Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -337,45 +337,58 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
 def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
     stat_df = torch.zeros(size=(1,0))
     demo_df = torch.zeros(size=(1,0))
+    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_df = torch.zeros(size=(1,0))
+    demo_df = torch.zeros(size=(1,0))
+    feat_lab = False
     
     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)
     dyn,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds)
-    print(dyn.columns)
+
     ###########""
-    charts = dyn['CHART']
-    charts=charts.to_numpy()
-    charts = torch.tensor(charts)
-    charts = charts.unsqueeze(0)
-    charts = torch.tensor(charts)
-    charts = charts.type(torch.LongTensor)
+    if feat_chart:
+        charts = dyn['CHART']
+        charts=charts.to_numpy()
+        charts = torch.tensor(charts)
+        charts = charts.unsqueeze(0)
+        charts = torch.tensor(charts)
+        charts = charts.type(torch.LongTensor)
     
-    meds = dyn['MEDS']
-    meds=meds.to_numpy()
-    meds = torch.tensor(meds)
-    meds = meds.unsqueeze(0)
-    meds = torch.tensor(meds)
-    meds = meds.type(torch.LongTensor)
-
-    proc = dyn['PROC']
-    proc=proc.to_numpy()
-    proc = torch.tensor(proc)
-    proc = proc.unsqueeze(0)
-    proc = torch.tensor(proc)
-    proc = proc.type(torch.LongTensor)
-
-    out = dyn['OUT']
-    out=out.to_numpy()
-    out = torch.tensor(out)
-    out = out.unsqueeze(0)
-    out = torch.tensor(out)
-    out = out.type(torch.LongTensor)
-
-    lab = dyn['LAB']
-    lab=lab.to_numpy()
-    lab = torch.tensor(lab)
-    lab = lab.unsqueeze(0)
-    lab = torch.tensor(lab)
-    lab = lab.type(torch.LongTensor)
+    if feat_meds:
+        meds = dyn['MEDS']
+        meds=meds.to_numpy()
+        meds = torch.tensor(meds)
+        meds = meds.unsqueeze(0)
+        meds = torch.tensor(meds)
+        meds = meds.type(torch.LongTensor)
+
+    if feat_proc:
+        proc = dyn['PROC']
+        proc=proc.to_numpy()
+        proc = torch.tensor(proc)
+        proc = proc.unsqueeze(0)
+        proc = torch.tensor(proc)
+        proc = proc.type(torch.LongTensor)
+
+    if feat_out:
+        out = dyn['OUT']
+        out=out.to_numpy()
+        out = torch.tensor(out)
+        out = out.unsqueeze(0)
+        out = torch.tensor(out)
+        out = out.type(torch.LongTensor)
+
+    if feat_lab:
+        lab = dyn['LAB']
+        lab=lab.to_numpy()
+        lab = torch.tensor(lab)
+        lab = lab.unsqueeze(0)
+        lab = torch.tensor(lab)
+        lab = lab.type(torch.LongTensor)
 
 ####################""