Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -200,7 +200,6 @@ def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag):
         
         return ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict 
 
-
 def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
     meds=data['Med']
     proc = data['Proc']
@@ -376,7 +375,6 @@ def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
     
     return stat, demo, meds, chart, out, proc, lab, y
 
-
 def getXY(dyn,stat,demo,concat_cols,concat):
     X_df=pd.DataFrame()
     if concat:
@@ -449,17 +447,6 @@ def generate_split(path,task,concat,feat_cond,feat_chart,feat_proc, feat_meds, f
     X_df = encoding(X_df)
     return X_df
 
-def generate_split_deep(path,task,feat_cond,feat_chart,feat_proc, feat_meds, feat_out):
-    with open(path, 'rb') as fp:
-        dico = pickle.load(fp)
-    X = pd.DataFrame.from_dict(dico, orient='index')
-    X_dict = {}
-    taskf=task.replace(" ","_")
-    for hid, data in tqdm(X.iterrows(),desc='Encoding Splits Data for '+task+' task'):
-        stat, demo, meds, chart, out, proc, lab, y = getXY_deep(data, taskf, feat_cond, feat_proc, feat_out, feat_chart,feat_meds)
-        X_dict[hid] = {'stat': stat, 'demo': demo, 'meds': meds, 'chart': chart, 'out': out, 'proc': proc, 'lab': lab, 'label': y}
-        
-    return X_dict
 
 
 def task_cohort(task, mimic_path, config_path):
@@ -527,6 +514,7 @@ def task_cohort(task, mimic_path, config_path):
     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-------------------------------------------------------
@@ -715,7 +703,8 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         config = self.config_path.split('/')[-1]
 
         #####################create task cohort
-        task_cohort(self.config.name.replace(" ","_"),self.mimic_path,config)
+        if not os.path.exists(data_dir):
+            task_cohort(self.config.name.replace(" ","_"),self.mimic_path,config)
 
         #####################Split data into train, test and val
         with open(data_dir, 'rb') as fp:
@@ -941,16 +930,6 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             yield i, row.to_dict()
 ######################################################DEEP###############################################################
     def _info_deep(self):
-        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)
-
-        with open(self.path+"/X_train_deep.pkl", 'wb') as f:
-            pickle.dump(X_train_deep, f)
-        with open(self.path+"/X_test_deep.pkl", 'wb') as f:
-            pickle.dump(X_test_deep, f)
-        with open(self.path+"/X_val_deep.pkl", 'wb') as f:
-            pickle.dump(X_val_deep, f)
         features = datasets.Features(
             {
                 "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
@@ -983,7 +962,6 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
     def _generate_examples_deep(self, filepath):
         with open(filepath, 'rb') as fp:
             dico = pickle.load(fp)
-        
         task=self.config.name.replace(" ","_")
         if 'Custom' in task:
             task = task.rsplit('_', 1)[0]