Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -536,7 +536,11 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         self.config_path = kwargs.pop("config_path",None)
         self.test_size = kwargs.pop("test_size",0.2)
         self.val_size = kwargs.pop("val_size",0.1)
-        
+        self.generate_cohort = kwargs.pop("generate_cohort",True)
+        if self.encoding == 'onehot':
+            self.concat = True
+        else:
+            self.concat = False
         super().__init__(**kwargs)
         
         
@@ -656,8 +660,8 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         config = self.config_path.split('/')[-1]
 
         #####################create task cohort
-        #if not os.path.exists(data_dir):
-        task_cohort(self.config.name.replace(" ","_"),self.mimic_path,config)
+        if self.generate_cohort:
+            task_cohort(self.config.name.replace(" ","_"),self.mimic_path,config)
 
         #####################Split data into train, test and val
         with open(data_dir, 'rb') as fp:
@@ -892,7 +896,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             demo['insurance']=ins_encoder.transform(demo['insurance'])
             label = data['label']
             demo=demo.drop(['label'],axis=1)
-            X= getXY(dyn_df,cond_df,demo,concat_cols,True)
+            X= getXY(dyn_df,cond_df,demo,concat_cols,self.concat)
             X=X.values.tolist()[0]
             yield int(i), {
                 "label": label,
@@ -946,6 +950,9 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 
         if self.encoding == 'onehot' :
             return self._info_encoded()
+
+        elif self.encoding == 'aggreg' :
+            return self._info_encoded()
         
         elif self.encoding == 'deep' :
             return self._info_deep()
@@ -968,6 +975,9 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         if self.encoding == 'onehot' :
             yield from self._generate_examples_encoded(filepath)
         
+        elif self.encoding == 'aggreg' :
+            yield from self._generate_examples_encoded(filepath)
+        
         elif self.encoding == 'deep' :
             yield from self._generate_examples_deep(filepath)
         else :