Update dataset_utils.py

dataset_utils.py

@@ -130,12 +130,12 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             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])
             procs=pd.DataFrame(columns=feat)
             for p,v in zip(feat,proc_val):
                 procs[p]=v
-            procs.columns=pd.MultiIndex.from_product([["PROC"], procs.columns])
+            features=features.drop(columns=procs.columns.to_list())
             proc_df = pd.concat([features,procs],axis=1).fillna(0)
+            proc_df.columns=pd.MultiIndex.from_product([["PROC"], proc_df.columns])
         else:
             procedures=pd.DataFrame(procDic,columns=['PROC'])
             features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
@@ -152,12 +152,12 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             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])
             outs=pd.DataFrame(columns=feat)
             for o,v in zip(feat,out_val):
                 outs[o]=v
-            outs.columns=pd.MultiIndex.from_product([["OUT"], outs.columns])
+            features=features.drop(columns=outs.columns.to_list())
             out_df = pd.concat([features,outs],axis=1).fillna(0)
+            out_df.columns=pd.MultiIndex.from_product([["OUT"], out_df.columns])
         else:
             outputs=pd.DataFrame(outDic,columns=['OUT'])
             features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
@@ -175,13 +175,12 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             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])
-            
             chart=pd.DataFrame(columns=feat)
             for c,v in zip(feat,chart_val):
                 chart[c]=v
-            chart.columns=pd.MultiIndex.from_product([["CHART"], chart.columns])
+            features=features.drop(columns=chart.columns.to_list())
             chart_df = pd.concat([features,chart],axis=1).fillna(0)
+            chart_df.columns=pd.MultiIndex.from_product([["CHART"], chart_df.columns])
         else:
             charts=pd.DataFrame(chartDic,columns=['CHART'])
             features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
@@ -198,13 +197,13 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             chart_val=[charts[key] for key in feat]
             charts=pd.DataFrame(chartDic,columns=['LAB'])
             features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
-            features.columns=pd.MultiIndex.from_product([["LAB"], features.columns])
-            
             chart=pd.DataFrame(columns=feat)
             for c,v in zip(feat,chart_val):
                 chart[c]=v
+            features=features.drop(columns=chart.columns.to_list())
             chart.columns=pd.MultiIndex.from_product([["LAB"], chart.columns])
             chart_df = pd.concat([features,chart],axis=1).fillna(0)
+            chart_df.columns=pd.MultiIndex.from_product([["LAB"], chart_df.columns])
         else:
             charts=pd.DataFrame(chartDic,columns=['LAB'])
             features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
@@ -221,13 +220,12 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             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])
-            
             med=pd.DataFrame(columns=feat)
             for m,v in zip(feat,med_val):
                 med[m]=v
-            med.columns=pd.MultiIndex.from_product([["MEDS"], med.columns])
+            features=features.drop(columns=med.columns.to_list())
             meds_df = pd.concat([features,med],axis=1).fillna(0)
+            meds_df.columns=pd.MultiIndex.from_product([["MEDS"], meds_df.columns])
         else:
             meds=pd.DataFrame(medDic,columns=['MEDS'])
             features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])