Update dataset_utils.py

dataset_utils.py

@@ -109,19 +109,13 @@ def open_dict(task,cond, proc, out, chart, lab, med):
     
     return condDict, procDict, outDict, chartDict, medDict
 
-def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
+def concat_data(data,interval,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
     meds=data['Med']
     proc = data['Proc']
     out = data['Out']
-    chart = data['Chart']
+    charts = data['Chart']['val']
     cond= data['Cond']['fids']
 
-    cond_df=pd.DataFrame()
-    proc_df=pd.DataFrame()
-    out_df=pd.DataFrame()
-    chart_df=pd.DataFrame()
-    meds_df=pd.DataFrame()
-
     #demographic
     demo=pd.DataFrame(columns=['Age','gender','ethnicity','label','insurance'])
     new_row = {'Age': data['age'], 'gender': data['gender'], 'ethnicity': data['ethnicity'], 'label': data['label'], 'insurance': data['insurance']}
@@ -129,102 +123,67 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
 
     ##########COND#########
     if (feat_cond):
-        conds=pd.DataFrame(condDict,columns=['COND'])
-        features=pd.DataFrame(np.zeros([1,len(conds)]),columns=conds['COND'])
-
-        #onehot encode
-        if(cond ==[]):
-            cond_df=pd.DataFrame(np.zeros([1,len(features)]),columns=features['COND'])
-            cond_df=cond_df.fillna(0)
-        else:
-            cond_df=pd.DataFrame(cond,columns=['COND'])
-            cond_df['val']=1
-            cond_df=(cond_df.drop_duplicates()).pivot(columns='COND',values='val').reset_index(drop=True)
-            cond_df=cond_df.fillna(0)
-            oneh = cond_df.sum().to_frame().T
-            combined_df = pd.concat([features,oneh],ignore_index=True).fillna(0)
-            combined_oneh=combined_df.sum().to_frame().T
-            cond_df=combined_oneh
-            for c in cond_df.columns :
-                if c not in features: 
-                    cond_df=cond_df.drop(columns=[c])
+        cond_df=pd.DataFrame(np.zeros([1,len(condDict)]),columns=condDict)
+        if cond:
+            for c in cond : cond_df[c]=1
 
     ##########PROC#########
     if (feat_proc):
         if proc :
             feat=proc.keys()
             proc_val=[proc[key] for key in feat]
-            procedures=pd.DataFrame(procDict,columns=['PROC'])
-            features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
-            procs=pd.DataFrame(columns=feat)
+            proc_df=pd.DataFrame(np.zeros([interval,len(procDict)]),columns=procDict)
             for p,v in zip(feat,proc_val):
-                procs[p]=v
-            features=features.drop(columns=procs.columns.to_list())
-            proc_df = pd.concat([features,procs],axis=1).fillna(0)
+                proc_df[p]=v
             proc_df.columns=pd.MultiIndex.from_product([["PROC"], proc_df.columns])
+            print(proc_df)
         else:
             procedures=pd.DataFrame(procDict,columns=['PROC'])
-            features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
+            features=pd.DataFrame(np.zeros([interval,len(procedures)]),columns=procedures['PROC'])
             features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
-            proc_df=features.fillna(0)
 
     ##########OUT#########
     if (feat_out):
         if out :
             feat=out.keys()
             out_val=[out[key] for key in feat]
-            outputs=pd.DataFrame(outDict,columns=['OUT'])
-            features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
-            outs=pd.DataFrame(columns=feat)
+            out_df=pd.DataFrame(np.zeros([interval,len(outDict)]),columns=outDict)
             for o,v in zip(feat,out_val):
-                outs[o]=v
-            features=features.drop(columns=outs.columns.to_list())
-            out_df = pd.concat([features,outs],axis=1).fillna(0)
+                out_df[o]=v
             out_df.columns=pd.MultiIndex.from_product([["OUT"], out_df.columns])
         else:
             outputs=pd.DataFrame(outDict,columns=['OUT'])
-            features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
+            features=pd.DataFrame(np.zeros([interval,len(outputs)]),columns=outputs['OUT'])
             features.columns=pd.MultiIndex.from_product([["OUT"], features.columns])
             out_df=features.fillna(0)
 
     ##########CHART#########
     if (feat_chart):
-        if chart:
-            charts=chart['val']
+        if charts:
             feat=charts.keys()
             chart_val=[charts[key] for key in feat]
-            charts=pd.DataFrame(chartDict,columns=['CHART'])
-            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
-            chart=pd.DataFrame(columns=feat)
+            chart_df=pd.DataFrame(np.zeros([interval,len(chartDict)]),columns=chartDict)
             for c,v in zip(feat,chart_val):
-                chart[c]=v
-            features=features.drop(columns=chart.columns.to_list())
-            chart_df = pd.concat([features,chart],axis=1).fillna(0)
+                chart_df[c]=v
             chart_df.columns=pd.MultiIndex.from_product([["CHART"], chart_df.columns])
         else:
             charts=pd.DataFrame(chartDict,columns=['CHART'])
-            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
+            features=pd.DataFrame(np.zeros([interval,len(charts)]),columns=charts['CHART'])
             features.columns=pd.MultiIndex.from_product([["CHART"], features.columns])
             chart_df=features.fillna(0)
         ##########LAB#########
     
     if (feat_lab):
-        if chart:
-            charts=chart['val']
+        if charts:
             feat=charts.keys()
             chart_val=[charts[key] for key in feat]
-            charts=pd.DataFrame(chartDict,columns=['LAB'])
-            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
-            chart=pd.DataFrame(columns=feat)
+            chart_df=pd.DataFrame(np.zeros([interval,len(chartDict)]),columns=chartDict)
             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[c]=v
             chart_df.columns=pd.MultiIndex.from_product([["LAB"], chart_df.columns])
         else:
             charts=pd.DataFrame(chartDict,columns=['LAB'])
-            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
+            features=pd.DataFrame(np.zeros([interval,len(charts)]),columns=charts['LAB'])
             features.columns=pd.MultiIndex.from_product([["LAB"], features.columns])
             chart_df=features.fillna(0)
     
@@ -233,17 +192,13 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
         if meds:
             feat=meds['signal'].keys()
             med_val=[meds['amount'][key] for key in feat]
-            meds=pd.DataFrame(medDict,columns=['MEDS'])
-            features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
-            med=pd.DataFrame(columns=feat)
+            meds_df=pd.DataFrame(np.zeros([interval,len(medDict)]),columns=medDict)           
             for m,v in zip(feat,med_val):
-                med[m]=v
-            features=features.drop(columns=med.columns.to_list())
-            meds_df = pd.concat([features,med],axis=1).fillna(0)
+                meds_df[m]=v
             meds_df.columns=pd.MultiIndex.from_product([["MEDS"], meds_df.columns])
         else:
             meds=pd.DataFrame(medDict,columns=['MEDS'])
-            features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
+            features=pd.DataFrame(np.zeros([interval,len(meds)]),columns=meds['MEDS'])
             features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns])
             meds_df=features.fillna(0)
 
@@ -252,7 +207,7 @@ 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,condDict, procDict, outDict, chartDict, medDict):
+def generate_deep(data,interval,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
     meds = []
     charts = []
     proc = []
@@ -262,7 +217,7 @@ def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,fe
     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)
-    dyn,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict)
+    dyn,cond_df,demo=concat_data(data,interval,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict)
     if feat_chart:
         charts = dyn['CHART'].fillna(0).values
     if feat_meds: