Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -244,7 +244,7 @@ def concat_data(data,task,feat_cond=False,feat_proc=False,feat_out=False,feat_ch
     dyn_df = pd.concat([meds_df,proc_df,out_df,chart_df], axis=1)
     return dyn_df,cond_df,demo
 
-def getXY_deep(X_df,task,feat_cond=True,feat_chart=True,feat_proc=True, feat_meds=True, feat_out=False):
+def getXY_deep(X_df,task,feat_cond,feat_chart,feat_proc, feat_meds, feat_out):
     eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
     dyn_df=[]
     meds=torch.zeros(size=(0,0))
@@ -401,7 +401,7 @@ def generate_split_deep(path,task,feat_cond,feat_chart,feat_proc, feat_meds, fea
     df = pd.DataFrame.from_dict(dico, orient='index')
     X_df=pd.DataFrame()
     taskf=task.replace(" ","_")
-    meds,chart,out,proc,lab ,stat_df, demo_df, y_df = getXY_deep(df,taskf,feat_cond,feat_chart,feat_proc, feat_meds, feat_out)
+    meds,chart,out,proc,lab ,stat_df, demo_df, y_df  = getXY_deep(df,taskf,feat_cond,feat_chart,feat_proc, feat_meds, feat_out)
     X_df=pd.concat([X_df,meds],axis=1)
     X_df=pd.concat([X_df,chart],axis=1)
     X_df=pd.concat([X_df,out],axis=1)