Update dataset_utils.py

dataset_utils.py

@@ -291,26 +291,30 @@ def generate_ml(dyn, stat, demo, concat_cols, concat):
     X_df = pd.DataFrame()
     
     if concat:
-        dyna = dyn.copy()
-        dyna.columns = dyna.columns.droplevel(0)
-        dyna = dyna.to_numpy()
-        dyna = np.nan_to_num(dyna, copy=False)
-        dyna = dyna.reshape(1, -1)
-        dyn_df = pd.DataFrame(data=dyna, columns=concat_cols)
+        dyna=dyn.copy()
+        dyna.columns=dyna.columns.droplevel(0)
+        dyna=dyna.to_numpy()
+        dyna=np.nan_to_num(dyna, copy=False)
+        dyna=dyna.reshape(1,-1)
+        dyn_df=pd.DataFrame(data=dyna,columns=concat_cols)
     else:
-        dyn_df_list = []
-        
-        for key in dyn.columns.levels[0]:
-            dyn_temp = dyn[key]
-            if key in ["CHART", "MEDS"]:
-                agg = np.nanmean(dyn_temp.to_numpy(), axis=0)
+        dyn_df=pd.DataFrame()
+        for key in dyn.columns.levels[0]:     
+            dyn_temp=dyn[key]
+            if ((key=="CHART") or (key=="MEDS")):
+                agg=dyn_temp.aggregate("mean")
+                agg=agg.reset_index()
             else:
-                agg = np.nanmax(dyn_temp.to_numpy(), axis=0)
-            
-            agg = pd.DataFrame(data=agg.reshape(1, -1), columns=dyn_temp.columns)
-            dyn_df_list.append(agg)
-        
-        dyn_df = pd.concat(dyn_df_list, axis=0, ignore_index=True)
+                agg=dyn_temp.aggregate("max")
+                agg=agg.reset_index()
+
+            if dyn_df.empty:
+                dyn_df=agg
+            else:
+                dyn_df=pd.concat([dyn_df,agg],axis=0)
+        dyn_df=dyn_df.T
+        dyn_df.columns = dyn_df.iloc[0]
+        dyn_df=dyn_df.iloc[1:,:]
         
     X_df = pd.concat([dyn_df, stat, demo], axis=1)
     return X_df