Update dataset_utils.py

dataset_utils.py

@@ -77,7 +77,7 @@ def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag):
         
         return len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict
 
-def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,outDict,chartDict,condDict,procDict,medDict):
+def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab):
     meds=data['Med']
     proc = data['Proc']
     out = data['Out']
@@ -98,7 +98,9 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
     ##########COND#########
     if (feat_cond):
         #get all conds
-        conds=pd.DataFrame(condDict,columns=['COND'])
+        with open("./data/dict/"+task+"/condVocab", 'rb') as fp:
+            conDict = pickle.load(fp)
+        conds=pd.DataFrame(conDict,columns=['COND'])
         features=pd.DataFrame(np.zeros([1,len(conds)]),columns=conds['COND'])
 
         #onehot encode
@@ -120,11 +122,13 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
 
     ##########PROC#########
     if (feat_proc):
+        with open("./data/dict/"+task+"/procVocab", 'rb') as fp:
+            procDic = pickle.load(fp)
 
         if proc :
             feat=proc.keys()
             proc_val=[proc[key] for key in feat]
-            procedures=pd.DataFrame(procDict,columns=['PROC'])
+            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)
@@ -140,11 +144,13 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
 
     ##########OUT#########
     if (feat_out):
+        with open("./data/dict/"+task+"/outVocab", 'rb') as fp:
+            outDic = pickle.load(fp)
 
         if out :
             feat=out.keys()
             out_val=[out[key] for key in feat]
-            outputs=pd.DataFrame(outDict,columns=['OUT'])
+            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)
@@ -153,60 +159,68 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             outs.columns=pd.MultiIndex.from_product([["OUT"], outs.columns])
             out_df = pd.concat([features,outs],ignore_index=True).fillna(0)
         else:
-            outputs=pd.DataFrame(outDict,columns=['OUT'])
+            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])
             out_df=features.fillna(0)
 
     ##########CHART#########
     if (feat_chart):
+        with open("./data/dict/"+task+"/chartVocab", 'rb') as fp:
+            chartDic = pickle.load(fp)
+
         if chart:
             charts=chart['val']
             feat=charts.keys()
             chart_val=[charts[key] for key in feat]
-            charts=pd.DataFrame(chartDict,columns=['CHART'])
+            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])
             chart_df = pd.concat([features,chart],ignore_index=True).fillna(0)
-            
         else:
-            charts=pd.DataFrame(chartDict,columns=['CHART'])
+            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_df=features.fillna(0)
         
         ##########LAB#########
     if (feat_lab):
+        with open("./data/dict/"+task+"/labsVocab", 'rb') as fp:
+            chartDic = pickle.load(fp)
+
         if chart:
             charts=chart['val']
             feat=charts.keys()
             chart_val=[charts[key] for key in feat]
-            charts=pd.DataFrame(chartDict,columns=['LAB'])
+            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
             chart.columns=pd.MultiIndex.from_product([["LAB"], chart.columns])
             chart_df = pd.concat([features,chart],ignore_index=True).fillna(0)
         else:
-            charts=pd.DataFrame(chartDict,columns=['LAB'])
+            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_df=features.fillna(0)
-        
+    
     ###MEDS
     if (feat_meds):
+        with open("./data/dict/"+task+"/medVocab", 'rb') as fp:
+                medDic = pickle.load(fp)
+
         if meds:
             feat=meds['signal'].keys()
             med_val=[meds['amount'][key] for key in feat]
-            meds=pd.DataFrame(medDict,columns=['MEDS'])
+            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])
             
@@ -216,7 +230,7 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             med.columns=pd.MultiIndex.from_product([["MEDS"], med.columns])
             meds_df = pd.concat([features,med],ignore_index=True).fillna(0)
         else:
-            meds=pd.DataFrame(medDict,columns=['MEDS'])
+            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])
             meds_df=features.fillna(0)
@@ -330,8 +344,6 @@ def generate_ml(dyn,stat,demo,concat_cols,concat):
             else:
                 dyn_df=pd.concat([dyn_df,agg],axis=0)
         dyn_df=dyn_df.T
-        print(dyn_df.columns)
-        print(dyn_df)
         dyn_df.columns = dyn_df.iloc[0]
         dyn_df=dyn_df.iloc[1:,:]