Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -218,13 +218,13 @@ def generate_split(path,task,concat,feat_cond=True,feat_chart=True,feat_proc=Tru
     with open(path, 'rb') as fp:
         dico = pickle.load(fp)
     df = pd.DataFrame.from_dict(dico, orient='index')
-    task=task.replace(" ","_")
     X_df=pd.DataFrame()   
     #y_df=pd.DataFrame(df['label'],columns=['label'])
-    for _, data in tqdm(df.iterrows()):
+    taskf=task.replace(" ","_")
+    for _, data in tqdm(df.iterrows(),desc='Encoding Data for '+task+' task'):
         concat_cols=[]
         sample=data
-        dyn_df,cond_df,demo=onehot(sample,task,feat_cond,feat_chart,feat_proc, feat_meds, feat_out)
+        dyn_df,cond_df,demo=onehot(sample,taskf,feat_cond,feat_chart,feat_proc, feat_meds, feat_out)
         dyn=dyn_df.copy()
         dyn.columns=dyn.columns.droplevel(0)
         cols=dyn.columns
@@ -263,12 +263,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         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()
-        print("init dataset")
-        
         
         
     BUILDER_CONFIGS = [
@@ -327,7 +322,6 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             raise ValueError(f"Unsupported dtype: {dtype}")
         
     def create_cohort(self):
-        print("init cohort")
         if self.config.name == 'Phenotype' : self.config_path = _CONFIG_URLS['phenotype'] 
         if self.config.name == 'Readmission' : self.config_path = _CONFIG_URLS['readmission'] 
         if self.config.name == 'Length of Stay' : self.config_path = _CONFIG_URLS['los'] 
@@ -603,7 +597,6 @@ 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}
         features = datasets.Features(columns)
         return datasets.DatasetInfo(
@@ -625,7 +618,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
     def _generate_examples_encoded(self, filepath):
         df = pd.read_csv(filepath, header=0)
         for i, row in df.iterrows():
-            yield i, row.to_dict('index')
+            yield i, row.to_dict()
         
 
 #############################################################################################################################