Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -200,11 +200,11 @@ def encoding(X_data):
     X_data['insurance']=ins_encoder.transform(X_data['insurance'])
     return X_data
 
-def generate_split(df,task,concat,feat_cond=True,feat_chart=True,feat_proc=True, feat_meds=True, feat_out=False):
+def generate_split(path,task,concat,feat_cond=True,feat_chart=True,feat_proc=True, feat_meds=True, feat_out=False):
+    df = pd.DataFrame.from_dict(path)
     task=task.replace(" ","_")
     X_df=pd.DataFrame()   
     #y_df=pd.DataFrame(df['label'],columns=['label'])
-    
     for hid, data in tqdm(df.iterrows()):
         concat_cols=[]
         sample=data
@@ -222,7 +222,6 @@ def generate_split(df,task,concat,feat_cond=True,feat_chart=True,feat_proc=True,
              X_df=pd.concat([X_df,X],axis=1)
         else:
             X_df = pd.concat([X_df, X], axis=0)
-    
     X_df=X_df.fillna(0) 
     X_df = encoding(X_df)
     #X_df=X_df.drop(['label'], axis=1)
@@ -250,8 +249,15 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 
         
         super().__init__(**kwargs)
-        self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out = self.create_cohort()
-        
+        self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out,path = self.create_cohort()
+
+        if self.encoding:
+            X_train_encoded=generate_split(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(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(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(path+"/X_train_encoded.csv", index=False)
+            X_test_encoded.to_csv(path+"/X_test_encoded.csv", index=False)
+            X_val_encoded.to_csv(path+"/X_val_encoded.csv", index=False)
 
     BUILDER_CONFIGS = [
         Mimic4DatasetConfig(
@@ -399,25 +405,24 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         train_data, test_data = train_test_split(data, test_size=self.test_size, random_state=42)
         train_data, val_data = train_test_split(test_data, test_size=self.val_size, random_state=42)
         
-        csv_dir = "./data/dict/"+self.config.name.replace(" ","_")
-
-        train_data.to_csv(csv_dir+'/train_data.csv',index=False)
-        val_data.to_csv(csv_dir+'/val_data.csv',index=False)
-        test_data.to_csv(csv_dir+'/test_data.csv',index=False)
-
+        dict_dir = "./data/dict/"+self.config.name.replace(" ","_")
         train_dic = train_data.to_dict('index')
         test_dic = test_data.to_dict('index')
         val_dic = val_data.to_dict('index')
 
-        with open(csv_dir+'/train_data.pkl', 'wb') as f:
+        train_path = dict_dir+'/train_data.pkl'
+        test_path = dict_dir+'/test_data.pkl'
+        val_path = dict_dir+'/val_data.pkl'
+        
+        with open(train_path, 'wb') as f:
             pickle.dump(train_dic, f)
-        with open(csv_dir+'/val_data.pkl', 'wb') as f:
+        with open(val_path, 'wb') as f:
             pickle.dump(val_dic, f)
-        with open(csv_dir+'/test_data.pkl', 'wb') as f:
+        with open(test_path, 'wb') as f:
             pickle.dump(test_dic, f)
 
         
-        return feat_cond, feat_chart, feat_proc, feat_meds, feat_out
+        return feat_cond, feat_chart, feat_proc, feat_meds, feat_out, dict_dir
   
 ###########################################################RAW##################################################################
 
@@ -585,7 +590,8 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 ###########################################################ENCODED##################################################################
        
     def _info_encoded(self):
-        X_df = pd.read_csv("./data/dict/"+self.config.name.replace(" ","_")+'/train_data_encoded.csv', header=0)
+        #X_df = pd.read_csv("./data/dict/"+self.config.name.replace(" ","_")+'/train_data_encoded.csv', header=0)
+        X_df = pd.read_csv("./data/dict/"+self.config.name.replace(" ","_")+'/X_train_encoded.csv', header=0)
         columns = {col: self.map_dtype(X_df[col].dtype) for col in X_df.columns}
         features = datasets.Features(columns)
         return datasets.DatasetInfo(
@@ -596,17 +602,19 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         )
     
     def __split_generators_encoded(self):
-        csv_dir = "./data/dict/"+self.config.name.replace(" ","_")
+        data_dir = "./data/dict/"+self.config.name.replace(" ","_")
 
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": csv_dir+'/train_data_encoded.csv'}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": csv_dir+'/val_data_encoded.csv'}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": csv_dir+'/test_data_encoded.csv'}),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir+'/X_train_encoded.csv'}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir+'/X_val_encoded.csv'}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir+'/X_test_encoded.csv'}),
             ]
 
     def _generate_examples_encoded(self, filepath):
         df = pd.read_csv(filepath, header=0)
-        X_df=generate_split(df,self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
+        for i, row in df.iterrows():
+            yield i, row.to_dict('index')
+        
 
 #############################################################################################################################
     def _info(self):