Update dataset_utils.py

dataset_utils.py

@@ -77,7 +77,39 @@ 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):
+def open_dict(task,cond, proc, out, chart, lab, med):
+    if cond:
+        with open("./data/dict/"+task+"/condVocab", 'rb') as fp:
+            condDict = pickle.load(fp)
+    else:
+        condDict = None
+    if proc:
+        with open("./data/dict/"+task+"/procVocab", 'rb') as fp:
+            procDict = pickle.load(fp)
+    else:
+        procDict = None
+    if out:
+        with open("./data/dict/"+task+"/outVocab", 'rb') as fp:
+            outDict = pickle.load(fp)
+    else:
+        outDict = None
+    if chart:
+        with open("./data/dict/"+task+"/chartVocab", 'rb') as fp:
+            chartDict = pickle.load(fp)
+    elif lab:
+        with open("./data/dict/"+task+"/labsVocab", 'rb') as fp:
+            chartDict = pickle.load(fp)
+    else:
+        chartDict = None
+    if med:
+        with open("./data/dict/"+task+"/medVocab", 'rb') as fp:
+            medDict = pickle.load(fp)
+    else:
+        medDict = None
+    
+    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):
     meds=data['Med']
     proc = data['Proc']
     out = data['Out']
@@ -97,10 +129,7 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
 
     ##########COND#########
     if (feat_cond):
-        #get all conds
-        with open("./data/dict/"+task+"/condVocab", 'rb') as fp:
-            conDict = pickle.load(fp)
-        conds=pd.DataFrame(conDict,columns=['COND'])
+        conds=pd.DataFrame(condDict,columns=['COND'])
         features=pd.DataFrame(np.zeros([1,len(conds)]),columns=conds['COND'])
 
         #onehot encode
@@ -122,13 +151,10 @@ 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(procDic,columns=['PROC'])
+            procedures=pd.DataFrame(procDict,columns=['PROC'])
             features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
             procs=pd.DataFrame(columns=feat)
             for p,v in zip(feat,proc_val):
@@ -137,20 +163,17 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             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'])
+            procedures=pd.DataFrame(procDict,columns=['PROC'])
             features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
             features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
             proc_df=features.fillna(0)
 
     ##########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(outDic,columns=['OUT'])
+            outputs=pd.DataFrame(outDict,columns=['OUT'])
             features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
             outs=pd.DataFrame(columns=feat)
             for o,v in zip(feat,out_val):
@@ -159,21 +182,18 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             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'])
+            outputs=pd.DataFrame(outDict,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(chartDic,columns=['CHART'])
+            charts=pd.DataFrame(chartDict,columns=['CHART'])
             features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
             chart=pd.DataFrame(columns=feat)
             for c,v in zip(feat,chart_val):
@@ -182,20 +202,18 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             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'])
+            charts=pd.DataFrame(chartDict,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(chartDic,columns=['LAB'])
+            charts=pd.DataFrame(chartDict,columns=['LAB'])
             features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
             chart=pd.DataFrame(columns=feat)
             for c,v in zip(feat,chart_val):
@@ -205,20 +223,17 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             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'])
+            charts=pd.DataFrame(chartDict,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(medDic,columns=['MEDS'])
+            meds=pd.DataFrame(medDict,columns=['MEDS'])
             features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
             med=pd.DataFrame(columns=feat)
             for m,v in zip(feat,med_val):
@@ -227,7 +242,7 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
             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'])
+            meds=pd.DataFrame(medDict,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)
@@ -237,7 +252,7 @@ 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):
+def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
     meds = []
     charts = []
     proc = []
@@ -247,7 +262,7 @@ 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)
+    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)
     if feat_chart:
         charts = dyn['CHART'].values