Update cohort.py

cohort.py

@@ -14,7 +14,107 @@ def task_cohort(task,mimic_path,path_benchmark, config_path):
         config = yaml.safe_load(f)
     version_path = mimic_path
     version = version_path.split('/')[-1][0]
-    print(version)
+    start = time.time()
+    #----------------------------------------------config----------------------------------------------------
+    with open(config) as f:
+           config = yaml.safe_load(f)
+
+    disease_label =  config['disease_label']
+    tim = config['time']
+    label = config['label']
+    timeW = config['timeW']
+    include=int(timeW.split()[1])
+    bucket = config['bucket']
+    radimp = config['radimp']
+    predW = config['predW']
+    diag_flag = config['diagnosis']
+    out_flag = config['output']
+    chart_flag = config['chart']
+    proc_flag= config['proc']
+    med_flag = config['meds']
+    disease_filter = config['disease_filter']
+    icu_no_icu = config['icu_no_icu']
+    groupingICD = config['groupingICD']
+    # -------------------------------------------------------------------------------------------------------------
+
+    data_icu=icu_no_icu=="ICU"
+    data_mort=label=="Mortality"
+    data_admn=label=='Readmission'
+    data_los=label=='Length of Stay'
+
+    if (disease_filter=="Heart Failure"):
+        icd_code='I50'
+    elif (disease_filter=="CKD"):
+        icd_code='N18'
+    elif (disease_filter=="COPD"):
+        icd_code='J44'
+    elif (disease_filter=="CAD"):
+        icd_code='I25'
+    else:
+        icd_code='No Disease Filter'
+
+    #-----------------------------------------------EXTRACT MIMIC-----------------------------------------------------
+    if version == '2':
+        cohort_output = day_intervals_cohort_v2.extract_data(icu_no_icu,label,tim,icd_code, root_dir,disease_label)
+    elif version == '1':
+        cohort_output = day_intervals_cohort.extract_data(icu_no_icu,label,tim,icd_code, root_dir,disease_label)
+    end = time.time()
+    print("Time elapsed : ", round((end - start)/60,2),"mins")
+    #----------------------------------------------FEATURES-------------------------------------------------------
+    if data_icu :
+        feature_selection_icu.feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag)
+    end = time.time()
+    print("Time elapsed : ", round((end - start)/60,2),"mins")
+    #----------------------------------------------GROUPING-------------------------------------------------------
+    group_diag=False
+    group_med=False
+    group_proc=False
+    if data_icu:
+        if diag_flag:
+            group_diag=groupingICD
+        feature_selection_icu.preprocess_features_icu(cohort_output, diag_flag, group_diag,False,False,False,0,0)
+    end = time.time()
+    print("Time elapsed : ", round((end - start)/60,2),"mins")
+    #----------------------------------------------SUMMARY-------------------------------------------------------
+    if data_icu:
+        feature_selection_icu.generate_summary_icu(cohort_output,diag_flag,proc_flag,med_flag,out_flag,chart_flag)
+    end = time.time()
+    print("Time elapsed : ", round((end - start)/60,2),"mins")
+    #----------------------------------------------FEATURE SELECTION---------------------------------------------
+
+    select_diag= config['select_diag']
+    select_med= config['select_med']
+    select_proc=  config['select_proc']
+    #select_lab= config['select_lab']
+    select_out= config['select_out']
+    select_chart=  config['select_chart']
+
+    feature_selection_icu.features_selection_icu(cohort_output, diag_flag,proc_flag,med_flag,out_flag, chart_flag,select_diag,select_med,select_proc,select_out,select_chart)
+    end = time.time()
+    print("Time elapsed : ", round((end - start)/60,2),"mins")
+    #---------------------------------------CLEANING OF FEATURES-----------------------------------------------
+    thresh=0
+    if data_icu:
+        if chart_flag:
+            outlier_removal=config['outlier_removal']
+            clean_chart=outlier_removal!='No outlier detection'
+            impute_outlier_chart=outlier_removal=='Impute Outlier (default:98)'
+            thresh=config['outlier']
+            left_thresh=config['left_outlier']
+        feature_selection_icu.preprocess_features_icu(cohort_output, False, False,chart_flag,clean_chart,impute_outlier_chart,thresh,left_thresh)
+    end = time.time()
+    print("Time elapsed : ", round((end - start)/60,2),"mins")
+    # ---------------------------------------tim-Series Representation--------------------------------------------
+    if radimp == 'forward fill and mean' :
+        impute='Mean'
+    elif radimp =='forward fill and median':
+        impute = 'Median'
+    else :
+        impute = False
+    
+    if data_icu:
+        gen=data_generation_icu_modify.Generator(task,cohort_output,data_mort,data_admn,data_los,diag_flag,proc_flag,out_flag,chart_flag,med_flag,impute,include,bucket,predW)
+    print("[============TASK COHORT SUCCESSFULLY CREATED============]")
 
 if __name__ == '__main__':