', 'voicemail_vect', 'vomit_vect', 'vote_vect', 'voucher_vect', 'vouchers_vect', 'vry_vect', 'vth_vect', 'w45wq_vect', 'wa_vect', 'wah_vect', 'wait_vect', 'waited_vect', 'waitin_vect', 'waiting_vect', 'wake_vect', 'waking_vect', 'wales_vect', 'walk_vect', 'walked_vect', 'walking_vect', 'walmart_vect', 'wan_vect', 'wana_vect', 'want_vect', 'wanted_vect', 'wanting_vect', 'wants_vect', 'wap_vect', 'warm_vect', 'warner_vect', 'waste_vect', 'wasted_vect', 'wat_vect', 'watch_vect', 'watching_vect', 'water_vect', 'wats_vect', 'way_vect', 'wc1n3xx_vect', 'we_vect', 'weak_vect', 'wear_vect', 'wearing_vect', 'weather_vect', 'web_vect', 'website_vect', 'wed_vect', 'wedding_vect', 'wednesday_vect', 'wee_vect', 'weed_vect', 'week_vect', 'weekend_vect', 'weekends_vect', 'weekly_vect', 'weeks_vect', 'weigh_vect', 'weight_vect', 'weird_vect', 'welcome_vect', 'well_vect', 'welp_vect', 'wen_vect', 'went_vect', 'west_vect', 'wet_vect', 'what_vect', 'whatever_vect', 'whats_vect', 'whenever_vect', 'whenevr_vect', 'wherever_vect', 'whether_vect', 'white_vect', 'whn_vect', 'whole_vect', 'whos_vect', 'whose_vect', 'wid_vect', 'widelivecomindex_vect', 'wif_vect', 'wife_vect', 'wil_vect', 'willing_vect', 'win_vect', 'wind_vect', 'wine_vect', 'winner_vect', 'winning_vect', 'wins_vect', 'wipro_vect', 'wisdom_vect', 'wise_vect', 'wish_vect', 'wishes_vect', 'wishing_vect', 'wit_vect', 'within_vect', 'without_vect', 'wiv_vect', 'wk_vect', 'wkend_vect', 'wkg_vect', 'wkly_vect', 'wks_vect', 'wld_vect', 'wml_vect', 'wn_vect', 'wnt_vect', 'wo_vect', 'woke_vect', 'woken_vect', 'woman_vect', 'women_vect', 'wonder_vect', 'wonderful_vect', 'wondering_vect', 'wont_vect', 'woot_vect', 'word_vect', 'words_vect', 'work_vect', 'workin_vect', 'working_vect', 'works_vect', 'world_vect', 'worried_vect', 'worries_vect', 'worry_vect', 'worse_vect', 'worst_vect', 'worth_vect', 'wot_vect', 'would_vect', 'wow_vect', 'write_vect', 'wrong_vect', 'wtf_vect', 'wud_vect', 'wuld_vect', 'wun_vect', 'www4tcbiz_vect', 'wwwcomuknet_vect', 'wwwetlpcoukexpressoffer_vect', 'wwwgetzedcouk_vect', 'wwwldewcom_vect', 'wwwldewcom1win150ppmx3age16_vect', 'wwwmovietriviatv_vect', 'wwwringtonescouk_vect', 'wwwsmsconet_vect', 'wwwtxttowincouk_vect', 'wwwurawinnercom_vect', 'wylie_vect', 'xchat_vect', 'xmas_vect', 'xuhui_vect', 'xx_vect', 'xxx_vect', 'xxxx_vect', 'xxxxx_vect', 'xy_vect', 'ya_vect', 'yahoo_vect', 'yan_vect', 'yar_vect', 'yay_vect', 'yck_vect', 'yeah_vect', 'year_vect', 'years_vect', 'yelling_vect', 'yellow_vect', 'yep_vect', 'yes_vect', 'yest_vect', 'yesterday_vect', 'yet_vect', 'yetunde_vect', 'yijue_vect', 'ym_vect', 'yo_vect', 'yoga_vect', 'yogasana_vect', 'yor_vect', 'you_vect', 'yr_vect', 'yrs_vect', 'yummy_vect', 'yun_vect', 'yuo_vect', 'yup_vect', 'zed_vect', 'zindgi_vect', 'ìï_vect', 'ûò_vect']}, 'training': {'algo': 'Logistic Regression', 'model_file': 'AI0110_1.sav'}} deployer = get_deployer('classification',params=config) deployer.run( ) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os import platform import json import shutil import logging class outputFormatter: def __init__(self): self.log = logging.getLogger('eion') self.log.info('========> Inside Output Formatter') def crate_output_format_file(self,deploy_path,learner_type,modelType,model,output_label,threshold,trained_data_file,dictDiffCount,targetFeature,features,datetimeFeature): self.output_formatfile = 'import json' self.output_formatfile += '\n' self.output_formatfile += 'import numpy as np' self.output_formatfile += '\n' self.output_formatfile += 'import pandas as pd' self.output_formatfile += '\n' self.output_formatfile += 'import os' self.output_formatfile += '\n' self.output_formatfile += 'from pathlib import Path' self.output_formatfile += '\n' if((model.lower() in ['autoencoder','dbscan']) and modelType.lower()=="anomaly_detection"): self.output_formatfile += 'from script.aion_granularity import aion_gettimegranularity' self.output_formatfile += '\n' self.output_formatfile += 'class output_format(object):' self.output_formatfile += '\n' if(model == 'VAR'): self.output_formatfile += ' def invertTransformation(self,predictions):' self.output_formatfile += '\n' self.output_formatfile += ' datasetdf = pd.read_csv(os.path.join(os.path.dirname(os.path.abspath(__file__)),"..","data","trainingdata.csv"))' self.output_formatfile += '\n' self.output_formatfile += ' dictDiffCount = '+str(dictDiffCount) self.output_formatfile += '\n' self.output_formatfile += ' targetFeature = "'+str(targetFeature)+'"' self.output_formatfile += '\n' self.output_formatfile += ' columns = targetFeature.split(",")' self.output_formatfile += '\n' self.output_formatfile += ' pred = pd.DataFrame(index=range(0,len(predictions)),columns=columns)' self.output_formatfile += '\n' self.output_formatfile += ' for j in range(0,len(columns)):' self.output_formatfile += '\n' self.output_formatfile += ' for i in range(0, len(predictions)):' self.output_formatfile += '\n' self.output_formatfile += ' pred.iloc[i][j] = round(predictions[i][j],2)' self.output_formatfile += '\n' self.output_formatfile += ' prediction = pred' self.output_formatfile += '\n' self.output_formatfile += ' for col in columns:' self.output_formatfile += '\n' self.output_formatfile += ' if col in dictDiffCount:' self.output_formatfile += '\n' self.output_formatfile += ' if dictDiffCount[col]==2:' self.output_formatfile += '\n' self.output_formatfile += ' prediction[col] = (datasetdf[col].iloc[-1]-datasetdf[col].iloc[-2]) + prediction[col].cumsum()' self.output_formatfile += '\n' self.output_formatfile += ' prediction[col] = datasetdf[col].iloc[-1] + prediction[col].cumsum()' self.output_formatfile += '\n' self.output_formatfile += ' prediction = pred' self.output_formatfile += '\n' self.output_formatfile += ' return(prediction)' self.output_formatfile += '\n' self.log.info("op:modelType: \n"+str(modelType)) if((model.lower() in ['autoencoder','dbscan']) and modelType.lower()=="anomaly_detection"): # if modelType == 'anomaly_detection': self.output_formatfile += ' def find_point_subsequence_anomalies(self,datetime_column,dataframe=None):' self.output_formatfile += '\n' self.output_formatfile += ' try:' self.output_formatfile += '\n' self.output_formatfile += ' dataframe[datetime_column] = pd.to_datetime(dataframe[datetime_column]) ' self.output_formatfile += '\n' self.output_formatfile += ' aion_gettimegranularity_obj=aion_gettimegranularity(dataframe,datetime_column) ' self.output_formatfile += '\n' self.output_formatfile += ' anomaly_info_df=aion_gettimegranularity_obj.get_granularity() ' self.output_formatfile += '\n' self.output_formatfile += ' except Exception as e:' self.output_formatfile += '\n' self.output_formatfile += ' print(f"find_point_subsequence_anomalies,: aion_gettimegranularity err msg:{e} ")\n' self.output_formatfile += ' return anomaly_info_df' self.output_formatfile += '\n' if((model.lower() in ['autoencoder','dbscan']) and modelType.lower()=="anomaly_detection"): if (datetimeFeature!='' and datetimeFeature!='NA'): self.output_formatfile += ' def apply_output_format(self,df,modeloutput,datetimeFeature):' self.output_formatfile += '\n' else: self.output_formatfile += ' def apply_output_format(self,df,modeloutput):' self.output_formatfile += '\n' else: self.output_formatfile += ' def apply_output_format(self,df,modeloutput):' self.output_formatfile += '\n' if modelType.lower() == 'classification': self.output_formatfile += ' modeloutput = round(modeloutput,2)' self.output_formatfile += '\n' if(learner_type == 'ImageClassification'): if(str(output_label) != '{}'): inv_mapping_dict = {v: k for k, v in output_label.items()} self.output_formatfile += ' le_dict = '+ str(inv_mapping_dict) self.output_formatfile += '\n' self.output_formatfile += ' predictions = []' self.output_formatfile += '\n' self.output_formatfile += ' for x in modeloutput:' self.output_formatfile += '\n' self.output_formatfile += ' x = le_dict[x]' self.output_formatfile += '\n' self.output_formatfile += ' predictions.append(x)' self.output_formatfile += '\n' else: self.output_formatfile += ' predictions=modeloutput' self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = predictions' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = df.to_json(orient=\'records\')' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}' self.output_formatfile += '\n' elif(learner_type == 'Text Similarity'): self.output_formatfile += ' df[\'prediction\'] = np.where(modeloutput > '+str(threshold)+',1,0)' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = modeloutput' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = df.to_json(orient=\'records\',double_precision=2)' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}' self.output_formatfile += '\n' elif(learner_type == 'TS'): if(model == 'VAR'): self.output_formatfile += ' modeloutput = self.invertTransformation(modeloutput)' self.output_formatfile += '\n' self.output_formatfile += ' modeloutput = modeloutput.to_json(orient=\'records\',double_precision=2)' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(modeloutput)}' elif(model.lower() == 'fbprophet'): self.output_formatfile += ' modeloutput = modeloutput.to_json(orient=\'records\')' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(modeloutput)}' elif((model.lower() == 'lstm' or model.lower() == 'mlp') and len(features) >= 1): self.output_formatfile += ' modeloutput = modeloutput.round(2)\n' self.output_formatfile += ' modeloutput = modeloutput.to_json(orient=\'records\')\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(modeloutput)}\n' else: self.output_formatfile += ' modeloutput = modeloutput.round(2)' self.output_formatfile += '\n' self.output_formatfile += ' modeloutput = json.dumps(modeloutput.tolist())' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":eval(modeloutput)}' self.output_formatfile += '\n' elif(learner_type in ['RecommenderSystem','similarityIdentification','contextualSearch']): self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = modeloutput' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = df.to_json(orient=\'records\',double_precision=2)' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}' self.output_formatfile += '\n' else: if(modelType == 'Classification' or modelType == 'TLClassification' or modelType == 'anomaly_detection'): if(modelType == 'Classification' or modelType == 'TLClassification' or modelType == 'anomaly_detection'): if(str(output_label) != '{}'): inv_mapping_dict = {v: k for k, v in output_label.items()} self.output_formatfile += ' le_dict = '+ str(inv_mapping_dict) self.output_formatfile += '\n' ''' if(model in ['SGDClassifier']): self.output_formatfile += ' modeloutput = modeloutput.replace({"predict_class": le_dict})' else: self.output_formatfile += ' modeloutput = modeloutput.rename(columns=le_dict)' ''' if modelType != 'anomaly_detection': self.output_formatfile += ' modeloutput = modeloutput.rename(columns=le_dict)' self.output_formatfile += '\n' if(threshold != -1): ''' if(model in ['SGDClassifier']): self.output_formatfile += ' df[\'prediction\'] = np.where(modeloutput[\'probability\'] > '+str(threshold)+',1,0)' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = modeloutput[\'probability\']' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = ""' self.output_formatfile += '\n' else: self.output_formatfile += ' predictedData = modeloutput.iloc[:,1]' self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = np.where(predictedData > '+str(threshold)+',modeloutput.columns[1],modeloutput.columns[0])' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = np.where(df[\'prediction\'] == modeloutput.columns[1],modeloutput.iloc[:,1],modeloutput.iloc[:,0])' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)' self.output_formatfile += '\n' ''' self.output_formatfile += ' predictedData = modeloutput.iloc[:,1]' self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = np.where(predictedData > '+str(threshold)+',modeloutput.columns[1],modeloutput.columns[0])' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = np.where(df[\'prediction\'] == modeloutput.columns[1],modeloutput.iloc[:,1],modeloutput.iloc[:,0])' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)' self.output_formatfile += '\n' else: ''' if(model in ['SGDClassifier']): self.output_formatfile += ' df[\'prediction\'] = modeloutput[\'predict_class\']' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = ""' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = "NA"' self.output_formatfile += '\n' else: self.output_formatfile += ' df[\'prediction\'] = modeloutput.idxmax(axis=1)' self.output_formatfile += '\n' self.output_formatfile += ' df[\'probability\'] = modeloutput.max(axis=1)' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)' self.output_formatfile += '\n' ''' if modelType == 'anomaly_detection': # if (model.lower()=='autoencoder'): if model.lower() in ['autoencoder']: if (datetimeFeature != '' and datetimeFeature.lower() != 'na'): self.output_formatfile += ' df[modeloutput.columns] = modeloutput\n' self.output_formatfile += ' anomaly_df=df[df[\'anomaly\'] == True]\n' self.output_formatfile += ' anomaly_prediction_df=self.find_point_subsequence_anomalies(datetimeFeature,anomaly_df)\n' self.output_formatfile += ' new_dir = str(Path(__file__).parent.parent/\'data\')\n' self.output_formatfile += ' anomaly_prediction_df.to_csv(f"{new_dir}/anomaly_data.csv")\n' self.output_formatfile += ' try:\n' self.output_formatfile += ' anomaly_prediction_df[datetimeFeature]=pd.to_datetime(anomaly_prediction_df[datetimeFeature])\n' self.output_formatfile += ' df[datetimeFeature]=pd.to_datetime(df[datetimeFeature])\n' self.output_formatfile += ' anomaly_prediction_df.drop("Time_diff",axis=1,inplace=True)\n' self.output_formatfile += ' except:\n' self.output_formatfile += ' pass\n' self.output_formatfile += ' try:\n' self.output_formatfile += ' df_out = pd.merge(df, anomaly_prediction_df, on=df.columns.values.tolist(), how=\'left\')\n' self.output_formatfile += ' df_out[\'anomaly\'].replace([\'None\', \'NaN\', np.nan], "Normal", inplace=True)\n' self.output_formatfile += ' df_out[\'anomalyType\'].replace([\'None\', \'NaN\', np.nan], "Normal", inplace=True)\n' self.output_formatfile += ' df_out.to_csv(f"{new_dir}/overall_ad_output.csv") \n' self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str) \n' self.output_formatfile += ' df_out.drop("time_diff",axis=1,inplace=True)\n' self.output_formatfile += ' except Exception as e:\n' self.output_formatfile += ' print("anomaly data updated issue",e)\n' self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str)\n' self.output_formatfile += ' df=df_out \n' else: self.output_formatfile += ' df[modeloutput.columns] = modeloutput\n' elif (model.lower()=='dbscan'): if (datetimeFeature != '' and datetimeFeature.lower() != 'na'): self.output_formatfile += ' df[\'anomaly\'] = modeloutput[\'cluster\']== -1\n' self.output_formatfile += ' anomaly_df=df[df[\'anomaly\'] == True]\n' self.output_formatfile += ' anomaly_prediction_df=self.find_point_subsequence_anomalies(datetimeFeature,anomaly_df)\n' self.output_formatfile += ' new_dir = str(Path(__file__).parent.parent/\'data\')\n' self.output_formatfile += ' try:\n' self.output_formatfile += ' anomaly_prediction_df[datetimeFeature]=pd.to_datetime(anomaly_prediction_df[datetimeFeature])\n' self.output_formatfile += ' df[datetimeFeature]=pd.to_datetime(df[datetimeFeature])\n' self.output_formatfile += ' except:\n' self.output_formatfile += ' pass\n' self.output_formatfile += ' try:\n' self.output_formatfile += ' df_out = pd.merge(df, anomaly_prediction_df, on=df.columns.values.tolist(), how=\'left\')\n' self.output_formatfile += ' df_out[\'anomaly\'].replace([\'None\', \'NaN\', np.nan], "Normal", inplace=True)\n' self.output_formatfile += ' df_out.to_csv(f"{new_dir}/overall_ad_output.csv") \n' self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str)\n' self.output_formatfile += ' except Exception as e:\n' self.output_formatfile += ' print("anomaly data updated.")\n' self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str)\n' self.output_formatfile += ' df=df_out \n' else: self.output_formatfile += ' df[\'anomaly\'] = modeloutput[\'cluster\']== -1\n' self.output_formatfile += ' df.sort_values(by=[\'anomaly\'], ascending=False, inplace=True)\n' else: self.output_formatfile += ' df[\'prediction\'] = modeloutput' self.output_formatfile += '\n' else: self.output_formatfile += ' df[\'prediction\'] = modeloutput.idxmax(axis=1)' self.output_formatfile += '\n' if learner_type != 'DL': self.output_formatfile += ' df[\'probability\'] = modeloutput.max(axis=1).round(2)' self.output_formatfile += '\n' self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)' self.output_formatfile += '\n' else: if model == 'COX': self.output_formatfile += '\n' self.output_formatfile += ' modeloutput[0] = modeloutput[0].round(2)' self.output_formatfile += '\n' #self.output_formatfile += ' modeloutput = modeloutput[0].to_json(orient=\'records\',double_precision=2)' #self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = modeloutput' self.output_formatfile += '\n' else: self.output_formatfile += ' df[\'prediction\'] = modeloutput[0]' if(learner_type == 'objectDetection'): self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = df[\'prediction\']' else: self.output_formatfile += '\n' self.output_formatfile += ' df[\'prediction\'] = df[\'prediction\'].round(2)' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = df.to_json(orient=\'records\',double_precision=2)' self.output_formatfile += '\n' self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}' self.output_formatfile += '\n' self.output_formatfile += ' return(json.dumps(outputjson))' filename = os.path.join(deploy_path,'script','output_format.py') #print(deploy_path) f = open(filename, "wb") self.log.info('-------> Output Mapping File Location :'+filename) f.write(str(self.output_formatfile).encode('utf8')) f.close() ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import numpy as np import scipy import warnings import scipy.stats as st import logging import json class inputdrift(): def __init__(self,conf): self.log = logging.getLogger('eion') def get_input_drift(self,ndf,hdf,outputfolder): selectedColumns = self.features.split(',') dataalertcount=0 distributionChangeColumns="" distributionChangeMessage=[] for i in range(0,len(selectedColumns)): data1=hdf[selectedColumns[i]] data2=ndf[selectedColumns[i]] if(data1.dtype !="str" and data2.dtype !="str" ): cumulativeData=data1.append(data2) teststaticValue=teststatic(self,data1,data2) if (teststaticValue < 0.05): distributionName1,sse1=DistributionFinder(self,data1) distributionName2,sse2=DistributionFinder(self,data2) if(distributionName1 == distributionName2): dataalertcount = dataalertcount else: dataalertcount = dataalertcount+1 distributionChangeColumns=distributionChangeColumns+selectedColumns[i]+"," changedColumn = {} changedColumn['Feature'] = selectedColumns[i] changedColumn['KS_Training'] = teststaticValue changedColumn['Training_Distribution'] = distributionName1 changedColumn['New_Distribution'] = distributionName2 distributionChangeMessage.append(changedColumn) else : dataalertcount = dataalertcount else : response ="Selected Columns should be Numerical Values" if(dataalertcount == 0): resultStatus="Model is working as expected" else : resultStatus=json.dumps(distributionChangeMessage) return(dataalertcount,resultStatus) def DistributionFinder(self,data): try: distributionName ="" sse =0.0 KStestStatic=0.0 dataType="" if(data.dtype == "float64"): dataType ="Continuous" elif(data.dtype =="int"): dataType="Discrete" elif(data.dtype =="int64"): dataType="Discrete" if(dataType == "Discrete"): distributions= [st.bernoulli,st.binom,st.geom,st.nbinom,st.poisson] index, counts = np.unique(data.astype(int),return_counts=True) if(len(index)>=2): best_sse = np.inf y1=[] total=sum(counts) mean=float(sum(index*counts))/total variance=float((sum(index**2*counts) -total*mean**2))/(total-1) dispersion=mean/float(variance) theta=1/float(dispersion) r=mean*(float(theta)/1-theta) for j in counts: y1.append(float(j)/total) pmf1=st.bernoulli.pmf(index,mean) pmf2=st.binom.pmf(index,len(index),p=mean/len(index)) pmf3=st.geom.pmf(index,1/float(1+mean)) pmf4=st.nbinom.pmf(index,mean,r) pmf5=st.poisson.pmf(index,mean) sse1 = np.sum(np.power(y1 - pmf1, 2.0)) sse2 = np.sum(np.power(y1 - pmf2, 2.0)) sse3 = np.sum(np.power(y1 - pmf3, 2.0)) sse4 = np.sum(np.power(y1 - pmf4, 2.0)) sse5 = np.sum(np.power(y1- pmf5, 2.0)) sselist=[sse1,sse2,sse3,sse4,sse5] for i in range(0,len(sselist)): if best_sse > sselist[i] > 0: best_distribution = distributions[i].name best_sse = sselist[i] elif (len(index) == 1): best_distribution = "Constant Data-No Distribution" best_sse = 0.0 distributionName =best_distribution sse=best_sse elif(dataType == "Continuous"): distributions = [st.uniform,st.expon,st.weibull_max,st.weibull_min,st.chi,st.norm,st.lognorm,st.t,st.gamma,st.beta] best_distribution = st.norm.name best_sse = np.inf datamin=data.min() datamax=data.max() nrange=datamax-datamin y, x = np.histogram(data.astype(float), bins='auto', density=True) x = (x + np.roll(x, -1))[:-1] / 2.0 for distribution in distributions: with warnings.catch_warnings(): warnings.filterwarnings('ignore') params = distribution.fit(data.astype(float)) # Separate parts of parameters arg = params[:-2] loc = params[-2] scale = params[-1] # Calculate fitted PDF and error with fit in distribution pdf = distribution.pdf(x, loc=loc, scale=scale, *arg) sse = np.sum(np.power(y - pdf, 2.0)) if(best_sse >sse > 0): best_distribution = distribution.name best_sse = sse distributionName =best_distribution sse=best_sse except: response = str(sys.exc_info()[0]) message='Job has Failed'+response print(message) return distributionName,sse ##KStestStatic -pvalue finding def teststatic(self,data1,data2): try: teststatic =st.ks_2samp(data1,data2) pValue=0.0 scipyVersion =scipy.__version__ if(scipyVersion <= "0.14.1"): pValue =teststatic[1] else: pValue =teststatic.pvalue except: response = str(sys.exc_info()[0]) print("Input Drift Job Failed "+response) return pValue ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import os,sys import platform import json import shutil import logging from pathlib import Path def create_selector_file(self,deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature, model_type,model,config=None): self.selectorfile += 'import pandas as pd' self.selectorfile += '\n' self.selectorfile += 'import joblib' self.selectorfile += '\n' self.selectorfile += 'import os' self.selectorfile += '\n' self.selectorfile += 'import numpy as np' self.selectorfile += '\n' self.selectorfile += 'class selector(object):' self.selectorfile += '\n' self.selectorfile += ' def apply_selector(self,df):' self.selectorfile += '\n' if pcaModel_pickle_file != '': self.selectorfile += " pcaModel = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+pcaModel_pickle_file+"'))" self.selectorfile += '\n' self.selectorfile += ' bpca_features = '+str(bpca_features) self.selectorfile += '\n' self.selectorfile += ' apca_features = '+str(apca_features) self.selectorfile += '\n' self.selectorfile += ' df = pcaModel.transform(df[bpca_features])' self.selectorfile += '\n' self.selectorfile += ' df = pd.DataFrame(df,columns=apca_features)' self.selectorfile += '\n' if(len(features) != 0) and model_type != 'BM25': if model_type.lower()!='anomaly_detection' and model.lower() != 'autoencoder': self.selectorfile += ' df = df['+str(features)+']' self.selectorfile += '\n' self.selectorfile += ' return(df)' filename = os.path.join(deploy_path,'script','selector.py') f = open(filename, "wb") self.log.info('-------> Feature Selector File Location :'+filename) f.write(str(self.selectorfile).encode('utf8')) f.close() featurefile = 'import json' featurefile +='\n' featurefile += 'def getfeatures():' featurefile +='\n' featurefile +=' try:' featurefile +='\n' featurelist = [] if 'profiler' in config: if 'input_features_type' in config['profiler']: inputfeatures = config['profiler']['input_features_type'] for x in inputfeatures: featurelt={} featurelt['feature'] = x print(x,inputfeatures[x]) if x == targetFeature: featurelt['Type'] = 'Target' else: if inputfeatures[x] in ['int','int64','float','float64']: featurelt['Type'] = 'Numeric' elif inputfeatures[x] == 'object': featurelt['Type'] = 'Text' elif inputfeatures[x] == 'category': featurelt['Type'] = 'Category' else: featurelt['Type'] = 'Unknown' featurelist.append(featurelt) featurefile +=' features = '+str(featurelist) featurefile +='\n' featurefile +=' outputjson = {"status":"SUCCESS","features":features}' featurefile +='\n' featurefile +=' output = json.dumps(outputjson)' featurefile +='\n' featurefile +=' print("Features:",output)' featurefile +='\n' featurefile +=' return(output)' featurefile +='\n' featurefile +=' except Exception as e:' featurefile +='\n' featurefile +=' output = {"status":"FAIL","message":str(e).strip(\'"\')}' featurefile +='\n' featurefile +=' print("Features:",json.dumps(output))' featurefile +='\n' featurefile +=' return (json.dumps(output))' featurefile +='\n' featurefile +='if __name__ == "__main__":' featurefile +='\n' featurefile +=' output = getfeatures()' filename = os.path.join(deploy_path,'featureslist.py') f = open(filename, "wb") f.write(str(featurefile).encode('utf8')) f.close() def create_init_function_for_classification(self,modelfile,classes,learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,modelName,model_type,imageconfig): self.modelfile += ' def __init__(self):' self.modelfile += '\n' if (learner_type == 'ML' and model_type.lower()=='anomaly_detection' and modelName.lower()=="autoencoder"): modelfile=modelfile.replace('.sav','') self.modelfile+=" self.model = tf.keras.models.load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif(learner_type == 'TextDL' or learner_type == 'DL'): if modelName.lower() == 'googlemodelsearch': self.modelfile += ' import autokeras as ak' self.modelfile += '\n' self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','modelsearch_rootdir','saved_model_onnx.onnx'))" self.modelfile += '\n' else: if scoreParam == 'recall': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'recall': recall_m},compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[recall_m])' self.modelfile += '\n' elif scoreParam == 'precision': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'precision': precision_m},compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[precision_m])' self.modelfile += '\n' elif scoreParam == 'roc_auc': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[tf.keras.metrics.AUC()])' self.modelfile += '\n' elif scoreParam == 'f1_score': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'f1_score': f1_m},compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[f1_m])' self.modelfile += '\n' elif scoreParam == 'r2': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'r2': r_square},compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[r_square])' self.modelfile += '\n' elif scoreParam == 'rmse': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'rmse': rmse_m},compile=False)" self.modelfile += '\n' self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[rmse_m])' self.modelfile += '\n' elif scoreParam == 'mse': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif scoreParam == 'mae': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif scoreParam == 'accuracy': self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' else: self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif(learner_type == 'Text Similarity'): self.modelfile += " self.preprocessing = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+preprocessing_pipe+"'))" self.modelfile += '\n' self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'), custom_objects={'cosine_distance': cosine_distance, 'cos_dist_output_shape': cos_dist_output_shape})" self.modelfile += '\n' elif(learner_type in ['similarityIdentification','contextualSearch']): if scoreParam == 'VectorDB Cosine': vectorfiledbname = 'trainingdataVecDB' self.modelfile += f"\ \n persist_directory = os.path.join(os.path.dirname(__file__),'..','data')\ \n client = chromadb.PersistentClient(path=persist_directory)\ \n self.collection_name = '{vectorfiledbname}'\ \n self.collection = client.get_collection(self.collection_name)\n" else: self.modelfile += " self.train_input = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','data','trainingdata.csv'))\n\n" elif(learner_type == 'ImageClassification'): self.modelfile += ' self.config='+str(imageconfig) self.modelfile += '\n' if(modelName.lower() == 'densenet'): self.modelfile += ' baseModel = tf.keras.applications.DenseNet121(weights="imagenet", include_top=False, input_tensor=Input(shape=(self.config[\'img_width\'],self.config[\'img_height\'],self.config[\'img_channel\'])))' else: self.modelfile += ' baseModel = tensorflow.keras.applications.InceptionV3(weights="imagenet", include_top=False, input_tensor=Input(shape=(self.config[\'img_width\'],self.config[\'img_height\'],self.config[\'img_channel\'])))' self.modelfile += '\n' self.modelfile += ' headModel = baseModel.output' self.modelfile += '\n' self.modelfile += ' headModel = Flatten(name="flatten")(headModel)' self.modelfile += '\n' self.modelfile += ' headModel = Dense(1024, activation=\'relu\')(headModel)' self.modelfile += '\n' self.modelfile += ' headModel = Dropout(0.5)(headModel)' self.modelfile += '\n' self.modelfile += ' headModel = Dense(2, activation=\'sigmoid\')(headModel)' self.modelfile += '\n' self.modelfile += ' headModel = self.model = Model(inputs=baseModel.input, outputs=headModel)' self.modelfile += '\n' self.modelfile += ' opt = Adam(lr=self.config[\'lr\'])' self.modelfile += '\n' self.modelfile += ' self.model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])' self.modelfile += '\n' self.modelfile += " self.model.load_weights(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif(learner_type == 'objectDetection'): self.modelfile += " self.MODEL_LOCATION = os.path.join(os.path.dirname(__file__),'..','model')\n" self.modelfile += ' PATH_TO_CFG = self.MODEL_LOCATION+"/export/pipeline.config"\n' self.modelfile += ' PATH_TO_CKPT = self.MODEL_LOCATION+"/export/checkpoint/"\n' self.modelfile += ' PATH_TO_LABELS = self.MODEL_LOCATION+"/export/label_map.pbtxt"\n' self.modelfile += ' configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)\n' self.modelfile += ' self.detection_model = model_builder.build(model_config=configs["model"], is_training=False)\n' self.modelfile += ' ckpt = tf.compat.v2.train.Checkpoint(model=self.detection_model)\n' self.modelfile += ' ckpt.restore(os.path.join(PATH_TO_CKPT, "ckpt-0")).expect_partial()\n' self.modelfile += ' self.category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\ use_display_name=True)\n' elif learner_type == 'TS' and (modelName.lower() == 'lstm' or modelName.lower() == 'mlp'): self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' elif modelName.lower() == 'neural architecture search': self.modelfile += ' import autokeras as ak' self.modelfile += '\n' self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects=ak.CUSTOM_OBJECTS)" self.modelfile += '\n' else: self.modelfile += " self.model = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))" self.modelfile += '\n' def create_predict(self,learner_type,method,model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,feature,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scoreParam=None): self.modelfile += ' def predict(self,X,features_names):' self.modelfile += '\n' if (learner_type == 'ML' and model_type.lower()=='anomaly_detection' and model.lower()=="autoencoder"): self.modelfile += f" X=X[{feature}]\n" self.modelfile += f" X = np.asarray(X).astype('float32')\n" self.modelfile += f" reconstructed = self.model.predict(X)\n" self.modelfile += f" predict_loss = tf.keras.losses.mae(reconstructed,X)\n" self.modelfile += ' max_threshold = np.mean(predict_loss) + 2*np.std(predict_loss)\n' self.modelfile += ' min_threshold = np.mean(predict_loss) - 2*np.std(predict_loss)\n' self.modelfile += ' prediction_df = pd.DataFrame()\n' self.modelfile += ' prediction_df["loss"] = predict_loss\n' self.modelfile += ' prediction_df["max_threshold"] = max_threshold\n' self.modelfile += ' prediction_df["min_threshold"] = min_threshold\n' self.modelfile += ' prediction_df["anomaly"] = np.where((prediction_df["loss"] > prediction_df["max_threshold"]) | (prediction_df["loss"] <= prediction_df["min_threshold"]), True, False)\n' self.modelfile += ' return prediction_df\n' elif(learner_type == 'RecommenderSystem'): self.modelfile += ' predictions = []' self.modelfile += '\n' self.modelfile += ' for index,row in X.iterrows():' self.modelfile += '\n' self.modelfile += ' score = self.model.predict(int(row["uid"]),int(row["iid"]))' self.modelfile += '\n' self.modelfile += ' predictions.append(score.est)' self.modelfile += '\n' self.modelfile += ' return predictions' elif(learner_type in ['similarityIdentification','contextualSearch']): tfeatures = list(modelFeatures.split(",")) if indexFeature != '' and indexFeature != 'NA': ifeatures = indexFeature.split(",") for ifes in ifeatures: if ifes not in tfeatures: tfeatures.append(ifes) if model_type == 'BM25': self.modelfile += f"\n\ tokenized_corpus =[doc.split(' ') for doc in self.train_input.tokenize]\n\ bm25 = BM25Okapi(tokenized_corpus)\n\ tokenized_query = [doc.split(' ') for doc in X.tokenize]\n\ logcnt = 5\n\ output = []\n\ for query in tokenized_query:\n\ doc_scores = bm25.get_scores(query)\n\ related_docs_indices = np.argsort(doc_scores)[::-1][:logcnt]\n\ x = self.train_input[{tfeatures}].loc[self.train_input.index[related_docs_indices]]\n\ x['Score'] = doc_scores[related_docs_indices]\n\ x['Score'] = round(x['Score'],2).astype(str)+'%'\n\ output.append(x)\n\ return output\n" elif scoreParam == 'VectorDB Cosine': featuresVecDB = modelFeatures.split(",") self.modelfile += ' logcnt = 5\n' self.modelfile += f" columns = {featuresVecDB}\n" self.modelfile += f"\ \n output = []\ \n for rowindex, row in X.iterrows():\ \n queryembedding = X.iloc[rowindex:rowindex+1].to_numpy()\ \n results = self.collection.query(\ \n query_embeddings=queryembedding.tolist(),\ \n n_results=logcnt\ \n )\ \n x = pd.DataFrame(columns=columns)\ \n for i in range(0, len(results['ids'][0])):\ \n documentAry = results['documents'][0][i]\ \n documentAry = documentAry.split(' ~&~ ')\ \n for j in range(0, len(documentAry)):\ \n x.at[i,columns[j]] = documentAry[j]\ \n x.at[i,'Score'] = results['distances'][0][i]\ \n output.append(x)\ \n return output" else: self.modelfile += ' columns = self.train_input.columns.tolist()\n' self.modelfile += ' logcnt = 5\n' self.modelfile += f" train_input = self.train_input[{tfeatures}]\n" for tf in tfeatures: self.modelfile += f" columns.remove('{tf}')\n" self.modelfile += f"\ \n results = cosine_similarity(self.train_input[columns],X)\ \n output = []\ \n for i in range(results.shape[1]):\ \n related_docs_indices = results[:,i].argsort(axis=0)[:-(int(logcnt) + 1):-1]\ \n x=self.train_input[{tfeatures}].loc[self.train_input.index[related_docs_indices]]\ \n scores = []\ \n for j in range(0,logcnt):\ \n scores.append(str(round((results[related_docs_indices][j][i])*100))+'%')\ \n x['Score'] = scores\ \n output.append(x)\ \n return output" elif(learner_type == 'Text Similarity'): self.modelfile += ' X["'+firstDocFeature+'"] = X["'+firstDocFeature+'"].astype(str)' self.modelfile += '\n' self.modelfile += ' X["'+secondDocFeature+'"] = X["'+secondDocFeature+'"].astype(str)' self.modelfile += '\n' self.modelfile += ' test_sentence1 = self.preprocessing.texts_to_sequences(X["'+firstDocFeature+'"].values)' self.modelfile += '\n' self.modelfile += ' test_sentence2 = self.preprocessing.texts_to_sequences(X["'+secondDocFeature+'"].values)' self.modelfile += '\n' self.modelfile += ' test_sentence1 = pad_sequences(test_sentence1, maxlen='+str(padding_length)+', padding=\'post\')' self.modelfile += '\n' self.modelfile += ' test_sentence2 = pad_sequences(test_sentence2, maxlen='+str(padding_length)+', padding=\'post\')' self.modelfile += '\n' self.modelfile += ' prediction = self.model.predict([test_sentence1, test_sentence2 ])' self.modelfile += '\n' self.modelfile += ' return(prediction)' self.modelfile += '\n' elif(learner_type == 'ImageClassification'): self.modelfile += ' predictions = []' self.modelfile += '\n' self.modelfile += ' for index, row in X.iterrows(): ' self.modelfile += '\n' self.modelfile += ' img = cv2.imread(row[\'imagepath\'])' self.modelfile += '\n' self.modelfile += ' img = cv2.resize(img, (self.config[\'img_width\'],self.config[\'img_height\']))' self.modelfile += '\n' self.modelfile += ' img = image.img_to_array(img)' self.modelfile += '\n' self.modelfile += ' img = np.expand_dims(img, axis=0)' self.modelfile += '\n' self.modelfile += ' img = img/255' self.modelfile += '\n' self.modelfile += ' prediction = self.model.predict(img)' self.modelfile += '\n' self.modelfile += ' prediction = np.argmax(prediction,axis=1)' self.modelfile += '\n' self.modelfile += ' predictions.append(prediction[0])' self.modelfile += '\n' self.modelfile += ' return(predictions)' self.modelfile += '\n' elif(learner_type == 'objectDetection'): self.modelfile += ' @tf.function\n' self.modelfile += ' def detect_fn(image):\n' self.modelfile += ' image, shapes = self.detection_model.preprocess(image)\n' self.modelfile += ' prediction_dict = self.detection_model.predict(image, shapes)\n' self.modelfile += ' detections = self.detection_model.postprocess(prediction_dict, shapes)\n' self.modelfile += ' return detections\n' self.modelfile += ' def load_image_into_numpy_array(path):\n' self.modelfile += ' return np.array(Image.open(path))\n' self.modelfile += ' imageLocation = []\n' self.modelfile += ' for i, row in X.iterrows():\n' self.modelfile += ' if ("confidance" in row) and row["confidance"] <= 1.0:\n' self.modelfile += ' confidance = row["confidance"]\n' self.modelfile += ' else:\n' self.modelfile += ' confidance = 0.8\n' self.modelfile += ' imageName = str(Path(row["imagepath"]).stem)+"_output"+str(Path(row["imagepath"]).suffix)\n' self.modelfile += ' image_np = load_image_into_numpy_array(row["imagepath"])\n' self.modelfile += ' input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n' self.modelfile += ' detections = detect_fn(input_tensor)\n' self.modelfile += ' num_detections = int(detections.pop("num_detections"))\n' self.modelfile += ' detections = {key: value[0, :num_detections].numpy()\n\ for key, value in detections.items()}\n' self.modelfile += ' detections["num_detections"] = num_detections\n' self.modelfile += ' detections["detection_classes"] = detections["detection_classes"].astype(np.int64)\n' self.modelfile += ' label_id_offset = 1\n' self.modelfile += ' image_np_with_detections = image_np.copy()\n' self.modelfile += ' viz_utils.visualize_boxes_and_labels_on_image_array(\n\ image_np_with_detections,\n\ detections["detection_boxes"],\n\ detections["detection_classes"]+label_id_offset,\n\ detections["detection_scores"],\n\ self.category_index,\n\ use_normalized_coordinates=True,\n\ max_boxes_to_draw=200,\n\ min_score_thresh=confidance,\n\ agnostic_mode=False)\n' self.modelfile += ' plt.figure()\n' self.modelfile += ' plt.imsave(os.path.join(self.MODEL_LOCATION,imageName), image_np_with_detections)\n' self.modelfile += ' imageLocation.append(os.path.join(self.MODEL_LOCATION,imageName))\n' self.modelfile += ' plt.show()\n' self.modelfile += ' return imageLocation\n' else: if(learner_type == 'DL' and model != 'Neural Network'): self.modelfile += ' X = np.expand_dims(X, axis=2)' self.modelfile += '\n' if(learner_type == 'TextDL'): self.modelfile += ' return pd.DataFrame(np.argmax(self.model.predict(X),axis=1))' self.modelfile += '\n' elif(learner_type == 'TextML'): self.modelfile += ' return pd.DataFrame(self.model.predict_proba(X),columns=self.model.classes_)' self.modelfile += '\n' elif(learner_type == 'DL' and model_type == 'Classification'): self.modelfile += ' X = X.astype(np.float32)' self.modelfile += '\n' self.modelfile += ' return pd.DataFrame(np.argmax(self.model.predict(X),axis=1))' self.modelfile += '\n' else: if(model_type == 'Classification' or model_type == 'TLClassification'): if model == 'Neural Architecture Search': self.modelfile += ' X = X.astype(np.float32)' self.modelfile += '\n' self.modelfile += ' return pd.DataFrame(self.model.predict(X))' self.modelfile += '\n' else: if optimizationmethod == 'genetic': self.modelfile += '\n' self.modelfile += ' try:' self.modelfile += '\n' self.modelfile += ' return pd.DataFrame(self.model.predict_proba(X))' self.modelfile += '\n' self.modelfile += ' except:' self.modelfile += '\n' self.modelfile += ' return pd.DataFrame(self.model.predict(X))' else: self.modelfile += ' X = X.astype(np.float32)' self.modelfile += '\n' if model.lower() == 'deep q network' or model.lower() == 'dueling deep q network': self.modelfile += ' q, _ = self.model(np.array(X), step_type=constant([time_step.StepType.FIRST] * np.array(X).shape[0]), training=False)' self.modelfile += '\n' self.modelfile += ' return pd.DataFrame(q.numpy())' else: self.modelfile += ' return pd.DataFrame(self.model.predict_proba(X), columns=self.model.classes_)' self.modelfile += '\n' elif model_type == 'Regression' and model == 'NAS': self.modelfile += \ """ X = X.astype(np.float32) return self.model.predict(X) """ elif(learner_type == 'TS'): if model.lower() == 'fbprophet': self.modelfile += ' sessonal_freq="'+str(sessonal_freq)+'"' self.modelfile += '\n' self.modelfile += ' ts_prophet_future = self.model.make_future_dataframe(periods=int(X["noofforecasts"][0]),freq=sessonal_freq,include_history = False)' self.modelfile += '\n' if (additional_regressors): self.modelfile += '\n' self.modelfile += ' additional_regressors='+str(additional_regressors) self.modelfile += '\n' self.modelfile += ' ts_prophet_future[additional_regressors] = dataFrame[additional_regressors]' self.modelfile += '\n' self.modelfile += ' ts_prophet_future.reset_index(drop=True)' self.modelfile += '\n' self.modelfile += ' ts_prophet_future=ts_prophet_future.dropna()' self.modelfile += '\n' self.modelfile += ' train_forecast = self.model.predict(ts_prophet_future)' self.modelfile += '\n' self.modelfile += ' prophet_forecast_tail=train_forecast[[\'ds\', \'yhat\', \'yhat_lower\',\'yhat_upper\']].tail( int(X["noofforecasts"][0]))' self.modelfile += '\n' self.modelfile += ' return(prophet_forecast_tail)' elif model.lower() == 'lstm' or model.lower() == 'mlp': self.modelfile += ' lag_order='+str(lag_order) self.modelfile += '\n' self.modelfile += ' xt = X.values' self.modelfile += '\n' scalertransformationFile = scalertransformationFile.split('\\')[-1] self.modelfile += ' loaded_scaler_model = joblib.load(os.path.join(os.path.dirname(__file__),\'..\',\'model\',\''+scalertransformationFile+'\'))' self.modelfile += '\n' self.modelfile += ' xt = xt.astype(\'float32\')' self.modelfile += '\n' self.modelfile += ' xt = loaded_scaler_model.transform(xt)' self.modelfile += '\n' self.modelfile += ' noOfPredictions = 10' self.modelfile += '\n' self.modelfile += ' pred_data = xt' self.modelfile += '\n' self.modelfile += ' y_future = []' self.modelfile += '\n' self.modelfile += ' for i in range(noOfPredictions):' self.modelfile += '\n' if len(feature) == 1: self.modelfile += ' pred_data = pred_data[-lag_order:]' self.modelfile += '\n' if model.lower() == 'mlp': self.modelfile += ' pred_data = pred_data.reshape((1,lag_order))' else: self.modelfile += ' pred_data = pred_data.reshape((1,lag_order,1))' self.modelfile += '\n' self.modelfile += ' pred = self.model.predict(pred_data)' self.modelfile += '\n' self.modelfile += ' predoutput = loaded_scaler_model.inverse_transform(pred) ' self.modelfile += '\n' self.modelfile += ' y_future.append(predoutput.flatten()[-1])' self.modelfile += '\n' self.modelfile += ' pred_data = np.append(pred_data,pred)' self.modelfile += '\n' self.modelfile += ' pred = pd.DataFrame(index=range(0,len(y_future)),columns='+str(feature)+')' self.modelfile += '\n' self.modelfile += ' for i in range(0, len(y_future)):' self.modelfile += '\n' self.modelfile += ' pred.iloc[i] = y_future[i]' self.modelfile += '\n' self.modelfile += ' return pred' else: self.modelfile += ' pdata = pred_data[-lag_order:]' self.modelfile += '\n' self.modelfile += ' pdata = pdata.reshape((1,lag_order,'+str(len(feature))+'))' self.modelfile += '\n' self.modelfile += ' pred = self.model.predict(pdata)' self.modelfile += '\n' self.modelfile += ' predoutput = loaded_scaler_model.inverse_transform(pred) ' self.modelfile += '\n' self.modelfile += ' y_future.append(predoutput)' self.modelfile += '\n' self.modelfile += ' pred_data = np.append(pred_data,pred,axis=0)' self.modelfile += '\n' self.modelfile += ' pred = pd.DataFrame(index=range(0,len(y_future)),columns='+str(feature)+')' self.modelfile += '\n' self.modelfile += ' for i in range(0, len(y_future)):' self.modelfile += '\n' self.modelfile += ' pred.iloc[i] = y_future[i]' self.modelfile += '\n' self.modelfile += ' return pred' else: self.modelfile += ' return self.model.predict(n_periods=int(X["noofforecasts"][0]))' else: if model == 'KaplanMeierFitter': self.modelfile += '\n' self.modelfile += ' res = self.model.predict(X[\''+feature[0]+'\'].astype(int))' self.modelfile += '\n' self.modelfile += ' if isinstance(res, pd.DataFrame):\n' self.modelfile += ' return res.values.reshape(1,-1)\n' self.modelfile += ' else:\n' self.modelfile += ' return np.array([res])\n' elif model == 'COX': self.modelfile += ' res = []\n' self.modelfile += ' for idx,row in X.iterrows():\n' self.modelfile += ' res.append(self.model.predict_survival_function(X, times=row[self.model.duration_col])[idx].values[0])\n' self.modelfile += ' return pd.DataFrame(res)' #self.modelfile += ' return self.model.predict_survival_function(X, times=X[self.model.duration_col])' self.modelfile += '\n' elif(learner_type == 'DL' and model_type in ['Classification','Regression']): self.modelfile += ' X = X.astype(np.float32)' self.modelfile += '\n' self.modelfile += ' return self.model.predict(X).reshape(1, -1)' self.modelfile += '\n' elif (model_type == 'Clustering' and model == 'DBSCAN'): self.modelfile += ' return self.model.fit_predict(X)' elif(model_type.lower() == 'anomaly_detection' and model.lower() == 'dbscan'): self.modelfile += " pred=self.model.fit_predict(X)\n" self.modelfile += " X.loc[:,'cluster'] = self.model.labels_ \n" self.modelfile += ' return X\n' elif model_type.lower() == 'anomaly_detection': self.modelfile += ' X = X.astype(np.float32)\n' self.modelfile += ' return self.model.predict(X)' else: if model_type != 'Clustering': self.modelfile += ' X = X.astype(np.float32)' self.modelfile += '\n' #self.modelfile += ' return self.model.predict(X).reshape(1, -1)' self.modelfile += \ """ if isinstance(self.model, LatentDirichletAllocation): output = np.matrix(self.model.transform(X)).argmax(axis=1) return output.flatten().tolist() return self.model.predict(X).reshape(1, -1) """ ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from pathlib import Path from AION.prediction_package.imports import importModule from AION.prediction_package.aion_prediction import aionPrediction from AION.prediction_package.utility import TAB_CHAR from AION.prediction_package import utility from AION.prediction_package import common def file_header( usecase=''): return '' class deployer(): """ base deployer class which can be used to generate the deployemnt code. This class will be inherited by deployer specific to problem type. """ def __init__(self, params={}): if not params['paths']['deploy']: raise ValueError('Deploy path is not provided') self.deploy_path = Path(params['paths']['deploy']) if not self.deploy_path.exists(): self.deploy_path.mkdir(parents=True, exist_ok=True) self.name = params.get('problem_type', '') self.params = params self.importer = importModule() self.feature_reducer = False def profiler_code(self): return common.profiler_code(self.params['profiler']) def feature_engg_code(self): if self.params['selector'].get('reducer',False): code, modules = common.feature_reducer_code(self.params['selector']) else: code, modules = common.feature_selector_code(self.params['selector']) utility.import_modules(self.importer, modules) return code def training_code(self): return common.training_code(self.params['training']) def formatter_code(self): return '' def run(self): """ run function will be called to start the deployment process. This function will create following files inputprofiler.py for preprocessing the input aion_predict.py for prediction model service file """ code = self.predict_code( ) with open(self.deploy_path/'aion_predict.py', 'w') as f: f.write(code) profiler_code = self.profiler_code() with open(self.deploy_path/'script'/'inputprofiler.py', 'w') as f: f.write(profiler_code) self.create_model_service( ) self.create_publish_service() self.create_idrift() self.create_odrift() common.create_feature_list(self.params, self.params['features']['target_feat'], self.deploy_path) common.requirement_file(self.deploy_path,self.params['training']['algo'],self.params['features']['text_feat']) common.create_readme_file(self.deploy_path, self.params['training']['model_file'], self.params['features']['input_feat']) self.create_utils_folder() def predict_code(self): imported_modules = [ {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None} ] utility.import_modules(self.importer, imported_modules) self.importer.addLocalModule(module='inputprofiler',mod_from='script.inputprofiler') code_text = "" code_text += self.feature_engg_code() code_text += self.training_code() code_text += self.formatter_code() code_text += common.main_code() code = file_header() code += self.importer.getCode() return code + code_text def create_model_service(self): service_name = '{}{}{}'.format(self.params['usecase_name'], '_' if self.params['usecase_ver'] != '' else '', self.params['usecase_ver']) obj = aionPrediction() obj.create_model_service(self.deploy_path, service_name, self.name) def create_publish_service(self): obj = aionPrediction() obj.create_publish_service(self.params['paths']['usecase'], self.params['usecase_name'],self.params['usecase_ver'], self.name) def create_idrift(self): pass def create_odrift(self): pass def create_utils_folder(self): common.create_util_folder(self.deploy_path) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from pathlib import Path from AION.prediction_package.imports import importModule from AION.prediction_package.aion_prediction import aionPrediction from AION.prediction_package.utility import TAB_CHAR from AION.prediction_package import utility from AION.prediction_package.base import deployer from AION.prediction_package import common import numpy as np def get_deployer( params): if params['training']['algo'] == 'ARIMA': return arima(params) elif params['training']['algo'] == 'LSTM': return lstm(params) elif params['training']['algo'] == 'ENCODER_DECODER_LSTM_MVI_UVO': return lstmencdec_mviuvo(params) elif params['training']['algo'] == 'MLP': return mlp(params) elif params['training']['algo'] == 'VAR': return var(params) elif params['training']['algo'] == 'FBPROPHET': return fbprophet(params) else: raise ValueError(f"Algorithm {params['training']['algo']} for time series forecasting is not supported") def _profiler_code(params, importer): """ This will create the profiler file based on the config file. separated file is created as profiler is required for input drift also. """ imported_modules = [ {'module': 'json', 'mod_from': None, 'mod_as': None}, {'module': 'scipy', 'mod_from': None, 'mod_as': None}, {'module': 'joblib', 'mod_from': None, 'mod_as': None}, {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, {'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None} ] utility.import_modules(importer, imported_modules) if 'code' in params['profiler'].get('preprocess',{}).keys(): code = params['profiler']['preprocess']['code'] else: code = "" code += """ class inputprofiler(): """ init_code = """ def __init__(self): """ init_code += """ # preprocessing preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl' if not preprocess_path.exists(): raise ValueError(f'Preprocess model file not found: {preprocess_path}') self.profiler = joblib.load(preprocess_path) """ run_code = """ def run(self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) """ if 'code' in params['profiler'].get('preprocess',{}).keys(): run_code += """ df = preprocess( df)""" if params['profiler'].get('unpreprocessed_columns'): run_code += f""" unpreprocessed_data = df['{params['profiler']['unpreprocessed_columns'][0]}'] df.drop(['{params['profiler']['unpreprocessed_columns'][0]}'], axis=1,inplace=True) """ if params['profiler'].get('force_numeric_conv'): run_code += f""" df[{params['profiler']['force_numeric_conv']}] = df[{params['profiler']['force_numeric_conv']}].apply(pd.to_numeric,errors='coerce')""" run_code += _profiler_main_code(params) if params['profiler'].get('unpreprocessed_columns'): run_code += f""" df['{params['profiler'].get('unpreprocessed_columns')[0]}'] = unpreprocessed_data """ run_code += """ return df """ utility.import_modules(importer, imported_modules) import_code = importer.getCode() return import_code + code + init_code + run_code def _profiler_main_code(params): code = f""" df = self.profiler.transform(df) columns = {params['profiler']['output_features']} if isinstance(df, scipy.sparse.spmatrix): df = pd.DataFrame(df.toarray(), columns=columns) else: df = pd.DataFrame(df, columns=columns) """ return code class arima( deployer): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' def profiler_code( self): imported_modules = [ {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, ] importer = importModule() utility.import_modules(importer, imported_modules) code = """ class inputprofiler(): def __init__(self): pass def run( self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) return df[['noofforecasts']] """ return importer.getCode() + code def feature_engg_code(self): self.importer.addModule(module='pandas',mod_as='pd') return f""" class selector(): def __init__(self): pass def run(self, df): return df """ def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') self.importer.addModule(module='Path',mod_from='pathlib') self.importer.addModule(module='numpy',mod_as='np') self.importer.addModule(module='joblib') return f""" class trainer(): def __init__(self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = joblib.load(model_file) def run(self,df): return self.model.predict(n_periods=int(df["noofforecasts"][0])) """ def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__( self): pass def run(self,raw_df,df): df = df.round(2) df = json.dumps(df.tolist()) outputjson = {"status":"SUCCESS","data":eval(df)} return(json.dumps(outputjson)) """ class lstm( deployer): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' def profiler_code(self): importer = importModule() return _profiler_code( self.params, importer) def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') self.importer.addModule(module='Path',mod_from='pathlib') code = f""" class trainer(): """ init_code, run_code = self._get_train_code() return code + init_code + run_code def _get_train_code(self): self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = load_model(model_file) """ run_code = f""" def run(self, df): lag_order={self.params['training']['lag_order']} xt = df.values scaler_file = (Path(__file__).parent/"model")/"{self.params['training']['scaler_file']}" if not scaler_file.exists(): raise ValueError(f'Scaling file not found: {{scaler_file}}') loaded_scaler_model = joblib.load(scaler_file) xt = xt.astype('float32') xt = loaded_scaler_model.transform(xt) noOfPredictions = 10 pred_data = xt y_future = [] for i in range(noOfPredictions): """ if len(self.params['selector']['output_features']) == 1: run_code += f""" pred_data = pred_data[-lag_order:] pred_data = pred_data.reshape((1,lag_order,1)) pred = self.model.predict(pred_data) predoutput = loaded_scaler_model.inverse_transform(pred) y_future.append(predoutput.flatten()[-1]) pred_data = np.append(pred_data,pred) pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']}) for i in range(0, len(y_future)): pred.iloc[i] = y_future[i] return pred """ else: run_code += f""" pdata = pred_data[-lag_order:] pdata = pdata.reshape((1,lag_order,{len(self.params['selector']['output_features'])})) pred = self.model.predict(pdata) predoutput = loaded_scaler_model.inverse_transform(pred) y_future.append(predoutput) pred_data = np.append(pred_data,pred,axis=0) pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']}) for i in range(0, len(y_future)): pred.iloc[i] = y_future[i] return pred """ return init_code, run_code def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__( self): pass def run(self,raw_df,df): df = df.round(2) df = df.to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(df)} return(json.dumps(outputjson)) """ class lstmencdec_mviuvo( deployer): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' outputFeatrues = params['profiler']['output_features'] self.targetColIndx = outputFeatrues.index(params['features']['target_feat']) selectedColDict = params['selector']['output_features'] self.selectedCols = list() for col in selectedColDict: self.selectedCols.append(col) def profiler_code(self): importer = importModule() return _profiler_code( self.params, importer) def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') self.importer.addModule(module='Path',mod_from='pathlib') code = f""" class trainer(): """ init_code, run_code = self._get_train_code() return code + init_code + run_code def _get_train_code(self): self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = load_model(model_file) """ run_code = f""" def run(self, df): targetColIndx = {self.targetColIndx} lag_order={self.params['training']['lag_order']} xt = df.values scaler_file = (Path(__file__).parent/"model")/"{self.params['training']['scaler_file']}" if not scaler_file.exists(): raise ValueError(f'Scaling file not found: {{scaler_file}}') loaded_scaler_model = joblib.load(scaler_file) xt = xt.astype('float32') xt = loaded_scaler_model.transform(xt) noOfPredictions = 10 pred_data = xt y_future = [] pdata = pred_data[-lag_order:] pdata = pdata.reshape((1,lag_order,{len(self.params['selector']['output_features'])})) pred = self.model.predict(pdata) pred_1d = pred.ravel() pdata_2d = pdata.ravel().reshape(len(pdata) * lag_order, {len(self.params['selector']['output_features'])}) pdata_2d[:,targetColIndx] = pred_1d pred_2d_inv = loaded_scaler_model.inverse_transform(pdata_2d) predout = pred_2d_inv[:, targetColIndx] predout = predout.reshape(len(pred_1d),1) pred = pd.DataFrame(index=range(0,len(predout)),columns=['{self.params['features']['target_feat']}']) for i in range(0, len(predout)): pred.iloc[i] = predout[i] return pred """ return init_code, run_code def feature_engg_code(self): self.importer.addModule(module='pandas',mod_as='pd') return f""" class selector(): def __init__(self): pass def run(self, df): return df[{self.selectedCols}] """ def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__( self): pass def run(self,raw_df,df): df = df.round(2) df = df.to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(df)} return(json.dumps(outputjson)) """ class mlp( lstm): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') self.importer.addModule(module='Path',mod_from='pathlib') code = f""" class trainer(): """ init_code, run_code = self._get_train_code() return code + init_code + run_code def _get_train_code(self): self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models') init_code = f""" def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = load_model(model_file)""" run_code = f""" def run(self, df): lag_order={self.params['training']['lag_order']} xt = df.values scaler_file = (Path(__file__).parent/"model")/"{self.params['training']['scaler_file']}" if not scaler_file.exists(): raise ValueError(f'Scaling file not found: {{scaler_file}}') loaded_scaler_model = joblib.load(scaler_file) xt = xt.astype('float32') xt = loaded_scaler_model.transform(xt) noOfPredictions = 10 pred_data = xt y_future = [] for i in range(noOfPredictions): """ if len(self.params['selector']['output_features']) == 1: run_code += f""" pred_data = pred_data[-lag_order:] pred_data = pred_data.reshape((1,lag_order)) pred = self.model.predict(pred_data) predoutput = loaded_scaler_model.inverse_transform(pred) y_future.append(predoutput.flatten()[-1]) pred_data = np.append(pred_data,pred) pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']}) for i in range(0, len(y_future)): pred.iloc[i] = y_future[i] return pred """ else: run_code += f""" pdata = pred_data[-lag_order:] pdata = pdata.reshape((1,lag_order,{len(self.params['selector']['output_features'])})) pred = self.model.predict(pdata) predoutput = loaded_scaler_model.inverse_transform(pred) y_future.append(predoutput) pred_data = np.append(pred_data,pred,axis=0) pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']}) for i in range(0, len(y_future)): pred.iloc[i] = y_future[i] return pred """ return init_code, run_code class var( deployer): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' def profiler_code(self): importer = importModule() code = _profiler_code( self.params, importer) return code def feature_engg_code(self): self.importer.addModule(module='pandas',mod_as='pd') return f""" class selector(): def __init__(self): pass def run(self, df): return df[{self.params['selector']['output_features']}] """ def training_code( self): self.importer.addModule(module='joblib') return f""" class trainer(): def __init__( self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = joblib.load(model_file) def run(self,df): lag_order = self.model.k_ar return self.model.forecast(df.values[-lag_order:],steps={self.params['training']['no_of_prediction']}) """ def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return f""" class output_format(): def __init__( self): pass def invertTransformation(self,predictions): datasetdf = pd.read_csv((Path(__file__).parent/"data")/"trainingdata.csv") dictDiffCount = {self.params['training']['dictDiffCount']} target_features = "{self.params['features']['target_feat']}" columns = target_features.split(',') pred = pd.DataFrame(index=range(0,len(predictions)),columns=columns) for j in range(0,len(columns)): for i in range(0, len(predictions)): pred.iloc[i][j] = round(predictions[i][j],2) prediction = pred for col in columns: if col in dictDiffCount: if dictDiffCount[col]==2: prediction[col] = (datasetdf[col].iloc[-1]-datasetdf[col].iloc[-2]) + prediction[col].cumsum() prediction[col] = datasetdf[col].iloc[-1] + prediction[col].cumsum() prediction = pred return(prediction) def run(self,raw_df,df): df = self.invertTransformation(df) df = df.to_json(orient='records',double_precision=2) outputjson = {{"status":"SUCCESS","data":json.loads(df)}} return(json.dumps(outputjson)) """ class fbprophet( deployer): def __init__(self, params={}): super().__init__( params) self.name = 'timeseriesforecasting' def profiler_code( self): imported_modules = [ {'module': 'numpy', 'mod_from': None, 'mod_as': 'np'}, {'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}, ] importer = importModule() utility.import_modules(importer, imported_modules) code = """ class inputprofiler(): def __init__(self): pass def run( self,df): df = df.replace(r'^\s*$', np.NaN, regex=True) return df[['noofforecasts']] """ return importer.getCode() + code def feature_engg_code(self): self.importer.addModule(module='pandas',mod_as='pd') return f""" class selector(): def __init__(self): pass def run(self, df): return df """ def training_code( self): self.importer.addModule(module='pandas',mod_as='pd') self.importer.addModule(module='Path',mod_from='pathlib') self.importer.addModule(module='joblib') code = f""" class trainer(): def __init__(self): model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}" if not model_file.exists(): raise ValueError(f'Trained model file not found: {{model_file}}') self.model = joblib.load(model_file) """ code += f""" def run(self,df): sessonal_freq = '{self.params['training']['sessonal_freq']}' ts_prophet_future = self.model.make_future_dataframe(periods=int(df["noofforecasts"][0]),freq=sessonal_freq,include_history = False) """ if (self.params['training']['additional_regressors']): code += f""" additional_regressors={self.params['training']['additional_regressors']} ts_prophet_future[additional_regressors] = dataFrame[additional_regressors] ts_prophet_future.reset_index(drop=True) ts_prophet_future=ts_prophet_future.dropna() """ code += """ train_forecast = self.model.predict(ts_prophet_future) prophet_forecast_tail=train_forecast[[\'ds\', \'yhat\', \'yhat_lower\',\'yhat_upper\']].tail( int(df["noofforecasts"][0])) return(prophet_forecast_tail)""" return code def formatter_code(self): self.importer.addModule('json') self.importer.addModule('pandas', mod_as='pd') return """ class output_format(): def __init__( self): pass def run(self,raw_df,df): df = df.to_json(orient='records') outputjson = {"status":"SUCCESS","data":json.loads(df)} return(json.dumps(outputjson)) """ import paramiko from pathlib import Path import logging import json import os import sys import pandas as pd import time import timeit import re running_state_code = 16 stopped_state_code = 80 #prompt_command = '/home/aion/AION/llm/sbin/run_inference.sh' log_file_path = '/home/aion/data/log/fine_tuning_log.log' def read_file_from_server(ip,username,password,pem_file,remote_file_name,localfilepath): host = ip client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) if pem_file != '': client.connect(host, username=username, key_filename=pem_file) else: client.connect(host, username=username, password=password) sftp = client.open_sftp() sftp.get(remote_file_name,localfilepath) sftp.close() client.close() def read_log_file(ip,username,password,pem_file): host = ip client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) if pem_file != '': client.connect(host, username=username, key_filename=pem_file) else: client.connect(host, username=username, password=password) #log_file_path = '/home/ubuntu/AION/data/log/fine_tuning_log.log' stdin, stdout, stderr = client.exec_command(f'tail -n 500 {log_file_path}') time.sleep(5) client.close() return stdout.read().decode() def run_ssh_cmd(ip,pem_file,username,password,log,command): try: buf = '' host = ip client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) startTime = timeit.default_timer() while True: try: if pem_file != '': client.connect(host, username=username, key_filename=pem_file) else: client.connect(host, username=username, password=password) break except Exception as e: print(e) dataLoadTime = timeit.default_timer() - startTime if dataLoadTime >= 600: break time.sleep(10) stdin, stdout, stderr =client.exec_command(command) for line in stdout: if log != '': log.info(line) else: # if buf != '': # buf= buf+'\n' buf = buf+line client.close() return buf except Exception as e: print(str(e)) raise Exception(str(e)) def copy_files_to_server(ip, pem_file,local_data_file_path,local_config_file_path,username,password,remote_data_dir,remote_config_dir): try: host = ip client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) startTime = timeit.default_timer() while True: try: if pem_file != '': client.connect(host, username=username, key_filename=pem_file) elif password != '': client.connect(host, username=username, password=password) sftp = client.open_sftp() break except Exception as e: print(e) time.sleep(10) dataLoadTime = timeit.default_timer() - startTime if dataLoadTime >= 600: break try: sftp.stat(remote_data_dir) print(f"Path: '{remote_data_dir}' already exist") except FileNotFoundError: sftp.mkdir(remote_data_dir) try: sftp.stat(remote_config_dir) print(f"Path: '{remote_config_dir}' already exist") except FileNotFoundError: sftp.mkdir(remote_config_dir) Train_Data_InRemoteArtifacts = sftp.listdir(remote_data_dir) for traindata_files in Train_Data_InRemoteArtifacts: if not traindata_files.endswith('.ipynb_checkpoints'): sftp.remove(remote_data_dir +'/'+ traindata_files) if os.path.isdir(local_data_file_path): list_pdf_json_files = os.listdir(local_data_file_path) for file_pdf_json in list_pdf_json_files: sftp.put(local_data_file_path+'/'+file_pdf_json, remote_data_dir+'/'+ file_pdf_json) print(file_pdf_json + " data copied successfully") else: filename = os.path.basename(local_data_file_path) directoryname= os.path.dirname(local_data_file_path) sftp.put(directoryname+'/'+filename, remote_data_dir+'/'+ filename) print(filename + " data copied successfully") if local_config_file_path != '': config_InRemoteArtifacts = sftp.listdir(remote_config_dir) for config_file in config_InRemoteArtifacts: print(config_file) if not config_file.endswith('.ipynb_checkpoints'): sftp.remove(remote_config_dir +'/'+ config_file) if os.path.isdir(local_config_file_path): list_pdf_json_files = os.listdir(local_config_file_path) for file_pdf_json in list_pdf_json_files: sftp.put(local_config_file_path+'/'+file_pdf_json, remote_config_dir+'/'+ file_pdf_json) print(file_pdf_json + " config copied successfully") else: # updated_config_file_path = create_config(local_config_file_path) filename = os.path.basename(local_config_file_path) directoryname= os.path.dirname(local_config_file_path) sftp.put(directoryname+'/'+filename, remote_config_dir+'/'+ 'config.json') print(filename + " config copied successfully") remote_files = sftp.listdir(remote_config_dir) print("List of uploaded files",remote_files) sftp.close() client.close() except Exception as e: print(e) import json import ast import sys import time from pathlib import Path import pandas as pd from AION.llm import llm_utils bench_mark_file = {'code':'code_eval.sh', 'doc': 'doc_eval.sh'} DB_TABLE = 'llm_benchmarking' def bench_mark(hypervisor,instanceid,model,usecaseid,eval='code'): output = {} started = False if eval not in bench_mark_file.keys(): raise ValueError(f"Evaluation for '{eval}' is not supported.\nSupported types are {list(bench_mark_file.keys())}") db = benchmark_db( DB_TABLE, usecaseid) db.update_state('running') try: server = llm_utils.hypervisor( hypervisor,instanceid) if not server.is_already_running(): started, msg = server.start() if not started: raise ValueError( msg) ssh = server.ssh_details() pem_file = str(Path(__file__).parent/ssh['keyFilePath']) sh_file = llm_utils.remote_code_dir(as_str=True) + '/' + bench_mark_file[eval] cmd = sh_file + ' ' + usecaseid + ' '+ str(model) print(cmd) from llm.ssh_command import run_ssh_cmd buf = run_ssh_cmd(server.ip, pem_file, ssh['userName'], '', '', cmd) if isinstance( buf, str): print( buf) else: print( buf.decode('utf-8')) if buf: if 'eval_output:' in buf: output = buf.split('eval_output:')[-1].rstrip() output = ast.literal_eval( output) record = {} record['state'] = 'Success' record['eval_type'] = eval record['result_type'] = 'value' if eval =='code' else 'dict' record['result'] = output db.put_record( record) else: record = {} record['state'] = 'Error' record['eval_type'] = eval db.put_record( record) return output except Exception as e: print(e) record = {} record['state'] = 'Error' record['eval_type'] = eval record['result_type'] = 'value' if eval =='code' else 'dict' record['result'] = [{'error': str(e)}] db.put_record( record) output = {'status':'Error','msg':str(e)} return output class benchmark_db(): def __init__(self, table_name, usecaseid): from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = str(Path(DATA_DIR)/'sqlite') self.sqlite_obj = sqlite_db(file_path,'config.db') self.table_name = table_name self.usecaseid = usecaseid self.columns = ['usecaseid','state','eval_type','result_type','result'] self.sqlite_obj.create_table(self.table_name, self.columns, ['TEXT' for x in self.columns]) def put_record(self, record={}): db_data = self.sqlite_obj.get_data(self.table_name,'usecaseid',self.usecaseid) if (len(db_data) > 0): self.sqlite_obj.update_dict_data(record,f'"usecaseid"="{self.usecaseid}"',self.table_name) else: data = {x:[str(record[x])] if x in record.keys() else [''] for x in self.columns} data['usecaseid'] = self.usecaseid self.sqlite_obj.write_data(pd.DataFrame.from_dict(data),self.table_name) def update_state(self, state, error=None): data = {x:'' for x in self.columns} data['state'] = state data['usecaseid'] = self.usecaseid if error: data['result'] = error self.put_record( data) def read_data(self): return self.sqlite_obj.read_data(self.table_name) if __name__ == '__main__': run_code_benchmarking = False if run_code_benchmarking: #for code bench_mark('aws','i-0c7bfeddd00658f45','CodeLLaMA-2-7B','AI0025_1',eval='code') else: # for document bench_mark('aws','i-0c7bfeddd00658f45','LLaMA-2-7B','AI0041_1',eval='doc') from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = str(Path(DATA_DIR)/'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') data = sqlite_obj.read_data('llm_benchmarking') print(data) import json import os import time from pathlib import Path BASE_DIR = '/home/aion' DATA_DIR = '/home/aion/data' CONFIG_DIR = '/home/aion/data/config' PROMPT_DATA_DIR = '/home/aion/data/prompt_data' CODE_DIR = '/home/aion/llm/sbin' def remote_base_dir(as_str=False): if as_str: return BASE_DIR return Path( BASE_DIR) def remote_data_dir(as_str=False): if as_str: return DATA_DIR return Path( DATA_DIR) def remote_config_dir(as_str=False): if as_str: return CONFIG_DIR return Path( CONFIG_DIR) def remote_code_dir(as_str=False): if as_str: return CODE_DIR return Path( CODE_DIR) def remote_prompt_data_dir(as_str=False): if as_str: return DATA_DIR return Path( DATA_DIR) def get_ami_details(config,selectedAMI): y = {} for x in config: if x['id'] == selectedAMI: return x return y def get_ip(cloudconfig,instanceid,hypervisor,region,image): from AION.appbe.compute import readComputeConfig cloud_infra = readComputeConfig() if hypervisor == 'AWS': aws_access_key_id = cloud_infra['awsCredentials']['accessKey'] aws_secret_key = cloud_infra['awsCredentials']['secretAccessKey'] if image != '' and image != 'NA': amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['amis'], image) else: amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], instanceid) if region == '' or region == 'NA': region = amiDetails['regionName'] from llm.aws_instance_api import get_instance_ip return get_instance_ip(aws_access_key_id, aws_secret_key, instanceid,region) elif hypervisor == 'GCP': credentialsJson = cloud_infra['gcpCredentials']['gcpCredentials'] amiDetails = getAMIDetails(cloud_infra['GCP']['instances'], instanceid) zone = amiDetails['regionName'] projectID = cloud_infra['gcpCredentials']['projectID'] from llm.gcp_compute_api import check_instance status,ip = check_instance(credentialsJson,projectID, zone, instanceid) return ip else: raise ValueError(f"Given hypervisor '{hypervisor}' is not supported") def hypervisor( hypervisor,instanceid='', image=''): if not hypervisor: raise ValueError('No hypervisor provided') if hypervisor.lower() == 'aws': return aws_server(instanceid, image) elif hypervisor.lower() == 'gcp': return gcp_server(instanceid, image) else: raise ValueError(f"Hyperviser '{hypervisor}' is not supported") class gcp_server(): def __init__( self, instanceid='', image=''): self.hypervisor_name = 'gcp' from AION.appbe.compute import readComputeConfig self.cloud_infra = readComputeConfig() if image and image.lower() != 'na': self.ami_details = get_ami_details(self.cloud_infra['GCP']['machineImage'], image) self.instanceid = '' elif instanceid and instanceid.lower() != 'na': self.ami_details = get_ami_details( self.cloud_infra['GCP']['instances'], instanceid) self.instanceid = instanceid else: raise ValueError("Either provide 'image name' or 'instance id'") self.credentialsJson = self.cloud_infra['gcpCredentials']['gcpCredentials'] self.projectID = self.cloud_infra['gcpCredentials']['projectID'] self.zone = self.ami_details['regionName'] self.stopped = False self.ip = '' self.created = False def is_machine_running(self): from llm.gcp_compute_api import check_instance status,self.ip = check_instance(self.credentialsJson,self.projectID,self.zone,self.instanceid) return 'running' == status.lower() def start(self): from AION.llm.gcp_compute_api import is_running from AION.llm.gcp_compute_api import check_instance from AION.llm.gcp_compute_api import start_instance status = is_running(self.credentialsJson, self.projectID, self.zone, self.instanceid).lower() if 'running' == status: stratus, self.ip = check_instance(self.credentialsJson, self.projectID, self.zone, self.instanceid) self.already_running = True return True else: status, self.ip = start_instance(self.credentialsJson, self.projectID, self.zone, self.instanceid) return status == 'Success' def create(self,key_name = None): from AION.llm.gcp_compute_api import create_instance machineImageName = self.ami_details['id'] machineImageProjectID = self.ami_details['machineImageProjectID'] self.ip,msg = create_instance(self.credentialsJson,self.projectID,self.zone,key_name,machineImageName,machineImageProjectID) if self.ip != '': self.created = True return self.ip,msg def stop(self): if self.already_running or self.stopped: return True from AION.llm.gcp_compute_api import stop_server_instance status = stop_server_instance(self.credentialsJson,self.projectID, self.zone, self.instanceid) if status == 'Process Completed': self.stopped = True return True return False def showndown(self): if self.created: self.terminate() else: if self.already_running or self.stopped: return True from AION.llm.gcp_compute_api import stop_server_instance status = stop_server_instance(self.credentialsJson,self.projectID, self.zone, self.instanceid) if status == 'Process Completed': self.stopped = True return True return False def terminate(self): from AION.llm.gcp_compute_api import terminate_instance msg, status = terminate_instance(self.projectID, self.zone, self.instanceid) print(msg) return status == 'success' def ssh_details(self): return self.ami_details['ssh'] class aws_server(): def __init__(self, instanceid='', image='', boot_up_time=0): self.hypervisor_name = 'aws' from AION.appbe.compute import readComputeConfig self.cloud_infra = readComputeConfig() if image and image.lower() != 'na': self.ami_details = get_ami_details(self.cloud_infra['AWS_EC2']['amis'], image) self.instanceid = '' self.machine_type = 'ami' elif instanceid and instanceid.lower() != 'na': self.ami_details = get_ami_details( self.cloud_infra['AWS_EC2']['instances'], instanceid) self.instanceid = instanceid self.machine_type = 'instance' else: raise ValueError("Either provide 'image name' or 'instance id'") self.access_key = self.cloud_infra['awsCredentials']['accessKey'] self.secret_key = self.cloud_infra['awsCredentials']['secretAccessKey'] self.securitygroupid = self.cloud_infra['awsCredentials']['securitygroupid'] self.region = self.ami_details['regionName'] self.already_running = False self.boot_up_time = boot_up_time self.stopped = False self.created = False def is_already_running(self): return self.already_running def is_machine_running(self): from AION.llm.aws_instance_api import is_running status = is_running(self.instanceid, self.region, self.access_key, self.secret_key).lower() return 'running' == status.lower() def start(self): from AION.llm.aws_instance_api import is_running from AION.llm.aws_instance_api import get_instance_ip from AION.llm.aws_instance_api import start_instance status = is_running(self.instanceid, self.region, self.access_key, self.secret_key).lower() if 'running' == status: self.ip = get_instance_ip(self.access_key, self.secret_key, self.instanceid, self.region) self.already_running = True return True, 'already running' else: status, msg, self.ip = start_instance(self.access_key, self.secret_key, self.instanceid, self.region) return status == 'Success', msg def create(self, key_name=None): from AION.llm.aws_instance_api import create_instance image_id = self.ami_details['id'] security_group_ids = self.ami_details['instanceSetting']['securityGroupIds'] if not key_name: key_name = self.ami_details['instanceSetting']['keyName'] instance_type = self.ami_details['instanceSetting']['instanceType'] self.instanceid,msg = create_instance(image_id, instance_type, self.securitygroupid, self.region, key_name, self.access_key, self.secret_key) if self.instanceid != '': self.created = True return self.instanceid,msg def showndown(self): from AION.llm.aws_instance_api import stop_server_instance if self.created: return self.terminate() else: if self.already_running or self.stopped: return True status = stop_server_instance(self.access_key, self.secret_key, self.instanceid, self.region) if status == 'Process Completed': self.stopped = True return True return False def stop(self): from AION.llm.aws_instance_api import stop_server_instance if self.already_running or self.stopped: return True status = stop_server_instance(self.access_key, self.secret_key, self.instanceid, self.region) if status == 'Process Completed': self.stopped = True return True return False def terminate(self): from AION.llm.aws_instance_api import terminate_instance msg, status = terminate_instance(self.instanceid, self.region, self.access_key, self.secret_key) print(msg) return status == 'success' def ssh_details(self): return self.ami_details['ssh'] class CachePrompt: tableName = 'cachePrompts' def __init__(self, conn): self.conn = conn def selectFromCache(self,prompt ,usecaseId ,modelType,temperature=None ,max_token=None): searchFromLLMFlag = False try: query = f'''SELECT * FROM {CachePrompt.tableName} WHERE usecaseId= "{usecaseId}" AND prompt = "{prompt}" AND modelType = "{modelType}"''' if temperature: query += f''' AND temperature= "{temperature}"''' if max_token: query += f''' AND max_token= "{max_token}"''' cursor = self.conn.execute(query) results = [x for x in cursor.fetchone()] column_names = list(map(lambda x:x[0],cursor.description)) response = results[column_names.index('response')] return searchFromLLMFlag,response except Exception as e: print(e) searchFromLLMFlag = True return searchFromLLMFlag,'' def deleteRecord(self ,usecaseId,max_records=5): q_exitingRecords = f'''SELECT count(*) FROM {CachePrompt.tableName} WHERE usecaseId= "{usecaseId}" ''' cursor = self.conn.execute(q_exitingRecords) numberOfRecords = cursor.fetchone()[0] if numberOfRecords >= max_records: idForDeletion = f'SELECT * FROM {CachePrompt.tableName} WHERE usecaseId= "{usecaseId}" ORDER BY created_at ASC LIMIT 1;' cursor = self.conn.execute(idForDeletion) id2del =[x[0] for x in cursor][0] sql_delete_query = f"""DELETE from {CachePrompt.tableName} WHERE id = {id2del};""" self.conn.execute(sql_delete_query) self.conn.commit() def insertRecord(self,prompt,response,usecaseId ,modelType,temperature=None ,max_token=None, max_records=5): self.conn.execute('''CREATE TABLE IF NOT EXISTS cachePrompts (ID INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, prompt TEXT NOT NULL, context TEXT , usecaseId text NOT NULL, modelType text NOT NULL, temperature float NOT NULL, max_token INT, response TEXT NOT NULL, created_at TEXT DEFAULT CURRENT_TIMESTAMP );''') cur = self.conn.execute(f"select * from {CachePrompt.tableName}").fetchall() print(cur) self.deleteRecord(usecaseId,max_records=5) self.conn.execute(f"INSERT INTO {CachePrompt.tableName} (prompt, usecaseId,modelType,temperature,max_token,response) \ VALUES (?, ?, ?,?, ?, ?)", (prompt, usecaseId,modelType,temperature, max_token, response)); self.conn.commit() import json import os import sys import pandas as pd import time from stat import S_ISDIR, S_ISREG import paramiko from pathlib import Path import logging import boto3 from botocore.exceptions import ClientError import re remote_data_dir = '/home/ubuntu/AION/data/storage' remote_config_dir = '/home/ubuntu/AION/data/config' running_state_code = 16 stopped_state_code = 80 sh_file_path = '/home/ubuntu/AION/llm/sbin/run_experiment.sh ' prompt_command = '/home/ubuntu/AION/llm/sbin/run_inference.sh' def create_instance(image_id, instance_type, security_group_id,region,instance_name,aws_access_key_id,aws_secret_key): try: ec2 = boto3.client('ec2', aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_key, region_name=region) response = ec2.run_instances(ImageId=image_id, InstanceType=instance_type, SecurityGroupIds=[security_group_id], MaxCount=1, MinCount=1, TagSpecifications=[{'ResourceType': 'instance', 'Tags': [{'Key': 'Name', 'Value': instance_name}]}]) #print('Instance ID:', response['Instances'][0]['InstanceId']) return response['Instances'][0]['InstanceId'],'' except Exception as e: print(e) return '',str(e) def check_instance(aws_access_key_id, aws_secret_key, instance_id,region): ip = '' ec2 = boto3.client('ec2', region_name=region, aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_key) response = ec2.describe_instance_status(InstanceIds=[instance_id],IncludeAllInstances=True) if response['InstanceStatuses'][0]['InstanceState']['Name'] == 'running': ip = response['Reservations'][0]['Instances'][0]['PublicIpAddress'] return 'Running',ip else: return 'NotRunning',ip def get_instance_ip(aws_access_key_id, aws_secret_key, instance_id,region): try: ec2 = boto3.client('ec2', region_name=region, aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_key) response = ec2.describe_instances(InstanceIds=[instance_id]) ip = response['Reservations'][0]['Instances'][0]['PublicIpAddress'] return ip except Exception as e: print(e) return '' def start_instance(aws_access_key_id, aws_secret_key, instance_id,region): ip = '' ec2 = boto3.client('ec2', region_name=region, aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_key) response = ec2.describe_instance_status(InstanceIds=[instance_id],IncludeAllInstances=True) if response['InstanceStatuses'][0]['InstanceState']['Name'] == 'running': print("Instance is already running") try: response = ec2.start_instances(InstanceIds=[instance_id], DryRun=False) #print(response) instance_status_code = 0 while instance_status_code != running_state_code: response = ec2.describe_instances(InstanceIds=[instance_id]) instance_status_code = response['Reservations'][0]['Instances'][0]['State']['Code'] if instance_status_code == running_state_code: ip = response['Reservations'][0]['Instances'][0]['PublicIpAddress'] break