Update dataset_utils.py

dataset_utils.py

@@ -238,13 +238,13 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
 
 
 def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab):
-    meds = torch.zeros(size=(0,0))
-    charts = torch.zeros(size=(0,0))
-    proc = torch.zeros(size=(0,0))
-    out = torch.zeros(size=(0,0))
-    lab = torch.zeros(size=(0,0))
-    stat = torch.zeros(size=(1,0))
-    demo = torch.zeros(size=(1,0))
+    meds = []
+    charts = []
+    proc = []
+    out = []
+    lab = []
+    stat = []
+    demo = []
     
     
     size_cond, size_proc, size_meds, size_out, size_chart, size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
@@ -256,32 +256,19 @@ def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,fe
         #charts = charts.tolist()
         
     if feat_meds:
-        meds = dyn['MEDS']
-        meds=meds.to_numpy()
-        meds = torch.tensor(meds, dtype=torch.long)
-        meds = meds.tolist()
+        meds = dyn['MEDS'].values
 
     if feat_proc:
         proc = dyn['PROC']
-        proc=proc.to_numpy()
-        proc = torch.tensor(proc, dtype=torch.long)
-        proc = proc.tolist()
 
     if feat_out:
         out = dyn['OUT']
-        out=out.to_numpy()
-        out = torch.tensor(out, dtype=torch.long)
-        out = out.tolist()
 
     if feat_lab:
         lab = dyn['LAB']
-        lab=lab.to_numpy()
-        lab = torch.tensor(lab, dtype=torch.long)
-        lab = lab.tolist()
 
     if feat_cond:
         stat=cond_df.values[0]
-        print(stat)
         #stat = stat.to_numpy()
         
         #stat = torch.tensor(stat)
@@ -303,7 +290,6 @@ def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,fe
     demo["Age"].replace(age_vocab, inplace=True)
     demo=demo[["gender","ethnicity","insurance","Age"]]
     demo = demo.values[0]
-    print(demo)
     #demo = torch.tensor(demo)
     #if demo_df[0].nelement():
      #   demo_df = torch.cat((demo_df,demo),0)