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import os
import json
import uuid
import pandas as pd
from importlib import resources as pkg_resources
from openfactcheck.lib.logger import logger
from openfactcheck.core.base import OpenFactCheck
from openfactcheck.evaluator.llm.evaluate_snowballing import SnowballingEvaluator
from openfactcheck.evaluator.llm.evaluate_selfaware import SelfAwareEvaluator
from openfactcheck.evaluator.llm.evaluate_freshqa import FreshQAEvaluator
from openfactcheck.evaluator.llm.evaluate_freetext import FreeTextEvaluator
from openfactcheck.evaluator.llm.report import create_report
from openfactcheck import data as data_dir
# Import LLM Evaluation Dataset
default_dataset_path = str(pkg_resources.files(data_dir))
default_output_path = "tmp/output/llm_evaluator"
class LLMEvaluator(SnowballingEvaluator, SelfAwareEvaluator, FreshQAEvaluator, FreeTextEvaluator):
"""
This class is used to evaluate the performance of a Language Model.
Parameters
----------
model_name : str
The name of the Language Model.
input_path : Union[str, pd.DataFrame]
The path to the CSV file or the DataFrame containing the LLM responses.
The CSV file should have the following two columns:
- index: The index of the response.
- response: The response generated by the LLM.
output_path : str
The path to store the output files.
dataset_path : str
The path to the dataset file containing the questions.
datasets : list
The list of datasets to evaluate the LLM on.
analyze : bool
Whether to analyze the results.
save_plots : bool
Whether to save the plots.
save_report : bool
Whether to save the report.
Attributes
----------
model_name : str
The name of the Language Model.
run_id : str
The unique identifier for the run.
input_path : Union[str, pd.DataFrame]
The path to the CSV file or the DataFrame containing the LLM responses.
output_path : str
The path to store the output files.
dataset_path : str
The path to the dataset file containing the questions.
datasets : list
The list of datasets to evaluate the LLM on.
combined_result : dict
The combined evaluation results for all datasets.
Methods
-------
evaluate(model_name: str, input_path: Union[str, pd.DataFrame], output_path: str = "", dataset_path: str = "", datasets: list = ["snowballing"], analyze: bool = True, save_plots: bool = True, save_report: bool = True):
This function evaluates the performance of the Language Model.
read_input():
This function reads the input file and dataset file and returns a DataFrame containing the combined data.
filter_responses(df: pd.DataFrame, dataset: str):
Filter the responses based on the dataset.
generate_plots(fig_path, save_plots=True):
Generate plots for the evaluation
"""
def __init__(self, ofc: OpenFactCheck):
SnowballingEvaluator.__init__(self)
SelfAwareEvaluator.__init__(self)
FreshQAEvaluator.__init__(self)
FreeTextEvaluator.__init__(self, ofc)
self.logger = logger
# Set the attributes
self.model_name = None
self.run_id = str(uuid.uuid4().hex)
self.input_path = None
self.dataset_path = None
self.output_path = None
self.datasets = None
self.combined_result = None
self.labels = None
self.predictions = None
self.logger.info(f"LLM Evaluator initialized with run_id: {self.run_id}")
def read_input(self):
"""
This function reads the input file and dataset file and returns a DataFrame containing the combined data.
"""
# Check if the input_path is a DataFrame
if isinstance(self.input_path, pd.DataFrame):
df_responses = self.input_path
else:
# Read the CSV file
self.logger.info(f"Reading the LLM responses from {self.input_path}...")
df_responses = pd.read_csv(self.input_path)
# Check the number of columns and if any response is missing
assert df_responses.shape[1] == 2, "The LLM responses should have 2 columns."
# Use the first column as index and rename the index and response column
df_responses.set_index(df_responses.columns[0], inplace=True)
df_responses.index.name = None
df_responses.columns = ["response"]
# Read the avaliable datasets
self.logger.info(f"Reading the dataset from {self.dataset_path}...")
df_dataset = pd.DataFrame()
# Loop through each file in the directory
for filename in os.listdir(self.dataset_path):
if filename.endswith('.jsonl'): # Check if the file is a JSONL file
file_path = os.path.join(self.dataset_path, filename)
logger.info(f"Reading {filename.split('.')[0]} dataset...")