except Exception as e: print(e) return 'Fail',str(e),'' return 'Success','Success',ip def is_running(instance_id,region,aws_access_key_id,aws_secret_key): try: ec2 = boto3.client('ec2', region_name=region, aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_key) response = ec2.describe_instance_status(InstanceIds=[instance_id], IncludeAllInstances=True) if 'Reservations' in response and len(response['Reservations']) > 0: state = response['Reservations'][0]['Instances'][0]['State']['Name'] return state elif 'InstanceStatuses' in response: return response['InstanceStatuses'][0]['InstanceState']['Name'] else : return 'failed' except: return "error" def terminate_instance(instance_id,region,aws_access_key_id,aws_secret_key): try: ec2 = boto3.client('ec2', aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_key, region_name=region) response = ec2.terminate_instances(InstanceIds=[instance_id]) return response['TerminatingInstances'][0]['InstanceId'],'success' except Exception as e: print(e),'failed' def copy_files_to_server(ip, pem_file,local_data_file_path,local_config_file_path,username): try: host = ip client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) while True: try: client.connect(host, username=username, key_filename=pem_file) sftp = client.open_sftp() break except: time.sleep(10) try: sftp.stat(remote_data_dir) print(f"Path: '{remote_data_dir}' already exist") except FileNotFoundError: sftp.mkdir(remote_data_dir) try: sftp.stat(remote_config_dir) print(f"Path: '{remote_config_dir}' already exist") except FileNotFoundError: sftp.mkdir(remote_config_dir) Train_Data_InRemoteArtifacts = sftp.listdir(remote_data_dir) for traindata_files in Train_Data_InRemoteArtifacts: print(traindata_files) if not traindata_files.endswith('.ipynb_checkpoints'): sftp.remove(remote_data_dir +'/'+ traindata_files) if os.path.isdir(local_data_file_path): list_pdf_json_files = os.listdir(local_data_file_path) for file_pdf_json in list_pdf_json_files: sftp.put(local_data_file_path+'/'+file_pdf_json, remote_data_dir+'/'+ file_pdf_json) print(file_pdf_json + " data copied successfully") else: filename = os.path.basename(local_data_file_path) directoryname= os.path.dirname(local_data_file_path) sftp.put(directoryname+'/'+filename, remote_data_dir+'/'+ filename) print(filename + " data copied successfully") config_InRemoteArtifacts = sftp.listdir(remote_config_dir) for config_file in config_InRemoteArtifacts: print(config_file) if not config_file.endswith('.ipynb_checkpoints'): sftp.remove(remote_config_dir +'/'+ config_file) if local_config_file_path != '': if os.path.isdir(local_config_file_path): list_pdf_json_files = os.listdir(local_config_file_path) for file_pdf_json in list_pdf_json_files: sftp.put(local_config_file_path+'/'+file_pdf_json, remote_config_dir+'/'+ file_pdf_json) print(file_pdf_json + " config copied successfully") else: # updated_config_file_path = create_config(local_config_file_path) filename = os.path.basename(local_config_file_path) directoryname= os.path.dirname(local_config_file_path) sftp.put(directoryname+'/'+filename, remote_config_dir+'/'+ 'config.json') print(filename + " config copied successfully") remote_files = sftp.listdir(remote_config_dir) print("List of uploaded files",remote_files) sftp.close() client.close() except Exception as e: print(e) def check_status(ip,username,pem_file): logoutput = read_log_file(ip,username,pem_file) if "aion_llmfinetuning_Status" in logoutput: return True else: return False def read_log_file(ip,username,pem_file): host = ip client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) client.connect(host, username=username, key_filename=pem_file) log_file_path = '/home/ubuntu/AION/data/log/fine_tuning_log.log' stdin, stdout, stderr = client.exec_command(f'tail -n 500 {log_file_path}') time.sleep(5) client.close() return stdout.read().decode() def run_ssh_cmd(ip,pem_file,username,log,command): try: buf = '' host = ip client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) while True: try: client.connect(host, username=username, key_filename=pem_file) break except: time.sleep(10) stdin, stdout, stderr =client.exec_command(command) for line in stdout: if log != '': log.info(line.strip()) else: if buf != '': buf= buf+'\n' buf = buf+line.strip() print(buf) client.close() return buf except Exception as e: print(str(e)) raise Exception(str(e)) def start_logging(deployFolder,modelName,version): try: deployLocation = Path(deployFolder)/modelName/str(version)/'log' deployLocation.mkdir(parents=True, exist_ok=True) name = 'model_training_logs.log' filehandler = logging.FileHandler(deployLocation/name, 'w','utf-8') log = logging.getLogger('log_llm') log.propagate = False for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): log.removeHandler(hdlr) log.addHandler(filehandler) log.setLevel(logging.INFO) return log except Exception as e: print(str(e)) def update_sqllite_data(usecaseid,variable,variable_value): try: print(usecaseid) from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') if sqlite_obj.table_exists("LLMTuning"): data = sqlite_obj.get_data('LLMTuning','usecaseid',usecaseid) if (len(data) > 0): sqlite_obj.update_data('"'+variable+'"="'+variable_value+'"','"usecaseid"="'+str(usecaseid)+'"','LLMTuning') return('Success') data = dict(usecaseid=usecaseid,ip='',instance='',hypervisor='AWS',status='NA') data.update({variable:variable_value}) df = pd.DataFrame(data, index=[0]) sqlite_obj.write_data(df,'LLMTuning') return('Success') except Exception as e: print(e) return('Error') def LLM_predict(cloudconfig,instanceid,promptfile): with open(cloudconfig, 'r') as config_f: cloud_infra = json.load(config_f) config_f.close() aws_access_key_id = cloud_infra['AWS_EC2']['AWSAccessKeyID'] aws_secret_key = cloud_infra['AWS_EC2']['AWSSecretAccessKey'] region = cloud_infra['AWS_EC2']['LLaMa7B']['RegionName'] ip = start_instance(aws_access_key_id, aws_secret_key, instanceid,region) currentDirectory = os.path.dirname(os.path.abspath(__file__)) pem_file = os.path.join(currentDirectory,cloud_infra['AWS_EC2']['LLaMa7B']['ssh']['keyFilePath']) username = cloud_infra['AWS_EC2']['LLaMa7B']['ssh']['userName'] copy_files_to_server(ip,pem_file,promptfile,'',username) promptfile = os.path.basename(promptfile) command = prompt_command+' '+remote_data_dir+'/'+ promptfile buf = run_ssh_cmd(ip, pem_file, username,'',command) return buf def LLM_tuning_lemma7b(config,cloudconfig): with open(config, 'r') as config_f: config_data = json.load(config_f) config_f.close() modelid = config_data['basic']['modelName']+'_'+config_data['basic']['modelVersion'] log = start_logging(config_data['basic']['deployLocation'],config_data['basic']['modelName'],config_data['basic']['modelVersion']) with open(cloudconfig, 'r') as config_f: cloud_infra = json.load(config_f) config_f.close() currentDirectory = os.path.dirname(os.path.abspath(__file__)) aws_access_key_id = cloud_infra['AWS_EC2']['AWSAccessKeyID'] aws_secret_key = cloud_infra['AWS_EC2']['AWSSecretAccessKey'] instance_type = cloud_infra['AWS_EC2']['LLaMa7B']['InstanceSetting']['InstanceType'] security_group_id = cloud_infra['AWS_EC2']['LLaMa7B']['InstanceSetting']['SecurityGroupId'] region = cloud_infra['AWS_EC2']['LLaMa7B']['RegionName'] image_id = cloud_infra['AWS_EC2']['LLaMa7B']['amiId'] pem_file = os.path.join(currentDirectory,cloud_infra['AWS_EC2']['LLaMa7B']['ssh']['keyFilePath']) username = cloud_infra['AWS_EC2']['LLaMa7B']['ssh']['userName'] datafile = config_data['basic']['dataLocation'] instance_name = config_data['basic']['modelName']+'_'+str(config_data['basic']['modelVersion'])+'_LLMTuning' configfile = config ip = cloud_infra['AWS_EC2']['LLaMa7B']['InstanceIP'] if image_id != '': log.info("Status:-|... Create Instance") instance_id = create_instance(image_id, instance_type, security_group_id,region,instance_name) elif cloud_infra['AWS_EC2']['LLaMa7B']['InstanceId'] != '': instance_id = cloud_infra['AWS_EC2']['LLaMa7B']['InstanceId'] update_sqllite_data(modelid,'instance',instance_id) print(instance_id) else: instance_id = '' ip = cloud_infra['AWS_EC2']['LLaMa7B']['InstanceIP'] if instance_id != '': log.info("Status:-|... Start Instance") update_sqllite_data(modelid,'status','Initialize') ip = start_instance(aws_access_key_id, aws_secret_key, instance_id,region) print(ip) if ip != '': update_sqllite_data(modelid,'ip',ip) try: log.info("Status:-|... Copy Files") copy_files_to_server(ip,pem_file,datafile,configfile,username) log.info("Status:-|... Start LLM Tuning") update_sqllite_data(modelid,'status','Tuning') run_ssh_cmd(ip, pem_file, username,log,sh_file_path) log_data = read_log_file(ip,username,pem_file) outputStr = re.search(r'aion_learner_status:(.*)', str(log_data), re.IGNORECASE).group(1) outputStr = outputStr.strip() outputStr = json.loads(outputStr) from llm.llm_tuning import save_output outputStr = save_output(config_data['basic']['deployLocation'],config_data['basic']['modelName'],config_data['basic']['modelVersion'],outputStr) print(outputStr) if "Tuning completed Successfully" in log_data: update_sqllite_data(modelid,'status','Success') output = json.dumps(outputStr) print(f"\naion_learner_status:{output}\n") return output else: update_sqllite_data(modelid,'status','Error') output = json.dumps(outputStr) print(f"\naion_learner_status:{output}\n") return output except Exception as e: print(e) log.info(str(e)) output = {'status':'FAIL','message':str(e),'LogFile':''} output = json.dumps(output) print(f"\naion_learner_status:{output}\n") return output else: output = {'status':'FAIL','message':'Not Configured Properly','LogFile':''} output = json.dumps(output) print(f"\naion_learner_status:{output}\n") return output def stop_server_instance(aws_access_key_id, aws_secret_access_key, instance_id,region): ec2 = boto3.client('ec2', region_name=region, aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key) try: ec2.stop_instances(InstanceIds=[instance_id, ], DryRun=True) except ClientError as e: if 'DryRunOperation' not in str(e): raise # Dry run succeeded, call stop_instances without dryrun try: response = ec2.stop_instances(InstanceIds=[instance_id], DryRun=False) response = ec2.describe_instances(InstanceIds=[instance_id]) instance_status_code = 0 while instance_status_code != stopped_state_code: response = ec2.describe_instances(InstanceIds=[instance_id]) instance_status_code = response['Reservations'][0]['Instances'][0]['State']['Code'] if instance_status_code == stopped_state_code: print("Instance Stopped") break return "Process Completed" except ClientError as e: print(e) return "Process failed" if __name__ == "__main__": status,msg = LLM_tuning_lemma7b(sys.argv[1],sys.argv[2]) print(status, msg) def check_file_on_server(file_path, ip, pem_file): is_wait = True try: host = ip username = "ubuntu" client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) client.connect(host, username=username, key_filename=pem_file) sftp = client.open_sftp() sftp.stat(file_path) print("Model File created On Server") is_wait = False except IOError: is_wait = True print("Model training is in progress..") return is_wait def removeremotefolder_files(sftp, path): try: files = sftp.listdir(path=path) for f in files: filepath = path + "/" + f print(filepath) if isdir(sftp, filepath): removeremotefolder_files(sftp, filepath) else: sftp.remove(filepath) sftp.rmdir(path) except IOError as e: print(e) def isdir(sftp, path): try: return S_ISDIR(sftp.stat(path).st_mode) except IOError: return False def get_remote_folder(ip, remotedir, localdir, pem_file, preserve_mtime=False): host = ip username = "ubuntu" client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) client.connect(host, username=username, key_filename=pem_file) sftp = client.open_sftp() for entry in sftp.listdir(remotedir): remotepath = remotedir + "/" + entry localpath = os.path.join(localdir, entry) mode = sftp.stat(remotepath).st_mode if S_ISDIR(mode): try: os.mkdir(localpath, mode=777) except OSError: pass get_remote_folder(ip, remotepath, localpath, pem_file, preserve_mtime) elif S_ISREG(mode): sftp.get(remotepath, localpath) print("{} downloaded successfully".format(entry)) import json import os import sys import pandas as pd import time from stat import S_ISDIR, S_ISREG from pathlib import Path import logging import re import tarfile from llm import llm_utils #remote_data_dir = '/home/ubuntu/AION/data/storage' remote_data_rawdata_dir = '/home/aion/data/storage/raw_data' remote_data_processeddata_dir = '/home/aion/data/storage/processed_data' remote_config_dir = '/home/aion/data/config' sh_file_path = '/home/aion/llm/sbin/llm_model_finetuning.sh' unstructured_script_path = '/home/aion/llm/sbin/llm_model_finetuning.sh' def start_logging(deployFolder,modelName,version): try: deployLocation = Path(deployFolder)/modelName/str(version)/'log' deployLocation.mkdir(parents=True, exist_ok=True) name = 'model_training_logs.log' filehandler = logging.FileHandler(deployLocation/name, 'w','utf-8') log = logging.getLogger('log_llm') log.propagate = False for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): log.removeHandler(hdlr) log.addHandler(filehandler) log.setLevel(logging.INFO) return log except Exception as e: print(str(e)) def update_sqllite_data(usecaseid,variable,variable_value): try: from appbe.sqliteUtility import sqlite_db from appbe.dataPath import DATA_DIR file_path = os.path.join(DATA_DIR,'sqlite') sqlite_obj = sqlite_db(file_path,'config.db') if sqlite_obj.table_exists("LLMTuning"): column_names = sqlite_obj.column_names('LLMTuning') #print(column_names) if 'region' not in column_names: query = 'Alter Table LLMTuning ADD region TEXT' sqlite_obj.execute_query(query) if 'image' not in column_names: query = 'Alter Table LLMTuning ADD image TEXT' sqlite_obj.execute_query(query) data = sqlite_obj.get_data('LLMTuning','usecaseid',usecaseid) if (len(data) > 0): sqlite_obj.update_data('"'+variable+'"="'+variable_value+'"','"usecaseid"="'+str(usecaseid)+'"','LLMTuning') return('Success') data = dict(usecaseid=usecaseid,ip='',instance='',hypervisor='NA',status='NA',region='',image='') data.update({variable:variable_value}) df = pd.DataFrame(data, index=[0]) sqlite_obj.write_data(df,'LLMTuning') return('Success') except Exception as e: print(e) return('Error') def save_output(deployFolder,modelName,version,outputstr,hypervisor,instance): try: deployLocation = Path(deployFolder)/modelName/str(version)/'etc' deployLocation.mkdir(parents=True, exist_ok=True) name = 'output.json' dpath = Path(deployFolder)/modelName/str(version) outputstr['data']['deployLocation'] = str(dpath) outputstr['data']['vmDetails'] = str(hypervisor)+' Instance: '+str(instance) outputstr['data']['LogFile'] = str(dpath/'log'/'model_training_logs.log') with open(deployLocation/name, 'w',encoding='utf-8') as f: json.dump(outputstr, f) f.close() return (outputstr) except Exception as e: print(str(e)) print(outputstr) def llm_logs(config,cloudconfig,instanceid,hypervisor,mlmodels): try: with open(config, 'r') as config_f: config_data = json.load(config_f) config_f.close() modelid = config_data['basic']['modelName']+'_'+config_data['basic']['modelVersion'] from appbe.compute import readComputeConfig cloud_infra = readComputeConfig() if hypervisor == 'AWS': aws_access_key_id = cloud_infra['awsCredentials']['accessKey'] aws_secret_key = cloud_infra['awsCredentials']['secretAccessKey'] amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], instanceid) region = amiDetails['regionName'] from llm.aws_instance_api import check_instance status,ip = check_instance(aws_access_key_id, aws_secret_key, instanceid,region) if status.lower() == 'running': currentDirectory = os.path.dirname(os.path.abspath(__file__)) pem_file = os.path.join(currentDirectory,amiDetails['ssh']['keyFilePath']) username = amiDetails['ssh']['userName'] from llm.ssh_command import read_log_file logs = read_log_file(ip,username,'',pem_file) deployFolder = config_data['basic']['deployLocation'] modelName = config_data['basic']['modelName'] version = config_data['basic']['modelVersion'] deployLocation = Path(deployFolder)/modelName/str(version)/'log' deployLocation.mkdir(parents=True, exist_ok=True) name = 'model_training_logs.log' with open(deployLocation/name, 'r+',encoding='utf-8') as f: lines = [line.rstrip('\n') for line in f] for log in logs: if log not in lines: # inserts on top, elsewise use lines.append(name) to append at the end of the file. lines.insert(0, log) f.seek(0) # move to first position in the file, to overwrite ! f.write('\n'.join(lines)) else: status = {'status':'Error','msg':'Instance not running'} output = json.dumps(status) deployFolder = config_data['basic']['deployLocation'] modelName = config_data['basic']['modelName'] version = config_data['basic']['modelVersion'] deployLocation = Path(deployFolder)/modelName/str(version)/'log' deployLocation.mkdir(parents=True, exist_ok=True) name = 'model_training_logs.log' with open(deployLocation/name, 'r+',encoding='utf-8') as f: f.write('aion_learner_status:'+str(output)) f.close() else: credentialsJson = cloud_infra['gcpCredentials']['gcpCredentials'] projectID = cloud_infra['gcpCredentials']['projectID'] amiDetails = getAMIDetails(cloud_infra['GCP']['instances'], instanceid) zone = amiDetails['regionName'] username = username = amiDetails['ssh']['userName'] currentDirectory = os.path.dirname(os.path.abspath(__file__)) pem_file = os.path.join(currentDirectory,amiDetails['ssh']['keyFilePath']) password = '' from llm.gcp_compute_api import check_instance status,ip = check_instance(credentialsJson,projectID, zone, instanceid) if status.lower() == 'running': from llm.ssh_command import read_log_file logs = read_log_file(ip,username,'',pem_file) deployFolder = config_data['basic']['deployLocation'] modelName = config_data['basic']['modelName'] version = config_data['basic']['modelVersion'] deployLocation = Path(deployFolder)/modelName/str(version)/'log' deployLocation.mkdir(parents=True, exist_ok=True) name = 'model_training_logs.log' with open(deployLocation/name, 'r+',encoding='utf-8') as f: lines = [line.rstrip('\n') for line in f] for log in logs: if log not in lines: lines.insert(0, log) f.seek(0) # move to first position in the file, to overwrite ! f.write('\n'.join(lines)) else: status = {'status':'Error','msg':'Instance not running'} output = json.dumps(status) deployFolder = config_data['basic']['deployLocation'] modelName = config_data['basic']['modelName'] version = config_data['basic']['modelVersion'] deployLocation = Path(deployFolder)/modelName/str(version)/'log' deployLocation.mkdir(parents=True, exist_ok=True) name = 'model_training_logs.log' with open(deployLocation/name, 'r+',encoding='utf-8') as f: f.write('aion_learner_status:'+str(output)) f.close() except Exception as e: print(e) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def tardirectory(path,tarfilepath,ext): with tarfile.open(tarfilepath, 'w:tar') as tarhandle: for root, dirs, files in os.walk(path): for f in files: if ext == 'doc': if f.endswith('.' + 'doc') or f.endswith('.' + 'docx'): tarhandle.add(os.path.join(root, f), arcname=f) else: if f.endswith('.'+ext): tarhandle.add(os.path.join(root, f),arcname=f) tarhandle.close() def getAMIDetails(config,selectedAMI): y = {} for x in config: if x['id'] == selectedAMI: return x return y def run(config): with open(config, 'r') as config_f: config_data = json.load(config_f) config_f.close() modelid = config_data['basic']['modelName']+'_'+config_data['basic']['modelVersion'] log = start_logging(config_data['basic']['deployLocation'],config_data['basic']['modelName'],config_data['basic']['modelVersion']) from appbe.compute import readComputeConfig cloud_infra = readComputeConfig() currentDirectory = os.path.dirname(os.path.abspath(__file__)) filetimestamp = str(int(time.time())) instance_name = config_data['basic']['modelName']+'-'+str(config_data['basic']['modelVersion'])+'-LLM-'+filetimestamp instance_name = instance_name.lower() if config_data['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'True': if os.path.isdir(config_data['basic']['dataLocation']): from appbe.dataPath import DATA_FILE_PATH filetimestamp = str(int(time.time())) tarfilepath = os.path.join(DATA_FILE_PATH,filetimestamp+'.tar') tardirectory(config_data['basic']['dataLocation'],tarfilepath,config_data['basic']['folderSettings']['fileExtension']) config_data['basic']['dataLocationUnstructured'] = tarfilepath with open(config, "w") as outfile: json.dump(config_data, outfile) outfile.close() if cloud_infra['computeInfrastructure'] == 'GCP': log.info("Status:-|... Compute Infrastructure GCP GCE") credentialsJson = cloud_infra['gcpCredentials']['gcpCredentials'] #credentialsJson = "C:\AION\GCP-Instance-Utilityv2\GCP-Instance-Utility\ers-research.json" selectedID = cloud_infra['gcpCredentials']['selectedID'] projectID = cloud_infra['gcpCredentials']['projectID'] zone = cloud_infra['gcpCredentials']['regionName'] selectMachineType = cloud_infra['gcpCredentials']['machineType'] if selectMachineType.lower() == 'image': amiDetails = getAMIDetails(cloud_infra['GCP']['machineImage'],selectedID) machineImageName = amiDetails['id'] else: amiDetails = getAMIDetails(cloud_infra['GCP']['instances'], selectedID) zone = amiDetails['regionName'] machineImageName = '' instance_name = selectedID pem_file = os.path.join(currentDirectory,amiDetails['ssh']['keyFilePath']) username = amiDetails['ssh']['userName'] if machineImageName != '': log.info("Status:-|... Create Instance Start") try: server = llm_utils.gcp_server("",machineImageName) ip,msg = server.create(instance_name) log.info("Status:-|... Create Instance End") if ip == '': if "resources available" in msg: msg = "The respective zone (or region) does not have enough resources available to fulfill the request. Please try after some time." output_json = {"status": "FAIL", "message": str(msg), "LogFile": ''} output = json.dumps(output_json) log.info("Status:-|... Failed to create the instance. "+str(msg)) print(f"\naion_learner_status:{output}\n") return output except Exception as e: print(str(e)) output_json = {"status":"FAIL","message":'Failed to create the instance.',"LogFile":''} output = json.dumps(output_json) log.info("Status:-|... Failed to create the instance.") print(f"\naion_learner_status:{output}\n") return output else: server = llm_utils.gcp_server(instance_name, "") server.start() ip = server.ip if ip != '': time.sleep(20) if selectMachineType.lower() == 'machineimage': update_sqllite_data(modelid, 'image', machineImageName) update_sqllite_data(modelid,'hypervisor','GCP') update_sqllite_data(modelid, 'region', zone) update_sqllite_data(modelid,'ip',ip) update_sqllite_data(modelid,'instance',instance_name) from llm.ssh_command import copy_files_to_server if config_data['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'True': datafile = config_data['basic']['dataLocationUnstructured'] else: datafile = config_data['basic']['dataLocation'] log.info("Status:-|... Upload tuning data Start") try: if config_data['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'True': copy_files_to_server(ip,pem_file,datafile,config,username,'',remote_data_rawdata_dir,remote_config_dir) else: copy_files_to_server(ip, pem_file, datafile, config, username,'', remote_data_processeddata_dir,remote_config_dir) time.sleep(20) log.info("Status:-|... Upload tuning data End") log.info("Status:-|... Start LLM Tuning") update_sqllite_data(modelid,'status','Tuning') from llm.ssh_command import run_ssh_cmd if config_data['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'True': script_path = unstructured_script_path else: script_path = sh_file_path print(script_path) run_ssh_cmd(ip,pem_file, username,'',log,script_path) from llm.ssh_command import read_log_file log_data = read_log_file(ip,username,'',pem_file) outputStr = re.search(r'aion_learner_status:(.*)', str(log_data), re.IGNORECASE).group(1) outputStr = outputStr.strip() outputStr = json.loads(outputStr) outputStr = save_output(config_data['basic']['deployLocation'],config_data['basic']['modelName'],config_data['basic']['modelVersion'],outputStr,'GCP GCE',instance_name) vmRunning = config_data['basic'].get('vmRunning','KeepRunning') if vmRunning.lower() != 'keeprunning': from llm.gcp_compute_api import stop_instance server.stop() if "Tuning Completed Successfully" in log_data: update_sqllite_data(modelid,'status','Success') output = json.dumps(outputStr) print(f"\naion_learner_status:{output}\n") return output else: server.showndown() update_sqllite_data(modelid,'status','Error') output = json.dumps(outputStr) print(f"\naion_learner_status:{output}\n") return output except Exception as e: print(e) server.showndown() output_json = {"status": "FAIL", "message": str(e), "LogFile": ''} output = json.dumps(output_json) log.info("Status:-|... " + str(e)) print(f"\naion_learner_status:{output}\n") return output else: output_json = {"status":"FAIL","message":'Failed to initialize the instance',"LogFile":''} output = json.dumps(output_json) log.info("Status:-|... Failed to initialize the instance") print(f"\naion_learner_status:{output}\n") return output elif cloud_infra['computeInfrastructure'] == 'AWS': log.info("Status:-|... Compute Infrastructure AWS EC2") aws_access_key_id = cloud_infra['awsCredentials']['accessKey'] aws_secret_key = cloud_infra['awsCredentials']['secretAccessKey'] selectMachineType = cloud_infra['awsCredentials']['machineType'] selectedID = cloud_infra['awsCredentials']['selectedID'] region = cloud_infra['awsCredentials']['regionName'] if selectMachineType.lower() == 'ami': amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['amis'],selectedID) instance_type = amiDetails['instanceSetting']['instanceType'] security_group_id = cloud_infra['awsCredentials']['securitygroupid'] else: amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], selectedID) region = amiDetails['regionName'] #region = cloud_infra['AWS_EC2']['LLaMa7B']['RegionName'] image_id = amiDetails['id'] pem_file = os.path.join(currentDirectory,amiDetails['ssh']['keyFilePath']) username = amiDetails['ssh']['userName'] if config_data['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'True': datafile = config_data['basic']['dataLocationUnstructured'] else: datafile = config_data['basic']['dataLocation'] if selectMachineType.lower() == 'ami': log.info("Status:-|... Create Instance Start") server = llm_utils.aws_server('', image_id) instance_id,msg = server.create(instance_name) if instance_id == '': output_json = {"status":"FAIL","message":'Failed to initialize the instance. '+str(msg),"LogFile":''} output = json.dumps(output_json) log.info("Status:-|... Failed to initialize the instance") print(f"\naion_learner_status:{output}\n") log.info(f"\naion_learner_status:{output}\n") return output log.info("Status:-|... Create Instance End") elif selectMachineType.lower() == 'instance': instance_id = image_id update_sqllite_data(modelid,'instance',instance_id) server = llm_utils.aws_server( instance_id, '') else: output_json = {"status":"FAIL","message":'AMI is not configured',"LogFile":''} output = json.dumps(output_json) log.info("Status:-|... AMI is not configured") print(f"\naion_learner_status:{output}\n") log.info(f"\naion_learner_status:{output}\n") return output # instance_id = '' # ip = cloud_infra['AWS_EC2']['LLaMa7B']['InstanceIP'] try: from appbe.models import get_published_models already_published,published_usecase = get_published_models(instance_id) if already_published: Msg = f'Fine Tuned Model-{published_usecase} is already published at the same instance, Please unpublish the mentioned model to proceed.' output_json = {"status":"Error","message":Msg,"LogFile":''} output = json.dumps(output_json) log.info("Status:-|... A Model is already Published at the same instance.") print(f"\naion_learner_status:{output}\n") log.info(f"\naion_learner_status:{output}\n") return output except Exception as e: log.info(str(e)) print(str(e)) if instance_id != '': log.info("Status:-|... Start Instance") if selectMachineType.lower() == 'ami': update_sqllite_data(modelid, 'image', image_id) update_sqllite_data(modelid, 'region', region) update_sqllite_data(modelid,'instance',instance_id) update_sqllite_data(modelid,'hypervisor','AWS') update_sqllite_data(modelid,'status','Initialize') status,msg = server.start() ip = server.ip time.sleep(20) if status and ip != '': update_sqllite_data(modelid,'ip',ip) try: log.info("Status:-|... Copy Files") from llm.ssh_command import copy_files_to_server if config_data['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'True': print(ip,pem_file,datafile,config,username,'',remote_data_rawdata_dir,remote_config_dir) copy_files_to_server(ip,pem_file,datafile,config,username,'',remote_data_rawdata_dir,remote_config_dir) else: print(ip, pem_file, datafile, config, username, '', remote_data_processeddata_dir, remote_config_dir) copy_files_to_server(ip, pem_file, datafile, config, username, '', remote_data_processeddata_dir,remote_config_dir) time.sleep(20) log.info("Status:-|... Start LLM Tuning") update_sqllite_data(modelid,'status','Tuning') from llm.ssh_command import run_ssh_cmd if config_data['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'True': script_path = unstructured_script_path else: script_path = sh_file_path #print(script_path) #sys.exit() run_ssh_cmd(ip, pem_file, username,'',log,script_path) from llm.ssh_command import read_log_file log_data = read_log_file(ip,username,'',pem_file) outputStr = re.search(r'aion_learner_status:(.*)', str(log_data), re.IGNORECASE).group(1) outputStr = outputStr.strip() outputStr = json.loads(outputStr) outputStr = save_output(config_data['basic']['deployLocation'],config_data['basic']['modelName'],config_data['basic']['modelVersion'],outputStr,'AWS EC2',instance_id) vmRunning = config_data['basic'].get('vmRunning','KeepRunning') if vmRunning.lower() != 'keeprunning': server.stop() if "Tuning Completed Successfully" in log_data: update_sqllite_data(modelid,'status','Success') output = json.dumps(outputStr) print(f"\naion_learner_status:{output}\n") log.info(f"\naion_learner_status:{output}\n") return output else: server.showndown() update_sqllite_data(modelid,'status','Error') output = json.dumps(outputStr) print(f"\naion_learner_status:{output}\n") log.info(f"\naion_learner_status:{output}\n") return output except Exception as e: print(e) log.info(str(e)) server.showndown() output = {'status': 'FAIL', 'message': str(e), 'LogFile': ''} output = json.dumps(output) print(f"\naion_learner_status:{output}\n") log.info(f"\naion_learner_status:{output}\n") return output else: output = {'status':'FAIL','message':msg,'LogFile':''} output = json.dumps(output) print(f"\naion_learner_status:{output}\n") log.info(f"\naion_learner_status:{output}\n") return output else: output_json = {"status":"FAIL","message":'Failed to initialize the instance',"LogFile":''} output = json.dumps(output_json) log.info("Status:-|... Failed to initialize the instance") print(f"\naion_learner_status:{output}\n") return output import json import os import sys import pandas as pd import time from stat import S_ISDIR, S_ISREG from pathlib import Path import logging import re remote_data_dir = '/home/ubuntu/AION/data/storage' remote_config_dir = '/home/ubuntu/AION/data/config' sh_file_path = '/home/ubuntu/AION/llm/sbin/run_experiment.sh' import os import tarfile def tardirectory(path,tarfilepath): with tarfile.open(tarfilepath, 'w:tar') as tarhandle: for root, dirs, files in os.walk(path): for f in files: tarhandle.add(os.path.join(root, f),arcname=f) tarhandle.close() def createCodeSummary(codedir,cloudconfig,filetype): try: from appbe.dataPath import DATA_FILE_PATH filetimestamp = str(int(time.time())) tarfilepath = os.path.join(DATA_FILE_PATH,filetimestamp+'.tar') tardirectory(codedir,tarfilepath) with open(cloudconfig, 'r') as config_f: cloud_infra = json.load(config_f) config_f.close() aws_access_key_id = cloud_infra['AWS_EC2']['AWSAccessKeyID'] aws_secret_key = cloud_infra['AWS_EC2']['AWSSecretAccessKey'] instance_type = cloud_infra['AWS_EC2']['CodeSummarization']['InstanceSetting']['InstanceType'] security_group_id = cloud_infra['AWS_EC2']['CodeSummarization']['InstanceSetting']['SecurityGroupId'] region = cloud_infra['AWS_EC2']['CodeSummarization']['RegionName'] image_id = cloud_infra['AWS_EC2']['CodeSummarization']['amiId'] currentDirectory = os.path.dirname(os.path.abspath(__file__)) pem_file = os.path.join(currentDirectory,cloud_infra['AWS_EC2']['CodeSummarization']['ssh']['keyFilePath']) username = cloud_infra['AWS_EC2']['CodeSummarization']['ssh']['userName'] instance_id = '' if image_id != '': from llm.aws_instance_api import create_instance instance_id = create_instance(image_id, instance_type, security_group_id,region,instance_name,aws_access_key_id, aws_secret_key) if instance_id == '': return 'Failed','Instance Creation Failed' if instance_id == '': if cloud_infra['AWS_EC2']['CodeSummarization']['InstanceId'] != '': instance_id = cloud_infra['AWS_EC2']['CodeSummarization']['InstanceId'] else: return 'Failed','Instance Creation Failed.' if instance_id != '': from llm.aws_instance_api import start_instance ip = start_instance(aws_access_key_id, aws_secret_key, instance_id,region) if ip != '': from llm.ssh_command import copy_files_to_server copy_files_to_server(ip,pem_file,tarfilepath,'',username,'',remote_data_dir,remote_config_dir) from llm.ssh_command import run_ssh_cmd command = 'rm -r /home/ubuntu/AION/data/code' buf = run_ssh_cmd(ip, pem_file, username,'','',command) command = 'mkdir -p /home/ubuntu/AION/data/code' buf = run_ssh_cmd(ip, pem_file, username,'','',command) command = 'tar -xvf '+remote_data_dir+'/'+filetimestamp+'.tar -C /home/ubuntu/AION/data/code' print(command) buf = run_ssh_cmd(ip, pem_file, username,'','',command) command = sh_file_path+' '+'/home/ubuntu/AION/data/code'+' '+filetype print(command) buf = run_ssh_cmd(ip, pem_file, username,'','',command) from llm.ssh_command import read_file_from_server filetimestamp = str(int(time.time())) codesummar = os.path.join(DATA_FILE_PATH,filetimestamp+'.csv') read_file_from_server(ip,username,'',pem_file,'/home/ubuntu/AION/data/storage/code_summararies.csv',codesummar) return 'Success',codesummar else: return 'Failed','Instance Initialization Failed.' else: return 'Failed','Instance Initialization Failed . AMI/Instance is not configured. Please check with ERS Research' except Exception as e: print(e) return 'Failed','Code Summarization Failed' from google.cloud import compute_v1 import os PROJECT_ID = 'ers-research' ZONE = 'us-west1-b' INSTANCE_NAME = 'aion-llm-a100-vm1' MACHINE_IMAGE_NAME = 'aion-40gb-a100-image' MACHINE_IMAGE_PROJECT_ID = 'ers-research' def create_instance(credentialsJson,project_id, zone, instance_name, machine_image_name, machine_image_project_id): try: os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentialsJson compute = compute_v1.InstancesClient() machine_image = compute_v1.MachineImagesClient().get(project=machine_image_project_id, machine_image=machine_image_name) instance = compute_v1.Instance() instance.name = instance_name instance.machine_type = f"zones/{zone}/machineTypes/a2-ultragpu-1g" instance.source_machine_image = machine_image.self_link boot_disk = compute_v1.AttachedDisk() boot_disk.auto_delete = True boot_disk.boot = True instance.disks = [boot_disk] network_interface = compute_v1.NetworkInterface() access_config = compute_v1.AccessConfig() access_config.type = "ONE_TO_ONE_NAT" network_interface.access_configs = [access_config] instance.network_interfaces = [network_interface] operation = compute.insert(project=project_id, zone=zone, instance_resource=instance) operation.result() instance = compute.get(project=project_id, zone=zone, instance=instance_name) print("--->instace created ") print(instance.network_interfaces[0]) return instance.network_interfaces[0].access_configs[0].nat_i_p,'' except Exception as e: print(e) return '',str(e) def is_running(credentialsJson,project_id, zone, instance_name): os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentialsJson compute = compute_v1.InstancesClient() instance = compute.get(project=project_id, zone=zone, instance=instance_name) status = instance.status return status def check_instance(credentialsJson,project_id, zone, instance_name): os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentialsJson compute = compute_v1.InstancesClient() instance = compute.get(project=project_id, zone=zone, instance=instance_name) status = instance.status if status.lower() == 'running': print(instance.network_interfaces[0].access_configs[0].nat_i_p) ip = instance.network_interfaces[0].access_configs[0].nat_i_p else: ip = '' return status,ip def start_instance(credentialsJson,project_id, zone, instance_name): try: os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentialsJson compute = compute_v1.InstancesClient() operation = compute.start(project=project_id, zone=zone, instance=instance_name) operation.result() instance = compute.get(project=project_id, zone=zone, instance=instance_name) status = instance.status if status.lower() == 'running': print(instance.network_interfaces[0].access_configs[0]) ip = instance.network_interfaces[0].access_configs[0].nat_i_p else: ip = '' except Exception as e: print(e) status = 'Error' ip = '' return status,ip def stop_instance(credentialsJson,project_id, zone, instance_name): os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentialsJson compute = compute_v1.InstancesClient() operation = compute.stop(project=project_id, zone=zone, instance=instance_name) operation.result() def terminate_instance(project_id, zone, instance_name): try: compute = compute_v1.InstancesClient() operation = compute.delete(project=project_id, zone=zone, instance=instance_name) operation.result() return "","suceess" except Exception as e: return str(e),"error" # if __name__ == '__main__': # ip_address = create_instance(PROJECT_ID, ZONE, INSTANCE_NAME, MACHINE_IMAGE_NAME, MACHINE_IMAGE_PROJECT_ID) # print(f"IP address of the new VM: {ip_address}") # #start_instance(PROJECT_ID, ZONE, INSTANCE_NAME) # # stop_instance(PROJECT_ID, ZONE, INSTANCE_NAME) # # terminate_instance(PROJECT_ID, ZONE, INSTANCE_NAME) import json import os import time remote_data_dir = '/home/aion/data/storage/prompt' remote_config_dir = '/home/aion/data/config' prompt_command = '/home/aion/llm/sbin/llm_predict.sh' command_prepare_model = '/home/aion/llm/sbin/llm_merge_weights.sh' command_start_service = '/home/aion/llm/sbin/llm_publish_model.sh' command_stop_service = 'publish.py' from AION.llm import llm_utils from pathlib import Path def getAMIDetails(config,selectedAMI): y = {} for x in config: print(x) if x['id'] == selectedAMI: return x return y def get_ip(cloudconfig,instanceid,hypervisor,region,image): try: # with open(cloudconfig, 'r') as config_f: # cloud_infra = json.load(config_f) # config_f.close() from appbe.compute import readComputeConfig cloud_infra = readComputeConfig() if hypervisor == 'AWS': aws_access_key_id = cloud_infra['awsCredentials']['accessKey'] aws_secret_key = cloud_infra['awsCredentials']['secretAccessKey'] if image != '' and image != 'NA': amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['amis'], image) else: amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], instanceid) if region == '' or region == 'NA': region = amiDetails['regionName'] from llm.aws_instance_api import get_instance_ip return get_instance_ip(aws_access_key_id, aws_secret_key, instanceid,region) elif hypervisor == 'GCP': #print(hypervisor,instanceid) server = llm_utils.hypervisor( hypervisor,instanceid) if server.is_machine_running(): return server.ip else: return '' except Exception as e: print(e) raise Exception def kill_inference_server(cloudconfig,instanceid,hypervisor,region,image): # with open(cloudconfig, 'r') as config_f: # cloud_infra = json.load(config_f) # config_f.close() from appbe.compute import readComputeConfig cloud_infra = readComputeConfig() if hypervisor == 'AWS': ip = get_ip(cloudconfig,instanceid,hypervisor,region,image) if ip == '': print("Machine is not running.") else: amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], instanceid) currentDirectory = os.path.dirname(os.path.abspath(__file__)) pem_file = os.path.join(currentDirectory,amiDetails['ssh']['keyFilePath']) username = amiDetails['ssh']['userName'] command = 'pkill -f'+ ' '+command_stop_service from llm.ssh_command import run_ssh_cmd buf = run_ssh_cmd(ip, pem_file, username, '', '', command) elif hypervisor == 'GCP': server = llm_utils.hypervisor( hypervisor,instanceid) if server.is_machine_running(): ssh = server.ssh_details() pem_file = str(Path(__file__).parent/ssh['keyFilePath']) from llm.ssh_command import run_ssh_cmd command = 'pkill -f'+ ' '+command_stop_service buf = run_ssh_cmd(server.ip, pem_file, ssh['userName'],'','',command) else: raise Exception("Error") def LLM_publish(cloudconfig,instanceid,hypervisor,model,usecaseid,region,image): from appbe.compute import readComputeConfig cloud_infra = readComputeConfig() # with open(cloudconfig, 'r') as config_f: # cloud_infra = json.load(config_f) # config_f.close() if hypervisor == 'AWS': aws_access_key_id = cloud_infra['awsCredentials']['accessKey'] aws_secret_key = cloud_infra['awsCredentials']['secretAccessKey'] if image != '' and image != 'NA': amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['amis'], image) else: amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], instanceid) if region == '' or region == 'NA': region = amiDetails['regionName'] from llm.aws_instance_api import start_instance status,msg,ip = start_instance(aws_access_key_id, aws_secret_key, instanceid, region) print(status,msg,ip) if status.lower() == 'success': currentDirectory = os.path.dirname(os.path.abspath(__file__)) pem_file = os.path.join(currentDirectory,amiDetails['ssh']['keyFilePath']) username = amiDetails['ssh']['userName'] command = command_prepare_model + ' ' + usecaseid + ' '+ str(model) print(command) from llm.ssh_command import run_ssh_cmd buf = run_ssh_cmd(ip, pem_file, username, '', '', command) if "Error" in buf: print("Error in Merging model") raise Exception("Error in Merging model") print("merging finished") command = command_start_service+' '+ usecaseid buf = run_ssh_cmd(ip, pem_file, username, '', '', command) print("inference server running") return buf else: print(msg) return msg elif hypervisor == 'GCP': amiDetails = getAMIDetails(cloud_infra['GCP']['instances'], instanceid) if region == '' or region == 'NA': region = amiDetails['regionName'] server = llm_utils.hypervisor(hypervisor,instanceid) if not server.is_machine_running(): started, msg = server.start() if not started: raise ValueError( msg) ssh = server.ssh_details() pem_file = str(Path(__file__).parent/ssh['keyFilePath']) from llm.ssh_command import run_ssh_cmd #print(model) #print(usecaseid) command = command_prepare_model + ' ' + usecaseid + ' '+ str(model) buf = run_ssh_cmd(server.ip, pem_file, ssh['userName'], '', '', command) if "Error" in buf: print("Error in Merging model") raise Exception("Error in Merging model") #print("merging finished") command = command_start_service+' '+ usecaseid buf = run_ssh_cmd(server.ip, pem_file, ssh['userName'], '', '', command) #print("inference server running") return buf else: print("Not configured for gcp") raise Exception("Eror") def LLM_predict(cloudconfig,instanceid,promptfile,hypervisor,model,usecaseid,region,image,temperature,maxtokens,modelType): from appbe.compute import readComputeConfig cloud_infra = readComputeConfig() try: temperature = float(temperature) except: temperature = 0.4 try: maxtokens = int(maxtokens) except: maxtokens = 2048 print("====") print(float(temperature)) print("====") if hypervisor == 'AWS': aws_access_key_id = cloud_infra['awsCredentials']['accessKey'] aws_secret_key = cloud_infra['awsCredentials']['secretAccessKey'] currentDirectory = os.path.dirname(os.path.abspath(__file__)) if image != '' and image != 'NA': amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['amis'], image) else: amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], instanceid) if region == '' or region == 'NA': region = amiDetails['regionName'] from llm.aws_instance_api import start_instance #print(aws_access_key_id, aws_secret_key, instanceid, region) status,msg,ip = start_instance(aws_access_key_id, aws_secret_key, instanceid, region) if status.lower() == 'success': pem_file = os.path.join(currentDirectory,amiDetails['ssh']['keyFilePath']) username = amiDetails['ssh']['userName'] from llm.ssh_command import copy_files_to_server #print(ip,pem_file,promptfile,'',username,'',remote_data_dir,remote_config_dir) copy_files_to_server(ip,pem_file,promptfile,'',username,'',remote_data_dir,remote_config_dir) promptfile = os.path.basename(promptfile) if modelType == 'BaseModel': command = prompt_command + ' ' + 'BaseModel' + ' ' + remote_data_dir + '/' + promptfile + ' ' + str( model) + ' ' + str(temperature) + ' ' + str(maxtokens) else: command = prompt_command+' '+usecaseid+' '+remote_data_dir+'/'+ promptfile+' '+str(model)+' '+str(temperature)+' '+str(maxtokens) print(command) from llm.ssh_command import run_ssh_cmd buf = run_ssh_cmd(ip, pem_file, username,'','',command) return buf else: return msg else: server = llm_utils.hypervisor( hypervisor,instanceid) if not server.is_machine_running(): started, msg = server.start() if not started: raise ValueError( msg) ssh = server.ssh_details() pem_file = str(Path(__file__).parent/ssh['keyFilePath']) from llm.ssh_command import copy_files_to_server copy_files_to_server(server.ip,pem_file,promptfile,'',ssh['userName'],'',remote_data_dir,remote_config_dir) promptfile = os.path.basename(promptfile) if modelType == 'BaseModel': command = prompt_command + ' ' + 'BaseModel' + ' ' + remote_data_dir + '/' + promptfile + ' ' + str( model) + ' ' + str(temperature) + ' ' + str(maxtokens) else: command = prompt_command+' '+usecaseid+' '+remote_data_dir+'/'+ promptfile+' '+str(model)+' '+str(temperature)+' '+str(maxtokens) #command = '/home/aion/llm/sbin/llm_model_finetuning.sh' #print(command) from llm.ssh_command import run_ssh_cmd #print(ssh['userName'],pem_file) buf = run_ssh_cmd(server.ip, pem_file, ssh['userName'],'','',command) return buf ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import pandas as pd import matplotlib.pyplot as plt from lifelines import KaplanMeierFitter, CoxPHFitter from lifelines.utils import datetimes_to_durations import logging import numpy as np import re import sys import os class SurvivalAnalysis(object): def __init__(self, df, pipe, method, event_column, duration_column, filterExpression, train_features_type,start=None, end=None): pd.options.display.width = 30 self.df = df self.pipe = pipe self.train_features_type = train_features_type self.filterExpression = filterExpression self.covariateExpression = filterExpression self.method = method self.event_column = event_column if start is not None and end is not None: self.df['duration'], _ = datetimes_to_durations(start, end) self.duration_column = 'duration' else: self.duration_column = duration_column self.models = [] self.score = 0 self.log = logging.getLogger('eion') self.plots = [] def transform_filter_expression(self, covariate, covariate_input): ''' Filter expression given by user will be encoded if it is categorical and if it is a numerical feature that is normalised in data profiler, in filter expression feature also it will be converted to normalised value ''' cols = list(self.df.columns) if self.duration_column in cols: cols.remove(self.duration_column) if self.event_column in cols: cols.remove(self.event_column) df_filter = pd.DataFrame([{covariate:covariate_input}], columns=cols) df_filter[covariate] = df_filter[covariate].astype(self.train_features_type[covariate]) df_transform_array = self.pipe.transform(df_filter) df_transform = pd.DataFrame(df_transform_array, columns=cols) return df_transform[covariate].iloc[0] def learn(self): self.log.info('\n---------- SurvivalAnalysis learner has started ----------') self.log.info('\n---------- SurvivalAnalysis learner method is "%s" ----------' % self.method) if self.method.lower() in ['kaplanmeierfitter', 'kaplanmeier', 'kaplan-meier', 'kaplan meier', 'kaplan', 'km', 'kmf']: self.log.info('\n---------- SurvivalAnalysis learner method "%s" has started ----------' % self.method) kmf = KaplanMeierFitter() T = self.df[self.duration_column] E = self.df[self.event_column] self.log.info('\n T : \n%s' % str(T)) self.log.info('\n E : \n%s' % str(E)) K = kmf.fit(T, E) kmf_sf = K.survival_function_ kmf_sf_json = self.survival_probability_to_json(kmf_sf) self.models.append(K) if isinstance(self.filterExpression, str): df_f, df_n, refined_filter_expression = self.parse_filterExpression() kmf1 = KaplanMeierFitter() kmf2 = KaplanMeierFitter() self.log.info( '\n---------- SurvivalAnalysis learner "%s" fitting for filter expression has started----------' % self.method) T1 = df_f[self.duration_column] E1 = df_f[self.event_column] T2 = df_n[self.duration_column] E2 = df_n[self.event_column] kmf1.fit(T1, E1) fig, ax = plt.subplots(1, 1) ax = kmf1.plot_survival_function(ax=ax, label='%s' % refined_filter_expression) self.log.info( '\n---------- SurvivalAnalysis learner "%s" fitting for filter expression has ended----------' % self.method) plt.title("KM Survival Functions - Filter vs Negation") self.log.info( '\n---------- SurvivalAnalysis learner "%s" fitting for negation has started----------' % self.method) kmf2.fit(T2, E2) ax = kmf2.plot_survival_function(ax=ax, label='~%s' % refined_filter_expression) self.log.info( '\n---------- SurvivalAnalysis learner "%s" fitting for negation has ended----------' % self.method) self.models.extend([kmf1, kmf2]) kmf1_sf = kmf1.survival_function_ kmf2_sf = kmf2.survival_function_ kmf1_sf_json = self.survival_probability_to_json(kmf1_sf) self.plots.append(fig) self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------' % self.method) self.log.info('\n---------- SurvivalAnalysis learner has ended ----------') self.log.info('Status:- |... Algorithm applied: KaplanMeierFitter') return kmf1_sf_json else: fig, ax = plt.subplots(1, 1) ax = kmf_sf.plot(ax=ax) plt.title("KM Survival Functions") self.plots.append(fig) self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------' % self.method) self.log.info('\n---------- SurvivalAnalysis learner has ended ----------') self.log.info('Status:- |... Algorithm applied: KaplanMeierFitter') return kmf_sf_json elif self.method.lower() in ['coxphfitter', 'coxregression', 'cox-regression', 'cox regression', 'coxproportionalhazard', 'coxph', 'cox', 'cph']: self.log.info('\n---------- SurvivalAnalysis learner method "%s" has started ----------' % self.method) cph = CoxPHFitter(penalizer=0.1) self.df = self.drop_constant_features(self.df) C = cph.fit(self.df, self.duration_column, self.event_column) self.models.append(C) cph_sf = C.baseline_survival_ self.score = C.score(self.df, scoring_method="concordance_index") self.log.info( '\n---------- SurvivalAnalysis learner "%s" score is "%s"----------' % (self.method, str(self.score))) cph_sf_json = self.survival_probability_to_json(cph_sf) if isinstance(self.covariateExpression, str): covariate, covariate_inputs, covariate_values = self.parse_covariateExpression() fig, (ax1, ax2) = plt.subplots(1, 2) fig.tight_layout() ax1 = C.plot(ax=ax1, hazard_ratios=True) self.log.info('\n Summary : \n%s' % str(C.summary)) ax1.set_title("COX hazard ratio") ax2 = C.plot_partial_effects_on_outcome(covariate, covariate_values, ax=ax2) mylabels = [covariate + '=' + str(x) for x in covariate_inputs] mylabels.append('baseline') ax2.legend(labels=mylabels) ax2.set_title("Covariate Plot") self.plots.append(fig) else: fig = plt.figure() ax1 = C.plot(hazard_ratios=True) self.log.info('\n Summary : \n%s' % str(C.summary)) plt.title("COX hazard ratio") self.plots.append(fig) self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------' % self.method) self.log.info('\n---------- SurvivalAnalysis learner has ended ----------') self.log.info('Status:- |... Algorithm applied: CoxPHFitter') return cph_sf_json def parse_filterExpression(self): import operator self.log.info('\n---------- Filter Expression parsing has started ----------') self.log.info('Filter Expression provided : %s' % self.filterExpression) self.log.info('Shape before filter : %s' % str(self.df.shape)) f = self.filterExpression.split('&') f = list(filter(None, f)) if len(f) == 1: p = '[<>=!]=?' op = re.findall(p, self.filterExpression)[0] covariate, covariate_input = [x.strip().strip('\'').strip('\"') for x in self.filterExpression.split(op)] refined_filter_expression = covariate + op + covariate_input self.log.info('Final refined filter : %s' % refined_filter_expression) ops = {"==": operator.eq, ">": operator.gt, "<": operator.lt, ">=": operator.ge, "<=": operator.le, "!=": operator.ne} try: fv = self.transform_filter_expression(covariate, covariate_input) df_f = self.df[ops[op](self.df[covariate], fv)] self.log.info('Shape after filter : %s' % str(df_f.shape)) df_n = self.df[~self.df[covariate].isin(df_f[covariate])] self.log.info('Shape of negation : %s' % str(df_n.shape)) self.log.info('---------- Filter Expression has ended ----------') return df_f, df_n, refined_filter_expression except Exception: self.log.info('\n-----> Filter Expression parsing encountered error!!!') exc_type, exc_obj, exc_tb = sys.exc_info() if exc_type == IndexError or ValueError or KeyError: self.log.info('----->Given filter expression '+ self.filterExpression +' is invalid') self.log.info('Valid examples are "A>100", "B==category1", "C>=10 && C<=20" etc..') fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname) + ' ' + str(exc_tb.tb_lineno)) raise Exception(str(exc_type)+str(exc_obj)) else: full_f = [] try: for filterExpression in f: p = '[<>=!]=?' op = re.findall(p, filterExpression)[0] covariate, covariate_input = [x.strip().strip('\'').strip('\"') for x in filterExpression.split(op)] full_f.append(covariate + op + covariate_input) ops = {"==": operator.eq, ">": operator.gt, "<": operator.lt, ">=": operator.ge, "<=": operator.le, "!=": operator.ne} fv = self.transform_filter_expression(covariate, covariate_input) df_f = self.df[ops[op](self.df[covariate], fv)] df_n = self.df[~self.df[covariate].isin(df_f[covariate])] refined_filter_expression = " & ".join(full_f) self.log.info('Final refined filter : %s' % refined_filter_expression) self.log.info('Shape after filter : %s' % str(df_f.shape)) self.log.info('Shape of negation : %s' % str(df_n.shape)) self.log.info('---------- Filter Expression has ended ----------') return df_f, df_n, refined_filter_expression # except (IndexError, ValueError, KeyError): except Exception: self.log.info('\n-----> Filter Expression parsing encountered error!!!') exc_type, exc_obj, exc_tb = sys.exc_info() if exc_type == IndexError or ValueError or KeyError: self.log.info('----->Given filter expression '+ self.filterExpression +' is invalid') self.log.info('Valid examples are "A>100", "B==category1", "C>=10 && C<=20" etc..') fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname) + ' ' + str(exc_tb.tb_lineno)) raise Exception(str(exc_type)+str(exc_obj)) def parse_covariateExpression(self): self.log.info('\n---------- Covariate Expression parsing has started ----------') self.log.info('\n Covariate Expression provided : %s' % self.covariateExpression) import ast p = '[=:]' try: op = re.findall(p, self.covariateExpression)[0] covariate, covariate_inputs = [x.strip().strip('\'').strip('\"') for x in self.covariateExpression.split(op)] covariate_inputs = ast.literal_eval(covariate_inputs) covariate_values = [self.transform_filter_expression(covariate, x) for x in covariate_inputs] self.log.info('\n---------- Covariate Expression parsing has ended ----------') return covariate, covariate_inputs, covariate_values except Exception: self.log.info('\n-----> Covariate Expression parsing encountered error!!!') exc_type, exc_obj, exc_tb = sys.exc_info() if exc_type == IndexError or ValueError or KeyError: self.log.info('----->Given covariate expression '+ self.filterExpression +' is invalid') self.log.info("\n Valid examples are A=['Yes','No'] or B=[100,500,1000]") fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname) + ' ' + str(exc_tb.tb_lineno)) raise Exception(str(exc_type)+str(exc_obj)) def survival_probability_to_json(self, sf): ''' sf = Survival function i.e. KaplanMeierFitter.survival_function_ or CoxPHFitter.baseline_survival_ returns json of survival probabilities ''' sf = sf[sf.columns[0]].apply(lambda x: "%4.2f" % (x * 100)) self.log.info('\n Survival probabilities : \n%s' % str(sf)) sf = sf.reset_index() sf = sf.sort_values(sf.columns[0]) sf_json = sf.to_json(orient='records') self.log.info('\n Survival probability json : \n%s' % str(sf_json)) return sf_json def drop_constant_features(self, df): dropped = [] for col in df.columns: if (len(df[col].unique()) == 1) and (col not in [self.duration_column, self.event_column]): df.drop(col, inplace=True, axis=1) dropped.append(col) if len(dropped) != 0: self.log.info('\n Dropping constant features %s' % str(col)) self.log.info('\n After dropping constant features : \n%s' % str(df)) return df def predict(self): if self.method == 'KaplanMeierFitter': return self.model.predict(self.test[self.duration_column]) elif self.method == 'CoxPHFitter': res = [] for idx, row in self.test.iterrows(): res.append( self.model.predict_survival_function(self.test, times=row[self.model.duration_col])[idx].values[0]) return pd.DataFrame(res) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import warnings import sys warnings.simplefilter(action='ignore', category=FutureWarning) import xgboost as xgb import dask.array as da import shutil import dask.distributed import dask.dataframe as dd import dask_ml import logging from sklearn.metrics import accuracy_score, recall_score, \ roc_auc_score, precision_score, f1_score, \ mean_squared_error, mean_absolute_error, \ r2_score, classification_report, confusion_matrix, \ mean_absolute_percentage_error import lightgbm as lgb import re from sklearn.pipeline import Pipeline from sklearn.base import BaseEstimator, TransformerMixin from dask_ml.impute import SimpleImputer from dask_ml.compose import ColumnTransformer from dask_ml.decomposition import TruncatedSVD, PCA from dask_ml.preprocessing import StandardScaler, \ MinMaxScaler, \ OneHotEncoder, LabelEncoder from dask_ml.wrappers import ParallelPostFit import numpy as np import json import time from sklearn.ensemble import IsolationForest import joblib import pickle as pkl import os predict_config={} dask.config.set({"distributed.workers.memory.terminate": 0.99}) dask.config.set({"array.chunk-size": "128 MiB"}) dask.config.set({"distributed.admin.tick.limit": "3h"}) # dask.config.set({"distributed.workers.memory.pause": 0.9}) class MinImputer(BaseEstimator, TransformerMixin): def fit(self, X, y=None): return self def transform(self, X, y=None): # to_fillna = ['public_meeting', 'scheme_management', 'permit'] # X[to_fillna] = X[to_fillna].fillna(value='NaN') # X[to_fillna] = X[to_fillna].astype(str) X = X.fillna(value=X.min()) # X = X.astype(str) return X class MaxImputer(BaseEstimator, TransformerMixin): def fit(self, X, y=None): return self def transform(self, X, y=None): X = X.fillna(value=X.max()) return X class DropImputer(BaseEstimator, TransformerMixin): def fit(self, X, y=None): return self def transform(self, X, y=None): X = X.dropna() return X class ModeCategoricalImputer(BaseEstimator, TransformerMixin): def fit(self, X, y=None): return self def transform(self, X, y=None): X = X.fillna(value=X.mode()) return X class IsoForestOutlierExtractor(TransformerMixin): def fit(self, X, y=None): return self def transform(self, X, y): lcf = IsolationForest() with joblib.parallel_backend('dask'): lcf.fit(X) y_pred_train = lcf.predict(X) y_pred_train = y_pred_train == 1 return X def load_config_json(json_file): with open(json_file, 'r') as j: contents = json.loads(j.read()) return contents def load_data_dask(data_file, npartitions=500): big_df = dd.read_csv(data_file, # sep=r'\s*,\s*', assume_missing=True, parse_dates=True, infer_datetime_format=True, sample=1000000, # dtype={'caliper': 'object', # 'timestamp': 'object'}, # dtype='object', na_values=['-','?'] ) big_df = big_df.repartition(npartitions) return big_df def get_dask_eda(df_dask): descr = df_dask.describe().compute() corr = df_dask.corr().compute() return descr, corr def normalization(config): scaler = config["advance"] \ ["profiler"]["normalization"] scaler_method = None if scaler["minMax"] == "True": scaler_method = MinMaxScaler() if scaler["standardScaler"] == "True": scaler_method = StandardScaler() return scaler_method def categorical_encoding(config): encoder = config["advance"]["profiler"] \ ["categoryEncoding"] encoder_method = None if encoder["OneHotEncoding"] == "True": encoder_method = OneHotEncoder() # OneHotEncoder(handle_unknown='ignore', sparse=False) if encoder["LabelEncoding"] == "True": encoder_method = LabelEncoder() return encoder_method def numeric_feature_imputing(config): imputer_numeric_method = None imputer_numeric = config["advance"] \ ["profiler"]["numericalFillMethod"] if imputer_numeric["Median"] == "True": print("Median Simple Imputer") imputer_numeric_method = SimpleImputer(strategy='median') if imputer_numeric["Mean"] == "True": print("Mean Simple Imputer") imputer_numeric_method = SimpleImputer(strategy='mean') if imputer_numeric["Min"] == "True": print("Min Simple Imputer") imputer_numeric_method = MinImputer() if imputer_numeric["Max"] == "True": print("Max Simple Imputer") imputer_numeric_method = MaxImputer() if imputer_numeric["Zero"] == "True": print("Zero Simple Imputer") imputer_numeric_method = SimpleImputer(strategy='constant', fill_value=0) # if imputer_numeric["Drop"] == "True": # print("Median Simple Imputer") # imputer_numeric_method = DropImputer() return imputer_numeric_method def categorical_feature_imputing(config): imputer_categorical_method = None imputer_categorical = config["advance"] \ ["profiler"]["categoricalFillMethod"] if imputer_categorical["MostFrequent"] == "True": imputer_categorical_method = SimpleImputer(strategy='most_frequent') if imputer_categorical["Mode"] == "True": imputer_categorical_method = ModeCategoricalImputer() if imputer_categorical["Zero"] == "True": imputer_categorical_method = SimpleImputer(strategy='constant', fill_value=0) return imputer_categorical_method def preprocessing_pipeline(config, X_train): print("Start preprocessing") scaler_method = normalization(config) encoding_method = categorical_encoding(config) imputer_numeric_method = numeric_feature_imputing(config) imputer_categorical_method = categorical_feature_imputing(config) numeric_pipeline = Pipeline(steps=[ ('impute', imputer_numeric_method), ('scale', scaler_method) ]) categorical_pipeline = Pipeline(steps=[ ('impute', imputer_categorical_method), ('encoding', encoding_method) ]) numerical_features = X_train._get_numeric_data().columns.values.tolist() categorical_features = list(set(X_train.columns) - set(X_train._get_numeric_data().columns)) print("numerical_features: ", numerical_features) print("categorical_features: ", categorical_features) full_processor = ColumnTransformer(transformers=[ ('number', numeric_pipeline, numerical_features), # ('category', categorical_pipeline, categorical_features) ]) return full_processor def full_pipeline(X_train, X_test, config): full_processor = preprocessing_pipeline(config, X_train) reduce_dim = config["advance"] \ ["selector"]["featureEngineering"] feature_reduce = None if reduce_dim["SVD"] == "True": feature_reduce = TruncatedSVD(n_components=3) if reduce_dim["PCA"] == "True": feature_reduce = PCA(n_components=3) X_train = full_processor.fit_transform(X_train) # joblib.dump(full_processor, 'full_processor_pipeline.pkl') deploy_location = config["basic"]["modelLocation"] profiler_file = os.path.join(deploy_location,'model','profiler.pkl') selector_file = os.path.join(deploy_location,'model','selector.pkl') save_pkl(full_processor, profiler_file) X_test = full_processor.transform(X_test) predict_config['profilerLocation'] = 'profiler.pkl' if feature_reduce != None: X_train = feature_reduce.fit_transform(X_train.to_dask_array(lengths=True)) save_pkl(feature_reduce, selector_file) predict_config['selectorLocation'] = 'selector.pkl' # joblib.dump(feature_reduce, 'feature_reduce_pipeline.pkl') X_test = feature_reduce.transform(X_test.to_dask_array(lengths=True)) X_train = dd.from_dask_array(X_train) X_test = dd.from_dask_array(X_test) else: predict_config['selectorLocation'] = '' return X_train, X_test def train_xgb_classification(client, X_train, y_train, X_test, config): print("Training XGBoost classification") model_hyperparams = config["advance"] \ ["distributedlearner_config"] \ ["modelParams"] \ ["classifierModelParams"] \ ["Distributed Extreme Gradient Boosting (XGBoost)"] dask_model = xgb.dask.DaskXGBClassifier( tree_method=model_hyperparams["tree_method"], n_estimators=int(model_hyperparams["n_estimators"]), max_depth=int(model_hyperparams["max_depth"]), gamma=float(model_hyperparams["gamma"]), min_child_weight=float(model_hyperparams["min_child_weight"]), subsample=float(model_hyperparams["subsample"]), colsample_bytree=float(model_hyperparams["colsample_bytree"]), learning_rate=float(model_hyperparams["learning_rate"]), reg_alpha=float(model_hyperparams["reg_alpha"]), reg_lambda=float(model_hyperparams["reg_lambda"]), random_state=int(model_hyperparams["random_state"]), verbosity=3) dask_model.client = client X_train, X_test = full_pipeline(X_train, X_test, config) dask_model.fit(X_train, y_train) save_model(config, dask_model) save_config(config) return dask_model, X_train, X_test def train_xgb_regression(client, X_train, y_train, X_test, config): model_hyperparams = config["advance"] \ ["distributedlearner_config"] \ ["modelParams"] \ ["regressorModelParams"] \ ["Distributed Extreme Gradient Boosting (XGBoost)"] print("Training XGBoost regression") dask_model = xgb.dask.DaskXGBRegressor( tree_method=model_hyperparams["tree_method"], n_estimators=int(model_hyperparams["n_estimators"]), max_depth=int(model_hyperparams["max_depth"]), gamma=float(model_hyperparams["gamma"]), min_child_weight=float(model_hyperparams["min_child_weight"]), subsample=float(model_hyperparams["subsample"]), colsample_bytree=float(model_hyperparams["colsample_bytree"]), learning_rate=float(model_hyperparams["learning_rate"]), reg_alpha=float(model_hyperparams["reg_alpha"]), reg_lambda=float(model_hyperparams["reg_lambda"]), random_state=int(model_hyperparams["random_state"]), verbosity=3) dask_model.client = client X_train, X_test = full_pipeline(X_train, X_test, config) dask_model.fit(X_train, y_train) # dask_model.fit(X_train, y_train, eval_set=[(X_test, y_test)]) save_model(config, dask_model) save_config(config) return dask_model, X_train, X_test def train_lgbm_regression(client, X_train, y_train, X_test, config): print("Training lightGBM regression") model_hyperparams = config["advance"] \ ["distributedlearner_config"] \ ["modelParams"] \ ["regressorModelParams"] \ ["Distributed Light Gradient Boosting (LightGBM)"] dask_model = lgb.DaskLGBMRegressor( client=client, n_estimators=int(model_hyperparams["n_estimators"]), num_leaves=int(model_hyperparams["num_leaves"]), max_depth =int(model_hyperparams["max_depth"]), learning_rate=float(model_hyperparams["learning_rate"]), min_child_samples=int(model_hyperparams["min_child_samples"]), reg_alpha=int(model_hyperparams["reg_alpha"]), subsample=float(model_hyperparams["subsample"]), reg_lambda=int(model_hyperparams["reg_lambda"]), colsample_bytree=float(model_hyperparams["colsample_bytree"]), n_jobs=4, verbosity=3) X_train, X_test = full_pipeline(X_train, X_test, config) # print("before X_train.shape, y_train.shape", # X_train.shape, # y_train.shape) # indices = dask_findiforestOutlier(X_train) # print("X_train type: ", type(X_train)) # print("y_train type: ", type(y_train)) # X_train, y_train = X_train.iloc[indices, :], \ # y_train.iloc[indices] # print("after X_train.shape, y_train.shape", # X_train.shape, # y_train.shape) dask_model.fit(X_train, y_train) # dask_model.fit(X_train, y_train, # # eval_set=[(X_test,y_test), # # (X_train,y_train)], # verbose=20,eval_metric='l2') save_model(config, dask_model) save_config(config) return dask_model, X_train, X_test def train_lgbm_classification(client, X_train, y_train, X_test, config): print("Training lightGBM classification") model_hyperparams = config["advance"] \ ["distributedlearner_config"] \ ["modelParams"] \ ["classifierModelParams"] \ ["Distributed Light Gradient Boosting (LightGBM)"] dask_model = lgb.DaskLGBMClassifier( client=client, num_leaves=int(model_hyperparams["num_leaves"]), learning_rate=float(model_hyperparams["learning_rate"]), feature_fraction=float(model_hyperparams["feature_fraction"]), bagging_fraction=float(model_hyperparams["bagging_fraction"]), bagging_freq=int(model_hyperparams["bagging_freq"]), max_depth=int(model_hyperparams["max_depth"]), min_data_in_leaf=int(model_hyperparams["min_data_in_leaf"]), n_estimators=int(model_hyperparams["n_estimators"]), verbosity=3) X_train, X_test = full_pipeline(X_train, X_test, config) dask_model.fit(X_train, y_train) # dask_model.fit(X_train, y_train, # eval_set=[(X_test,y_test), # (X_train,y_train)], # verbose=20,eval_metric='logloss') save_model(config, dask_model) save_config(config) return dask_model, X_train, X_test def evaluate_model_classification(model, config, X_test, y_test, class_names): metrics = config["basic"]["scoringCriteria"]["classification"] y_test = y_test.to_dask_array().compute() log = logging.getLogger('eion') X_test = X_test.to_dask_array(lengths=True) y_pred = model.predict(X_test) if metrics["Accuracy"] == "True": # ParallelPostFit(estimator=model, scoring='accuracy') # score = model.score(X_test, y_test) * 100.0 score = accuracy_score(y_test, y_pred) * 100.0 type = 'Accuracy' log.info('Status:-|... Accuracy Score '+str(score)) if metrics["Recall"] == "True": score = recall_score(y_test, y_pred) type = 'Recall' log.info('Status:-|... Recall Score '+str(score)) if metrics["Precision"] == "True": score = precision_score(y_test, y_pred) type = 'Precision' log.info('Status:-|... Precision Score '+str(score)) if metrics["F1_Score"] == "True": score = f1_score(y_test, y_pred) type = 'F1' log.info('Status:-|... F1 Score '+str(score)) y_pred_prob = model.predict_proba(X_test) if len(class_names) == 2: roc_auc = roc_auc_score(y_test, y_pred) else: roc_auc = roc_auc_score(y_test, y_pred_prob, multi_class='ovr') if metrics["ROC_AUC"] == "True": score = roc_auc type = 'ROC_AUC' log.info('Status:-|... ROC AUC Score '+str(score)) class_report = classification_report(y_test, y_pred, output_dict=True, target_names=class_names) conf_matrix = confusion_matrix(y_test, y_pred) return type, score, class_report, conf_matrix, roc_auc def evaluate_model_regression(model, config, X_test, y_test): metrics = config["basic"]["scoringCriteria"]["regression"] y_pred = model.predict(X_test).compute() y_test = y_test.to_dask_array().compute() X_test = X_test.to_dask_array(lengths=True) log = logging.getLogger('eion') mse = mean_squared_error(y_test, y_pred) rmse = mean_squared_error(y_test, y_pred, squared=False) norm_rmse = rmse * 100 / (y_test.max() - y_test.min()) mape = mean_absolute_percentage_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) mae = mean_absolute_error(y_test, y_pred) if metrics["Mean Squared Error"] == "True": type = 'Mean Squared Error' score = mse log.info('Status:-|... Mean Squared Error '+str(score)) if metrics["Root Mean Squared Error"] == "True": type = 'Root Mean Squared Error' score = rmse log.info('Status:-|... Root Mean Square Error '+str(score)) if metrics["R-Squared"] == "True": type = 'R-Squared' score = r2 log.info('Status:-|... R Squared Error '+str(score)) if metrics["Mean Absolute Error"] == "True": type = 'Mean Absolute Error' score = mae log.info('Status:-|... Mean Absolute Error '+str(score)) return type, score, mse, rmse, norm_rmse, r2, mae, mape def save_config(config): deploy_location = config["basic"]["modelLocation"] saved_model_file = os.path.join(deploy_location,'etc','config.json') print(predict_config) with open (saved_model_file,'w') as f: json.dump(predict_config, f) f.close() def save_model(config, model): model_name = config["basic"]["modelName"] model_version = config["basic"]["modelVersion"] analysis_type = config["basic"]["analysisType"] deploy_location = config["basic"]["modelLocation"] if analysis_type["classification"] == "True": problem_type = "classification" if analysis_type["regression"] == "True": problem_type = "regression" print("model_name", model_name) print("model_version", model_version) print("problem_type", problem_type) print("deploy_location", deploy_location) file_name = problem_type + '_' + model_version + ".sav" saved_model = os.path.join(deploy_location,'model',file_name) print("Save trained model to directory: ", save_model) with open (saved_model,'wb') as f: pkl.dump(model,f) f.close() predict_config['modelLocation'] = file_name def save_pkl(model, filename): with open(filename, 'wb') as f: pkl.dump(model, f, protocol=pkl.HIGHEST_PROTOCOL) def dask_findiforestOutlier(X): print("Outlier removal with Isolation Forest...") isolation_forest = IsolationForest(n_estimators=100) with joblib.parallel_backend('dask'): isolation_forest.fit(X) y_pred_train = isolation_forest.fit_predict(X) mask_isoForest = y_pred_train != -1 return mask_isoForest def training(configFile): start_time = time.time() config = load_config_json(configFile) data_dir = config["basic"]["dataLocation"] n_workers = int(config["advance"] ["distributedlearner_config"] ["n_workers"]) npartitions = int(config["advance"] ["distributedlearner_config"] ["npartitions"]) threads_per_worker = int(config["advance"] ["distributedlearner_config"] ["threads_per_worker"]) predict_config['modelName'] = config["basic"]["modelName"] predict_config['modelVersion'] = config["basic"]["modelVersion"] predict_config['targetFeature'] = config["basic"]["targetFeature"] predict_config['trainingFeatures'] = config["basic"]["trainingFeatures"] predict_config['dataLocation'] = config["basic"]["dataLocation"] predict_config['n_workers'] = n_workers predict_config['npartitions'] = npartitions predict_config['threads_per_worker'] = threads_per_worker if config['basic']['analysisType']["classification"] == "True": problemType = "classification" oProblemType = "Distributed Classification" if config['basic']['analysisType']["regression"] == "True": problemType = "regression" oProblemType = "Distributed Regression" predict_config['analysisType'] = problemType predict_config['scoringCriteria'] = '' target_feature = config["basic"]["targetFeature"] training_features = config["basic"]["trainingFeatures"] deploy_location = config["basic"]["deployLocation"] is_xgb_class = config["basic"] \ ["algorithms"]["classification"] \ ["Distributed Extreme Gradient Boosting (XGBoost)"] is_lgbm_class = config["basic"] \ ["algorithms"]["classification"] \ ["Distributed Light Gradient Boosting (LightGBM)"] is_xgb_regress = config["basic"] \ ["algorithms"]["regression"] \ ["Distributed Extreme Gradient Boosting (XGBoost)"] is_lgbm_regress = config["basic"] \ ["algorithms"]["regression"] \ ["Distributed Light Gradient Boosting (LightGBM)"] if is_xgb_class=="True" or is_xgb_regress=="True": algorithm = "Distributed Extreme Gradient Boosting (XGBoost)" predict_config['algorithm'] = algorithm if is_lgbm_class=="True" or is_lgbm_regress=="True": algorithm = "Distributed Light Gradient Boosting (LightGBM)" predict_config['algorithm'] = algorithm cluster = dask.distributed.LocalCluster(n_workers=n_workers, threads_per_worker=threads_per_worker, # dashboard_address="127.0.0.1:8787" ) client = dask.distributed.Client(cluster) df_dask = load_data_dask(data_dir, npartitions=npartitions) deployFolder = config["basic"]["deployLocation"] modelName = config["basic"]["modelName"] modelName = modelName.replace(" ", "_") modelVersion = config["basic"]["modelVersion"] modelLocation = os.path.join(deployFolder,modelName) os.makedirs(modelLocation,exist_ok = True) deployLocation = os.path.join(modelLocation,modelVersion) predict_config['deployLocation'] = deployLocation try: os.makedirs(deployLocation) except OSError as e: shutil.rmtree(deployLocation) time.sleep(2) os.makedirs(deployLocation) modelFolderLocation = os.path.join(deployLocation,'model') try: os.makedirs(modelFolderLocation) except OSError as e: print("\nModel Folder Already Exists") etcFolderLocation = os.path.join(deployLocation,'etc') try: os.makedirs(etcFolderLocation) except OSError as e: print("\ETC Folder Already Exists") logFolderLocation = os.path.join(deployLocation,'log') try: os.makedirs(logFolderLocation) except OSError as e: print("\nLog Folder Already Exists") logFileName=os.path.join(logFolderLocation,'model_training_logs.log') outputjsonFile=os.path.join(deployLocation,'etc','output.json') filehandler = logging.FileHandler(logFileName, 'w','utf-8') formatter = logging.Formatter('%(message)s') filehandler.setFormatter(formatter) log = logging.getLogger('eion') log.propagate = False for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): log.removeHandler(hdlr) log.addHandler(filehandler) log.setLevel(logging.INFO) log.info('Status:-|... Distributed Learning Started') config['basic']['modelLocation'] = deployLocation # Get input for EDA # descr, corr = get_dask_eda(df_dask=df_dask) #print(descr) # print(corr) #print(df_dask.columns) #print("target feature", target_feature) df_dask = df_dask.dropna(subset=[target_feature]) if is_xgb_class == "True" or is_lgbm_class == "True": df_dask = df_dask.categorize(columns=[target_feature]) df_dask[target_feature] = df_dask[target_feature].astype('category') df_dask[target_feature] = df_dask[target_feature].cat.as_known() label_mapping = dict(enumerate(df_dask[target_feature].cat.categories)) df_dask[target_feature] = df_dask[target_feature].cat.codes label_mapping_file =os.path.join(deployLocation,'etc','label_mapping.json') with open(label_mapping_file, 'w') as f: json.dump(label_mapping, f) if config["advance"]["profiler"]["removeDuplicate"] == "True": df_dask = df_dask.drop_duplicates() # Need to dropna for case of categoricalFillMethod # if config["advance"]["profiler"]["numericalFillMethod"]["Drop"] == "True": # df_dask = df_dask.dropna() trainingFeatures = config["basic"]["trainingFeatures"].split(',') if target_feature not in trainingFeatures: trainingFeatures.append(target_feature) df_dask = df_dask[trainingFeatures] y = df_dask[target_feature] X = df_dask.drop(target_feature, axis=1) print("after X.shape, y.shape", X.shape, y.shape) X_train, X_test, y_train, y_test = dask_ml.model_selection.train_test_split(X, y, test_size=0.2, random_state=0) trainingFeatures = config["basic"]["trainingFeatures"].split(',') outputJson = None conf_matrix_dict = {} train_conf_matrix_dict = {} try: if is_xgb_class == "True": modelName = 'Distributed Extreme Gradient Boosting (XGBoost)' dask_model, X_train, X_test = train_xgb_classification(client, X_train, y_train, X_test, config) class_names = list(label_mapping.values()) _, _, train_class_report, train_conf_matrix, train_roc_auc = evaluate_model_classification(dask_model, config, X_train, y_train, class_names) scoringCreteria,score, class_report, conf_matrix, roc_auc = evaluate_model_classification(dask_model, config, X_test, y_test, class_names) for i in range(len(conf_matrix)): conf_matrix_dict_1 = {} for j in range(len(conf_matrix[i])): conf_matrix_dict_1['pre:' + str(class_names[j])] = int(conf_matrix[i][j]) conf_matrix_dict['act:'+ str(class_names[i])] = conf_matrix_dict_1 for i in range(len(train_conf_matrix)): train_conf_matrix_dict_1 = {} for j in range(len(train_conf_matrix[i])): train_conf_matrix_dict_1['pre:' + str(class_names[j])] = int(train_conf_matrix[i][j]) train_conf_matrix_dict['act:'+ str(class_names[i])] = train_conf_matrix_dict_1 # print(roc_auc) outputJson = {'status':'SUCCESS','data':{'ModelType':oProblemType,\ 'deployLocation':deployLocation,'BestModel':modelName,'BestScore':score,'ScoreType':scoringCreteria,\ 'matrix':{'ConfusionMatrix':conf_matrix_dict,'ClassificationReport':class_report,'ROC_AUC_SCORE':roc_auc},\ 'trainmatrix':{'ConfusionMatrix':train_conf_matrix_dict,'ClassificationReport':train_class_report,'ROC_AUC_SCORE':train_roc_auc},\ 'featuresused':trainingFeatures,'targetFeature':target_feature,'EvaluatedModels':[{'Model':modelName,'Score':score}], 'LogFile':logFileName}} if is_lgbm_class == "True": modelName = 'Distributed Light Gradient Boosting (LightGBM)' dask_model, X_train, X_test = train_lgbm_classification(client, X_train, y_train, X_test, config) class_names = list(label_mapping.values()) _, _, train_class_report, train_conf_matrix, train_roc_auc = evaluate_model_classification(dask_model, config, X_train, y_train, class_names) scoringCreteria,score, class_report, conf_matrix, roc_auc = evaluate_model_classification(dask_model, config, X_test, y_test, class_names) for i in range(len(conf_matrix)): conf_matrix_dict_1 = {} for j in range(len(conf_matrix[i])): conf_matrix_dict_1['pre:' + str(class_names[j])] = int(conf_matrix[i][j]) conf_matrix_dict['act:'+ str(class_names[i])] = conf_matrix_dict_1 for i in range(len(train_conf_matrix)): train_conf_matrix_dict_1 = {} for j in range(len(train_conf_matrix[i])): train_conf_matrix_dict_1['pre:' + str(class_names[j])] = int(train_conf_matrix[i][j]) train_conf_matrix_dict['act:'+ str(class_names[i])] = train_conf_matrix_dict_1 outputJson = {'status':'SUCCESS','data':{'ModelType':oProblemType,\ 'deployLocation':deployLocation,'BestModel':modelName,'BestScore':score,'ScoreType':scoringCreteria,\ 'matrix':{'ConfusionMatrix':conf_matrix_dict,'ClassificationReport':class_report,'ROC_AUC_SCORE':roc_auc},\ 'trainmatrix':{'ConfusionMatrix':train_conf_matrix_dict,'ClassificationReport':train_class_report,'ROC_AUC_SCORE':train_roc_auc},\ 'featuresused':trainingFeatures,'targetFeature':target_feature,'EvaluatedModels':[{'Model':modelName,'Score':score}], 'LogFile':logFileName}} if is_xgb_regress == "True": modelName = 'Distributed Extreme Gradient Boosting (XGBoost)' dask_model, X_train, X_test = train_xgb_regression(client, X_train, y_train, X_test, config) _, _, train_mse, train_rmse, train_norm_rmse, train_r2, train_mae, train_mape = evaluate_model_regression(dask_model, config, X_train, y_train) scoringCreteria, score, mse, rmse, norm_rmse, r2, mae, mape = evaluate_model_regression(dask_model, config, X_test, y_test) outputJson = {'status':'SUCCESS','data':{'ModelType':oProblemType,\ 'deployLocation':deployLocation,'BestModel':modelName,'BestScore':score,'ScoreType':scoringCreteria,\ 'matrix':{'MAE':mae,'R2Score':r2,'MSE':mse,'MAPE':mape,'RMSE':rmse,'Normalised RMSE(%)':norm_rmse}, \ 'trainmatrix':{'MAE':train_mae,'R2Score':train_r2,'MSE':train_mse,'MAPE':train_mape,'RMSE':train_rmse,'Normalised RMSE(%)':train_norm_rmse}, \ 'featuresused':trainingFeatures,'targetFeature':target_feature,'EvaluatedModels':[{'Model':modelName,'Score':score}], 'LogFile':logFileName}} if is_lgbm_regress == "True": modelName = 'Distributed Light Gradient Boosting (LightGBM)' dask_model, X_train, X_test = train_lgbm_regression(client, X_train, y_train, X_test, config) _, _, train_mse, train_rmse, train_norm_rmse, train_r2, train_mae, train_mape = evaluate_model_regression(dask_model, config, X_train, y_train) scoringCreteria, score, mse, rmse, norm_rmse, r2, mae, mape = evaluate_model_regression(dask_model, config, X_test, y_test) outputJson = {'status':'SUCCESS','data':{'ModelType':oProblemType,\ 'deployLocation':deployLocation,'BestModel':modelName,'BestScore':score,'ScoreType':scoringCreteria,\ 'matrix':{'MAE':mae,'R2Score':r2,'MSE':mse,'MAPE':mape,'RMSE':rmse,'Normalised RMSE(%)':norm_rmse}, \ 'trainmatrix':{'MAE':train_mae,'R2Score':train_r2,'MSE':train_mse,'MAPE':train_mape,'RMSE':train_rmse,'Normalised RMSE(%)':train_norm_rmse}, \ 'featuresused':trainingFeatures,'targetFeature':target_feature,'EvaluatedModels':[{'Model':modelName,'Score':score}], 'LogFile':logFileName}} src = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','utilities','dl_aion_predict.py') shutil.copy2(src,deployLocation) os.rename(os.path.join(deployLocation,'dl_aion_predict.py'),os.path.join(deployLocation,'aion_predict.py')) except Exception as e: outputJson = {"status":"FAIL","message":str(e)} print(e) client.close() cluster.close() log.info('Status:-|... Distributed Learning Completed') with open(outputjsonFile, 'w') as f: json.dump(outputJson, f) f.close() output_json = json.dumps(outputJson) log.info('aion_learner_status:'+str(output_json)) for hdlr in log.handlers[:]: # remove the existing file handlers if isinstance(hdlr,logging.FileHandler): hdlr.close() log.removeHandler(hdlr) print("\n") print("aion_learner_status:",output_json) print("\n") end_time = time.time() print("--- %s processing time (sec) ---" % (end_time - start_time)) """ The :mod:`dataset <surprise.dataset>` module defines the :class:`Dataset` class and other subclasses which are used for managing datasets. Users may use both *built-in* and user-defined datasets (see the :ref:`getting_started` page for examples). Right now, three built-in datasets are available: * The `movielens-100k <https://grouplens.org/datasets/movielens/>`_ dataset. * The `movielens-1m <https://grouplens.org/datasets/movielens/>`_ dataset. * The `Jester <https://eigentaste.berkeley.edu/dataset/>`_ dataset 2. Built-in datasets can all be loaded (or downloaded if you haven't already) using the :meth:`Dataset.load_builtin` method. Summary: .. autosummary:: :nosignatures: Dataset.load_builtin Dataset.load_from_file Dataset.load_from_folds """ import itertools import os import sys from collections import defaultdict from .builtin_datasets import BUILTIN_DATASETS, download_builtin_dataset from .reader import Reader from .trainset import Trainset class Dataset: """Base class for loading datasets. Note that you should never instantiate the :class:`Dataset` class directly (same goes for its derived classes), but instead use one of the three available methods for loading datasets.""" def __init__(self, reader): self.reader = reader @classmethod def load_builtin(cls, name="ml-100k", prompt=True): """Load a built-in dataset. If the dataset has not already been loaded, it will be downloaded and saved. You will have to split your dataset using the :meth:`split <DatasetAutoFolds.split>` method. See an example in the :ref:`User Guide <cross_validate_example>`. Args: name(:obj:`string`): The name of the built-in dataset to load. Accepted values are 'ml-100k', 'ml-1m', and 'jester'. Default is 'ml-100k'. prompt(:obj:`bool`): Prompt before downloading if dataset is not already on disk. Default is True. Returns: A :obj:`Dataset` object. Raises: ValueError: If the ``name`` parameter is incorrect. """ try: dataset = BUILTIN_DATASETS[name] except KeyError: raise ValueError( "unknown dataset " + name + ". Accepted values are " + ", ".join(BUILTIN_DATASETS.keys()) + "." ) # if dataset does not exist, offer to download it if not os.path.isfile(dataset.path): answered = not prompt while not answered: print( "Dataset " + name + " could not be found. Do you want " "to download it? [Y/n] ", end="", ) choice = input().lower() if choice in ["yes", "y", "", "omg this is so nice of you!!"]: answered = True elif choice in ["no", "n", "hell no why would i want that?!"]: answered = True print("Ok then, I'm out!") sys.exit() download_builtin_dataset(name) reader = Reader(**dataset.reader_params) return cls.load_from_file(file_path=dataset.path, reader=reader) @classmethod def load_from_file(cls, file_path, reader): """Load a dataset from a (custom) file. Use this if you want to use a custom dataset and all of the ratings are stored in one file. You will have to split your dataset using the :meth:`split <DatasetAutoFolds.split>` method. See an example in the :ref:`User Guide <load_from_file_example>`. Args: file_path(:obj:`string`): The path to the file containing ratings. reader(:obj:`Reader <surprise.reader.Reader>`): A reader to read the file. """ return DatasetAutoFolds(ratings_file=file_path, reader=reader) @classmethod def load_from_folds(cls, folds_files, reader): """Load a dataset where folds (for cross-validation) are predefined by some files. The purpose of this method is to cover a common use case where a dataset is already split into predefined folds, such as the movielens-100k dataset which defines files u1.base, u1.test, u2.base, u2.test, etc... It can also be used when you don't want to perform cross-validation but still want to specify your training and testing data (which comes down to 1-fold cross-validation anyway). See an example in the :ref:`User Guide <load_from_folds_example>`. Args: folds_files(:obj:`iterable` of :obj:`tuples`): The list of the folds. A fold is a tuple of the form ``(path_to_train_file, path_to_test_file)``. reader(:obj:`Reader <surprise.reader.Reader>`): A reader to read the files. """ return DatasetUserFolds(folds_files=folds_files, reader=reader) @classmethod def load_from_df(cls, df, reader): """Load a dataset from a pandas dataframe. Use this if you want to use a custom dataset that is stored in a pandas dataframe. See the :ref:`User Guide<load_from_df_example>` for an example. Args: df(`Dataframe`): The dataframe containing the ratings. It must have three columns, corresponding to the user (raw) ids, the item (raw) ids, and the ratings, in this order. reader(:obj:`Reader <surprise.reader.Reader>`): A reader to read the file. Only the ``rating_scale`` field needs to be specified. """ return DatasetAutoFolds(reader=reader, df=df) def read_ratings(self, file_name): """Return a list of ratings (user, item, rating, timestamp) read from file_name""" with open(os.path.expanduser(file_name)) as f: raw_ratings = [ self.reader.parse_line(line) for line in itertools.islice(f, self.reader.skip_lines, None) ] return raw_ratings def construct_trainset(self, raw_trainset): raw2inner_id_users = {} raw2inner_id_items = {} current_u_index = 0 current_i_index = 0 ur = defaultdict(list) ir = defaultdict(list) # user raw id, item raw id, translated rating, time stamp for urid, irid, r, timestamp in raw_trainset: try: uid = raw2inner_id_users[urid] except KeyError: uid = current_u_index raw2inner_id_users[urid] = current_u_index current_u_index += 1 try: iid = raw2inner_id_items[irid] except KeyError: iid = current_i_index raw2inner_id_items[irid] = current_i_index current_i_index += 1 ur[uid].append((iid, r)) ir[iid].append((uid, r)) n_users = len(ur) # number of users n_items = len(ir) # number of items n_ratings = len(raw_trainset) trainset = Trainset( ur, ir, n_users, n_items, n_ratings, self.reader.rating_scale, raw2inner_id_users, raw2inner_id_items, ) return trainset def construct_testset(self, raw_testset): return [(ruid, riid, r_ui_trans) for (ruid, riid, r_ui_trans, _) in raw_testset] class DatasetUserFolds(Dataset): """A derived class from :class:`Dataset` for which folds (for cross-validation) are predefined.""" def __init__(self, folds_files=None, reader=None): Dataset.