Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -236,7 +236,6 @@ class Mimic4DatasetConfig(datasets.BuilderConfig):
         **kwargs,
     ):
         print("init config")
-        self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out,self.path = self.create_cohort()
         super().__init__(**kwargs)
         
 class Mimic4Dataset(datasets.GeneratorBasedBuilder):
@@ -248,21 +247,13 @@ 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.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out,self.path = self.create_cohort()
+
         super().__init__(**kwargs)
         print("init dataset")
         
         
-        if self.encoding:
-            print("init encod")
-            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(
@@ -597,7 +588,14 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
        
     def _info_encoded(self):
         print("init info encod")
-        X_train_encoded = pd.read_csv('./data/dict/'+self.config.name+'/X_train_encoded.csv', header=0)
+        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(