# Read the JSONL file and append it to the combined DataFrame
df = pd.read_json(file_path, lines=True)
df_dataset = pd.concat([df_dataset, df], ignore_index=True)
# Combine the responses and questions
assert len(df_responses) == len(df_dataset), "The number of responses and questions should be the same."
df_combined = pd.concat([df_dataset, df_responses], axis=1)
return df_combined
@staticmethod
def filter_responses(df: pd.DataFrame, dataset: str):
logger.info(f"Filtering responses for dataset: {dataset}...")
# Filter the DataFrame based on the 'source' column directly
if dataset == "snowballing":
# Filter the DataFrame based on the 'source' column directly
filtered_df = df[df['source'] == dataset]
# Create a new DataFrame with only the required columns
responses_df = filtered_df[['topic', 'response']].copy()
# Convert the DataFrame to a list of dictionaries if needed
responses = responses_df.to_dict(orient='records')
elif dataset == "selfaware":
selfaware_subset = [
"selfaware-hotpot_train",
"selfaware-squadqa_train",
"selfaware-triviaqa_train",
"selfaware-squadqa_dev",
"selfaware-hotpot_dev",
"selfaware-triviaqa_dev",
"selfaware-SelfAware",
]
responses = []
for k, row in df.iterrows():
if row["source"] in selfaware_subset:
responses.append({
"label_unanswerable": row["ability_to_test"].lstrip("answerable: ") == "False",
"response": row["response"]})
elif dataset == "freshqa":
responses = []
for k, row in df.iterrows():
if row["source"] == dataset:
responses.append(
{
"question": row["question"],
"reference_answer": row["reference_answer"],
"response": row["response"],
}
)
elif dataset in ["factoolqa", "felm-wk", "factcheck-bench"]:
responses = []
for k, row in df.iterrows():
if row["source"] == dataset:
responses.append(
{
"source": row["source"],
"prompt": row["prompt"],
"response": row["response"],
}
)
elif dataset == "factscore-bio":
factscore_subset = [
"factscore-labelled",
"factscore-unlabelled",
]
responses = []
for k, row in df.iterrows():
if row["source"] in factscore_subset:
responses.append(
{
"source": row["source"],
"prompt": row["prompt"],
"response": row["response"],
}
)
else:
raise ValueError(f"Dataset {dataset} is not supported.")
return responses
def generate_plots(self, fig_path: str = "", save_plots=True):
# Create a bar plot of the accuracy of the LLM responses on the Snowballing dataset
# for each topic and the overall accuracy.
plots = {}
for dataset in self.combined_result:
if dataset == "snowballing":
plots["snowballing"] = {}
plots["snowballing"]["barplot"] = self.snowballing_barplot(self.combined_result[dataset], fig_path, save=save_plots)
plots["snowballing"]["cm"] = self.snowballing_cm(self.labels[dataset], self.predictions[dataset], fig_path, save=save_plots)
elif dataset == "selfaware":
plots["selfaware"] = {}
plots["selfaware"]["barplot"] = self.selfaware_barplot(self.combined_result[dataset], fig_path, save=save_plots)
plots["selfaware"]["cm"] = self.selfaware_cm(self.labels[dataset], self.predictions[dataset], fig_path, save=save_plots)
elif dataset == "freshqa":
plots["freshqa"] = {}
plots["freshqa"]["piechart"] = self.freshqa_piechart(self.combined_result[dataset], fig_path, save=save_plots)
elif dataset == "freetext":
plots["freetext"] = {}
plots["freetext"]["barplot"] = self.freetext_barplot(self.combined_result["freetext"], fig_path, save=save_plots)
return plots
def generate_report(self, report_path: str):
# Create a LaTeX report and return the path to the generated PDF
return create_report(self.model_name, report_path)
def evaluate(self,
model_name: str,
input_path: str,
output_path: str = "",
dataset_path: str = "",
datasets: list = [
"snowballing",
"selfaware",
"freshqa",
"factoolqa",
"felm-wk",
"factcheck-bench",
"factscore-bio"
],
analyze: bool = True,
save_report: bool = True):
self.logger.info("Evaluating LLM responses...")