__init__(self, reader) self.folds_files = folds_files # check that all files actually exist. for train_test_files in self.folds_files: for f in train_test_files: if not os.path.isfile(os.path.expanduser(f)): raise ValueError("File " + str(f) + " does not exist.") class DatasetAutoFolds(Dataset): """A derived class from :class:`Dataset` for which folds (for cross-validation) are not predefined. (Or for when there are no folds at all).""" def __init__(self, ratings_file=None, reader=None, df=None): Dataset.__init__(self, reader) self.has_been_split = False # flag indicating if split() was called. if ratings_file is not None: self.ratings_file = ratings_file self.raw_ratings = self.read_ratings(self.ratings_file) elif df is not None: self.df = df self.raw_ratings = [ (uid, iid, float(r), None) for (uid, iid, r) in self.df.itertuples(index=False) ] else: raise ValueError("Must specify ratings file or dataframe.") def build_full_trainset(self): """Do not split the dataset into folds and just return a trainset as is, built from the whole dataset. User can then query for predictions, as shown in the :ref:`User Guide <train_on_whole_trainset>`. Returns: The :class:`Trainset <surprise.Trainset>`. """ return self.construct_trainset(self.raw_ratings) """ The :mod:`dump <surprise.dump>` module defines the :func:`dump` function. """ import pickle def dump(file_name, predictions=None, algo=None, verbose=0): """A basic wrapper around Pickle to serialize a list of prediction and/or an algorithm on drive. What is dumped is a dictionary with keys ``'predictions'`` and ``'algo'``. Args: file_name(str): The name (with full path) specifying where to dump the predictions. predictions(list of :obj:`Prediction\ <surprise.prediction_algorithms.predictions.Prediction>`): The predictions to dump. algo(:class:`Algorithm\ <surprise.prediction_algorithms.algo_base.AlgoBase>`, optional): The algorithm to dump. verbose(int): Level of verbosity. If ``1``, then a message indicates that the dumping went successfully. Default is ``0``. """ dump_obj = {"predictions": predictions, "algo": algo} pickle.dump(dump_obj, open(file_name, "wb"), protocol=pickle.HIGHEST_PROTOCOL) if verbose: print("The dump has been saved as file", file_name) def load(file_name): """A basic wrapper around Pickle to deserialize a list of prediction and/or an algorithm that were dumped on drive using :func:`dump() <surprise.dump.dump>`. Args: file_name(str): The path of the file from which the algorithm is to be loaded Returns: A tuple ``(predictions, algo)`` where ``predictions`` is a list of :class:`Prediction <surprise.prediction_algorithms.predictions.Prediction>` objects and ``algo`` is an :class:`Algorithm <surprise.prediction_algorithms.algo_base.AlgoBase>` object. Depending on what was dumped, some of these may be ``None``. """ dump_obj = pickle.load(open(file_name, "rb")) return dump_obj["predictions"], dump_obj["algo"] """ The :mod:`surprise.accuracy` module provides tools for computing accuracy metrics on a set of predictions. Available accuracy metrics: .. autosummary:: :nosignatures: rmse mse mae fcp """ from collections import defaultdict import numpy as np def rmse(predictions, verbose=True): """Compute RMSE (Root Mean Squared Error). .. math:: \\text{RMSE} = \\sqrt{\\frac{1}{|\\hat{R}|} \\sum_{\\hat{r}_{ui} \\in \\hat{R}}(r_{ui} - \\hat{r}_{ui})^2}. Args: predictions (:obj:`list` of :obj:`Prediction\ <surprise.prediction_algorithms.predictions.Prediction>`): A list of predictions, as returned by the :meth:`test() <surprise.prediction_algorithms.algo_base.AlgoBase.test>` method. verbose: If True, will print computed value. Default is ``True``. Returns: The Root Mean Squared Error of predictions. Raises: ValueError: When ``predictions`` is empty. """ if not predictions: raise ValueError("Prediction list is empty.") mse = np.mean( [float((true_r - est) ** 2) for (_, _, true_r, est, _) in predictions] ) rmse_ = np.sqrt(mse) if verbose: print(f"RMSE: {rmse_:1.4f}") return rmse_ def mse(predictions, verbose=True): """Compute MSE (Mean Squared Error). .. math:: \\text{MSE} = \\frac{1}{|\\hat{R}|} \\sum_{\\hat{r}_{ui} \\in \\hat{R}}(r_{ui} - \\hat{r}_{ui})^2. Args: predictions (:obj:`list` of :obj:`Prediction\ <surprise.prediction_algorithms.predictions.Prediction>`): A list of predictions, as returned by the :meth:`test() <surprise.prediction_algorithms.algo_base.AlgoBase.test>` method. verbose: If True, will print computed value. Default is ``True``. Returns: The Mean Squared Error of predictions. Raises: ValueError: When ``predictions`` is empty. """ if not predictions: raise ValueError("Prediction list is empty.") mse_ = np.mean( [float((true_r - est) ** 2) for (_, _, true_r, est, _) in predictions] ) if verbose: print(f"MSE: {mse_:1.4f}") return mse_ def mae(predictions, verbose=True): """Compute MAE (Mean Absolute Error). .. math:: \\text{MAE} = \\frac{1}{|\\hat{R}|} \\sum_{\\hat{r}_{ui} \\in \\hat{R}}|r_{ui} - \\hat{r}_{ui}| Args: predictions (:obj:`list` of :obj:`Prediction\ <surprise.prediction_algorithms.predictions.Prediction>`): A list of predictions, as returned by the :meth:`test() <surprise.prediction_algorithms.algo_base.AlgoBase.test>` method. verbose: If True, will print computed value. Default is ``True``. Returns: The Mean Absolute Error of predictions. Raises: ValueError: When ``predictions`` is empty. """ if not predictions: raise ValueError("Prediction list is empty.") mae_ = np.mean([float(abs(true_r - est)) for (_, _, true_r, est, _) in predictions]) if verbose: print(f"MAE: {mae_:1.4f}") return mae_ def fcp(predictions, verbose=True): """Compute FCP (Fraction of Concordant Pairs). Computed as described in paper `Collaborative Filtering on Ordinal User Feedback <https://www.ijcai.org/Proceedings/13/Papers/449.pdf>`_ by Koren and Sill, section 5.2. Args: predictions (:obj:`list` of :obj:`Prediction\ <surprise.prediction_algorithms.predictions.Prediction>`): A list of predictions, as returned by the :meth:`test() <surprise.prediction_algorithms.algo_base.AlgoBase.test>` method. verbose: If True, will print computed value. Default is ``True``. Returns: The Fraction of Concordant Pairs. Raises: ValueError: When ``predictions`` is empty. """ if not predictions: raise ValueError("Prediction list is empty.") predictions_u = defaultdict(list) nc_u = defaultdict(int) nd_u = defaultdict(int) for u0, _, r0, est, _ in predictions: predictions_u[u0].append((r0, est)) for u0, preds in predictions_u.items(): for r0i, esti in preds: for r0j, estj in preds: if esti > estj and r0i > r0j: nc_u[u0] += 1 if esti >= estj and r0i < r0j: nd_u[u0] += 1 nc = np.mean(list(nc_u.values())) if nc_u else 0 nd = np.mean(list(nd_u.values())) if nd_u else 0 try: fcp = nc / (nc + nd) except ZeroDivisionError: raise ValueError( "cannot compute fcp on this list of prediction. " + "Does every user have at least two predictions?" ) if verbose: print(f"FCP: {fcp:1.4f}") return fcp """This module contains the Reader class.""" from .builtin_datasets import BUILTIN_DATASETS class Reader: """The Reader class is used to parse a file containing ratings. Such a file is assumed to specify only one rating per line, and each line needs to respect the following structure: :: user ; item ; rating ; [timestamp] where the order of the fields and the separator (here ';') may be arbitrarily defined (see below). brackets indicate that the timestamp field is optional. For each built-in dataset, Surprise also provides predefined readers which are useful if you want to use a custom dataset that has the same format as a built-in one (see the ``name`` parameter). Args: name(:obj:`string`, optional): If specified, a Reader for one of the built-in datasets is returned and any other parameter is ignored. Accepted values are 'ml-100k', 'ml-1m', and 'jester'. Default is ``None``. line_format(:obj:`string`): The fields names, in the order at which they are encountered on a line. Please note that ``line_format`` is always space-separated (use the ``sep`` parameter). Default is ``'user item rating'``. sep(char): the separator between fields. Example : ``';'``. rating_scale(:obj:`tuple`, optional): The rating scale used for every rating. Default is ``(1, 5)``. skip_lines(:obj:`int`, optional): Number of lines to skip at the beginning of the file. Default is ``0``. """ def __init__( self, name=None, line_format="user item rating", sep=None, rating_scale=(1, 5), skip_lines=0, ): if name: try: self.__init__(**BUILTIN_DATASETS[name].reader_params) except KeyError: raise ValueError( "unknown reader " + name + ". Accepted values are " + ", ".join(BUILTIN_DATASETS.keys()) + "." ) else: self.sep = sep self.skip_lines = skip_lines self.rating_scale = rating_scale lower_bound, higher_bound = rating_scale splitted_format = line_format.split() entities = ["user", "item", "rating"] if "timestamp" in splitted_format: self.with_timestamp = True entities.append("timestamp") else: self.with_timestamp = False # check that all fields are correct if any(field not in entities for field in splitted_format): raise ValueError("line_format parameter is incorrect.") self.indexes = [splitted_format.index(entity) for entity in entities] def parse_line(self, line): """Parse a line. Ratings are translated so that they are all strictly positive. Args: line(str): The line to parse Returns: tuple: User id, item id, rating and timestamp. The timestamp is set to ``None`` if it does no exist. """ line = line.split(self.sep) try: if self.with_timestamp: uid, iid, r, timestamp = (line[i].strip() for i in self.indexes) else: uid, iid, r = (line[i].strip() for i in self.indexes) timestamp = None except IndexError: raise ValueError( "Impossible to parse line. Check the line_format" " and sep parameters." ) return uid, iid, float(r), timestamp from pkg_resources import get_distribution from . import dump, model_selection from .builtin_datasets import get_dataset_dir from .dataset import Dataset from .prediction_algorithms import ( AlgoBase, BaselineOnly, CoClustering, KNNBaseline, KNNBasic, KNNWithMeans, KNNWithZScore, NMF, NormalPredictor, Prediction, PredictionImpossible, SlopeOne, SVD, SVDpp, ) from .reader import Reader from .trainset import Trainset __all__ = [ "AlgoBase", "NormalPredictor", "BaselineOnly", "KNNBasic", "KNNWithMeans", "KNNBaseline", "SVD", "SVDpp", "NMF", "SlopeOne", "CoClustering", "PredictionImpossible", "Prediction", "Dataset", "Reader", "Trainset", "dump", "KNNWithZScore", "get_dataset_dir", "model_selection", ] __version__ = get_distribution("scikit-surprise").version """This module contains built-in datasets that can be automatically downloaded.""" import errno import os import zipfile from collections import namedtuple from os.path import join from urllib.request import urlretrieve def get_dataset_dir(): """Return folder where downloaded datasets and other data are stored. Default folder is ~/.surprise_data/, but it can also be set by the environment variable ``SURPRISE_DATA_FOLDER``. """ folder = os.environ.get( "SURPRISE_DATA_FOLDER", os.path.expanduser("~") + "/.surprise_data/" ) try: os.makedirs(folder) except OSError as e: if e.errno != errno.EEXIST: # reraise exception if folder does not exist and creation failed. raise return folder # a builtin dataset has # - an url (where to download it) # - a path (where it is located on the filesystem) # - the parameters of the corresponding reader BuiltinDataset = namedtuple("BuiltinDataset", ["url", "path", "reader_params"]) BUILTIN_DATASETS = { "ml-100k": BuiltinDataset( url="https://files.grouplens.org/datasets/movielens/ml-100k.zip", path=join(get_dataset_dir(), "ml-100k/ml-100k/u.data"), reader_params=dict( line_format="user item rating timestamp", rating_scale=(1, 5), sep="\t" ), ), "ml-1m": BuiltinDataset( url="https://files.grouplens.org/datasets/movielens/ml-1m.zip", path=join(get_dataset_dir(), "ml-1m/ml-1m/ratings.dat"), reader_params=dict( line_format="user item rating timestamp", rating_scale=(1, 5), sep="::" ), ), "jester": BuiltinDataset( url="https://eigentaste.berkeley.edu/dataset/archive/jester_dataset_2.zip", path=join(get_dataset_dir(), "jester/jester_ratings.dat"), reader_params=dict(line_format="user item rating", rating_scale=(-10, 10)), ), } def download_builtin_dataset(name): dataset = BUILTIN_DATASETS[name] print("Trying to download dataset from " + dataset.url + "...") tmp_file_path = join(get_dataset_dir(), "tmp.zip") urlretrieve(dataset.url, tmp_file_path) with zipfile.ZipFile(tmp_file_path, "r") as tmp_zip: tmp_zip.extractall(join(get_dataset_dir(), name)) os.remove(tmp_file_path) print("Done! Dataset", name, "has been saved to", join(get_dataset_dir(), name)) #!/usr/bin/env python import argparse import os import random as rd import shutil import sys import numpy as np import surprise.dataset as dataset from surprise import __version__ from surprise.builtin_datasets import get_dataset_dir from surprise.dataset import Dataset from surprise.model_selection import cross_validate, KFold, PredefinedKFold from surprise.prediction_algorithms import ( BaselineOnly, CoClustering, KNNBaseline, KNNBasic, KNNWithMeans, NMF, NormalPredictor, SlopeOne, SVD, SVDpp, ) from surprise.reader import Reader # noqa def main(): class MyParser(argparse.ArgumentParser): """A parser which prints the help message when an error occurs. Taken from https://stackoverflow.com/questions/4042452/display-help-message-with-python-argparse-when-script-is-called-without-any-argu.""" # noqa def error(self, message): sys.stderr.write("error: %s\n" % message) self.print_help() sys.exit(2) parser = MyParser( description="Evaluate the performance of a rating prediction " + "algorithm " + "on a given dataset using cross validation. You can use a built-in " + "or a custom dataset, and you can choose to automatically split the " + "dataset into folds, or manually specify train and test files. " + "Please refer to the documentation page " + "(https://surprise.readthedocs.io/) for more details.", epilog="""Example:\n surprise -algo SVD -params "{'n_epochs': 5, 'verbose': True}" -load-builtin ml-100k -n-folds 3""", ) algo_choices = { "NormalPredictor": NormalPredictor, "BaselineOnly": BaselineOnly, "KNNBasic": KNNBasic, "KNNBaseline": KNNBaseline, "KNNWithMeans": KNNWithMeans, "SVD": SVD, "SVDpp": SVDpp, "NMF": NMF, "SlopeOne": SlopeOne, "CoClustering": CoClustering, } parser.add_argument( "-algo", type=str, choices=algo_choices, help="The prediction algorithm to use. " + "Allowed values are " + ", ".join(algo_choices.keys()) + ".", metavar="<prediction algorithm>", ) parser.add_argument( "-params", type=str, metavar="<algorithm parameters>", default="{}", help="A kwargs dictionary that contains all the " + "algorithm parameters." + "Example: \"{'n_epochs': 10}\".", ) parser.add_argument( "-load-builtin", type=str, dest="load_builtin", metavar="<dataset name>", default="ml-100k", help="The name of the built-in dataset to use." + "Allowed values are " + ", ".join(dataset.BUILTIN_DATASETS.keys()) + ". Default is ml-100k.", ) parser.add_argument( "-load-custom", type=str, dest="load_custom", metavar="<file path>", default=None, help="A file path to custom dataset to use. " + "Ignored if " + "-loadbuiltin is set. The -reader parameter needs " + "to be set.", ) parser.add_argument( "-folds-files", type=str, dest="folds_files", metavar="<train1 test1 train2 test2... >", default=None, help="A list of custom train and test files. " + "Ignored if -load-builtin or -load-custom is set. " "The -reader parameter needs to be set.", ) parser.add_argument( "-reader", type=str, metavar="<reader>", default=None, help="A Reader to read the custom dataset. Example: " + "\"Reader(line_format='user item rating timestamp'," + " sep='\\t')\"", ) parser.add_argument( "-n-folds", type=int, dest="n_folds", metavar="<number of folds>", default=5, help="The number of folds for cross-validation. " + "Default is 5.", ) parser.add_argument( "-seed", type=int, metavar="<random seed>", default=None, help="The seed to use for RNG. " + "Default is the current system time.", ) parser.add_argument( "--with-dump", dest="with_dump", action="store_true", help="Dump the algorithm " + "results in a file (one file per fold). " + "Default is False.", ) parser.add_argument( "-dump-dir", dest="dump_dir", type=str, metavar="<dir>", default=None, help="Where to dump the files. Ignored if " + "with-dump is not set. Default is " + os.path.join(get_dataset_dir(), "dumps/"), ) parser.add_argument( "--clean", dest="clean", action="store_true", help="Remove the " + get_dataset_dir() + " directory and exit.", ) parser.add_argument("-v", "--version", action="version", version=__version__) args = parser.parse_args() if args.clean: folder = get_dataset_dir() shutil.rmtree(folder) print("Removed", folder) exit() # setup RNG rd.seed(args.seed) np.random.seed(args.seed) # setup algorithm params = eval(args.params) if args.algo is None: parser.error("No algorithm was specified.") algo = algo_choices[args.algo](**params) # setup dataset if args.load_custom is not None: # load custom and split if args.reader is None: parser.error("-reader parameter is needed.") reader = eval(args.reader) data = Dataset.load_from_file(args.load_custom, reader=reader) cv = KFold(n_splits=args.n_folds, random_state=args.seed) elif args.folds_files is not None: # load from files if args.reader is None: parser.error("-reader parameter is needed.") reader = eval(args.reader) folds_files = args.folds_files.split() folds_files = [ (folds_files[i], folds_files[i + 1]) for i in range(0, len(folds_files) - 1, 2) ] data = Dataset.load_from_folds(folds_files=folds_files, reader=reader) cv = PredefinedKFold() else: # load builtin dataset and split data = Dataset.load_builtin(args.load_builtin) cv = KFold(n_splits=args.n_folds, random_state=args.seed) cross_validate(algo, data, cv=cv, verbose=True) if __name__ == "__main__": main() """This module contains the Trainset class.""" import numpy as np class Trainset: """A trainset contains all useful data that constitute a training set. It is used by the :meth:`fit() <surprise.prediction_algorithms.algo_base.AlgoBase.fit>` method of every prediction algorithm. You should not try to build such an object on your own but rather use the :meth:`Dataset.folds() <surprise.dataset.Dataset.folds>` method or the :meth:`DatasetAutoFolds.build_full_trainset() <surprise.dataset.DatasetAutoFolds.build_full_trainset>` method. Trainsets are different from :class:`Datasets <surprise.dataset.Dataset>`. You can think of a :class:`Dataset <surprise.dataset.Dataset>` as the raw data, and Trainsets as higher-level data where useful methods are defined. Also, a :class:`Dataset <surprise.dataset.Dataset>` may be comprised of multiple Trainsets (e.g. when doing cross validation). Attributes: ur(:obj:`defaultdict` of :obj:`list`): The users ratings. This is a dictionary containing lists of tuples of the form ``(item_inner_id, rating)``. The keys are user inner ids. ir(:obj:`defaultdict` of :obj:`list`): The items ratings. This is a dictionary containing lists of tuples of the form ``(user_inner_id, rating)``. The keys are item inner ids. n_users: Total number of users :math:`|U|`. n_items: Total number of items :math:`|I|`. n_ratings: Total number of ratings :math:`|R_{train}|`. rating_scale(tuple): The minimum and maximal rating of the rating scale. global_mean: The mean of all ratings :math:`\\mu`. """ def __init__( self, ur, ir, n_users, n_items, n_ratings, rating_scale, raw2inner_id_users, raw2inner_id_items, ): self.ur = ur self.ir = ir self.n_users = n_users self.n_items = n_items self.n_ratings = n_ratings self.rating_scale = rating_scale self._raw2inner_id_users = raw2inner_id_users self._raw2inner_id_items = raw2inner_id_items self._global_mean = None # inner2raw dicts could be built right now (or even before) but they # are not always useful so we wait until we need them. self._inner2raw_id_users = None self._inner2raw_id_items = None def knows_user(self, uid): """Indicate if the user is part of the trainset. A user is part of the trainset if the user has at least one rating. Args: uid(int): The (inner) user id. See :ref:`this note<raw_inner_note>`. Returns: ``True`` if user is part of the trainset, else ``False``. """ return uid in self.ur def knows_item(self, iid): """Indicate if the item is part of the trainset. An item is part of the trainset if the item was rated at least once. Args: iid(int): The (inner) item id. See :ref:`this note<raw_inner_note>`. Returns: ``True`` if item is part of the trainset, else ``False``. """ return iid in self.ir def to_inner_uid(self, ruid): """Convert a **user** raw id to an inner id. See :ref:`this note<raw_inner_note>`. Args: ruid(str): The user raw id. Returns: int: The user inner id. Raises: ValueError: When user is not part of the trainset. """ try: return self._raw2inner_id_users[ruid] except KeyError: raise ValueError("User " + str(ruid) + " is not part of the trainset.") def to_raw_uid(self, iuid): """Convert a **user** inner id to a raw id. See :ref:`this note<raw_inner_note>`. Args: iuid(int): The user inner id. Returns: str: The user raw id. Raises: ValueError: When ``iuid`` is not an inner id. """ if self._inner2raw_id_users is None: self._inner2raw_id_users = { inner: raw for (raw, inner) in self._raw2inner_id_users.items() } try: return self._inner2raw_id_users[iuid] except KeyError: raise ValueError(str(iuid) + " is not a valid inner id.") def to_inner_iid(self, riid): """Convert an **item** raw id to an inner id. See :ref:`this note<raw_inner_note>`. Args: riid(str): The item raw id. Returns: int: The item inner id. Raises: ValueError: When item is not part of the trainset. """ try: return self._raw2inner_id_items[riid] except KeyError: raise ValueError("Item " + str(riid) + " is not part of the trainset.") def to_raw_iid(self, iiid): """Convert an **item** inner id to a raw id. See :ref:`this note<raw_inner_note>`. Args: iiid(int): The item inner id. Returns: str: The item raw id. Raises: ValueError: When ``iiid`` is not an inner id. """ if self._inner2raw_id_items is None: self._inner2raw_id_items = { inner: raw for (raw, inner) in self._raw2inner_id_items.items() } try: return self._inner2raw_id_items[iiid] except KeyError: raise ValueError(str(iiid) + " is not a valid inner id.") def all_ratings(self): """Generator function to iterate over all ratings. Yields: A tuple ``(uid, iid, rating)`` where ids are inner ids (see :ref:`this note <raw_inner_note>`). """ for u, u_ratings in self.ur.items(): for i, r in u_ratings: yield u, i, r def build_testset(self): """Return a list of ratings that can be used as a testset in the :meth:`test() <surprise.prediction_algorithms.algo_base.AlgoBase.test>` method. The ratings are all the ratings that are in the trainset, i.e. all the ratings returned by the :meth:`all_ratings() <surprise.Trainset.all_ratings>` generator. This is useful in cases where you want to to test your algorithm on the trainset. """ return [ (self.to_raw_uid(u), self.to_raw_iid(i), r) for (u, i, r) in self.all_ratings() ] def build_anti_testset(self, fill=None): """Return a list of ratings that can be used as a testset in the :meth:`test() <surprise.prediction_algorithms.algo_base.AlgoBase.test>` method. The ratings are all the ratings that are **not** in the trainset, i.e. all the ratings :math:`r_{ui}` where the user :math:`u` is known, the item :math:`i` is known, but the rating :math:`r_{ui}` is not in the trainset. As :math:`r_{ui}` is unknown, it is either replaced by the :code:`fill` value or assumed to be equal to the mean of all ratings :meth:`global_mean <surprise.Trainset.global_mean>`. Args: fill(float): The value to fill unknown ratings. If :code:`None` the global mean of all ratings :meth:`global_mean <surprise.Trainset.global_mean>` will be used. Returns: A list of tuples ``(uid, iid, fill)`` where ids are raw ids. """ fill = self.global_mean if fill is None else float(fill) anti_testset = [] for u in self.all_users(): user_items = {j for (j, _) in self.ur[u]} anti_testset += [ (self.to_raw_uid(u), self.to_raw_iid(i), fill) for i in self.all_items() if i not in user_items ] return anti_testset def all_users(self): """Generator function to iterate over all users. Yields: Inner id of users. """ return range(self.n_users) def all_items(self): """Generator function to iterate over all items. Yields: Inner id of items. """ return range(self.n_items) @property def global_mean(self): if self._global_mean is None: self._global_mean = np.mean([r for (_, _, r) in self.all_ratings()]) return self._global_mean """The utils module contains the get_rng function.""" import numbers import numpy as np def get_rng(random_state): """Return a 'validated' RNG. If random_state is None, use RandomState singleton from numpy. Else if it's an integer, consider it's a seed and initialized an rng with that seed. If it's already an rng, return it. """ if random_state is None: return np.random.mtrand._rand elif isinstance(random_state, (numbers.Integral, np.integer)): return np.random.RandomState(random_state) if isinstance(random_state, np.random.RandomState): return random_state raise ValueError( "Wrong random state. Expecting None, an int or a numpy " "RandomState instance, got a " "{}".format(type(random_state)) ) from abc import ABC, abstractmethod from itertools import product import numpy as np from joblib import delayed, Parallel from ..dataset import DatasetUserFolds from ..utils import get_rng from .split import get_cv from .validation import fit_and_score class BaseSearchCV(ABC): """Base class for hyper parameter search with cross-validation.""" @abstractmethod def __init__( self, algo_class, measures=["rmse", "mae"], cv=None, refit=False, return_train_measures=False, n_jobs=1, pre_dispatch="2*n_jobs", joblib_verbose=0, ): self.algo_class = algo_class self.measures = [measure.lower() for measure in measures] self.cv = cv if isinstance(refit, str): if refit.lower() not in self.measures: raise ValueError( "It looks like the measure you want to use " "with refit ({}) is not in the measures " "parameter" ) self.refit = refit.lower() elif refit is True: self.refit = self.measures[0] else: self.refit = False self.return_train_measures = return_train_measures self.n_jobs = n_jobs self.pre_dispatch = pre_dispatch self.joblib_verbose = joblib_verbose def _parse_options(self, params): # As sim_options and bsl_options are dictionaries, they require a # special treatment. if "sim_options" in params: sim_options = params["sim_options"] sim_options_list = [ dict(zip(sim_options, v)) for v in product(*sim_options.values()) ] params["sim_options"] = sim_options_list if "bsl_options" in params: bsl_options = params["bsl_options"] bsl_options_list = [ dict(zip(bsl_options, v)) for v in product(*bsl_options.values()) ] params["bsl_options"] = bsl_options_list return params def fit(self, data): """Runs the ``fit()`` method of the algorithm for all parameter combinations, over different splits given by the ``cv`` parameter. Args: data (:obj:`Dataset <surprise.dataset.Dataset>`): The dataset on which to evaluate the algorithm, in parallel. """ if self.refit and isinstance(data, DatasetUserFolds): raise ValueError( "refit cannot be used when data has been " "loaded with load_from_folds()." ) cv = get_cv(self.cv) delayed_list = ( delayed(fit_and_score)( self.algo_class(**params), trainset, testset, self.measures, self.return_train_measures, ) for params, (trainset, testset) in product( self.param_combinations, cv.split(data) ) ) out = Parallel( n_jobs=self.n_jobs, pre_dispatch=self.pre_dispatch, verbose=self.joblib_verbose, )(delayed_list) (test_measures_dicts, train_measures_dicts, fit_times, test_times) = zip(*out) # test_measures_dicts is a list of dict like this: # [{'mae': 1, 'rmse': 2}, {'mae': 2, 'rmse': 3} ...] # E.g. for 5 splits, the first 5 dicts are for the first param # combination, the next 5 dicts are for the second param combination, # etc... # We convert it into a dict of list: # {'mae': [1, 2, ...], 'rmse': [2, 3, ...]} # Each list is still of size n_parameters_combinations * n_splits. # Then, reshape each list to have 2-D arrays of shape # (n_parameters_combinations, n_splits). This way we can easily compute # the mean and std dev over all splits or over all param comb. test_measures = dict() train_measures = dict() new_shape = (len(self.param_combinations), cv.get_n_folds()) for m in self.measures: test_measures[m] = np.asarray([d[m] for d in test_measures_dicts]) test_measures[m] = test_measures[m].reshape(new_shape) if self.return_train_measures: train_measures[m] = np.asarray([d[m] for d in train_measures_dicts]) train_measures[m] = train_measures[m].reshape(new_shape) cv_results = dict() best_index = dict() best_params = dict() best_score = dict() best_estimator = dict() for m in self.measures: # cv_results: set measures for each split and each param comb for split in range(cv.get_n_folds()): cv_results[f"split{split}_test_{m}"] = test_measures[m][:, split] if self.return_train_measures: cv_results[f"split{split}_train_{m}"] = train_measures[m][:, split] # cv_results: set mean and std over all splits (testset and # trainset) for each param comb mean_test_measures = test_measures[m].mean(axis=1) cv_results[f"mean_test_{m}"] = mean_test_measures cv_results[f"std_test_{m}"] = test_measures[m].std(axis=1) if self.return_train_measures: mean_train_measures = train_measures[m].mean(axis=1) cv_results[f"mean_train_{m}"] = mean_train_measures cv_results[f"std_train_{m}"] = train_measures[m].std(axis=1) # cv_results: set rank of each param comb # also set best_index, and best_xxxx attributes indices = cv_results[f"mean_test_{m}"].argsort() cv_results[f"rank_test_{m}"] = np.empty_like(indices) if m in ("mae", "rmse", "mse"): cv_results[f"rank_test_{m}"][indices] = ( np.arange(len(indices)) + 1 ) # sklearn starts at 1 as well best_index[m] = mean_test_measures.argmin() elif m in ("fcp",): cv_results[f"rank_test_{m}"][indices] = np.arange(len(indices), 0, -1) best_index[m] = mean_test_measures.argmax() best_params[m] = self.param_combinations[best_index[m]] best_score[m] = mean_test_measures[best_index[m]] best_estimator[m] = self.algo_class(**best_params[m]) # Cv results: set fit and train times (mean, std) fit_times = np.array(fit_times).reshape(new_shape) test_times = np.array(test_times).reshape(new_shape) for s, times in zip(("fit", "test"), (fit_times, test_times)): cv_results[f"mean_{s}_time"] = times.mean(axis=1) cv_results[f"std_{s}_time"] = times.std(axis=1) # cv_results: set params key and each param_* values cv_results["params"] = self.param_combinations for param in self.param_combinations[0]: cv_results["param_" + param] = [ comb[param] for comb in self.param_combinations ] if self.refit: best_estimator[self.refit].fit(data.build_full_trainset()) self.best_index = best_index self.best_params = best_params self.best_score = best_score self.best_estimator = best_estimator self.cv_results = cv_results def test(self, testset, verbose=False): """Call ``test()`` on the estimator with the best found parameters (according the the ``refit`` parameter). See :meth:`AlgoBase.test() <surprise.prediction_algorithms.algo_base.AlgoBase.test>`. Only available if ``refit`` is not ``False``. """ if not self.refit: raise ValueError("refit is False, cannot use test()") return self.best_estimator[self.refit].test(testset, verbose) def predict(self, *args): """Call ``predict()`` on the estimator with the best found parameters (according the the ``refit`` parameter). See :meth:`AlgoBase.predict() <surprise.prediction_algorithms.algo_base.AlgoBase.predict>`. Only available if ``refit`` is not ``False``. """ if not self.refit: raise ValueError("refit is False, cannot use predict()") return self.best_estimator[self.refit].predict(*args) class GridSearchCV(BaseSearchCV): """The :class:`GridSearchCV` class computes accuracy metrics for an algorithm on various combinations of parameters, over a cross-validation procedure. This is useful for finding the best set of parameters for a prediction algorithm. It is analogous to `GridSearchCV <https://scikit-learn.org/stable/modules/generated/sklearn. model_selection.GridSearchCV.html>`_ from scikit-learn. See an example in the :ref:`User Guide <tuning_algorithm_parameters>`. Args: algo_class(:obj:`AlgoBase \ <surprise.prediction_algorithms.algo_base.AlgoBase>`): The class of the algorithm to evaluate. param_grid(dict): Dictionary with algorithm parameters as keys and list of values as keys. All combinations will be evaluated with desired algorithm. Dict parameters such as ``sim_options`` require special treatment, see :ref:`this note<grid_search_note>`. measures(list of string): The performance measures to compute. Allowed names are function names as defined in the :mod:`accuracy <surprise.accuracy>` module. Default is ``['rmse', 'mae']``. cv(cross-validation iterator, int or ``None``): Determines how the ``data`` parameter will be split (i.e. how trainsets and testsets will be defined). If an int is passed, :class:`KFold <surprise.model_selection.split.KFold>` is used with the appropriate ``n_splits`` parameter. If ``None``, :class:`KFold <surprise.model_selection.split.KFold>` is used with ``n_splits=5``. refit(bool or str): If ``True``, refit the algorithm on the whole dataset using the set of parameters that gave the best average performance for the first measure of ``measures``. Other measures can be used by passing a string (corresponding to the measure name). Then, you can use the ``test()`` and ``predict()`` methods. ``refit`` can only be used if the ``data`` parameter given to ``fit()`` hasn't been loaded with :meth:`load_from_folds() <surprise.dataset.Dataset.load_from_folds>`. Default is ``False``. return_train_measures(bool): Whether to compute performance measures on the trainsets. If ``True``, the ``cv_results`` attribute will also contain measures for trainsets. Default is ``False``. n_jobs(int): The maximum number of parallel training procedures. - If ``-1``, all CPUs are used. - If ``1`` is given, no parallel computing code is used at all,\ which is useful for debugging. - For ``n_jobs`` below ``-1``, ``(n_cpus + n_jobs + 1)`` are\ used. For example, with ``n_jobs = -2`` all CPUs but one are\ used. Default is ``1``. pre_dispatch(int or string): Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - ``None``, in which case all the jobs are immediately created\ and spawned. Use this for lightweight and fast-running\ jobs, to avoid delays due to on-demand spawning of the\ jobs. - An int, giving the exact number of total jobs that are\ spawned. - A string, giving an expression as a function of ``n_jobs``,\ as in ``'2*n_jobs'``. Default is ``'2*n_jobs'``. joblib_verbose(int): Controls the verbosity of joblib: the higher, the more messages. Attributes: best_estimator (dict of AlgoBase): Using an accuracy measure as key, get the algorithm that gave the best accuracy results for the chosen measure, averaged over all splits. best_score (dict of floats): Using an accuracy measure as key, get the best average score achieved for that measure. best_params (dict of dicts): Using an accuracy measure as key, get the parameters combination that gave the best accuracy results for the chosen measure (on average). best_index (dict of ints): Using an accuracy measure as key, get the index that can be used with ``cv_results`` that achieved the highest accuracy for that measure (on average). cv_results (dict of arrays): A dict that contains accuracy measures over all splits, as well as train and test time for each parameter combination. Can be imported into a pandas `DataFrame` (see :ref:`example <cv_results_example>`). """ def __init__( self, algo_class, param_grid, measures=["rmse", "mae"], cv=None, refit=False, return_train_measures=False, n_jobs=1, pre_dispatch="2*n_jobs", joblib_verbose=0, ): super().