Update dataset_utils.py

dataset_utils.py

@@ -279,16 +279,25 @@ def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,fe
         lab=lab.to_numpy()
         lab = torch.tensor(lab, dtype=torch.long)
         lab = lab.tolist()
-    
-    stat=cond_df
-    stat = stat.to_numpy()
-    stat = torch.tensor(stat)
-    if stat_df[0].nelement():
-        stat_df = torch.cat((stat_df,stat),0)
-    else:
-        stat_df = stat 
 
+    if feat_cond:
+        stat=cond_df
+        stat = stat.to_numpy()
+        stat = torch.tensor(stat)
+        if stat_df[0].nelement():
+            stat_df = torch.cat((stat_df,stat),0)
+        else:
+            stat_df = stat 
+
+        stat_df = torch.tensor(stat_df)
+        stat_df = stat_df.type(torch.LongTensor)
+        stat_df = stat_df.squeeze()
+
+        
     y = int(demo['label'])
+    y_df = torch.tensor(y)
+    y_df = y_df.type(torch.LongTensor)
+    
     demo["gender"].replace(gender_vocab, inplace=True)
     demo["ethnicity"].replace(eth_vocab, inplace=True)
     demo["insurance"].replace(ins_vocab, inplace=True)
@@ -300,14 +309,9 @@ def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,fe
         demo_df = torch.cat((demo_df,demo),0)
     else:
         demo_df = demo
-    stat_df = torch.tensor(stat_df)
-    stat_df = stat_df.type(torch.LongTensor)
-    stat_df = stat_df.squeeze()
     demo_df = torch.tensor(demo_df)
     demo_df = demo_df.type(torch.LongTensor)
     demo_df=demo_df.squeeze()
-    y_df = torch.tensor(y)
-    y_df = y_df.type(torch.LongTensor)
 
     return stat_df, demo_df, meds, charts, out, proc, lab, y