Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -82,13 +82,14 @@ def onehot(data,task,feat_cond=False,feat_proc=False,feat_out=False,feat_chart=F
         with open("./data/dict/"+task+"/procVocab", 'rb') as fp:
             procDic = pickle.load(fp)
 
-        feat=proc.keys()
-        proc_val=[proc[key] for key in feat]
-        procedures=pd.DataFrame(procDic,columns=['PROC'])
-        features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
-        features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
-        if feat==[]:
-            proc_df=features.fillna(0)
+        if proc :
+            feat=proc.keys()
+            proc_val=[proc[key] for key in feat]
+            procedures=pd.DataFrame(procDic,columns=['PROC'])
+            features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
+            features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
+            if feat==[]:
+                proc_df=features.fillna(0)
         else:
             procs=pd.DataFrame(columns=feat)
             for p,v in zip(feat,proc_val):
@@ -101,13 +102,14 @@ def onehot(data,task,feat_cond=False,feat_proc=False,feat_out=False,feat_chart=F
         with open("./data/dict/"+task+"/outVocab", 'rb') as fp:
             outDic = pickle.load(fp)
 
-        feat=out.keys()
-        out_val=[out[key] for key in feat]
-        outputs=pd.DataFrame(outDic,columns=['OUT'])
-        features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
-        features.columns=pd.MultiIndex.from_product([["OUT"], features.columns])
-        if feat==[]:
-            out_df=features.fillna(0)
+        if out :
+            feat=out.keys()
+            out_val=[out[key] for key in feat]
+            outputs=pd.DataFrame(outDic,columns=['OUT'])
+            features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
+            features.columns=pd.MultiIndex.from_product([["OUT"], features.columns])
+            if feat==[]:
+                out_df=features.fillna(0)
         else:
             outs=pd.DataFrame(columns=feat)
             for o,v in zip(feat,out_val):
@@ -120,14 +122,15 @@ def onehot(data,task,feat_cond=False,feat_proc=False,feat_out=False,feat_chart=F
         with open("./data/dict/"+task+"/chartVocab", 'rb') as fp:
             chartDic = pickle.load(fp)
 
-        charts=chart['val']
-        feat=charts.keys()
-        chart_val=[charts[key] for key in feat]
-        charts=pd.DataFrame(chartDic,columns=['CHART'])
-        features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
-        features.columns=pd.MultiIndex.from_product([["CHART"], features.columns])
-        if feat==[]:
-            chart_df=features.fillna(0)
+        if charts:
+            charts=chart['val']
+            feat=charts.keys()
+            chart_val=[charts[key] for key in feat]
+            charts=pd.DataFrame(chartDic,columns=['CHART'])
+            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
+            features.columns=pd.MultiIndex.from_product([["CHART"], features.columns])
+            if feat==[]:
+                chart_df=features.fillna(0)
         else:
             chart=pd.DataFrame(columns=feat)
             for c,v in zip(feat,chart_val):
@@ -140,13 +143,14 @@ def onehot(data,task,feat_cond=False,feat_proc=False,feat_out=False,feat_chart=F
         with open("./data/dict/"+task+"/medVocab", 'rb') as fp:
                 medDic = pickle.load(fp)
 
-        feat=meds['signal'].keys()
-        med_val=[meds['amount'][key] for key in feat]
-        meds=pd.DataFrame(medDic,columns=['MEDS'])
-        features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
-        features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns])
-        if feat==[]:
-                meds_df=features.fillna(0)
+        if meds:
+            feat=meds['signal'].keys()
+            med_val=[meds['amount'][key] for key in feat]
+            meds=pd.DataFrame(medDic,columns=['MEDS'])
+            features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
+            features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns])
+            if feat==[]:
+                    meds_df=features.fillna(0)
         else:
                 med=pd.DataFrame(columns=feat)
                 for m,v in zip(feat,med_val):