__init__( algo_class=algo_class, measures=measures, cv=cv, refit=refit, return_train_measures=return_train_measures, n_jobs=n_jobs, pre_dispatch=pre_dispatch, joblib_verbose=joblib_verbose, ) self.param_grid = self._parse_options(param_grid.copy()) self.param_combinations = [ dict(zip(self.param_grid, v)) for v in product(*self.param_grid.values()) ] class RandomizedSearchCV(BaseSearchCV): """The :class:`RandomizedSearchCV` class computes accuracy metrics for an algorithm on various combinations of parameters, over a cross-validation procedure. As opposed to GridSearchCV, which uses an exhaustive combinatorial approach, RandomizedSearchCV samples randomly from the parameter space. This is useful for finding the best set of parameters for a prediction algorithm, especially using a coarse to fine approach. It is analogous to `RandomizedSearchCV <https://scikit-learn.org/stable/ modules/generated/sklearn.model_selection.RandomizedSearchCV.html>`_ from scikit-learn. See an example in the :ref:`User Guide <tuning_algorithm_parameters>`. Args: algo_class(:obj:`AlgoBase \ <surprise.prediction_algorithms.algo_base.AlgoBase>`): The class of the algorithm to evaluate. param_distributions(dict): Dictionary with algorithm parameters as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. Parameters will be sampled n_iter times. n_iter(int): Number of times parameter settings are sampled. Default is ``10``. measures(list of string): The performance measures to compute. Allowed names are function names as defined in the :mod:`accuracy <surprise.accuracy>` module. Default is ``['rmse', 'mae']``. cv(cross-validation iterator, int or ``None``): Determines how the ``data`` parameter will be split (i.e. how trainsets and testsets will be defined). If an int is passed, :class:`KFold <surprise.model_selection.split.KFold>` is used with the appropriate ``n_splits`` parameter. If ``None``, :class:`KFold <surprise.model_selection.split.KFold>` is used with ``n_splits=5``. refit(bool or str): If ``True``, refit the algorithm on the whole dataset using the set of parameters that gave the best average performance for the first measure of ``measures``. Other measures can be used by passing a string (corresponding to the measure name). Then, you can use the ``test()`` and ``predict()`` methods. ``refit`` can only be used if the ``data`` parameter given to ``fit()`` hasn't been loaded with :meth:`load_from_folds() <surprise.dataset.Dataset.load_from_folds>`. Default is ``False``. return_train_measures(bool): Whether to compute performance measures on the trainsets. If ``True``, the ``cv_results`` attribute will also contain measures for trainsets. Default is ``False``. n_jobs(int): The maximum number of parallel training procedures. - If ``-1``, all CPUs are used. - If ``1`` is given, no parallel computing code is used at all,\ which is useful for debugging. - For ``n_jobs`` below ``-1``, ``(n_cpus + n_jobs + 1)`` are\ used. For example, with ``n_jobs = -2`` all CPUs but one are\ used. Default is ``1``. pre_dispatch(int or string): Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - ``None``, in which case all the jobs are immediately created\ and spawned. Use this for lightweight and fast-running\ jobs, to avoid delays due to on-demand spawning of the\ jobs. - An int, giving the exact number of total jobs that are\ spawned. - A string, giving an expression as a function of ``n_jobs``,\ as in ``'2*n_jobs'``. Default is ``'2*n_jobs'``. random_state(int, RandomState or None): Pseudo random number generator seed used for random uniform sampling from lists of possible values instead of scipy.stats distributions. If int, ``random_state`` is the seed used by the random number generator. If ``RandomState`` instance, ``random_state`` is the random number generator. If ``None``, the random number generator is the RandomState instance used by ``np.random``. Default is ``None``. joblib_verbose(int): Controls the verbosity of joblib: the higher, the more messages. Attributes: best_estimator (dict of AlgoBase): Using an accuracy measure as key, get the algorithm that gave the best accuracy results for the chosen measure, averaged over all splits. best_score (dict of floats): Using an accuracy measure as key, get the best average score achieved for that measure. best_params (dict of dicts): Using an accuracy measure as key, get the parameters combination that gave the best accuracy results for the chosen measure (on average). best_index (dict of ints): Using an accuracy measure as key, get the index that can be used with ``cv_results`` that achieved the highest accuracy for that measure (on average). cv_results (dict of arrays): A dict that contains accuracy measures over all splits, as well as train and test time for each parameter combination. Can be imported into a pandas `DataFrame` (see :ref:`example <cv_results_example>`). """ def __init__( self, algo_class, param_distributions, n_iter=10, measures=["rmse", "mae"], cv=None, refit=False, return_train_measures=False, n_jobs=1, pre_dispatch="2*n_jobs", random_state=None, joblib_verbose=0, ): super().__init__( algo_class=algo_class, measures=measures, cv=cv, refit=refit, return_train_measures=return_train_measures, n_jobs=n_jobs, pre_dispatch=pre_dispatch, joblib_verbose=joblib_verbose, ) self.n_iter = n_iter self.random_state = random_state self.param_distributions = self._parse_options(param_distributions.copy()) self.param_combinations = self._sample_parameters( self.param_distributions, self.n_iter, self.random_state ) @staticmethod def _sample_parameters(param_distributions, n_iter, random_state=None): """Samples ``n_iter`` parameter combinations from ``param_distributions`` using ``random_state`` as a seed. Non-deterministic iterable over random candidate combinations for hyper-parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Note that before SciPy 0.16, the ``scipy.stats.distributions`` do not accept a custom RNG instance and always use the singleton RNG from ``numpy.random``. Hence setting ``random_state`` will not guarantee a deterministic iteration whenever ``scipy.stats`` distributions are used to define the parameter search space. Deterministic behavior is however guaranteed from SciPy 0.16 onwards. Args: param_distributions(dict): Dictionary where the keys are parameters and values are distributions from which a parameter is to be sampled. Distributions either have to provide a ``rvs`` function to sample from them, or can be given as a list of values, where a uniform distribution is assumed. n_iter(int): Number of parameter settings produced. Default is ``10``. random_state(int, RandomState instance or None): Pseudo random number generator seed used for random uniform sampling from lists of possible values instead of scipy.stats distributions. If ``None``, the random number generator is the random state instance used by np.random. Default is ``None``. Returns: combos(list): List of parameter dictionaries with sampled values. """ # check if all distributions are given as lists # if so, sample without replacement all_lists = np.all( [not hasattr(v, "rvs") for v in param_distributions.values()] ) rnd = get_rng(random_state) # sort for reproducibility items = sorted(param_distributions.items()) if all_lists: # create exhaustive combinations param_grid = [ dict(zip(param_distributions, v)) for v in product(*param_distributions.values()) ] combos = np.random.choice(param_grid, n_iter, replace=False) else: combos = [] for _ in range(n_iter): params = dict() for k, v in items: if hasattr(v, "rvs"): params[k] = v.rvs(random_state=rnd) else: params[k] = v[rnd.randint(len(v))] combos.append(params) return combos from .search import GridSearchCV, RandomizedSearchCV from .split import ( KFold, LeaveOneOut, PredefinedKFold, RepeatedKFold, ShuffleSplit, train_test_split, ) from .validation import cross_validate __all__ = [ "KFold", "ShuffleSplit", "train_test_split", "RepeatedKFold", "LeaveOneOut", "PredefinedKFold", "cross_validate", "GridSearchCV", "RandomizedSearchCV", ] """ The validation module contains the cross_validate function, inspired from the mighty scikit learn. """ import time import numpy as np from joblib import delayed, Parallel from .. import accuracy from .split import get_cv def cross_validate( algo, data, measures=["rmse", "mae"], cv=None, return_train_measures=False, n_jobs=1, pre_dispatch="2*n_jobs", verbose=False, ): """ Run a cross validation procedure for a given algorithm, reporting accuracy measures and computation times. See an example in the :ref:`User Guide <cross_validate_example>`. Args: algo(:obj:`AlgoBase \ <surprise.prediction_algorithms.algo_base.AlgoBase>`): The algorithm to evaluate. data(:obj:`Dataset <surprise.dataset.Dataset>`): The dataset on which to evaluate the algorithm. measures(list of string): The performance measures to compute. Allowed names are function names as defined in the :mod:`accuracy <surprise.accuracy>` module. Default is ``['rmse', 'mae']``. cv(cross-validation iterator, int or ``None``): Determines how the ``data`` parameter will be split (i.e. how trainsets and testsets will be defined). If an int is passed, :class:`KFold <surprise.model_selection.split.KFold>` is used with the appropriate ``n_splits`` parameter. If ``None``, :class:`KFold <surprise.model_selection.split.KFold>` is used with ``n_splits=5``. return_train_measures(bool): Whether to compute performance measures on the trainsets. Default is ``False``. n_jobs(int): The maximum number of folds evaluated in parallel. - If ``-1``, all CPUs are used. - If ``1`` is given, no parallel computing code is used at all,\ which is useful for debugging. - For ``n_jobs`` below ``-1``, ``(n_cpus + n_jobs + 1)`` are\ used. For example, with ``n_jobs = -2`` all CPUs but one are\ used. Default is ``1``. pre_dispatch(int or string): Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - ``None``, in which case all the jobs are immediately created\ and spawned. Use this for lightweight and fast-running\ jobs, to avoid delays due to on-demand spawning of the\ jobs. - An int, giving the exact number of total jobs that are\ spawned. - A string, giving an expression as a function of ``n_jobs``,\ as in ``'2*n_jobs'``. Default is ``'2*n_jobs'``. verbose(int): If ``True`` accuracy measures for each split are printed, as well as train and test times. Averages and standard deviations over all splits are also reported. Default is ``False``: nothing is printed. Returns: dict: A dict with the following keys: - ``'test_*'`` where ``*`` corresponds to a lower-case accuracy measure, e.g. ``'test_rmse'``: numpy array with accuracy values for each testset. - ``'train_*'`` where ``*`` corresponds to a lower-case accuracy measure, e.g. ``'train_rmse'``: numpy array with accuracy values for each trainset. Only available if ``return_train_measures`` is ``True``. - ``'fit_time'``: numpy array with the training time in seconds for each split. - ``'test_time'``: numpy array with the testing time in seconds for each split. """ measures = [m.lower() for m in measures] cv = get_cv(cv) delayed_list = ( delayed(fit_and_score)(algo, trainset, testset, measures, return_train_measures) for (trainset, testset) in cv.split(data) ) out = Parallel(n_jobs=n_jobs, pre_dispatch=pre_dispatch)(delayed_list) (test_measures_dicts, train_measures_dicts, fit_times, test_times) = zip(*out) test_measures = dict() train_measures = dict() ret = dict() for m in measures: # transform list of dicts into dict of lists # Same as in GridSearchCV.fit() test_measures[m] = np.asarray([d[m] for d in test_measures_dicts]) ret["test_" + m] = test_measures[m] if return_train_measures: train_measures[m] = np.asarray([d[m] for d in train_measures_dicts]) ret["train_" + m] = train_measures[m] ret["fit_time"] = fit_times ret["test_time"] = test_times if verbose: print_summary( algo, measures, test_measures, train_measures, fit_times, test_times, cv.n_splits, ) return ret def fit_and_score(algo, trainset, testset, measures, return_train_measures=False): """Helper method that trains an algorithm and compute accuracy measures on a testset. Also report train and test times. Args: algo(:obj:`AlgoBase \ <surprise.prediction_algorithms.algo_base.AlgoBase>`): The algorithm to use. trainset(:obj:`Trainset <surprise.trainset.Trainset>`): The trainset. testset(:obj:`testset`): The testset. measures(list of string): The performance measures to compute. Allowed names are function names as defined in the :mod:`accuracy <surprise.accuracy>` module. return_train_measures(bool): Whether to compute performance measures on the trainset. Default is ``False``. Returns: tuple: A tuple containing: - A dictionary mapping each accuracy metric to its value on the testset (keys are lower case). - A dictionary mapping each accuracy metric to its value on the trainset (keys are lower case). This dict is empty if return_train_measures is False. - The fit time in seconds. - The testing time in seconds. """ start_fit = time.time() algo.fit(trainset) fit_time = time.time() - start_fit start_test = time.time() predictions = algo.test(testset) test_time = time.time() - start_test if return_train_measures: train_predictions = algo.test(trainset.build_testset()) test_measures = dict() train_measures = dict() for m in measures: f = getattr(accuracy, m.lower()) test_measures[m] = f(predictions, verbose=0) if return_train_measures: train_measures[m] = f(train_predictions, verbose=0) return test_measures, train_measures, fit_time, test_time def print_summary( algo, measures, test_measures, train_measures, fit_times, test_times, n_splits ): """Helper for printing the result of cross_validate.""" print( "Evaluating {} of algorithm {} on {} split(s).".format( ", ".join(m.upper() for m in measures), algo.__class__.__name__, n_splits ) ) print() row_format = "{:<18}" + "{:<8}" * (n_splits + 2) s = row_format.format( "", *[f"Fold {i + 1}" for i in range(n_splits)] + ["Mean"] + ["Std"] ) s += "\n" s += "\n".join( row_format.format( key.upper() + " (testset)", *[f"{v:1.4f}" for v in vals] + [f"{np.mean(vals):1.4f}"] + [f"{np.std(vals):1.4f}"], ) for (key, vals) in test_measures.items() ) if train_measures: s += "\n" s += "\n".join( row_format.format( key.upper() + " (trainset)", *[f"{v:1.4f}" for v in vals] + [f"{np.mean(vals):1.4f}"] + [f"{np.std(vals):1.4f}"], ) for (key, vals) in train_measures.items() ) s += "\n" s += row_format.format( "Fit time", *[f"{t:.2f}" for t in fit_times] + [f"{np.mean(fit_times):.2f}"] + [f"{np.std(fit_times):.2f}"], ) s += "\n" s += row_format.format( "Test time", *[f"{t:.2f}" for t in test_times] + [f"{np.mean(test_times):.2f}"] + [f"{np.std(test_times):.2f}"], ) print(s) """ The :mod:`model_selection.split<surprise.model_selection.split>` module contains various cross-validation iterators. Design and tools are inspired from the mighty scikit learn. The available iterators are: .. autosummary:: :nosignatures: KFold RepeatedKFold ShuffleSplit LeaveOneOut PredefinedKFold This module also contains a function for splitting datasets into trainset and testset: .. autosummary:: :nosignatures: train_test_split """ import numbers from collections import defaultdict from itertools import chain from math import ceil, floor import numpy as np from ..utils import get_rng def get_cv(cv): """Return a 'validated' CV iterator.""" if cv is None: return KFold(n_splits=5) if isinstance(cv, numbers.Integral): return KFold(n_splits=cv) if hasattr(cv, "split") and not isinstance(cv, str): return cv # str have split raise ValueError( "Wrong CV object. Expecting None, an int or CV iterator, " "got a {}".format(type(cv)) ) class KFold: """A basic cross-validation iterator. Each fold is used once as a testset while the k - 1 remaining folds are used for training. See an example in the :ref:`User Guide <use_cross_validation_iterators>`. Args: n_splits(int): The number of folds. random_state(int, RandomState instance from numpy, or ``None``): Determines the RNG that will be used for determining the folds. If int, ``random_state`` will be used as a seed for a new RNG. This is useful to get the same splits over multiple calls to ``split()``. If RandomState instance, this same instance is used as RNG. If ``None``, the current RNG from numpy is used. ``random_state`` is only used if ``shuffle`` is ``True``. Default is ``None``. shuffle(bool): Whether to shuffle the ratings in the ``data`` parameter of the ``split()`` method. Shuffling is not done in-place. Default is ``True``. """ def __init__(self, n_splits=5, random_state=None, shuffle=True): self.n_splits = n_splits self.shuffle = shuffle self.random_state = random_state def split(self, data): """Generator function to iterate over trainsets and testsets. Args: data(:obj:`Dataset<surprise.dataset.Dataset>`): The data containing ratings that will be divided into trainsets and testsets. Yields: tuple of (trainset, testset) """ if self.n_splits > len(data.raw_ratings) or self.n_splits < 2: raise ValueError( "Incorrect value for n_splits={}. " "Must be >=2 and less than the number " "of ratings".format(len(data.raw_ratings)) ) # We use indices to avoid shuffling the original data.raw_ratings list. indices = np.arange(len(data.raw_ratings)) if self.shuffle: get_rng(self.random_state).shuffle(indices) start, stop = 0, 0 for fold_i in range(self.n_splits): start = stop stop += len(indices) // self.n_splits if fold_i < len(indices) % self.n_splits: stop += 1 raw_trainset = [ data.raw_ratings[i] for i in chain(indices[:start], indices[stop:]) ] raw_testset = [data.raw_ratings[i] for i in indices[start:stop]] trainset = data.construct_trainset(raw_trainset) testset = data.construct_testset(raw_testset) yield trainset, testset def get_n_folds(self): return self.n_splits class RepeatedKFold: """ Repeated :class:`KFold` cross validator. Repeats :class:`KFold` n times with different randomization in each repetition. See an example in the :ref:`User Guide <use_cross_validation_iterators>`. Args: n_splits(int): The number of folds. n_repeats(int): The number of repetitions. random_state(int, RandomState instance from numpy, or ``None``): Determines the RNG that will be used for determining the folds. If int, ``random_state`` will be used as a seed for a new RNG. This is useful to get the same splits over multiple calls to ``split()``. If RandomState instance, this same instance is used as RNG. If ``None``, the current RNG from numpy is used. ``random_state`` is only used if ``shuffle`` is ``True``. Default is ``None``. shuffle(bool): Whether to shuffle the ratings in the ``data`` parameter of the ``split()`` method. Shuffling is not done in-place. Default is ``True``. """ def __init__(self, n_splits=5, n_repeats=10, random_state=None): self.n_repeats = n_repeats self.random_state = random_state self.n_splits = n_splits def split(self, data): """Generator function to iterate over trainsets and testsets. Args: data(:obj:`Dataset<surprise.dataset.Dataset>`): The data containing ratings that will be divided into trainsets and testsets. Yields: tuple of (trainset, testset) """ rng = get_rng(self.random_state) for _ in range(self.n_repeats): cv = KFold(n_splits=self.n_splits, random_state=rng, shuffle=True) yield from cv.split(data) def get_n_folds(self): return self.n_repeats * self.n_splits class ShuffleSplit: """A basic cross-validation iterator with random trainsets and testsets. Contrary to other cross-validation strategies, random splits do not guarantee that all folds will be different, although this is still very likely for sizeable datasets. See an example in the :ref:`User Guide <use_cross_validation_iterators>`. Args: n_splits(int): The number of folds. test_size(float or int ``None``): If float, it represents the proportion of ratings to include in the testset. If int, represents the absolute number of ratings in the testset. If ``None``, the value is set to the complement of the trainset size. Default is ``.2``. train_size(float or int or ``None``): If float, it represents the proportion of ratings to include in the trainset. If int, represents the absolute number of ratings in the trainset. If ``None``, the value is set to the complement of the testset size. Default is ``None``. random_state(int, RandomState instance from numpy, or ``None``): Determines the RNG that will be used for determining the folds. If int, ``random_state`` will be used as a seed for a new RNG. This is useful to get the same splits over multiple calls to ``split()``. If RandomState instance, this same instance is used as RNG. If ``None``, the current RNG from numpy is used. ``random_state`` is only used if ``shuffle`` is ``True``. Default is ``None``. shuffle(bool): Whether to shuffle the ratings in the ``data`` parameter of the ``split()`` method. Shuffling is not done in-place. Setting this to `False` defeats the purpose of this iterator, but it's useful for the implementation of :func:`train_test_split`. Default is ``True``. """ def __init__( self, n_splits=5, test_size=0.2, train_size=None, random_state=None, shuffle=True, ): if n_splits <= 0: raise ValueError( "n_splits = {} should be strictly greater than " "0.".format(n_splits) ) if test_size is not None and test_size <= 0: raise ValueError( "test_size={} should be strictly greater than " "0".format(test_size) ) if train_size is not None and train_size <= 0: raise ValueError( "train_size={} should be strictly greater than " "0".format(train_size) ) self.n_splits = n_splits self.test_size = test_size self.train_size = train_size self.random_state = random_state self.shuffle = shuffle def validate_train_test_sizes(self, test_size, train_size, n_ratings): if test_size is not None and test_size >= n_ratings: raise ValueError( "test_size={} should be less than the number of " "ratings {}".format(test_size, n_ratings) ) if train_size is not None and train_size >= n_ratings: raise ValueError( "train_size={} should be less than the number of" " ratings {}".format(train_size, n_ratings) ) if np.asarray(test_size).dtype.kind == "f": test_size = ceil(test_size * n_ratings) if train_size is None: train_size = n_ratings - test_size elif np.asarray(train_size).dtype.kind == "f": train_size = floor(train_size * n_ratings) if test_size is None: test_size = n_ratings - train_size if train_size + test_size > n_ratings: raise ValueError( "The sum of train_size and test_size ({}) " "should be smaller than the number of " "ratings {}.".format(train_size + test_size, n_ratings) ) return int(train_size), int(test_size) def split(self, data): """Generator function to iterate over trainsets and testsets. Args: data(:obj:`Dataset<surprise.dataset.Dataset>`): The data containing ratings that will be divided into trainsets and testsets. Yields: tuple of (trainset, testset) """ test_size, train_size = self.validate_train_test_sizes( self.test_size, self.train_size, len(data.raw_ratings) ) rng = get_rng(self.random_state) for _ in range(self.n_splits): if self.shuffle: permutation = rng.permutation(len(data.raw_ratings)) else: permutation = np.arange(len(data.raw_ratings)) raw_trainset = [data.raw_ratings[i] for i in permutation[:test_size]] raw_testset = [ data.raw_ratings[i] for i in permutation[test_size : (test_size + train_size)] ] trainset = data.construct_trainset(raw_trainset) testset = data.construct_testset(raw_testset) yield trainset, testset def get_n_folds(self): return self.n_splits def train_test_split( data, test_size=0.2, train_size=None, random_state=None, shuffle=True ): """Split a dataset into trainset and testset. See an example in the :ref:`User Guide <train_test_split_example>`. Note: this function cannot be used as a cross-validation iterator. Args: data(:obj:`Dataset <surprise.dataset.Dataset>`): The dataset to split into trainset and testset. test_size(float or int ``None``): If float, it represents the proportion of ratings to include in the testset. If int, represents the absolute number of ratings in the testset. If ``None``, the value is set to the complement of the trainset size. Default is ``.2``. train_size(float or int or ``None``): If float, it represents the proportion of ratings to include in the trainset. If int, represents the absolute number of ratings in the trainset. If ``None``, the value is set to the complement of the testset size. Default is ``None``. random_state(int, RandomState instance from numpy, or ``None``): Determines the RNG that will be used for determining the folds. If int, ``random_state`` will be used as a seed for a new RNG. This is useful to get the same splits over multiple calls to ``split()``. If RandomState instance, this same instance is used as RNG. If ``None``, the current RNG from numpy is used. ``random_state`` is only used if ``shuffle`` is ``True``. Default is ``None``. shuffle(bool): Whether to shuffle the ratings in the ``data`` parameter. Shuffling is not done in-place. Default is ``True``. """ ss = ShuffleSplit( n_splits=1, test_size=test_size, train_size=train_size, random_state=random_state, shuffle=shuffle, ) return next(ss.split(data)) class LeaveOneOut: """Cross-validation iterator where each user has exactly one rating in the testset. Contrary to other cross-validation strategies, ``LeaveOneOut`` does not guarantee that all folds will be different, although this is still very likely for sizeable datasets. See an example in the :ref:`User Guide <use_cross_validation_iterators>`. Args: n_splits(int): The number of folds. random_state(int, RandomState instance from numpy, or ``None``): Determines the RNG that will be used for determining the folds. If int, ``random_state`` will be used as a seed for a new RNG. This is useful to get the same splits over multiple calls to ``split()``. If RandomState instance, this same instance is used as RNG. If ``None``, the current RNG from numpy is used. ``random_state`` is only used if ``shuffle`` is ``True``. Default is ``None``. min_n_ratings(int): Minimum number of ratings for each user in the trainset. E.g. if ``min_n_ratings`` is ``2``, we are sure each user has at least ``2`` ratings in the trainset (and ``1`` in the testset). Other users are discarded. Default is ``0``, so some users (having only one rating) may be in the testset and not in the trainset. """ def __init__(self, n_splits=5, random_state=None, min_n_ratings=0): self.n_splits = n_splits self.random_state = random_state self.min_n_ratings = min_n_ratings def split(self, data): """Generator function to iterate over trainsets and testsets. Args: data(:obj:`Dataset<surprise.dataset.Dataset>`): The data containing ratings that will be divided into trainsets and testsets. Yields: tuple of (trainset, testset) """ # map ratings to the users ids user_ratings = defaultdict(list) for uid, iid, r_ui, _ in data.raw_ratings: user_ratings[uid].append((uid, iid, r_ui, None)) rng = get_rng(self.random_state) for _ in range(self.n_splits): # for each user, randomly choose a rating and put it in the # testset. raw_trainset, raw_testset = [], [] for uid, ratings in user_ratings.items(): if len(ratings) > self.min_n_ratings: i = rng.randint(0, len(ratings)) raw_testset.append(ratings[i]) raw_trainset += [ rating for (j, rating) in enumerate(ratings) if j != i ] if not raw_trainset: raise ValueError( "Could not build any trainset. Maybe " "min_n_ratings is too high?" ) trainset = data.construct_trainset(raw_trainset) testset = data.construct_testset(raw_testset) yield trainset, testset def get_n_folds(self): return self.n_splits class PredefinedKFold: """A cross-validation iterator to when a dataset has been loaded with the :meth:`load_from_folds <surprise.dataset.Dataset.load_from_folds>` method. See an example in the :ref:`User Guide <load_from_folds_example>`. """ def split(self, data): """Generator function to iterate over trainsets and testsets. Args: data(:obj:`Dataset<surprise.dataset.Dataset>`): The data containing ratings that will be divided into trainsets and testsets. Yields: tuple of (trainset, testset) """ self.n_splits = len(data.folds_files) for train_file, test_file in data.folds_files: raw_trainset = data.read_ratings(train_file) raw_testset = data.read_ratings(test_file) trainset = data.construct_trainset(raw_trainset) testset = data.construct_testset(raw_testset) yield trainset, testset def get_n_folds(self): return self.n_splits """ the :mod:`knns` module includes some k-NN inspired algorithms. """ import heapq import numpy as np from .algo_base import AlgoBase from .predictions import PredictionImpossible # Important note: as soon as an algorithm uses a similarity measure, it should # also allow the bsl_options parameter because of the pearson_baseline # similarity. It can be done explicitly (e.g. KNNBaseline), or implicetely # using kwargs (e.g. KNNBasic). class SymmetricAlgo(AlgoBase): """This is an abstract class aimed to ease the use of symmetric algorithms. A symmetric algorithm is an algorithm that can can be based on users or on items indifferently, e.g. all the algorithms in this module. When the algo is user-based x denotes a user and y an item. Else, it's reversed. """ def __init__(self, sim_options={}, verbose=True, **kwargs): AlgoBase.__init__(self, sim_options=sim_options, **kwargs) self.verbose = verbose def fit(self, trainset): AlgoBase.fit(self, trainset) ub = self.sim_options["user_based"] self.n_x = self.trainset.n_users if ub else self.trainset.n_items self.n_y = self.trainset.n_items if ub else self.trainset.n_users self.xr = self.trainset.ur if ub else self.trainset.ir self.yr = self.trainset.ir if ub else self.trainset.ur return self def switch(self, u_stuff, i_stuff): """Return x_stuff and y_stuff depending on the user_based field.""" if self.sim_options["user_based"]: return u_stuff, i_stuff else: return i_stuff, u_stuff class KNNBasic(SymmetricAlgo): """A basic collaborative filtering algorithm. The prediction :math:`\\hat{r}_{ui}` is set as: .. math:: \\hat{r}_{ui} = \\frac{ \\sum\\limits_{v \\in N^k_i(u)} \\text{sim}(u, v) \\cdot r_{vi}} {\\sum\\limits_{v \\in N^k_i(u)} \\text{sim}(u, v)} or .. math:: \\hat{r}_{ui} = \\frac{ \\sum\\limits_{j \\in N^k_u(i)} \\text{sim}(i, j) \\cdot r_{uj}} {\\sum\\limits_{j \\in N^k_u(i)} \\text{sim}(i, j)} depending on the ``user_based`` field of the ``sim_options`` parameter. Args: k(int): The (max) number of neighbors to take into account for aggregation (see :ref:`this note <actual_k_note>`). Default is ``40``. min_k(int): The minimum number of neighbors to take into account for aggregation. If there are not enough neighbors, the prediction is set to the global mean of all ratings. Default is ``1``. sim_options(dict): A dictionary of options for the similarity measure. See :ref:`similarity_measures_configuration` for accepted options. verbose(bool): Whether to print trace messages of bias estimation, similarity, etc. Default is True. """ def __init__(self, k=40, min_k=1, sim_options={}, verbose=True, **kwargs): SymmetricAlgo.__init__(self, sim_options=sim_options, verbose=verbose, **kwargs) self.k = k self.min_k = min_k def fit(self, trainset): SymmetricAlgo.fit(self, trainset) self.sim = self.compute_similarities() return self def estimate(self, u, i): if not (self.trainset.knows_user(u) and self.trainset.knows_item(i)): raise PredictionImpossible("User and/or item is unknown.") x, y = self.switch(u, i) neighbors = [(self.sim[x, x2], r) for (x2, r) in self.yr[y]] k_neighbors = heapq.nlargest(self.k, neighbors, key=lambda t: t[0]) # compute weighted average sum_sim = sum_ratings = actual_k = 0 for (sim, r) in k_neighbors: if sim > 0: sum_sim += sim sum_ratings += sim * r actual_k += 1 if actual_k < self.min_k: raise PredictionImpossible("Not enough neighbors.") est = sum_ratings / sum_sim details = {"actual_k": actual_k} return est, details class KNNWithMeans(SymmetricAlgo): """A basic collaborative filtering algorithm, taking into account the mean ratings of each user. The prediction :math:`\\hat{r}_{ui}` is set as: .. math:: \\hat{r}_{ui} = \\mu_u + \\frac{ \\sum\\limits_{v \\in N^k_i(u)} \\text{sim}(u, v) \\cdot (r_{vi} - \\mu_v)} {\\sum\\limits_{v \\in N^k_i(u)} \\text{sim}(u, v)} or .. math:: \\hat{r}_{ui} = \\mu_i + \\frac{ \\sum\\limits_{j \\in N^k_u(i)} \\text{sim}(i, j) \\cdot (r_{uj} - \\mu_j)} {\\sum\\limits_{j \\in N^k_u(i)} \\text{sim}(i, j)} depending on the ``user_based`` field of the ``sim_options`` parameter. Args: k(int): The (max) number of neighbors to take into account for aggregation (see :ref:`this note <actual_k_note>`). Default is ``40``. min_k(int): The minimum number of neighbors to take into account for aggregation. If there are not enough neighbors, the neighbor aggregation is set to zero (so the prediction ends up being equivalent to the mean :math:`\\mu_u` or :math:`\\mu_i`). Default is ``1``. sim_options(dict): A dictionary of options for the similarity measure. See :ref:`similarity_measures_configuration` for accepted options. verbose(bool): Whether to print trace messages of bias estimation, similarity, etc. Default is True. """ def __init__(self, k=40, min_k=1, sim_options={}, verbose=True, **kwargs): SymmetricAlgo.__init__(self, sim_options=sim_options, verbose=verbose, **kwargs) self.k = k self.min_k = min_k def fit(self, trainset): SymmetricAlgo.fit(self, trainset) self.sim = self.compute_similarities() self.means = np.zeros(self.n_x) for x, ratings in self.xr.items(): self.means[x] = np.mean([r for (_, r) in ratings]) return self def estimate(self, u, i): if not (self.trainset.knows_user(u) and self.trainset.knows_item(i)): raise PredictionImpossible("User and/or item is unknown.") x, y = self.switch(u, i) neighbors = [(x2, self.sim[x, x2], r) for (x2, r) in self.yr[y]] k_neighbors = heapq.nlargest(self.k, neighbors, key=lambda t: t[1]) est = self.means[x] # compute weighted average sum_sim = sum_ratings = actual_k = 0 for (nb, sim, r) in k_neighbors: if sim > 0: sum_sim += sim sum_ratings += sim * (r - self.means[nb]) actual_k += 1 if actual_k < self.min_k: sum_ratings = 0 try: est += sum_ratings / sum_sim except ZeroDivisionError: pass # return mean details = {"actual_k": actual_k} return est, details class KNNBaseline(SymmetricAlgo): """A basic collaborative filtering algorithm taking into account a *baseline* rating. The prediction :math:`\\hat{r}_{ui}` is set as: .. math:: \\hat{r}_{ui} = b_{ui} + \\frac{ \\sum\\limits_{v \\in N^k_i(u)} \\text{sim}(u, v) \\cdot (r_{vi} - b_{vi})} {\\sum\\limits_{v \\in N^k_i(u)} \\text{sim}(u, v)} or .. math:: \\hat{r}_{ui} = b_{ui} + \\frac{ \\sum\\limits_{j \\in N^k_u(i)} \\text{sim}(i, j) \\cdot (r_{uj} - b_{uj})} {\\sum\\limits_{j \\in N^k_u(i)} \\text{sim}(i, j)} depending on the ``user_based`` field of the ``sim_options`` parameter. For the best predictions, use the :func:`pearson_baseline <surprise.similarities.pearson_baseline>` similarity measure. This algorithm corresponds to formula (3), section 2.2 of :cite:`Koren:2010`. Args: k(int): The (max) number of neighbors to take into account for aggregation (see :ref:`this note <actual_k_note>`). Default is ``40``. min_k(int): The minimum number of neighbors to take into account for aggregation. If there are not enough neighbors, the neighbor aggregation is set to zero (so the prediction ends up being equivalent to the baseline). Default is ``1``. sim_options(dict): A dictionary of options for the similarity measure. See :ref:`similarity_measures_configuration` for accepted options. It is recommended to use the :func:`pearson_baseline <surprise.similarities.pearson_baseline>` similarity measure. bsl_options(dict): A dictionary of options for the baseline estimates computation. See :ref:`baseline_estimates_configuration` for accepted options. verbose(bool): Whether to print trace messages of bias estimation, similarity, etc. Default is True. """ def __init__( self, k=40, min_k=1, sim_options={}, bsl_options={}, verbose=True, **kwargs ): SymmetricAlgo.__init__( self, sim_options=sim_options, bsl_options=bsl_options, verbose=verbose, **kwargs ) self.k = k self.min_k = min_k def fit(self, trainset): SymmetricAlgo.fit(self, trainset) self.bu, self.bi = self.compute_baselines() self.bx, self.by = self.switch(self.bu, self.bi) self.sim = self.compute_similarities() return self def estimate(self, u, i): est = self.trainset.global_mean if self.trainset.knows_user(u): est += self.bu[u] if self.trainset.knows_item(i): est += self.bi[i] x, y = self.switch(u, i) if not (self.trainset.knows_user(u) and self.trainset.knows_item(i)): return est neighbors = [(x2, self.sim[x, x2], r) for (x2, r) in self.yr[y]] k_neighbors = heapq.nlargest(self.k, neighbors, key=lambda t: t[1]) # compute weighted average sum_sim = sum_ratings = actual_k = 0 for (nb, sim, r) in k_neighbors: if sim > 0: sum_sim += sim nb_bsl = self.trainset.global_mean + self.bx[nb] + self.by[y] sum_ratings += sim * (r - nb_bsl) actual_k += 1 if actual_k < self.min_k: sum_ratings = 0 try: est += sum_ratings / sum_sim except ZeroDivisionError: pass # just baseline again details = {"actual_k": actual_k} return est, details class KNNWithZScore(SymmetricAlgo): """A basic collaborative filtering algorithm, taking into account the z-score normalization of each user. The prediction :math:`\\hat{r}_{ui}` is set as: .. math:: \\hat{r}_{ui} = \\mu_u + \\sigma_u \\frac{ \\sum\\limits_{v \\in N^k_i(u)} \\text{sim}(u, v) \\cdot (r_{vi} - \\mu_v) / \\sigma_v} {\\sum\\limits_{v \\in N^k_i(u)} \\text{sim}(u, v)} or .. math:: \\hat{r}_{ui} = \\mu_i + \\sigma_i \\frac{ \\sum\\limits_{j \\in N^k_u(i)} \\text{sim}(i, j) \\cdot (r_{uj} - \\mu_j) / \\sigma_j} {\\sum\\limits_{j \\in N^k_u(i)} \\text{sim}(i, j)} depending on the ``user_based`` field of the ``sim_options`` parameter. If :math:`\\sigma` is 0, than the overall sigma is used in that case. Args: k(int): The (max) number of neighbors to take into account for aggregation (see :ref:`this note <actual_k_note>`). Default is ``40``. min_k(int): The minimum number of neighbors to take into account for aggregation. If there are not enough neighbors, the neighbor aggregation is set to zero (so the prediction ends up being equivalent to the mean :math:`\\mu_u` or :math:`\\mu_i`). Default is ``1``. sim_options(dict): A dictionary of options for the similarity measure. See :ref:`similarity_measures_configuration` for accepted options. verbose(bool): Whether to print trace messages of bias estimation, similarity, etc. Default is True. """ def __init__(self, k=40, min_k=1, sim_options={}, verbose=True, **kwargs): SymmetricAlgo.__init__(self, sim_options=sim_options, verbose=verbose, **kwargs) self.k = k self.min_k = min_k def fit(self, trainset): SymmetricAlgo.fit(self, trainset) self.means = np.zeros(self.n_x) self.sigmas = np.zeros(self.n_x) # when certain sigma is 0, use overall sigma self.overall_sigma = np.std([r for (_, _, r) in self.trainset.all_ratings()]) for x, ratings in self.xr.items(): self.means[x] = np.mean([r for (_, r) in ratings]) sigma = np.std([r for (_, r) in ratings]) self.sigmas[x] = self.overall_sigma if sigma == 0.0 else sigma self.sim = self.compute_similarities() return self def estimate(self, u, i): if not (self.trainset.knows_user(u) and self.trainset.knows_item(i)): raise PredictionImpossible("User and/or item is unknown.") x, y = self.switch(u, i) neighbors = [(x2, self.sim[x, x2], r) for (x2, r) in self.yr[y]] k_neighbors = heapq.nlargest(self.k, neighbors, key=lambda t: t[1]) est = self.means[x] # compute weighted average sum_sim = sum_ratings = actual_k = 0 for (nb, sim, r) in k_neighbors: if sim > 0: sum_sim += sim sum_ratings += sim * (r - self.means[nb]) / self.sigmas[nb] actual_k += 1 if actual_k < self.min_k: sum_ratings = 0 try: est += sum_ratings / sum_sim * self.sigmas[x] except ZeroDivisionError: pass # return mean details = {"actual_k": actual_k} return est, details """ The :mod:`surprise.prediction_algorithms.algo_base` module defines the base class :class:`AlgoBase` from which every single prediction algorithm has to inherit. """ import heapq from .. import similarities as sims from .optimize_baselines import baseline_als, baseline_sgd from .predictions import Prediction, PredictionImpossible class AlgoBase: """Abstract class where is defined the basic behavior of a prediction algorithm. Keyword Args: baseline_options(dict, optional): If the algorithm needs to compute a baseline estimate, the ``baseline_options`` parameter is used to configure how they are computed. See :ref:`baseline_estimates_configuration` for usage. """ def __init__(self, **kwargs): self.bsl_options = kwargs.get("bsl_options", {}) self.sim_options = kwargs.get("sim_options", {}) if "user_based" not in self.sim_options: self.sim_options["user_based"] = True def fit(self, trainset): """Train an algorithm on a given training set. This method is called by every derived class as the first basic step for training an algorithm. It basically just initializes some internal structures and set the self.trainset attribute. Args: trainset(:obj:`Trainset <surprise.Trainset>`) : A training set, as returned by the :meth:`folds <surprise.dataset.Dataset.folds>` method. Returns: self """ self.trainset = trainset # (re) Initialise baselines self.bu = self.bi = None return self def predict(self, uid, iid, r_ui=None, clip=True, verbose=False): """Compute the rating prediction for given user and item. The ``predict`` method converts raw ids to inner ids and then calls the ``estimate`` method which is defined in every derived class. If the prediction is impossible (e.g. because the user and/or the item is unknown), the prediction is set according to :meth:`default_prediction() <surprise.prediction_algorithms.algo_base.AlgoBase.default_prediction>`. Args: uid: (Raw) id of the user. See :ref:`this note<raw_inner_note>`. iid: (Raw) id of the item. See :ref:`this note<raw_inner_note>`. r_ui(float): The true rating :math:`r_{ui}`. Optional, default is ``None``. clip(bool): Whether to clip the estimation into the rating scale. For example, if :math:`\\hat{r}_{ui}` is :math:`5.5` while the rating scale is :math:`[1, 5]`, then :math:`\\hat{r}_{ui}` is set to :math:`5`. Same goes if :math:`\\hat{r}_{ui} < 1`. Default is ``True``. verbose(bool): Whether to print details of the prediction. Default is False. Returns: A :obj:`Prediction\ <surprise.prediction_algorithms.predictions.Prediction>` object containing: - The (raw) user id ``uid``. - The (raw) item id ``iid``. - The true rating ``r_ui`` (:math:`r_{ui}`). - The estimated rating (:math:`\\hat{r}_{ui}`). - Some additional details about the prediction that might be useful for later analysis. """ # Convert raw ids to inner ids try: iuid = self.trainset.to_inner_uid(uid) except ValueError: iuid = "UKN__" + str(uid) try: iiid = self.trainset.to_inner_iid(iid) except ValueError: iiid = "UKN__" + str(iid) details = {} try: est = self.estimate(iuid, iiid) # If the details dict was also returned if isinstance(est, tuple): est, details = est details["was_impossible"] = False except PredictionImpossible as e: est = self.default_prediction() details["was_impossible"] = True details["reason"] = str(e) # clip estimate into [lower_bound, higher_bound] if clip: lower_bound, higher_bound = self.trainset.rating_scale est = min(higher_bound, est) est = max(lower_bound, est) pred = Prediction(uid, iid, r_ui, est, details) if verbose: print(pred) return pred def default_prediction(self): """Used when the ``PredictionImpossible`` exception is raised during a call to :meth:`predict() <surprise.prediction_algorithms.algo_base.AlgoBase.predict>`. By default, return the global mean of all ratings (can be overridden in child classes). Returns: (float): The mean of all ratings in the trainset. """ return self.trainset.global_mean def test(self, testset, verbose=False): """Test the algorithm on given testset, i.e. estimate all the ratings in the given testset. Args: testset: A test set, as returned by a :ref:`cross-validation itertor<use_cross_validation_iterators>` or by the :meth:`build_testset() <surprise.Trainset.build_testset>` method. verbose(bool): Whether to print details for each predictions. Default is False. Returns: A list of :class:`Prediction\ <surprise.prediction_algorithms.predictions.Prediction>` objects that contains all the estimated ratings. """ # The ratings are translated back to their original scale. predictions = [ self.predict(uid, iid, r_ui_trans, verbose=verbose) for (uid, iid, r_ui_trans) in testset ] return predictions def compute_baselines(self): """Compute users and items baselines. The way baselines are computed depends on the ``bsl_options`` parameter passed at the creation of the algorithm (see :ref:`baseline_estimates_configuration`). This method is only relevant for algorithms using :func:`Pearson baseline similarity<surprise.similarities.pearson_baseline>` or the :class:`BaselineOnly <surprise.prediction_algorithms.baseline_only.BaselineOnly>` algorithm. Returns: A tuple ``(bu, bi)``, which are users and items baselines.""" # Firt of, if this method has already been called before on the same # trainset, then just return. Indeed, compute_baselines may be called # more than one time, for example when a similarity metric (e.g. # pearson_baseline) uses baseline estimates. if self.bu is not None: return self.bu, self.bi method = dict(als=baseline_als, sgd=baseline_sgd) method_name = self.bsl_options.get("method", "als") try: if getattr(self, "verbose", False): print("Estimating biases using", method_name + "...") self.bu, self.bi = method[method_name](self) return self.bu, self.bi except KeyError: raise ValueError( "Invalid method " + method_name + " for baseline computation." + " Available methods are als and sgd." ) def compute_similarities(self): """Build the similarity matrix. The way the similarity matrix is computed depends on the ``sim_options`` parameter passed at the creation of the algorithm (see :ref:`similarity_measures_configuration`). This method is only relevant for algorithms using a similarity measure, such as the :ref:`k-NN algorithms <pred_package_knn_inpired>`. Returns: The similarity matrix.""" construction_func = { "cosine": sims.cosine, "msd": sims.msd, "pearson": sims.pearson, "pearson_baseline": sims.pearson_baseline, } if self.sim_options["user_based"]: n_x, yr = self.trainset.n_users, self.trainset.ir else: n_x, yr = self.trainset.n_items, self.trainset.ur min_support = self.sim_options.get("min_support", 1) args = [n_x, yr, min_support] name = self.sim_options.get("name", "msd").lower() if name == "pearson_baseline": shrinkage = self.sim_options.get("shrinkage", 100) bu, bi = self.compute_baselines() if self.sim_options["user_based"]: bx, by = bu, bi else: bx, by = bi, bu args += [self.trainset.global_mean, bx, by, shrinkage] try: if getattr(self, "verbose", False): print(f"Computing the {name} similarity matrix...") sim = construction_func[name](*args) if getattr(self, "verbose", False): print("Done computing similarity matrix.") return sim except KeyError: raise NameError( "Wrong sim name " + name + ". Allowed values " + "are " + ", ".join(construction_func.keys()) + "." ) def get_neighbors(self, iid, k): """Return the ``k`` nearest neighbors of ``iid``, which is the inner id of a user or an item, depending on the ``user_based`` field of ``sim_options`` (see :ref:`similarity_measures_configuration`). As the similarities are computed on the basis of a similarity measure, this method is only relevant for algorithms using a similarity measure, such as the :ref:`k-NN algorithms <pred_package_knn_inpired>`. For a usage example, see the :ref:`FAQ <get_k_nearest_neighbors>`. Args: iid(int): The (inner) id of the user (or item) for which we want the nearest neighbors. See :ref:`this note<raw_inner_note>`. k(int): The number of neighbors to retrieve. Returns: The list of the ``k`` (inner) ids of the closest users (or items) to ``iid``. """ if self.sim_options["user_based"]: all_instances = self.trainset.all_users else: all_instances = self.trainset.all_items others = [(x, self.sim[iid, x]) for x in all_instances() if x != iid] others = heapq.nlargest(k, others, key=lambda tple: tple[1]) k_nearest_neighbors = [j for (j, _) in others] return k_nearest_neighbors """ Algorithm predicting a random rating. """ import numpy as np from .algo_base import AlgoBase class NormalPredictor(AlgoBase): """Algorithm predicting a random rating based on the distribution of the training set, which is assumed to be normal. The prediction :math:`\\hat{r}_{ui}` is generated from a normal distribution :math:`\\mathcal{N}(\\hat{\\mu}, \\hat{\\sigma}^2)` where :math:`\\hat{\\mu}` and :math:`\\hat{\\sigma}` are estimated from the training data using Maximum Likelihood Estimation: .. math:: \\hat{\\mu} &= \\frac{1}{|R_{train}|} \\sum_{r_{ui} \\in R_{train}} r_{ui}\\\\\\\\\ \\hat{\\sigma} &= \\sqrt{\\sum_{r_{ui} \\in R_{train}} \\frac{(r_{ui} - \\hat{\\mu})^2}{|R_{train}|}} """ def __init__(self): AlgoBase.__init__(self) def fit(self, trainset): AlgoBase.fit(self, trainset) num = sum( (r - self.trainset.global_mean) ** 2 for (_, _, r) in self.trainset.all_ratings() ) denum = self.trainset.n_ratings self.sigma = np.sqrt(num / denum) return self def estimate(self, *_): return np.random.normal(self.trainset.global_mean, self.sigma) """ The :mod:`surprise.prediction_algorithms.predictions` module defines the :class:`Prediction` named tuple and the :class:`PredictionImpossible` exception. """ from collections import namedtuple class PredictionImpossible(Exception): r"""Exception raised when a prediction is impossible. When raised, the estimation :math:`\hat{r}_{ui}` is set to the global mean of all ratings :math:`\mu`. """ pass class Prediction(namedtuple("Prediction", ["uid", "iid", "r_ui", "est", "details"])): """A named tuple for storing the results of a prediction. It's wrapped in a class, but only for documentation and printing purposes. Args: uid: The (raw) user id. See :ref:`this note<raw_inner_note>`. iid: The (raw) item id. See :ref:`this note<raw_inner_note>`. r_ui(float): The true rating :math:`r_{ui}`. est(float): The estimated rating :math:`\\hat{r}_{ui}`. details (dict): Stores additional details about the prediction that might be useful for later analysis. """ __slots__ = () # for memory saving purpose. def __str__(self): s = f"user: {self.uid:<10} " s += f"item: {self.iid:<10} " if self.r_ui is not None: s += f"r_ui = {self.r_ui:1.2f} " else: s += "r_ui = None " s += f"est = {self.est:1.2f} " s += str(self.details) return s """ The :mod:`prediction_algorithms` package includes the prediction algorithms available for recommendation. The available prediction algorithms are: .. autosummary:: :nosignatures: random_pred.NormalPredictor baseline_only.BaselineOnly knns.KNNBasic knns.KNNWithMeans knns.KNNWithZScore knns.KNNBaseline matrix_factorization.SVD matrix_factorization.SVDpp matrix_factorization.NMF slope_one.SlopeOne co_clustering.CoClustering """ from .algo_base import AlgoBase from .baseline_only import BaselineOnly from .co_clustering import CoClustering from .knns import KNNBaseline, KNNBasic, KNNWithMeans, KNNWithZScore from .matrix_factorization import NMF, SVD, SVDpp from .predictions import Prediction, PredictionImpossible from .random_pred import NormalPredictor from .slope_one import SlopeOne __all__ = [ "AlgoBase", "NormalPredictor", "BaselineOnly", "KNNBasic", "KNNBaseline", "KNNWithMeans", "SVD", "SVDpp", "NMF", "SlopeOne", "CoClustering", "PredictionImpossible", "Prediction", "KNNWithZScore", ] """ This class implements the baseline estimation. """ from .algo_base import AlgoBase class BaselineOnly(AlgoBase): r"""Algorithm predicting the baseline estimate for given user and item. :math:`\hat{r}_{ui} = b_{ui} = \mu + b_u + b_i` If user :math:`u` is unknown, then the bias :math:`b_u` is assumed to be zero. The same applies for item :math:`i` with :math:`b_i`. See section 2.1 of :cite:`Koren:2010` for details. Args: bsl_options(dict): A dictionary of options for the baseline estimates computation. See :ref:`baseline_estimates_configuration` for accepted options. verbose(bool): Whether to print trace messages of bias estimation, similarity, etc. Default is True. """ def __init__(self, bsl_options={}, verbose=True): AlgoBase.__init__(self, bsl_options=bsl_options) self.verbose = verbose def fit(self, trainset): AlgoBase.fit(self, trainset) self.bu, self.bi = self.compute_baselines() return self def estimate(self, u, i): est = self.trainset.global_mean if self.trainset.knows_user(u): est += self.bu[u] if self.trainset.knows_item(i): est += self.bi[i] return est import os import traceback import sys print("before function process") def process(version): print("inside fun process") currentDirectory = os.path.dirname(os.path.abspath(__file__)) print(currentDirectory) try: from os.path import expanduser import platform import subprocess import sys import demoji try: print('Downloading NLTK additional packages...') import nltk nltk.download('punkt') nltk.download('wordnet') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger') except Exception as e: print('NLTK Error: '+str(e)) pass from appbe.dataPath import DATA_DIR import shutil import importlib license_path = DATA_DIR if os.path.isdir(license_path) == False: os.makedirs(license_path) import warnings warnings.filterwarnings("ignore") LicenseFolder = os.path.join(license_path,'License') if os.path.isdir(LicenseFolder) == False: os.makedirs(LicenseFolder) sqlite_path = os.path.join(license_path,'sqlite') if os.path.isdir(sqlite_path) == False: os.makedirs(sqlite_path) pretrainedModel_path = os.path.join(license_path,'PreTrainedModels') if os.path.isdir(pretrainedModel_path) == False: os.makedirs(pretrainedModel_path) config_path = os.path.join(license_path,'config') if os.path.isdir(config_path) == False: os.makedirs(config_path) target_path = os.path.join(license_path,'target') if os.path.isdir(target_path) == False: os.makedirs(target_path) data_path = os.path.join(license_path,'storage') if os.path.isdir(data_path) == False: os.makedirs(data_path) log_path = os.path.join(license_path,'logs') if os.path.isdir(log_path) == False: os.makedirs(log_path) configFolder = os.path.join(currentDirectory,'..','config') for file in os.listdir(configFolder): if file.endswith(".var"): os.remove(os.path.join(configFolder,file)) versionfile = os.path.join(configFolder,str(version)+'.var') with open(versionfile, 'w') as fp: pass manage_path = os.path.join(currentDirectory,'..','aion.py') print('Setting up Django Environment for AION User Interface') proc = subprocess.Popen([sys.executable, manage_path, "-m","migrateappfe"],stdout=subprocess.PIPE, stderr=subprocess.PIPE) (stdout, stderr) = proc.communicate() if proc.returncode != 0: err_string = stderr.decode('utf8') import re result = re.search("No module named '(.*)'", err_string) if 'ModuleNotFoundError' in err_string: print('\n"{}" module is missing. The dependencies of AION were not installed properly. Uninstall and reinstall AION'.format(result.group(1))) else: print('\nThe dependencies of AION were not installed properly. Uninstall and reinstall AION') raise Exception(err_string) else: print('AION User Interface successfully set') print('--------------AION Installed Successfully--------------') except Exception as e: print(e) f = open(os.path.join(currentDirectory, 'workspace_error_logs.txt'), "w") f.write(str(traceback.format_exc())) f.close() pass if __name__ == "__main__": process(sys.argv[1]) import os import traceback def process(version): currentDirectory = os.path.dirname(os.path.abspath(__file__)) try: import win32com.client from os.path import expanduser import platform import subprocess import sys import demoji try: print('Downloading NLTK additional packages...') import nltk nltk.download('punkt') nltk.download('wordnet') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger') except Exception as e: print('NLTK Error: '+str(e)) pass from appbe.dataPath import DATA_DIR from win32com.shell import shell, shellcon import shutil import importlib license_path = DATA_DIR if os.path.isdir(license_path) == False: os.makedirs(license_path) import warnings warnings.filterwarnings("ignore") LicenseFolder = os.path.join(license_path,'License') if os.path.isdir(LicenseFolder) == False: os.makedirs(LicenseFolder) sqlite_path = os.path.join(license_path,'sqlite') if os.path.isdir(sqlite_path) == False: os.makedirs(sqlite_path) pretrainedModel_path = os.path.join(license_path,'PreTrainedModels') if os.path.isdir(pretrainedModel_path) == False: os.makedirs(pretrainedModel_path) config_path = os.path.join(license_path,'config') if os.path.isdir(config_path) == False: os.makedirs(config_path) target_path = os.path.join(license_path,'target') if os.path.isdir(target_path) == False: os.makedirs(target_path) data_path = os.path.join(license_path,'storage') if os.path.isdir(data_path) == False: os.makedirs(data_path) log_path = os.path.join(license_path,'logs') if os.path.isdir(log_path) == False: os.makedirs(log_path) configFolder = os.path.join(currentDirectory,'..','config') for file in os.listdir(configFolder): if file.endswith(".var"): os.remove(os.path.join(configFolder,file)) versionfile = os.path.join(configFolder,str(version)+'.var') with open(versionfile, 'w') as fp: pass manage_path = os.path.join(currentDirectory,'..','aion.py') print('Setting up Django Environment for AION User Interface') proc = subprocess.Popen([sys.executable, manage_path, "-m","migrateappfe"],stdout=subprocess.PIPE, stderr=subprocess.PIPE) (stdout, stderr) = proc.communicate() if proc.returncode != 0: err_string = stderr.decode('utf8') import re result = re.search("No module named '(.*)'", err_string) if 'ModuleNotFoundError' in err_string: print('\n"{}" module is missing. The dependencies of AION were not installed properly. Uninstall and reinstall AION'.format(result.group(1))) else: print('\nThe dependencies of AION were not installed properly. Uninstall and reinstall AION') raise Exception(err_string) else: print('AION User Interface successfully set') desktop = shell.SHGetFolderPath (0, shellcon.CSIDL_DESKTOP, 0, 0) #desktop = os.path.expanduser('~/Desktop') path = os.path.join(desktop, 'Explorer {0}.lnk'.format(version)) target = os.path.normpath(os.path.join(currentDirectory,'..', 'sbin', 'AION_Explorer.bat')) icon = os.path.join(currentDirectory,'icons','aion.ico') shell = win32com.client.Dispatch("WScript.Shell") shortcut = shell.CreateShortCut(path) shortcut.Targetpath = '"'+target+'"' shortcut.WorkingDirectory = currentDirectory #shortcut.WorkingDirectory = os.path.dirname(__file__) shortcut.IconLocation = icon shortcut.WindowStyle = 1 # 7 - Minimized, 3 - Maximized, 1 - Normal shortcut.save() path = os.path.join(desktop, 'Shell {0}.lnk'.format(version)) target = os.path.normpath(os.path.join(currentDirectory,'..','sbin', 'AION_Shell.bat')) icon = os.path.join(currentDirectory,'icons','aion_shell.ico') shell = win32com.client.Dispatch("WScript.Shell") shortcut = shell.CreateShortCut(path) shortcut.Targetpath = '"'+target+'"' shortcut.WorkingDirectory = currentDirectory #shortcut.WorkingDirectory = os.path.dirname(__file__) shortcut.IconLocation = icon shortcut.WindowStyle = 1 # 7 - Minimized, 3 - Maximized, 1 - Normal shortcut.save() print('--------------AION Installed Successfully--------------') except Exception as e: print(e) f = open(os.path.join(currentDirectory, 'workspace_error_logs.txt'), "w") f.write(str(traceback.format_exc())) f.close() pass ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import warnings import numpy as np import pandas as pd import sklearn.metrics as metrics from collections import defaultdict from sklearn.metrics import confusion_matrix import re import shutil import scipy.stats as st import json import os,sys import glob import logging from utils.file_ops import read_df_compressed class Visualization(): def __init__(self,usecasename,version,dataframe,visualizationJson,dateTimeColumn,deployPath,dataFolderLocation,numericContinuousFeatures,discreteFeatures,categoricalFeatures,modelFeatures,targetFeature,modeltype,original_data_file,profiled_data_file,trained_data_file,predicted_data_file,labelMaps,vectorizerFeatures,textFeatures,numericalFeatures,nonNumericFeatures,emptyFeatures,nrows,ncols,saved_model,scoreParam,learner_type,modelname,featureReduction,reduction_data_file): self.dataframe = dataframe self.displayjson = {} self.visualizationJson = visualizationJson self.dateTimeColumn = dateTimeColumn self.deployPath = deployPath #shutil.copy2(os.path.join(os.path.dirname(os.path.abspath(__file__)),'aion_portal.py'),self.deployPath) if learner_type == 'ML' and modelname != 'Neural Architecture Search': if(os.path.isfile(os.path.join(self.deployPath,'explainable_ai.py'))): os.remove(os.path.join(self.deployPath,'explainable_ai.py')) shutil.copy2(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','utilities','xai','explainable_ai.py'),self.deployPath) # os.rename(os.path.join(self.deployPath,'explainable_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) try: os.rename(os.path.join(self.deployPath,'explainable_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) except FileExistsError: os.remove(os.path.join(self.deployPath,'aion_xai.py')) os.rename(os.path.join(self.deployPath,'explainable_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) elif learner_type == 'DL' or modelname == 'Neural Architecture Search': if(os.path.isfile(os.path.join(self.deployPath,'explainable_ai.py'))): os.remove(os.path.join(self.deployPath,'explainable_ai.py')) shutil.copy2(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','utilities','xai','explainabledl_ai.py'),self.deployPath) # os.rename(os.path.join(self.deployPath,'explainabledl_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) try: os.rename(os.path.join(self.deployPath,'explainabledl_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) except FileExistsError: os.remove(os.path.join(self.deployPath,'aion_xai.py')) os.rename(os.path.join(self.deployPath,'explainabledl_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) self.jsondeployPath = deployPath #self.deployPath = self.deployPath+'visualization/' self.dataFolderLocation = dataFolderLocation self.vectorizerFeatures = vectorizerFeatures self.textFeatures = textFeatures self.emptyFeatures = emptyFeatures ''' try: os.makedirs(self.deployPath) except OSError as e: print("\nFolder Already Exists") ''' self.numericContinuousFeatures = numericContinuousFeatures self.discreteFeatures = discreteFeatures self.categoricalFeatures = categoricalFeatures self.modelFeatures = modelFeatures self.modeltype = modeltype self.targetFeature = targetFeature self.displayjson['usecasename'] = str(usecasename) self.displayjson['version'] = str(version) self.displayjson['problemType'] = str(self.modeltype) self.displayjson['targetFeature'] = self.targetFeature self.displayjson['numericalFeatures'] = numericalFeatures self.displayjson['nonNumericFeatures'] = nonNumericFeatures self.displayjson['modelFeatures'] = self.modelFeatures self.displayjson['textFeatures'] = self.textFeatures self.displayjson['emptyFeatures'] = self.emptyFeatures self.displayjson['modelname']= str(modelname) self.displayjson['preprocessedData'] = str(original_data_file) self.displayjson['nrows'] = str(nrows) self.displayjson['ncols'] = str(ncols) self.displayjson['saved_model'] = str(saved_model) self.displayjson['scoreParam'] = str(scoreParam) self.displayjson['labelMaps'] = eval(str(labelMaps)) self.original_data_file = original_data_file self.displayjson['featureReduction'] = featureReduction if featureReduction == 'True': self.displayjson['reduction_data_file'] = reduction_data_file else: self.displayjson['reduction_data_file'] = '' self.pred_filename = predicted_data_file self.profiled_data_file = profiled_data_file self.displayjson['predictedData'] = predicted_data_file self.displayjson['postprocessedData'] = profiled_data_file #self.trained_data_file = trained_data_file #self.displayjson['trainingData'] = trained_data_file #self.displayjson['categorialFeatures']=categoricalFeatures #self.displayjson['discreteFeatures']=discreteFeatures #self.displayjson['continuousFeatures']=numericContinuousFeatures #y = json.dumps(self.displayjson) #print(y) self.labelMaps = labelMaps self.log = logging.getLogger('eion') def visualizationrecommandsystem(self): try: import tensorflow.keras.utils as kutils datasetid = self.visualizationJson['datasetid'] self.log.info('\n================== Data Profiling Details==================') datacolumns=list(self.dataframe.columns) self.log.info('================== Data Profiling Details End ==================\n') self.log.info('================== Features Correlation Details ==================\n') self.log.info('\n================== Model Performance Analysis ==================') if os.path.exists(self.pred_filename): try: status,df=read_df_compressed(self.pred_filename) if self.modeltype == 'Classification' or self.modeltype == 'ImageClassification' or self.modeltype == 'anomaly_detection': y_actual = df['actual'].values y_predict = df['predict'].values y_actual = kutils.to_categorical(y_actual) y_predict = kutils.to_categorical(y_predict) classes = df.actual.unique() n_classes = y_actual.shape[1] self.log.info('-------> ROC AUC CURVE') roc_curve_dict = [] for i in classes: try: classname = i if str(self.labelMaps) != '{}': inv_map = {v: k for k, v in self.labelMaps.items()} classname = inv_map[i] fpr, tpr, threshold = metrics.roc_curve(y_actual[:,i],y_predict[:,i]) roc_auc = metrics.auc(fpr, tpr) class_roc_auc_curve = {} class_roc_auc_curve['class'] = str(classname) fprstring = ','.join(str(v) for v in fpr) tprstring = ','.join(str(v) for v in tpr) class_roc_auc_curve['FP'] = str(fprstring) class_roc_auc_curve['TP'] = str(tprstring) roc_curve_dict.append(class_roc_auc_curve) self.log.info('----------> Class: '+str(classname)) self.log.info('------------> ROC_AUC: '+str(roc_auc)) self.log.info('------------> False Positive Rate (x Points): '+str(fpr)) self.log.info('------------> True Positive Rate (y Points): '+str(tpr)) except: pass self.displayjson['ROC_AUC_CURVE'] = roc_curve_dict self.log.info('-------> Precision Recall CURVE') precision_recall_curve_dict = [] for i in range(n_classes): try: lr_precision, lr_recall, threshold = metrics.precision_recall_curve(y_actual[:,i],y_predict[:,i]) classname = i if str(self.labelMaps) != '{}': inv_map = {v: k for k, v in self.labelMaps.items()} classname = inv_map[i] roc_auc = metrics.auc(lr_recall,lr_precision) class_precision_recall_curve = {} class_precision_recall_curve['class'] = str(classname) Precisionstring = ','.join(str(round(v,2)) for v in lr_precision) Recallstring = ','.join(str(round(v,2)) for v in lr_recall) class_precision_recall_curve['Precision'] = str(Precisionstring) class_precision_recall_curve['Recall'] = str(Recallstring) precision_recall_curve_dict.append(class_precision_recall_curve) except: pass self.log.info('----------> Class: '+str(classname)) self.log.info('------------> ROC_AUC: '+str(roc_auc)) self.log.info('------------> Recall (x Points): '+str(lr_precision)) self.log.info('------------> Precision (y Points): '+str(lr_recall)) self.displayjson['PRECISION_RECALL_CURVE'] = precision_recall_curve_dict status,predictdataFrame=read_df_compressed(self.displayjson['predictedData']) except Exception as e: self.log.info('================== Error in Calculation ROC_AUC/Recall Precision Curve '+str(e)) self.log.info('================== Model Performance Analysis End ==================\n') self.log.info('\n================== For Descriptive Analysis of Model Features ==================') outputfile = os.path.join(self.jsondeployPath,'etc','display.json') with open(outputfile, 'w') as fp: json.dump(self.displayjson, fp) self.log.info('================== For Descriptive Analysis of Model Features End ==================\n') except Exception as inst: self.log.info('Visualization Failed !....'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def drawlinechart(self,xcolumn,ycolumn,deploy_path,datasetid): title = 'aion_visualization_'+xcolumn+"_"+ycolumn+"_linechart" yaxisname = 'Average '+ycolumn datasetindex = datasetid visulizationjson = '[{"_id": "543234","_type": "visualization","_source": {"title": "'+title+'",' visulizationjson = visulizationjson+'"visState": "{\\"title\\":\\"'+title+'\\",' visulizationjson = visulizationjson+'\\"type\\":\\"line\\",\\"params\\":{\\"type\\":\\"line\\",\\"grid\\":{\\"categoryLines\\":false,\\"style\\":{\\"color\\":\\"#eee\\"}},\\"categoryAxes\\":[{\\"id\\":\\"CategoryAxis-1\\",\\"type\\":\\"category\\",\\"position\\":\\"bottom\\",\\"show\\":true,\\"style\\":{},\\"scale\\":{\\"type\\":\\"linear\\"},\\"labels\\":{\\"show\\":true,\\"truncate\\":100},\\"title\\":{}}],\\"valueAxes\\":[{\\"id\\":\\"ValueAxis-1\\",\\"name\\":\\"LeftAxis-1\\",\\"type\\":\\"value\\",\\"position\\":\\"left\\",\\"show\\":true,\\"style\\":{},\\"scale\\":{\\"type\\":\\"linear\\",\\"mode\\":\\"normal\\"},\\"labels\\":{\\"show\\":true,\\"rotate\\":0,\\"filter\\":false,\\"truncate\\":100},\\"title\\":' visulizationjson = visulizationjson+'{\\"text\\":\\"'+yaxisname+'\\"}}],\\"seriesParams\\":[{\\"show\\":\\"true\\",\\"type\\":\\"line\\",\\"mode\\":\\"normal\\",\\"data\\":' visulizationjson = visulizationjson+'{\\"label\\":\\"'+yaxisname+'\\",\\"id\\":\\"1\\"},\\"valueAxis\\":\\"ValueAxis-1\\",\\"drawLinesBetweenPoints\\":true,\\"showCircles\\":true}],\\"addTooltip\\":true,\\"addLegend\\":true,\\"legendPosition\\":\\"right\\",\\"times\\":[],\\"addTimeMarker\\":false},\\"aggs\\":[{\\"id\\":\\"1\\",\\"enabled\\":true,\\"type\\":\\"avg\\",\\"schema\\":\\"metric\\",\\"params\\":{\\"field\\":\\"'+str(ycolumn)+'\\"}},{\\"id\\":\\"2\\",\\"enabled\\":true,\\"type\\":\\"terms\\",\\"schema\\":\\"segment\\",\\"params\\":{\\"field\\":\\"'+xcolumn+'\\",\\"size\\":100,\\"order\\":\\"desc\\",\\"orderBy\\":\\"1\\",\\"otherBucket\\":false,\\"otherBucketLabel\\":\\"Other\\",\\"missingBucket\\":false,\\"missingBucketLabel\\":\\"Missing\\"}}]}","uiStateJSON": "{}", "description": "","version": 1,"kibanaSavedObjectMeta": {"searchSourceJSON": "{\\"index\\":\\"'+datasetindex+'\\",\\"query\\":{\\"query\\":\\"\\",\\"language\\":\\"lucene\\"},\\"filter\\":[]}"}},"_migrationVersion": {"visualization": "6.7.2"}}]' filename = deploy_path+title+'.json' f = open(filename, "w") f.write(str(visulizationjson)) f.close() def drawbarchart(self,xcolumn,ycolumn,deploy_path,datasetid): title = 'aion_visualization_'+xcolumn+"_"+ycolumn+"_barchart" yaxisname = 'Average '+ycolumn datasetindex = datasetid visulizationjson = '[{"_id": "123456","_type": "visualization","_source": {"title":"'+title+'",' visulizationjson = visulizationjson+'"visState": "{\\"title\\":\\"'+title+'\\",' visulizationjson = visulizationjson+'\\"type\\":\\"histogram\\",\\"params\\":{\\"addLegend\\":true,\\"addTimeMarker\\":false,\\"addTooltip\\":true,\\"categoryAxes\\":[{\\"id\\":\\"CategoryAxis-1\\",\\"labels\\":{\\"show\\":true,\\"truncate\\":100},\\"position\\":\\"bottom\\",\\"scale\\":{\\"type\\":\\"linear\\"},\\"show\\":true,\\"style\\":{},\\"title\\":{},\\"type\\":\\"category\\"}],\\"grid\\":{\\"categoryLines\\":false,\\"style\\":{\\"color\\":\\"#eee\\"}},\\"legendPosition\\":\\"right\\",\\"seriesParams\\":[{\\"data\\":{\\"id\\":\\"1\\",' visulizationjson = visulizationjson+'\\"label\\":\\"'+yaxisname+'\\"},' visulizationjson = visulizationjson+'\\"drawLinesBetweenPoints\\":true,\\"mode\\":\\"stacked\\",\\"show\\":\\"true\\",\\"showCircles\\":true,\\"type\\":\\"histogram\\",\\"valueAxis\\":\\"ValueAxis-1\\"}],\\"times\\":[],\\"type\\":\\"histogram\\",\\"valueAxes\\":[{\\"id\\":\\"ValueAxis-1\\",\\"labels\\":{\\"filter\\":false,\\"rotate\\":0,\\"show\\":true,\\"truncate\\":100},\\"name\\":\\"LeftAxis-1\\",\\"position\\":\\"left\\",\\"scale\\":{\\"mode\\":\\"normal\\",\\"type\\":\\"linear\\"},\\"show\\":true,\\"style\\":{},\\"title\\":' visulizationjson = visulizationjson+'{\\"text\\":\\"'+yaxisname+'\\"},' visulizationjson = visulizationjson+'\\"type\\":\\"value\\"}]},\\"aggs\\":[{\\"id\\":\\"1\\",\\"enabled\\":true,\\"type\\":\\"avg\\",\\"schema\\":\\"metric\\",\\"params\\":{\\"field\\":\\"'+str(xcolumn)+'\\"}},{\\"id\\":\\"2\\",\\"enabled\\":true,\\"type\\":\\"terms\\",\\"schema\\":\\"segment\\",\\"params\\":{\\"field\\":\\"'+ycolumn+'\\",\\"size\\":100,\\"order\\":\\"asc\\",\\"orderBy\\":\\"1\\",\\"otherBucket\\":false,\\"otherBucketLabel\\":\\"Other\\",\\"missingBucket\\":false,\\"missingBucketLabel\\":\\"Missing\\"}}]}","uiStateJSON":"{}","description": "","version": 1,"kibanaSavedObjectMeta": {' visulizationjson = visulizationjson+'"searchSourceJSON": "{\\"index\\":\\"'+datasetindex+'\\",\\"query\\":{\\"language\\":\\"lucene\\",\\"query\\":\\"\\"},\\"filter\\":[]}"}},"_migrationVersion":{"visualization": "6.7.2"}}]' filename = deploy_path+title+'.json' f = open(filename, "w") f.write(str(visulizationjson)) f.close() def drawpiechart(self,xcolumn,deploy_path,datasetid): title = 'aion_visualization_'+xcolumn+"_piechart" datasetindex = datasetid visulizationjson = '[{"_id": "123456","_type": "visualization","_source": {"title":"'+title+'",' visulizationjson = visulizationjson+'"visState": "{\\"title\\":\\"'+title+'\\",' visulizationjson = visulizationjson+'\\"type\\":\\"pie\\",\\"params\\":{\\"type\\":\\"pie\\",\\"addTooltip\\":true,\\"addLegend\\":true,\\"legendPosition\\":\\"right\\",\\"isDonut\\":true,\\"labels\\":{\\"show\\":false,\\"values\\":true,\\"last_level\\":true,\\"truncate\\":100}},\\"aggs\\":[{\\"id\\":\\"1\\",\\"enabled\\":true,\\"type\\":\\"count\\",\\"schema\\":\\"metric\\",\\"params\\":{}},{\\"id\\":\\"2\\",\\"enabled\\":true,\\"type\\":\\"terms\\",\\"schema\\":\\"segment\\",\\"params\\":{\\"field\\":\\"'+xcolumn+'\\",\\"size\\":100,\\"order\\":\\"asc\\",\\"orderBy\\":\\"1\\",\\"otherBucket\\":false,\\"otherBucketLabel\\":\\"Other\\",\\"missingBucket\\":false,\\"missingBucketLabel\\":\\"Missing\\"}}]}",' visulizationjson = visulizationjson+'"uiStateJSON": "{}","description": "","version": 1,"kibanaSavedObjectMeta": {"searchSourceJSON":"{\\"index\\":\\"'+datasetid+'\\",\\"query\\":{\\"query\\":\\"\\",\\"language\\":\\"lucene\\"},\\"filter\\":[]}"}},"_migrationVersion": {"visualization": "6.7.2"}}]' filename = deploy_path+title+'.json' f = open(filename, "w") f.write(str(visulizationjson)) f.close() def get_confusion_matrix(self,df): setOfyTrue = set(df['actual']) unqClassLst = list(setOfyTrue) if(str(self.labelMaps) != '{}'): inv_mapping_dict = {v: k for k, v in self.labelMaps.items()} unqClassLst2 = (pd.Series(unqClassLst)).map(inv_mapping_dict) unqClassLst2 = list(unqClassLst2) else: unqClassLst2 = unqClassLst indexName = [] columnName = [] for item in unqClassLst2: indexName.append("act:"+str(item)) columnName.append("pre:"+str(item)) result = pd.DataFrame(confusion_matrix(df['actual'], df['predict'], labels = unqClassLst),index = indexName, columns = columnName) resultjson = result.to_json(orient='index') return(resultjson) def DistributionFinder(self,data): try: distributionName ="" sse =0.0 KStestStatic=0.0 dataType="" if(data.dtype == "float64"): dataType ="Continuous" elif(data.dtype =="int" or data.dtype =="int64"): dataType="Discrete" if(dataType == "Discrete"): distributions= [st.bernoulli,st.binom,st.geom,st.nbinom,st.poisson] index, counts = np.unique(abs(data.astype(int)),return_counts=True) if(len(index)>=2): best_sse = np.inf y1=[] total=sum(counts) mean=float(sum(index*counts))/total variance=float((sum(index**2*counts) -total*mean**2))/(total-1) dispersion=mean/float(variance) theta=1/float(dispersion) r=mean*(float(theta)/1-theta) for j in counts: y1.append(float(j)/total) pmf1=st.bernoulli.pmf(index,mean) pmf2=st.binom.pmf(index,len(index),p=mean/len(index)) pmf3=st.geom.pmf(index,1/float(1+mean)) pmf4=st.nbinom.pmf(index,mean,r) pmf5=st.poisson.pmf(index,mean) sse1 = np.sum(np.power(y1 - pmf1, 2.0)) sse2 = np.sum(np.power(y1 - pmf2, 2.0)) sse3 = np.sum(np.power(y1 - pmf3, 2.0)) sse4 = np.sum(np.power(y1 - pmf4, 2.0)) sse5 = np.sum(np.power(y1- pmf5, 2.0)) sselist=[sse1,sse2,sse3,sse4,sse5] for i in range(0,len(sselist)): if best_sse > sselist[i] > 0: best_distribution = distributions[i].name best_sse = sselist[i] elif (len(index) == 1): best_distribution = "Constant Data-No Distribution" best_sse = 0.0 distributionName =best_distribution sse=best_sse elif(dataType == "Continuous"): distributions = [st.uniform,st.expon,st.weibull_max,st.weibull_min,st.chi,st.norm,st.lognorm,st.t,st.gamma,st.beta] best_distribution = st.norm.name best_sse = np.inf datamin=data.min() datamax=data.max() nrange=datamax-datamin y, x = np.histogram(data.astype(float), bins='auto', density=True) x = (x + np.roll(x, -1))[:-1] / 2.0 for distribution in distributions: with warnings.catch_warnings(): warnings.filterwarnings('ignore') params = distribution.fit(data.astype(float)) # Separate parts of parameters arg = params[:-2] loc = params[-2] scale = params[-1] # Calculate fitted PDF and error with fit in distribution pdf = distribution.pdf(x, loc=loc, scale=scale, *arg) sse = np.sum(np.power(y - pdf, 2.0)) if(best_sse >sse > 0): best_distribution = distribution.name best_sse = sse distributionName =best_distribution sse=best_sse except: response = str(sys.exc_info()[0]) message='Job has Failed'+response print(message) return distributionName,sse import docker import json import logging def read_json(file_path): data = None with open(file_path,'r') as f: data = json.load(f) return data def run_pipeline(inputconfig): inputconfig = json.loads(inputconfig) logfilepath = inputconfig['logfilepath'] logging.basicConfig(level=logging.INFO,filename =logfilepath) usecasename = inputconfig['usecase'] logging.info("UseCaseName :"+str(usecasename)) version = inputconfig['version'] logging.info("version :"+str(version)) config = inputconfig['dockerlist'] persistancevolume = inputconfig['persistancevolume'] logging.info("PersistanceVolume :"+str(persistancevolume)) datasetpath = inputconfig['datasetpath'] logging.info("DataSet Path :"+str(datasetpath)) config = read_json(config) client = docker.from_env() inputconfig = {'modelName':usecasename,'modelVersion':str(version),'dataLocation':datasetpath} inputconfig = json.dumps(inputconfig) inputconfig = inputconfig.replace('"', '\\"') logging.info("===== Model Monitoring Container Start =====") outputStr = client.containers.run(config['ModelMonitoring'],'python code.py -i'+datasetpath,volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('ModelMonitoring: '+str(outputStr)) print('ModelMonitoring: '+str(outputStr)) logging.info("===== ModelMonitoring Stop =====") logging.info("===== Data Ingestion Container Start =====") outputStr = client.containers.run(config['DataIngestion'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('DataIngestion: '+str(outputStr)) print('DataIngestion: '+str(outputStr)) logging.info("===== Data Ingestion Container Stop =====") outputStr = outputStr.strip() decoded_data = json.loads(outputStr) status = decoded_data['Status'] if status != 'Success': output = {'Status':'Error','Msg':'Data Ingestion Fails'} logging.info("===== Transformation Container Start =====") outputStr = client.containers.run(config['DataTransformation'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('Data Transformations: '+str(outputStr)) print('Data Transformations: '+str(outputStr)) logging.info("===== Transformation Container Done =====") outputStr = outputStr.strip() decoded_data = json.loads(outputStr) status = decoded_data['Status'] if status != 'Success': output = {'Status':'Error','Msg':'Data Transformations Fails'} logging.info("===== Feature Engineering Container Start =====") outputStr = client.containers.run(config['FeatureEngineering'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('FeatureEngineering: '+str(outputStr)) print('FeatureEngineering: '+str(outputStr)) logging.info("===== Feature Engineering Container Done =====") outputStr = outputStr.strip() decoded_data = json.loads(outputStr) status = decoded_data['Status'] modeltraining = config['ModelTraining'] for mt in modeltraining: logging.info("===== Training Container Start =====") outputStr = client.containers.run(mt['Training'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('ModelTraining: '+str(outputStr)) print('ModelTraining: '+str(outputStr)) logging.info("===== Training Container Done =====") outputStr = outputStr.strip() try: decoded_data = json.loads(outputStr) status = decoded_data['Status'] except Exception as inst: logging.info(inst) logging.info("===== Model Registry Start =====") outputStr = client.containers.run(config['ModelRegistry'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('ModelRegistry: '+str(outputStr)) print('ModelRegistry: '+str(outputStr)) logging.info("===== ModelRegistry Done =====") logging.info("===== ModelServing Start =====") outputStr = client.containers.run(config['ModelServing'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('Prediction: '+str(outputStr)) print('Prediction: '+str(outputStr)) logging.info("===== ModelServing Done =====") import os import shutil import sys import subprocess from os.path import expanduser import platform import json def createDockerImage(model_name,model_version,module,folderpath): command = 'docker pull python:3.8-slim-buster' os.system(command); subprocess.check_call(["docker", "build", "-t",module+'_'+model_name.lower()+":"+model_version,"."], cwd=folderpath) def local_docker_build(config): print(config) config = json.loads(config) model_name = config['usecase'] model_version = config['version'] mlaac__code_path = config['mlacPath'] docker_images = {} docker_images['ModelMonitoring'] = 'modelmonitoring'+'_'+model_name.lower()+':'+model_version dataset_addr = os.path.join(mlaac__code_path,'ModelMonitoring') createDockerImage(model_name,model_version,'modelmonitoring',dataset_addr) docker_images['DataIngestion'] = 'dataingestion'+'_'+model_name.lower()+':'+model_version dataset_addr = os.path.join(mlaac__code_path,'DataIngestion') createDockerImage(model_name,model_version,'dataingestion',dataset_addr) transformer_addr = os.path.join(mlaac__code_path,'DataTransformation') docker_images['DataTransformation'] = 'datatransformation'+'_'+model_name.lower()+':'+model_version createDockerImage(model_name,model_version,'datatransformation',transformer_addr) featureengineering_addr = os.path.join(mlaac__code_path,'FeatureEngineering') docker_images['FeatureEngineering'] = 'featureengineering'+'_'+model_name.lower()+':'+model_version createDockerImage(model_name,model_version,'featureengineering',featureengineering_addr) from os import listdir arr = [filename for filename in os.listdir(mlaac__code_path) if filename.startswith("ModelTraining")] docker_training_images = [] for x in arr: dockertraing={} dockertraing['Training'] = str(x).lower()+'_'+model_name.lower()+':'+model_version docker_training_images.append(dockertraing) training_addri = os.path.join(mlaac__code_path,x) createDockerImage(model_name,model_version,str(x).lower(),training_addri) docker_images['ModelTraining'] = docker_training_images docker_images['ModelRegistry'] = 'modelregistry'+'_'+model_name.lower()+':'+model_version deploy_addr = os.path.join(mlaac__code_path,'ModelRegistry') createDockerImage(model_name,model_version,'modelregistry',deploy_addr) docker_images['ModelServing'] = 'modelserving'+'_'+model_name.lower()+':'+model_version deploy_addr = os.path.join(mlaac__code_path,'ModelServing') createDockerImage(model_name,model_version,'modelserving',deploy_addr) outputjsonFile = os.path.join(mlaac__code_path,'dockerlist.json') with open(outputjsonFile, 'w') as f: json.dump(docker_images, f) f.close() output = {'Status':'Success','Msg':outputjsonFile} output = json.dumps(output) print("aion_build_container:",output) import os import sys import json from pathlib import Path import subprocess import shutil import argparse def create_and_save_yaml(git_storage_path, container_label,usecasepath): file_name_prefix = 'gh-acr-' yaml_file = f"""\ name: gh-acr-{container_label} on: push: branches: main paths: {container_label}/** workflow_dispatch: jobs: gh-acr-build-push: runs-on: ubuntu-latest steps: - name: 'checkout action' uses: actions/checkout@main - name: 'azure login' uses: azure/login@v1 with: creds: ${{{{ secrets.AZURE_CREDENTIALS }}}} - name: 'build and push image' uses: azure/docker-login@v1 with: login-server: ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}} username: ${{{{ secrets.REGISTRY_USERNAME }}}} password: ${{{{ secrets.REGISTRY_PASSWORD }}}} - run: | docker build ./{container_label}/ModelMonitoring -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelmonitoring:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelmonitoring:{container_label} docker build ./{container_label}/DataIngestion -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/dataingestion:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/dataingestion:{container_label} docker build ./{container_label}/DataTransformation -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/datatransformation:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/datatransformation:{container_label} docker build ./{container_label}/FeatureEngineering -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/featureengineering:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/featureengineering:{container_label} docker build ./{container_label}/ModelRegistry -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelregistry:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelregistry:{container_label} docker build ./{container_label}/ModelServing -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelserving:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelserving:{container_label} """ arr = [filename for filename in os.listdir(usecasepath) if filename.startswith("ModelTraining")] for x in arr: yaml_file+=' docker build ./'+container_label+'/'+x+' -t ${{ secrets.REGISTRY_LOGIN_SERVER }}/'+x.lower()+':'+container_label yaml_file+='\n' yaml_file+=' docker push ${{ secrets.REGISTRY_LOGIN_SERVER }}/'+x.lower()+':'+container_label yaml_file+='\n' with open(Path(git_storage_path)/(file_name_prefix + container_label + '.yaml'), 'w') as f: f.write(yaml_file) def run_cmd(cmd): try: subprocess.check_output(cmd, stderr=subprocess.PIPE) except subprocess.CalledProcessError as e: if e.stderr: if isinstance(e.stderr, bytes): err_msg = e.stderr.decode(sys.getfilesystemencoding()) else: err_msg = e.stderr elif e.output: if isinstance(e.output, bytes): err_msg = e.output.decode(sys.getfilesystemencoding()) else: err_msg = e.output else: err_msg = str(e) return False, err_msg return True, "" def validate_config(config): non_null_keys = ['url','username', 'token', 'location', 'gitFolderLocation', 'email', 'modelName'] missing_keys = [k for k in non_null_keys if k not in config.keys()] if missing_keys: raise ValueError(f"following fields are missing in config file: {missing_keys}") for k,v in config.items(): if k in non_null_keys and not v: raise ValueError(f"Please provide value for '{k}' in config file.") def upload(config): validate_config(config) url_type = config.get('url_type','https') if url_type == 'https': https_str = "https://" url = https_str + config['username'] + ":" + config['token'] + "@" + config['url'][len(https_str):] else: url = config['url'] model_location = Path(config['location']) git_folder_location = Path(config['gitFolderLocation']) git_folder_location.mkdir(parents=True, exist_ok=True) (git_folder_location/'.github'/'workflows').mkdir(parents=True, exist_ok=True) if not model_location.exists(): raise ValueError('Trained model data not found') os.chdir(str(git_folder_location)) (git_folder_location/config['modelName']).mkdir(parents=True, exist_ok=True) shutil.copytree(model_location, git_folder_location/config['modelName'], dirs_exist_ok=True) create_and_save_yaml((git_folder_location/'.github'/'workflows'), config['modelName'],config['location']) if (Path(git_folder_location)/'.git').exists(): first_upload = False else: first_upload = True if first_upload: cmd = ['git','init'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','config','user.name',config['username']] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','config','user.email',config['email']] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','add', '-A'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','commit','-m',f"commit {config['modelName']}"] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','branch','-M','main'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) if first_upload: cmd = ['git','remote','add','origin', url] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','push','-f','-u','origin', 'main'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) else: cmd = ['git','push'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) return json.dumps({'Status':'SUCCESS'}) if __name__ == '__main__': try: if shutil.which('git') is None: raise ValueError("git is not installed on this system") parser = argparse.ArgumentParser() parser.add_argument('-c', '--config', help='Config file location or as a string') args = parser.parse_args() if Path(args.config).is_file() and Path(args.config).suffix == '.json': with open(args.config,'r') as f: config = json.load(f) else: config = json.loads(args.config) print(upload(config)) except Exception as e: status = {'Status':'Failure','msg':str(e)} print(json.dumps(status)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from kafka import KafkaConsumer from json import loads import pandas as pd import json import os,sys import time import multiprocessing from os.path import expanduser import platform import datetime modelDetails = {} class Process(multiprocessing.Process): def __init__(self, modelSignature,jsonData,predictedData,modelpath): super(Process, self).__init__() self.config = jsonData self.modelSignature = modelSignature self.data = predictedData self.modelpath = modelpath def run(self): #data = pd.json_normalize(self.data) minotoringService = self.config['minotoringService']['url'] trainingdatalocation = self.config['trainingDataLocation'][self.modelSignature] #filetimestamp = 'AION_'+str(int(time.time()))+'.csv' #data.to_csv(dataFile, index=False) inputFieldsJson = {"trainingDataLocation":trainingdatalocation,"currentDataLocation":self.data} inputFieldsJson = json.dumps(inputFieldsJson) ser_url = minotoringService+self.modelSignature+'/monitoring' driftTime = datetime.datetime.now() import requests try: response = requests.post(ser_url, data=inputFieldsJson,headers={"Content-Type":"application/json",}) outputStr=response.content outputStr = outputStr.decode('utf-8') outputStr = outputStr.strip() decoded_data = json.loads(outputStr) print(decoded_data) status = decoded_data['status'] msg = decoded_data['data'] except Exception as inst: if 'Failed to establish a new connection' in str(inst): status = 'Fail' msg = 'AION Service needs to be started' else: status = 'Fail' msg = 'Error during Drift Analysis' statusFile = os.path.join(self.modelpath,self.modelSignature+'_status.csv') df = pd.DataFrame(columns = ['dateTime', 'status', 'msg']) df = df.append({'dateTime' : driftTime, 'status' : status, 'msg' : msg},ignore_index = True) print(df) if (os.path.exists(statusFile)): df.to_csv(statusFile, mode='a', header=False,index=False) else: df.to_csv(statusFile, header=True,index=False) def launch_kafka_consumer(): from appbe.dataPath import DATA_DIR configfile = os.path.join(os.path.dirname(__file__),'..','config','kafkaConfig.conf') with open(configfile,'r',encoding='utf-8') as f: jsonData = json.load(f) f.close() kafkaIP=jsonData['kafkaCluster']['ip'] kafkaport = jsonData['kafkaCluster']['port'] topic = jsonData['kafkaCluster']['topic'] kafkaurl = kafkaIP+':'+kafkaport if jsonData['database']['csv'] == 'True': database = 'csv' elif jsonData['database']['mySql'] == 'True': database = 'mySql' else: database = 'csv' kafkaPath = os.path.join(DATA_DIR,'kafka') if not (os.path.exists(kafkaPath)): try: os.makedirs(kafkaPath) except OSError as e: pass consumer = KafkaConsumer(topic,bootstrap_servers=[kafkaurl],auto_offset_reset='earliest',enable_auto_commit=True,group_id='my-group',value_deserializer=lambda x: loads(x.decode('utf-8'))) for message in consumer: message = message.value data = message['data'] data = pd.json_normalize(data) modelname = message['usecasename'] version = message['version'] modelSignature = modelname+'_'+str(version) modelpath = os.path.join(kafkaPath,modelSignature) try: os.makedirs(modelpath) except OSError as e: pass secondsSinceEpoch = time.time() if modelSignature not in modelDetails: modelDetails[modelSignature] = {} modelDetails[modelSignature]['startTime'] = secondsSinceEpoch if database == 'csv': csvfile = os.path.join(modelpath,modelSignature+'.csv') if (os.path.exists(csvfile)): data.to_csv(csvfile, mode='a', header=False,index=False) else: data.to_csv(csvfile, header=True,index=False) modelTimeFrame = jsonData['timeFrame'][modelSignature] currentseconds = time.time() print(currentseconds - modelDetails[modelSignature]['startTime']) if (currentseconds - modelDetails[modelSignature]['startTime']) >= float(modelTimeFrame): csv_path = os.path.join(modelpath,modelSignature+'.csv') #predictedData = pd.read_csv(csv_path) ##predictedData = predictedData.to_json(orient="records") index = Process(modelSignature,jsonData,csv_path,modelpath) index.start() modelDetails[modelSignature]['startTime'] = secondsSinceEpoch

