Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -246,11 +246,17 @@ 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)
-
-        
         super().__init__(**kwargs)
-        self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out,self.path = self.create_cohort()
 
+        self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out,self.path = self.create_cohort()
+        if self.encoding:
+            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)
 
     BUILDER_CONFIGS = [
         Mimic4DatasetConfig(
@@ -583,13 +589,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 ###########################################################ENCODED##################################################################
        
     def _info_encoded(self):
-        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)
-        
+        X_train_encoded = pd.read_csv(self.path+'/X_train_encoded.csv', header=0)
         columns = {col: self.map_dtype(X_train_encoded[col].dtype) for col in X_train_encoded.columns}
         features = datasets.Features(columns)
         return datasets.DatasetInfo(