# Set the attributes
self.model_name = model_name
self.input_path = input_path
self.output_path = output_path
self.dataset_path = dataset_path
self.datasets = datasets
# Check if the output path is provided (if not, use the default template)
if self.output_path == "":
self.output_path = default_output_path
# Check if the output path exists (if not, create it)
if not os.path.exists(f"{self.output_path}/{self.run_id}"):
os.makedirs(f"{self.output_path}/{self.run_id}")
# Check if the questions path is provided (if not, use the default template)
if self.dataset_path == "":
self.dataset_path = default_dataset_path
# Read the input
self.logger.info("Reading the input...")
df = self.read_input()
self.logger.info(f"Combined data contains {len(df)} rows")
# Evaluate model responses over each dataset
self.combined_result = {}
self.labels = {}
self.predictions = {}
for dataset in self.datasets:
logger.info(f"Evaluating responses for dataset: {dataset}...")
if dataset == "snowballing":
# Filter responses based on the dataset
responses = self.filter_responses(df, dataset)
# Evaluate the responses
result, labels, preds = self.evaluate_snowballing(responses)
# Store the output and save the results
df_out = pd.DataFrame({"gold_labels": labels, "predictions": preds})
df_out.to_json(f"{self.output_path}/{self.run_id}/{dataset}_output.jsonl", orient="records", lines=True)
self.combined_result[dataset] = result
self.labels[dataset] = labels
self.predictions[dataset] = preds
elif dataset == "selfaware":
# Filter responses based on the dataset
responses = self.filter_responses(df, dataset)
# Evaluate the responses
result, labels, preds = self.evaluate_selfaware(responses[:30])
# Store the output and save the results
df_out = pd.DataFrame({"gold_labels": labels, "predictions": preds})
df_out.to_json(f"{self.output_path}/{self.run_id}/{dataset}_output.jsonl", orient="records", lines=True)
self.combined_result[dataset] = result
self.labels[dataset] = labels
self.predictions[dataset] = preds
elif dataset == "freshqa":
# Filter responses based on the dataset
responses = self.filter_responses(df, dataset)
# Evaluate the responses
result, raw_evals, preds = self.evaluate_freshqa(responses[:30])
# Store the output and save the results
df_out = pd.DataFrame({"raw_evaluations": raw_evals, "predictions": preds})
df_out.to_json(f"{self.output_path}/{self.run_id}/{dataset}_output.jsonl", orient="records", lines=True)
self.combined_result[dataset] = result
elif dataset in ["factoolqa", "felm-wk", "factcheck-bench", "factscore-bio"]:
# Check if the freetext key exists
if self.combined_result.get("freetext") is None:
self.combined_result["freetext"] = {}
# Filter responses based on the dataset
responses = self.filter_responses(df, dataset)
# Evaluate the responses
results, evaluations = self.evaluate_freetext(responses[:30], self.model_name, self.run_id)
# Store the output and save the results
df_out = pd.DataFrame(evaluations)
df_out.to_json(f"{self.output_path}/{self.run_id}/{dataset}_output.jsonl", orient="records", lines=True)
self.combined_result["freetext"][dataset] = results
else:
logger.error(f"Dataset {dataset} is not supported.")
raise ValueError(f"Dataset {dataset} is not supported.")
logger.info(f"Finished evaluating responses for dataset: {dataset}")
# save all evaluation results
with open(f"{self.output_path}/{self.run_id}/result.json", "w") as json_file:
json.dump(self.combined_result, json_file, indent=4)
# Analyze the results
if analyze:
self.logger.info("Analyzing the results...")
self.generate_plots(save_plots=True, fig_path=f"{self.output_path}/{self.run_id}")
# Create a report
if save_report:
self.logger.info("Creating the report...")
self.generate_report(report_path=f"{self.output_path}/{self.run_id}")
return self.combined_result
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