import os import traceback def process(version): currentDirectory = os.path.dirname(os.path.abspath(__file__)) try: import win32com.client from os.path import expanduser import platform import subprocess import sys import demoji try: print('Downloading NLTK additional packages...') import nltk nltk.download('punkt') nltk.download('wordnet') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger') except Exception as e: print('NLTK Error: '+str(e)) pass from appbe.dataPath import DATA_DIR from win32com.shell import shell, shellcon import shutil import importlib license_path = DATA_DIR if os.path.isdir(license_path) == False: os.makedirs(license_path) import warnings warnings.filterwarnings("ignore") LicenseFolder = os.path.join(license_path,'License') if os.path.isdir(LicenseFolder) == False: os.makedirs(LicenseFolder) sqlite_path = os.path.join(license_path,'sqlite') if os.path.isdir(sqlite_path) == False: os.makedirs(sqlite_path) pretrainedModel_path = os.path.join(license_path,'PreTrainedModels') if os.path.isdir(pretrainedModel_path) == False: os.makedirs(pretrainedModel_path) config_path = os.path.join(license_path,'config') if os.path.isdir(config_path) == False: os.makedirs(config_path) target_path = os.path.join(license_path,'target') if os.path.isdir(target_path) == False: os.makedirs(target_path) data_path = os.path.join(license_path,'storage') if os.path.isdir(data_path) == False: os.makedirs(data_path) log_path = os.path.join(license_path,'logs') if os.path.isdir(log_path) == False: os.makedirs(log_path) configFolder = os.path.join(currentDirectory,'..','config') for file in os.listdir(configFolder): if file.endswith(".var"): os.remove(os.path.join(configFolder,file)) versionfile = os.path.join(configFolder,str(version)+'.var') with open(versionfile, 'w') as fp: pass manage_path = os.path.join(currentDirectory,'..','aion.py') print('Setting up Django Environment for AION User Interface') proc = subprocess.Popen([sys.executable, manage_path, "-m","migrateappfe"],stdout=subprocess.PIPE, stderr=subprocess.PIPE) (stdout, stderr) = proc.communicate() if proc.returncode != 0: err_string = stderr.decode('utf8') import re result = re.search("No module named '(.*)'", err_string) if 'ModuleNotFoundError' in err_string: print('\n"{}" module is missing. The dependencies of AION were not installed properly. Uninstall and reinstall AION'.format(result.group(1))) else: print('\nThe dependencies of AION were not installed properly. Uninstall and reinstall AION') raise Exception(err_string) else: print('AION User Interface successfully set') desktop = shell.SHGetFolderPath (0, shellcon.CSIDL_DESKTOP, 0, 0) #desktop = os.path.expanduser('~/Desktop') path = os.path.join(desktop, 'Explorer {0}.lnk'.format(version)) target = os.path.normpath(os.path.join(currentDirectory,'..', 'sbin', 'AION_Explorer.bat')) icon = os.path.join(currentDirectory,'icons','aion.ico') shell = win32com.client.Dispatch("WScript.Shell") shortcut = shell.CreateShortCut(path) shortcut.Targetpath = '"'+target+'"' shortcut.WorkingDirectory = currentDirectory #shortcut.WorkingDirectory = os.path.dirname(__file__) shortcut.IconLocation = icon shortcut.WindowStyle = 1 # 7 - Minimized, 3 - Maximized, 1 - Normal shortcut.save() path = os.path.join(desktop, 'Shell {0}.lnk'.format(version)) target = os.path.normpath(os.path.join(currentDirectory,'..','sbin', 'AION_Shell.bat')) icon = os.path.join(currentDirectory,'icons','aion_shell.ico') shell = win32com.client.Dispatch("WScript.Shell") shortcut = shell.CreateShortCut(path) shortcut.Targetpath = '"'+target+'"' shortcut.WorkingDirectory = currentDirectory #shortcut.WorkingDirectory = os.path.dirname(__file__) shortcut.IconLocation = icon shortcut.WindowStyle = 1 # 7 - Minimized, 3 - Maximized, 1 - Normal shortcut.save() print('--------------AION Installed Successfully--------------') except Exception as e: print(e) f = open(os.path.join(currentDirectory, 'workspace_error_logs.txt'), "w") f.write(str(traceback.format_exc())) f.close() pass ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' import warnings import numpy as np import pandas as pd import sklearn.metrics as metrics from collections import defaultdict from sklearn.metrics import confusion_matrix import re import shutil import scipy.stats as st import json import os,sys import glob import logging from utils.file_ops import read_df_compressed class Visualization(): def __init__(self,usecasename,version,dataframe,visualizationJson,dateTimeColumn,deployPath,dataFolderLocation,numericContinuousFeatures,discreteFeatures,categoricalFeatures,modelFeatures,targetFeature,modeltype,original_data_file,profiled_data_file,trained_data_file,predicted_data_file,labelMaps,vectorizerFeatures,textFeatures,numericalFeatures,nonNumericFeatures,emptyFeatures,nrows,ncols,saved_model,scoreParam,learner_type,modelname,featureReduction,reduction_data_file): self.dataframe = dataframe self.displayjson = {} self.visualizationJson = visualizationJson self.dateTimeColumn = dateTimeColumn self.deployPath = deployPath #shutil.copy2(os.path.join(os.path.dirname(os.path.abspath(__file__)),'aion_portal.py'),self.deployPath) if learner_type == 'ML' and modelname != 'Neural Architecture Search': if(os.path.isfile(os.path.join(self.deployPath,'explainable_ai.py'))): os.remove(os.path.join(self.deployPath,'explainable_ai.py')) shutil.copy2(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','utilities','xai','explainable_ai.py'),self.deployPath) # os.rename(os.path.join(self.deployPath,'explainable_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) try: os.rename(os.path.join(self.deployPath,'explainable_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) except FileExistsError: os.remove(os.path.join(self.deployPath,'aion_xai.py')) os.rename(os.path.join(self.deployPath,'explainable_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) elif learner_type == 'DL' or modelname == 'Neural Architecture Search': if(os.path.isfile(os.path.join(self.deployPath,'explainable_ai.py'))): os.remove(os.path.join(self.deployPath,'explainable_ai.py')) shutil.copy2(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','utilities','xai','explainabledl_ai.py'),self.deployPath) # os.rename(os.path.join(self.deployPath,'explainabledl_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) try: os.rename(os.path.join(self.deployPath,'explainabledl_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) except FileExistsError: os.remove(os.path.join(self.deployPath,'aion_xai.py')) os.rename(os.path.join(self.deployPath,'explainabledl_ai.py'),os.path.join(self.deployPath,'aion_xai.py')) self.jsondeployPath = deployPath #self.deployPath = self.deployPath+'visualization/' self.dataFolderLocation = dataFolderLocation self.vectorizerFeatures = vectorizerFeatures self.textFeatures = textFeatures self.emptyFeatures = emptyFeatures ''' try: os.makedirs(self.deployPath) except OSError as e: print("\nFolder Already Exists") ''' self.numericContinuousFeatures = numericContinuousFeatures self.discreteFeatures = discreteFeatures self.categoricalFeatures = categoricalFeatures self.modelFeatures = modelFeatures self.modeltype = modeltype self.targetFeature = targetFeature self.displayjson['usecasename'] = str(usecasename) self.displayjson['version'] = str(version) self.displayjson['problemType'] = str(self.modeltype) self.displayjson['targetFeature'] = self.targetFeature self.displayjson['numericalFeatures'] = numericalFeatures self.displayjson['nonNumericFeatures'] = nonNumericFeatures self.displayjson['modelFeatures'] = self.modelFeatures self.displayjson['textFeatures'] = self.textFeatures self.displayjson['emptyFeatures'] = self.emptyFeatures self.displayjson['modelname']= str(modelname) self.displayjson['preprocessedData'] = str(original_data_file) self.displayjson['nrows'] = str(nrows) self.displayjson['ncols'] = str(ncols) self.displayjson['saved_model'] = str(saved_model) self.displayjson['scoreParam'] = str(scoreParam) self.displayjson['labelMaps'] = eval(str(labelMaps)) self.original_data_file = original_data_file self.displayjson['featureReduction'] = featureReduction if featureReduction == 'True': self.displayjson['reduction_data_file'] = reduction_data_file else: self.displayjson['reduction_data_file'] = '' self.pred_filename = predicted_data_file self.profiled_data_file = profiled_data_file self.displayjson['predictedData'] = predicted_data_file self.displayjson['postprocessedData'] = profiled_data_file #self.trained_data_file = trained_data_file #self.displayjson['trainingData'] = trained_data_file #self.displayjson['categorialFeatures']=categoricalFeatures #self.displayjson['discreteFeatures']=discreteFeatures #self.displayjson['continuousFeatures']=numericContinuousFeatures #y = json.dumps(self.displayjson) #print(y) self.labelMaps = labelMaps self.log = logging.getLogger('eion') def visualizationrecommandsystem(self): try: import tensorflow.keras.utils as kutils datasetid = self.visualizationJson['datasetid'] self.log.info('\n================== Data Profiling Details==================') datacolumns=list(self.dataframe.columns) self.log.info('================== Data Profiling Details End ==================\n') self.log.info('================== Features Correlation Details ==================\n') self.log.info('\n================== Model Performance Analysis ==================') if os.path.exists(self.pred_filename): try: status,df=read_df_compressed(self.pred_filename) if self.modeltype == 'Classification' or self.modeltype == 'ImageClassification' or self.modeltype == 'anomaly_detection': y_actual = df['actual'].values y_predict = df['predict'].values y_actual = kutils.to_categorical(y_actual) y_predict = kutils.to_categorical(y_predict) classes = df.actual.unique() n_classes = y_actual.shape[1] self.log.info('-------> ROC AUC CURVE') roc_curve_dict = [] for i in classes: try: classname = i if str(self.labelMaps) != '{}': inv_map = {v: k for k, v in self.labelMaps.items()} classname = inv_map[i] fpr, tpr, threshold = metrics.roc_curve(y_actual[:,i],y_predict[:,i]) roc_auc = metrics.auc(fpr, tpr) class_roc_auc_curve = {} class_roc_auc_curve['class'] = str(classname) fprstring = ','.join(str(v) for v in fpr) tprstring = ','.join(str(v) for v in tpr) class_roc_auc_curve['FP'] = str(fprstring) class_roc_auc_curve['TP'] = str(tprstring) roc_curve_dict.append(class_roc_auc_curve) self.log.info('----------> Class: '+str(classname)) self.log.info('------------> ROC_AUC: '+str(roc_auc)) self.log.info('------------> False Positive Rate (x Points): '+str(fpr)) self.log.info('------------> True Positive Rate (y Points): '+str(tpr)) except: pass self.displayjson['ROC_AUC_CURVE'] = roc_curve_dict self.log.info('-------> Precision Recall CURVE') precision_recall_curve_dict = [] for i in range(n_classes): try: lr_precision, lr_recall, threshold = metrics.precision_recall_curve(y_actual[:,i],y_predict[:,i]) classname = i if str(self.labelMaps) != '{}': inv_map = {v: k for k, v in self.labelMaps.items()} classname = inv_map[i] roc_auc = metrics.auc(lr_recall,lr_precision) class_precision_recall_curve = {} class_precision_recall_curve['class'] = str(classname) Precisionstring = ','.join(str(round(v,2)) for v in lr_precision) Recallstring = ','.join(str(round(v,2)) for v in lr_recall) class_precision_recall_curve['Precision'] = str(Precisionstring) class_precision_recall_curve['Recall'] = str(Recallstring) precision_recall_curve_dict.append(class_precision_recall_curve) except: pass self.log.info('----------> Class: '+str(classname)) self.log.info('------------> ROC_AUC: '+str(roc_auc)) self.log.info('------------> Recall (x Points): '+str(lr_precision)) self.log.info('------------> Precision (y Points): '+str(lr_recall)) self.displayjson['PRECISION_RECALL_CURVE'] = precision_recall_curve_dict status,predictdataFrame=read_df_compressed(self.displayjson['predictedData']) except Exception as e: self.log.info('================== Error in Calculation ROC_AUC/Recall Precision Curve '+str(e)) self.log.info('================== Model Performance Analysis End ==================\n') self.log.info('\n================== For Descriptive Analysis of Model Features ==================') outputfile = os.path.join(self.jsondeployPath,'etc','display.json') with open(outputfile, 'w') as fp: json.dump(self.displayjson, fp) self.log.info('================== For Descriptive Analysis of Model Features End ==================\n') except Exception as inst: self.log.info('Visualization Failed !....'+str(inst)) exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename) self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) def drawlinechart(self,xcolumn,ycolumn,deploy_path,datasetid): title = 'aion_visualization_'+xcolumn+"_"+ycolumn+"_linechart" yaxisname = 'Average '+ycolumn datasetindex = datasetid visulizationjson = '[{"_id": "543234","_type": "visualization","_source": {"title": "'+title+'",' visulizationjson = visulizationjson+'"visState": "{\\"title\\":\\"'+title+'\\",' visulizationjson = visulizationjson+'\\"type\\":\\"line\\",\\"params\\":{\\"type\\":\\"line\\",\\"grid\\":{\\"categoryLines\\":false,\\"style\\":{\\"color\\":\\"#eee\\"}},\\"categoryAxes\\":[{\\"id\\":\\"CategoryAxis-1\\",\\"type\\":\\"category\\",\\"position\\":\\"bottom\\",\\"show\\":true,\\"style\\":{},\\"scale\\":{\\"type\\":\\"linear\\"},\\"labels\\":{\\"show\\":true,\\"truncate\\":100},\\"title\\":{}}],\\"valueAxes\\":[{\\"id\\":\\"ValueAxis-1\\",\\"name\\":\\"LeftAxis-1\\",\\"type\\":\\"value\\",\\"position\\":\\"left\\",\\"show\\":true,\\"style\\":{},\\"scale\\":{\\"type\\":\\"linear\\",\\"mode\\":\\"normal\\"},\\"labels\\":{\\"show\\":true,\\"rotate\\":0,\\"filter\\":false,\\"truncate\\":100},\\"title\\":' visulizationjson = visulizationjson+'{\\"text\\":\\"'+yaxisname+'\\"}}],\\"seriesParams\\":[{\\"show\\":\\"true\\",\\"type\\":\\"line\\",\\"mode\\":\\"normal\\",\\"data\\":' visulizationjson = visulizationjson+'{\\"label\\":\\"'+yaxisname+'\\",\\"id\\":\\"1\\"},\\"valueAxis\\":\\"ValueAxis-1\\",\\"drawLinesBetweenPoints\\":true,\\"showCircles\\":true}],\\"addTooltip\\":true,\\"addLegend\\":true,\\"legendPosition\\":\\"right\\",\\"times\\":[],\\"addTimeMarker\\":false},\\"aggs\\":[{\\"id\\":\\"1\\",\\"enabled\\":true,\\"type\\":\\"avg\\",\\"schema\\":\\"metric\\",\\"params\\":{\\"field\\":\\"'+str(ycolumn)+'\\"}},{\\"id\\":\\"2\\",\\"enabled\\":true,\\"type\\":\\"terms\\",\\"schema\\":\\"segment\\",\\"params\\":{\\"field\\":\\"'+xcolumn+'\\",\\"size\\":100,\\"order\\":\\"desc\\",\\"orderBy\\":\\"1\\",\\"otherBucket\\":false,\\"otherBucketLabel\\":\\"Other\\",\\"missingBucket\\":false,\\"missingBucketLabel\\":\\"Missing\\"}}]}","uiStateJSON": "{}", "description": "","version": 1,"kibanaSavedObjectMeta": {"searchSourceJSON": "{\\"index\\":\\"'+datasetindex+'\\",\\"query\\":{\\"query\\":\\"\\",\\"language\\":\\"lucene\\"},\\"filter\\":[]}"}},"_migrationVersion": {"visualization": "6.7.2"}}]' filename = deploy_path+title+'.json' f = open(filename, "w") f.write(str(visulizationjson)) f.close() def drawbarchart(self,xcolumn,ycolumn,deploy_path,datasetid): title = 'aion_visualization_'+xcolumn+"_"+ycolumn+"_barchart" yaxisname = 'Average '+ycolumn datasetindex = datasetid visulizationjson = '[{"_id": "123456","_type": "visualization","_source": {"title":"'+title+'",' visulizationjson = visulizationjson+'"visState": "{\\"title\\":\\"'+title+'\\",' visulizationjson = visulizationjson+'\\"type\\":\\"histogram\\",\\"params\\":{\\"addLegend\\":true,\\"addTimeMarker\\":false,\\"addTooltip\\":true,\\"categoryAxes\\":[{\\"id\\":\\"CategoryAxis-1\\",\\"labels\\":{\\"show\\":true,\\"truncate\\":100},\\"position\\":\\"bottom\\",\\"scale\\":{\\"type\\":\\"linear\\"},\\"show\\":true,\\"style\\":{},\\"title\\":{},\\"type\\":\\"category\\"}],\\"grid\\":{\\"categoryLines\\":false,\\"style\\":{\\"color\\":\\"#eee\\"}},\\"legendPosition\\":\\"right\\",\\"seriesParams\\":[{\\"data\\":{\\"id\\":\\"1\\",' visulizationjson = visulizationjson+'\\"label\\":\\"'+yaxisname+'\\"},' visulizationjson = visulizationjson+'\\"drawLinesBetweenPoints\\":true,\\"mode\\":\\"stacked\\",\\"show\\":\\"true\\",\\"showCircles\\":true,\\"type\\":\\"histogram\\",\\"valueAxis\\":\\"ValueAxis-1\\"}],\\"times\\":[],\\"type\\":\\"histogram\\",\\"valueAxes\\":[{\\"id\\":\\"ValueAxis-1\\",\\"labels\\":{\\"filter\\":false,\\"rotate\\":0,\\"show\\":true,\\"truncate\\":100},\\"name\\":\\"LeftAxis-1\\",\\"position\\":\\"left\\",\\"scale\\":{\\"mode\\":\\"normal\\",\\"type\\":\\"linear\\"},\\"show\\":true,\\"style\\":{},\\"title\\":' visulizationjson = visulizationjson+'{\\"text\\":\\"'+yaxisname+'\\"},' visulizationjson = visulizationjson+'\\"type\\":\\"value\\"}]},\\"aggs\\":[{\\"id\\":\\"1\\",\\"enabled\\":true,\\"type\\":\\"avg\\",\\"schema\\":\\"metric\\",\\"params\\":{\\"field\\":\\"'+str(xcolumn)+'\\"}},{\\"id\\":\\"2\\",\\"enabled\\":true,\\"type\\":\\"terms\\",\\"schema\\":\\"segment\\",\\"params\\":{\\"field\\":\\"'+ycolumn+'\\",\\"size\\":100,\\"order\\":\\"asc\\",\\"orderBy\\":\\"1\\",\\"otherBucket\\":false,\\"otherBucketLabel\\":\\"Other\\",\\"missingBucket\\":false,\\"missingBucketLabel\\":\\"Missing\\"}}]}","uiStateJSON":"{}","description": "","version": 1,"kibanaSavedObjectMeta": {' visulizationjson = visulizationjson+'"searchSourceJSON": "{\\"index\\":\\"'+datasetindex+'\\",\\"query\\":{\\"language\\":\\"lucene\\",\\"query\\":\\"\\"},\\"filter\\":[]}"}},"_migrationVersion":{"visualization": "6.7.2"}}]' filename = deploy_path+title+'.json' f = open(filename, "w") f.write(str(visulizationjson)) f.close() def drawpiechart(self,xcolumn,deploy_path,datasetid): title = 'aion_visualization_'+xcolumn+"_piechart" datasetindex = datasetid visulizationjson = '[{"_id": "123456","_type": "visualization","_source": {"title":"'+title+'",' visulizationjson = visulizationjson+'"visState": "{\\"title\\":\\"'+title+'\\",' visulizationjson = visulizationjson+'\\"type\\":\\"pie\\",\\"params\\":{\\"type\\":\\"pie\\",\\"addTooltip\\":true,\\"addLegend\\":true,\\"legendPosition\\":\\"right\\",\\"isDonut\\":true,\\"labels\\":{\\"show\\":false,\\"values\\":true,\\"last_level\\":true,\\"truncate\\":100}},\\"aggs\\":[{\\"id\\":\\"1\\",\\"enabled\\":true,\\"type\\":\\"count\\",\\"schema\\":\\"metric\\",\\"params\\":{}},{\\"id\\":\\"2\\",\\"enabled\\":true,\\"type\\":\\"terms\\",\\"schema\\":\\"segment\\",\\"params\\":{\\"field\\":\\"'+xcolumn+'\\",\\"size\\":100,\\"order\\":\\"asc\\",\\"orderBy\\":\\"1\\",\\"otherBucket\\":false,\\"otherBucketLabel\\":\\"Other\\",\\"missingBucket\\":false,\\"missingBucketLabel\\":\\"Missing\\"}}]}",' visulizationjson = visulizationjson+'"uiStateJSON": "{}","description": "","version": 1,"kibanaSavedObjectMeta": {"searchSourceJSON":"{\\"index\\":\\"'+datasetid+'\\",\\"query\\":{\\"query\\":\\"\\",\\"language\\":\\"lucene\\"},\\"filter\\":[]}"}},"_migrationVersion": {"visualization": "6.7.2"}}]' filename = deploy_path+title+'.json' f = open(filename, "w") f.write(str(visulizationjson)) f.close() def get_confusion_matrix(self,df): setOfyTrue = set(df['actual']) unqClassLst = list(setOfyTrue) if(str(self.labelMaps) != '{}'): inv_mapping_dict = {v: k for k, v in self.labelMaps.items()} unqClassLst2 = (pd.Series(unqClassLst)).map(inv_mapping_dict) unqClassLst2 = list(unqClassLst2) else: unqClassLst2 = unqClassLst indexName = [] columnName = [] for item in unqClassLst2: indexName.append("act:"+str(item)) columnName.append("pre:"+str(item)) result = pd.DataFrame(confusion_matrix(df['actual'], df['predict'], labels = unqClassLst),index = indexName, columns = columnName) resultjson = result.to_json(orient='index') return(resultjson) def DistributionFinder(self,data): try: distributionName ="" sse =0.0 KStestStatic=0.0 dataType="" if(data.dtype == "float64"): dataType ="Continuous" elif(data.dtype =="int" or data.dtype =="int64"): dataType="Discrete" if(dataType == "Discrete"): distributions= [st.bernoulli,st.binom,st.geom,st.nbinom,st.poisson] index, counts = np.unique(abs(data.astype(int)),return_counts=True) if(len(index)>=2): best_sse = np.inf y1=[] total=sum(counts) mean=float(sum(index*counts))/total variance=float((sum(index**2*counts) -total*mean**2))/(total-1) dispersion=mean/float(variance) theta=1/float(dispersion) r=mean*(float(theta)/1-theta) for j in counts: y1.append(float(j)/total) pmf1=st.bernoulli.pmf(index,mean) pmf2=st.binom.pmf(index,len(index),p=mean/len(index)) pmf3=st.geom.pmf(index,1/float(1+mean)) pmf4=st.nbinom.pmf(index,mean,r) pmf5=st.poisson.pmf(index,mean) sse1 = np.sum(np.power(y1 - pmf1, 2.0)) sse2 = np.sum(np.power(y1 - pmf2, 2.0)) sse3 = np.sum(np.power(y1 - pmf3, 2.0)) sse4 = np.sum(np.power(y1 - pmf4, 2.0)) sse5 = np.sum(np.power(y1- pmf5, 2.0)) sselist=[sse1,sse2,sse3,sse4,sse5] for i in range(0,len(sselist)): if best_sse > sselist[i] > 0: best_distribution = distributions[i].name best_sse = sselist[i] elif (len(index) == 1): best_distribution = "Constant Data-No Distribution" best_sse = 0.0 distributionName =best_distribution sse=best_sse elif(dataType == "Continuous"): distributions = [st.uniform,st.expon,st.weibull_max,st.weibull_min,st.chi,st.norm,st.lognorm,st.t,st.gamma,st.beta] best_distribution = st.norm.name best_sse = np.inf datamin=data.min() datamax=data.max() nrange=datamax-datamin y, x = np.histogram(data.astype(float), bins='auto', density=True) x = (x + np.roll(x, -1))[:-1] / 2.0 for distribution in distributions: with warnings.catch_warnings(): warnings.filterwarnings('ignore') params = distribution.fit(data.astype(float)) # Separate parts of parameters arg = params[:-2] loc = params[-2] scale = params[-1] # Calculate fitted PDF and error with fit in distribution pdf = distribution.pdf(x, loc=loc, scale=scale, *arg) sse = np.sum(np.power(y - pdf, 2.0)) if(best_sse >sse > 0): best_distribution = distribution.name best_sse = sse distributionName =best_distribution sse=best_sse except: response = str(sys.exc_info()[0]) message='Job has Failed'+response print(message) return distributionName,sse import docker import json import logging def read_json(file_path): data = None with open(file_path,'r') as f: data = json.load(f) return data def run_pipeline(inputconfig): inputconfig = json.loads(inputconfig) logfilepath = inputconfig['logfilepath'] logging.basicConfig(level=logging.INFO,filename =logfilepath) usecasename = inputconfig['usecase'] logging.info("UseCaseName :"+str(usecasename)) version = inputconfig['version'] logging.info("version :"+str(version)) config = inputconfig['dockerlist'] persistancevolume = inputconfig['persistancevolume'] logging.info("PersistanceVolume :"+str(persistancevolume)) datasetpath = inputconfig['datasetpath'] logging.info("DataSet Path :"+str(datasetpath)) config = read_json(config) client = docker.from_env() inputconfig = {'modelName':usecasename,'modelVersion':str(version),'dataLocation':datasetpath} inputconfig = json.dumps(inputconfig) inputconfig = inputconfig.replace('"', '\\"') logging.info("===== Model Monitoring Container Start =====") outputStr = client.containers.run(config['ModelMonitoring'],'python code.py -i'+datasetpath,volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('ModelMonitoring: '+str(outputStr)) print('ModelMonitoring: '+str(outputStr)) logging.info("===== ModelMonitoring Stop =====") logging.info("===== Data Ingestion Container Start =====") outputStr = client.containers.run(config['DataIngestion'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('DataIngestion: '+str(outputStr)) print('DataIngestion: '+str(outputStr)) logging.info("===== Data Ingestion Container Stop =====") outputStr = outputStr.strip() decoded_data = json.loads(outputStr) status = decoded_data['Status'] if status != 'Success': output = {'Status':'Error','Msg':'Data Ingestion Fails'} logging.info("===== Transformation Container Start =====") outputStr = client.containers.run(config['DataTransformation'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('Data Transformations: '+str(outputStr)) print('Data Transformations: '+str(outputStr)) logging.info("===== Transformation Container Done =====") outputStr = outputStr.strip() decoded_data = json.loads(outputStr) status = decoded_data['Status'] if status != 'Success': output = {'Status':'Error','Msg':'Data Transformations Fails'} logging.info("===== Feature Engineering Container Start =====") outputStr = client.containers.run(config['FeatureEngineering'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('FeatureEngineering: '+str(outputStr)) print('FeatureEngineering: '+str(outputStr)) logging.info("===== Feature Engineering Container Done =====") outputStr = outputStr.strip() decoded_data = json.loads(outputStr) status = decoded_data['Status'] modeltraining = config['ModelTraining'] for mt in modeltraining: logging.info("===== Training Container Start =====") outputStr = client.containers.run(mt['Training'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('ModelTraining: '+str(outputStr)) print('ModelTraining: '+str(outputStr)) logging.info("===== Training Container Done =====") outputStr = outputStr.strip() try: decoded_data = json.loads(outputStr) status = decoded_data['Status'] except Exception as inst: logging.info(inst) logging.info("===== Model Registry Start =====") outputStr = client.containers.run(config['ModelRegistry'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('ModelRegistry: '+str(outputStr)) print('ModelRegistry: '+str(outputStr)) logging.info("===== ModelRegistry Done =====") logging.info("===== ModelServing Start =====") outputStr = client.containers.run(config['ModelServing'],'python code.py',volumes=[persistancevolume+':/aion']) outputStr = outputStr.decode('utf-8') logging.info('Prediction: '+str(outputStr)) print('Prediction: '+str(outputStr)) logging.info("===== ModelServing Done =====") import os import shutil import sys import subprocess from os.path import expanduser import platform import json def createDockerImage(model_name,model_version,module,folderpath): command = 'docker pull python:3.8-slim-buster' os.system(command); subprocess.check_call(["docker", "build", "-t",module+'_'+model_name.lower()+":"+model_version,"."], cwd=folderpath) def local_docker_build(config): print(config) config = json.loads(config) model_name = config['usecase'] model_version = config['version'] mlaac__code_path = config['mlacPath'] docker_images = {} docker_images['ModelMonitoring'] = 'modelmonitoring'+'_'+model_name.lower()+':'+model_version dataset_addr = os.path.join(mlaac__code_path,'ModelMonitoring') createDockerImage(model_name,model_version,'modelmonitoring',dataset_addr) docker_images['DataIngestion'] = 'dataingestion'+'_'+model_name.lower()+':'+model_version dataset_addr = os.path.join(mlaac__code_path,'DataIngestion') createDockerImage(model_name,model_version,'dataingestion',dataset_addr) transformer_addr = os.path.join(mlaac__code_path,'DataTransformation') docker_images['DataTransformation'] = 'datatransformation'+'_'+model_name.lower()+':'+model_version createDockerImage(model_name,model_version,'datatransformation',transformer_addr) featureengineering_addr = os.path.join(mlaac__code_path,'FeatureEngineering') docker_images['FeatureEngineering'] = 'featureengineering'+'_'+model_name.lower()+':'+model_version createDockerImage(model_name,model_version,'featureengineering',featureengineering_addr) from os import listdir arr = [filename for filename in os.listdir(mlaac__code_path) if filename.startswith("ModelTraining")] docker_training_images = [] for x in arr: dockertraing={} dockertraing['Training'] = str(x).lower()+'_'+model_name.lower()+':'+model_version docker_training_images.append(dockertraing) training_addri = os.path.join(mlaac__code_path,x) createDockerImage(model_name,model_version,str(x).lower(),training_addri) docker_images['ModelTraining'] = docker_training_images docker_images['ModelRegistry'] = 'modelregistry'+'_'+model_name.lower()+':'+model_version deploy_addr = os.path.join(mlaac__code_path,'ModelRegistry') createDockerImage(model_name,model_version,'modelregistry',deploy_addr) docker_images['ModelServing'] = 'modelserving'+'_'+model_name.lower()+':'+model_version deploy_addr = os.path.join(mlaac__code_path,'ModelServing') createDockerImage(model_name,model_version,'modelserving',deploy_addr) outputjsonFile = os.path.join(mlaac__code_path,'dockerlist.json') with open(outputjsonFile, 'w') as f: json.dump(docker_images, f) f.close() output = {'Status':'Success','Msg':outputjsonFile} output = json.dumps(output) print("aion_build_container:",output) import os import sys import json from pathlib import Path import subprocess import shutil import argparse def create_and_save_yaml(git_storage_path, container_label,usecasepath): file_name_prefix = 'gh-acr-' yaml_file = f"""\ name: gh-acr-{container_label} on: push: branches: main paths: {container_label}/** workflow_dispatch: jobs: gh-acr-build-push: runs-on: ubuntu-latest steps: - name: 'checkout action' uses: actions/checkout@main - name: 'azure login' uses: azure/login@v1 with: creds: ${{{{ secrets.AZURE_CREDENTIALS }}}} - name: 'build and push image' uses: azure/docker-login@v1 with: login-server: ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}} username: ${{{{ secrets.REGISTRY_USERNAME }}}} password: ${{{{ secrets.REGISTRY_PASSWORD }}}} - run: | docker build ./{container_label}/ModelMonitoring -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelmonitoring:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelmonitoring:{container_label} docker build ./{container_label}/DataIngestion -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/dataingestion:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/dataingestion:{container_label} docker build ./{container_label}/DataTransformation -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/datatransformation:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/datatransformation:{container_label} docker build ./{container_label}/FeatureEngineering -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/featureengineering:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/featureengineering:{container_label} docker build ./{container_label}/ModelRegistry -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelregistry:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelregistry:{container_label} docker build ./{container_label}/ModelServing -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelserving:{container_label} docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelserving:{container_label} """ arr = [filename for filename in os.listdir(usecasepath) if filename.startswith("ModelTraining")] for x in arr: yaml_file+=' docker build ./'+container_label+'/'+x+' -t ${{ secrets.REGISTRY_LOGIN_SERVER }}/'+x.lower()+':'+container_label yaml_file+='\n' yaml_file+=' docker push ${{ secrets.REGISTRY_LOGIN_SERVER }}/'+x.lower()+':'+container_label yaml_file+='\n' with open(Path(git_storage_path)/(file_name_prefix + container_label + '.yaml'), 'w') as f: f.write(yaml_file) def run_cmd(cmd): try: subprocess.check_output(cmd, stderr=subprocess.PIPE) except subprocess.CalledProcessError as e: if e.stderr: if isinstance(e.stderr, bytes): err_msg = e.stderr.decode(sys.getfilesystemencoding()) else: err_msg = e.stderr elif e.output: if isinstance(e.output, bytes): err_msg = e.output.decode(sys.getfilesystemencoding()) else: err_msg = e.output else: err_msg = str(e) return False, err_msg return True, "" def validate_config(config): non_null_keys = ['url','username', 'token', 'location', 'gitFolderLocation', 'email', 'modelName'] missing_keys = [k for k in non_null_keys if k not in config.keys()] if missing_keys: raise ValueError(f"following fields are missing in config file: {missing_keys}") for k,v in config.items(): if k in non_null_keys and not v: raise ValueError(f"Please provide value for '{k}' in config file.") def upload(config): validate_config(config) url_type = config.get('url_type','https') if url_type == 'https': https_str = "https://" url = https_str + config['username'] + ":" + config['token'] + "@" + config['url'][len(https_str):] else: url = config['url'] model_location = Path(config['location']) git_folder_location = Path(config['gitFolderLocation']) git_folder_location.mkdir(parents=True, exist_ok=True) (git_folder_location/'.github'/'workflows').mkdir(parents=True, exist_ok=True) if not model_location.exists(): raise ValueError('Trained model data not found') os.chdir(str(git_folder_location)) (git_folder_location/config['modelName']).mkdir(parents=True, exist_ok=True) shutil.copytree(model_location, git_folder_location/config['modelName'], dirs_exist_ok=True) create_and_save_yaml((git_folder_location/'.github'/'workflows'), config['modelName'],config['location']) if (Path(git_folder_location)/'.git').exists(): first_upload = False else: first_upload = True if first_upload: cmd = ['git','init'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','config','user.name',config['username']] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','config','user.email',config['email']] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','add', '-A'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','commit','-m',f"commit {config['modelName']}"] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','branch','-M','main'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) if first_upload: cmd = ['git','remote','add','origin', url] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) cmd = ['git','push','-f','-u','origin', 'main'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) else: cmd = ['git','push'] status, msg = run_cmd(cmd) if not status: raise ValueError(msg) return json.dumps({'Status':'SUCCESS'}) if __name__ == '__main__': try: if shutil.which('git') is None: raise ValueError("git is not installed on this system") parser = argparse.ArgumentParser() parser.add_argument('-c', '--config', help='Config file location or as a string') args = parser.parse_args() if Path(args.config).is_file() and Path(args.config).suffix == '.json': with open(args.config,'r') as f: config = json.load(f) else: config = json.loads(args.config) print(upload(config)) except Exception as e: status = {'Status':'Failure','msg':str(e)} print(json.dumps(status)) ''' * * ============================================================================= * COPYRIGHT NOTICE * ============================================================================= * @ Copyright HCL Technologies Ltd. 2021, 2022,2023 * Proprietary and confidential. All information contained herein is, and * remains the property of HCL Technologies Limited. Copying or reproducing the * contents of this file, via any medium is strictly prohibited unless prior * written permission is obtained from HCL Technologies Limited. * ''' from kafka import KafkaConsumer from json import loads import pandas as pd import json import os,sys import time import multiprocessing from os.path import expanduser import platform import datetime modelDetails = {} class Process(multiprocessing.Process): def __init__(self, modelSignature,jsonData,predictedData,modelpath): super(Process, self).__init__() self.config = jsonData self.modelSignature = modelSignature self.data = predictedData self.modelpath = modelpath def run(self): #data = pd.json_normalize(self.data) minotoringService = self.config['minotoringService']['url'] trainingdatalocation = self.config['trainingDataLocation'][self.modelSignature] #filetimestamp = 'AION_'+str(int(time.time()))+'.csv' #data.to_csv(dataFile, index=False) inputFieldsJson = {"trainingDataLocation":trainingdatalocation,"currentDataLocation":self.data} inputFieldsJson = json.dumps(inputFieldsJson) ser_url = minotoringService+self.modelSignature+'/monitoring' driftTime = datetime.datetime.now() import requests try: response = requests.post(ser_url, data=inputFieldsJson,headers={"Content-Type":"application/json",}) outputStr=response.content outputStr = outputStr.decode('utf-8') outputStr = outputStr.strip() decoded_data = json.loads(outputStr) print(decoded_data) status = decoded_data['status'] msg = decoded_data['data'] except Exception as inst: if 'Failed to establish a new connection' in str(inst): status = 'Fail' msg = 'AION Service needs to be started' else: status = 'Fail' msg = 'Error during Drift Analysis' statusFile = os.path.join(self.modelpath,self.modelSignature+'_status.csv') df = pd.DataFrame(columns = ['dateTime', 'status', 'msg']) df = df.append({'dateTime' : driftTime, 'status' : status, 'msg' : msg},ignore_index = True) print(df) if (os.path.exists(statusFile)): df.to_csv(statusFile, mode='a', header=False,index=False) else: df.to_csv(statusFile, header=True,index=False) def launch_kafka_consumer(): from appbe.dataPath import DATA_DIR configfile = os.path.join(os.path.dirname(__file__),'..','config','kafkaConfig.conf') with open(configfile,'r',encoding='utf-8') as f: jsonData = json.load(f) f.close() kafkaIP=jsonData['kafkaCluster']['ip'] kafkaport = jsonData['kafkaCluster']['port'] topic = jsonData['kafkaCluster']['topic'] kafkaurl = kafkaIP+':'+kafkaport if jsonData['database']['csv'] == 'True': database = 'csv' elif jsonData['database']['mySql'] == 'True': database = 'mySql' else: database = 'csv' kafkaPath = os.path.join(DATA_DIR,'kafka') if not (os.path.exists(kafkaPath)): try: os.makedirs(kafkaPath) except OSError as e: pass consumer = KafkaConsumer(topic,bootstrap_servers=[kafkaurl],auto_offset_reset='earliest',enable_auto_commit=True,group_id='my-group',value_deserializer=lambda x: loads(x.decode('utf-8'))) for message in consumer: message = message.value data = message['data'] data = pd.json_normalize(data) modelname = message['usecasename'] version = message['version'] modelSignature = modelname+'_'+str(version) modelpath = os.path.join(kafkaPath,modelSignature) try: os.makedirs(modelpath) except OSError as e: pass secondsSinceEpoch = time.time() if modelSignature not in modelDetails: modelDetails[modelSignature] = {} modelDetails[modelSignature]['startTime'] = secondsSinceEpoch if database == 'csv': csvfile = os.path.join(modelpath,modelSignature+'.csv') if (os.path.exists(csvfile)): data.to_csv(csvfile, mode='a', header=False,index=False) else: data.to_csv(csvfile, header=True,index=False) modelTimeFrame = jsonData['timeFrame'][modelSignature] currentseconds = time.time() print(currentseconds - modelDetails[modelSignature]['startTime']) if (currentseconds - modelDetails[modelSignature]['startTime']) >= float(modelTimeFrame): csv_path = os.path.join(modelpath,modelSignature+'.csv') #predictedData = pd.read_csv(csv_path) ##predictedData = predictedData.to_json(orient="records") index = Process(modelSignature,jsonData,csv_path,modelpath) index.start() modelDetails[modelSignature]['startTime'] = secondsSinceEpoch