Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -14,6 +14,9 @@ import numpy as np
 from tqdm import tqdm
 import yaml
 import torch
+import time
+from pathlib import Path
+import importlib
 
 
 _DESCRIPTION = """\
@@ -370,6 +373,116 @@ def generate_split_deep(path,task,feat_cond,feat_chart,feat_proc, feat_meds, fea
         
     return X_dict
 
+
+def task_cohort(task, mimic_path, config_path):
+    sys.path.append('./preprocessing/day_intervals_preproc')
+    sys.path.append('./utils')
+    sys.path.append('./preprocessing/hosp_module_preproc')
+    sys.path.append('./model')
+    import day_intervals_cohort_v22
+    import day_intervals_cohort
+    import feature_selection_icu
+    import data_generation_icu_modify
+    
+    root_dir = os.path.dirname(os.path.abspath('UserInterface.ipynb'))
+    config_path='./config/'+config_path
+    with open(config_path) as f:
+        config = yaml.safe_load(f)
+    version_path = mimic_path+'/'
+    version = mimic_path.split('/')[-1][0]
+    start = time.time()
+    #----------------------------------------------config----------------------------------------------------
+    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_v22.extract_data(icu_no_icu,label,tim,icd_code, root_dir,version_path,disease_label)
+
+    elif version == '1':
+        cohort_output = day_intervals_cohort.extract_data(icu_no_icu,label,tim,icd_code, root_dir,version_path,disease_label)
+    #----------------------------------------------FEATURES-------------------------------------------------------
+    print(data_icu)
+    if data_icu :
+        feature_selection_icu.feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag)
+    #----------------------------------------------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)
+    #----------------------------------------------SUMMARY-------------------------------------------------------
+    if data_icu:
+        feature_selection_icu.generate_summary_icu(diag_flag,proc_flag,med_flag,out_flag,chart_flag)
+    #----------------------------------------------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)
+    #---------------------------------------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)
+    # ---------------------------------------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)
+    end = time.time()
+    print("Time elapsed : ", round((end - start)/60,2),"mins")
+    print("[============TASK COHORT SUCCESSFULLY CREATED============]")
+
+
+ #############################################DATASET####################################################################   
 class Mimic4DatasetConfig(datasets.BuilderConfig):
     """BuilderConfig for Mimic4Dataset."""
 
@@ -519,12 +632,14 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         sys.path.append(path_bench)
         config = self.config_path.split('/')[-1]
 
-        script = 'python cohort.py '+ self.config.name.replace(" ","_") +" "+ self.mimic_path+ " "+path_bench+ " "+config
+        script = 'python cohort.py '+ self.config.name.replace(" ","_") +" "+ self.mimic_path+ " "+ " "+config
         
         #####################################CHANGE##########
         #if not os.path.exists(data_dir) :
-        os.system(script)  
+        #os.system(script)  
+        task_cohort(self.config.name.replace(" ","_"),self.mimic_path,config)
         #####################################CHANGE##########
+        
         config_path='./config/'+config
         with open(config_path) as f:
             config = yaml.safe_load(f)
@@ -560,8 +675,6 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 ###########################################################RAW##################################################################
 
     def _info_raw(self):
-        self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out,self.path = self.create_cohort()
-        
         features = datasets.Features(
             {
                 "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
@@ -725,7 +838,6 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 ###########################################################ENCODED##################################################################
        
     def _info_encoded(self):
-        self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out,self.path = self.create_cohort()
         X_train_encoded=generate_split(self.path+'/train_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
         X_test_encoded=generate_split(self.path+'/test_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
         X_val_encoded=generate_split(self.path+'/val_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
@@ -757,7 +869,6 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             yield i, row.to_dict()
 ######################################################DEEP###############################################################
     def _info_deep(self):
-        self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out,self.path = self.create_cohort()
         X_train_deep = generate_split_deep(self.path+'/train_data.pkl',self.config.name,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
         X_test_deep = generate_split_deep(self.path+'/test_data.pkl',self.config.name,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
         X_val_deep = generate_split_deep(self.path+'/val_data.pkl',self.config.name,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
@@ -823,7 +934,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
     
 #############################################################################################################################
     def _info(self):
-        
+        self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out,self.path = self.create_cohort()
         if self.encoding == 'onehot' :
             return self._info_encoded()