Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -560,6 +560,8 @@ 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"]),
@@ -722,8 +724,16 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 
 ###########################################################ENCODED##################################################################
        
-    def _info_encoded(self,X_encoded):
-        columns = {col: self.map_dtype(X_encoded[col].dtype) for col in X_encoded.columns}
+    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)
+        
+        X_train_encoded.to_csv(self.path+"/X_train_encoded.csv", index=False)
+        X_test_encoded.to_csv(self.path+"/X_test_encoded.csv", index=False)
+        X_val_encoded.to_csv(self.path+"/X_val_encoded.csv", index=False)
+        columns = {col: self.map_dtype(X_train_encoded[col].dtype) for col in X_train_encoded.columns}
         features = datasets.Features(columns)
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -747,6 +757,17 @@ 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)
+
+        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"]),
@@ -802,29 +823,11 @@ 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' :
-            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)
-            
-            X_train_encoded.to_csv(self.path+"/X_train_encoded.csv", index=False)
-            X_test_encoded.to_csv(self.path+"/X_test_encoded.csv", index=False)
-            X_val_encoded.to_csv(self.path+"/X_val_encoded.csv", index=False)
-            return self._info_encoded(X_train_encoded)
+            return self._info_encoded()
         
         elif self.encoding == 'deep' :
-            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)
-
             return self._info_deep()
         
         else: