Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -357,9 +357,7 @@ def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
         charts = torch.tensor(charts)
         charts = charts.unsqueeze(0)
         charts = torch.tensor(charts)
-        print('convert')
         charts = charts.type(torch.LongTensor)
-        print('charts ok')
     if feat_meds:
         meds = dyn['MEDS']
         meds=meds.to_numpy()
@@ -402,10 +400,10 @@ def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
         stat_df = torch.cat((stat_df,stat),0)
     else:
         stat_df = stat 
-    print('stat ok')
 
+    print('label')
     y = int(demo['label'])
-    
+    print('demo')
     demo["gender"].replace(gender_vocab, inplace=True)
     demo["ethnicity"].replace(eth_vocab, inplace=True)
     demo["insurance"].replace(ins_vocab, inplace=True)
@@ -413,21 +411,21 @@ def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
     demo=demo[["gender","ethnicity","insurance","Age"]]
     demo = demo.values
     demo = torch.tensor(demo)
-    
+    print('concat demo')
     if demo_df[0].nelement():
         demo_df = torch.cat((demo_df,demo),0)
     else:
         demo_df = demo
-
+    print('stat2')
     stat_df = torch.tensor(stat_df)
     stat_df = stat_df.type(torch.LongTensor)
-    
+    print('demo2')
     demo_df = torch.tensor(demo_df)
     demo_df = demo_df.type(torch.LongTensor)
-    
+    print('label2')
     y_df = torch.tensor(y)
     y_df = y_df.type(torch.LongTensor)
-   
+    print("end")
     return stat_df, demo_df, meds, charts, out, proc, lab, y_df
 
 def getXY(dyn,stat,demo,concat_cols,concat):