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 = []
@@ -247,25 +262,20 @@ 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
-   
     if feat_meds:
         meds = dyn['MEDS'].values
-
     if feat_proc:
         proc = dyn['PROC'].values
-
+        print(proc)
     if feat_out:
         out = dyn['OUT'].values
-
     if feat_lab:
         lab = dyn['LAB'].values
-
     if feat_cond:
         stat=cond_df.values[0]
-  
     y = int(demo['label'])
     
     demo["gender"].replace(gender_vocab, inplace=True)
@@ -274,34 +284,40 @@ def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,fe
     demo["Age"].replace(age_vocab, inplace=True)
     demo=demo[["gender","ethnicity","insurance","Age"]]
     demo = demo.values[0]
-    
     return stat, demo, meds, charts, out, proc, lab, y
 
 
-
-def generate_ml(dyn, stat, demo, concat_cols, concat):
+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 = np.nan_to_num(dyna, copy=False).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 = pd.DataFrame()
-        for key in dyn.columns.levels[0]:
-            dyn_temp = dyn[key]
-            if key in ["CHART", "MEDS"]:
-                agg = dyn_temp.aggregate("mean").reset_index()
+        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 = dyn_temp.aggregate("max").reset_index()
-
-            dyn_df = pd.concat([dyn_df, agg], axis=0)
+                agg=dyn_temp.aggregate("max")
+                agg=agg.reset_index()
 
-        dyn_df = dyn_df.T.iloc[1:]
+            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]
-
-    X_df = pd.concat([dyn_df, stat, demo], axis=1)
-    return X_df
- 
+        dyn_df=dyn_df.iloc[1:,:]
+        
+    X_df=pd.concat([dyn_df,stat],axis=1)
+    X_df=pd.concat([X_df,demo],axis=1)
+    return X_df  
 
 
 def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, feat_out):
@@ -324,7 +340,7 @@ def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, feat
             chart_mean = [round(np.mean(c), 3) for c in chart_val]
             feat_text = [(items[items['itemid'] == f]['label']).to_string(index=False) for f in feat]
             chart_text = '; '.join(f"{mean_val} for {feat_label}" for mean_val, feat_label in zip(chart_mean, feat_text))
-            chart_text = f"The chart events measured were: {chart_text}. "
+            chart_text = f"The chart events measured were: {chart_text}."
         else:
             chart_text = ''
     else:
@@ -340,7 +356,7 @@ def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, feat
             meds_mean = [round(np.mean(c), 3) for c in meds_val]
             feat_text = [(items[items['itemid'] == f]['label']).to_string(index=False) for f in feat]
             meds_text = '; '.join(f"{mean_val} of {feat_label}" for mean_val, feat_label in zip(meds_mean, feat_text))
-            meds_text = f"The mean amounts of medications administered during the episode were: {meds_text}. "
+            meds_text = f"The mean amounts of medications administered during the episode were: {meds_text}."
         else:
             meds_text = ''
     else:
@@ -352,8 +368,8 @@ def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, feat
         if proc:
             feat=proc.keys()
             feat_text = [(items[items['itemid']==f]['label']).to_string(index=False) for f in feat]
-            template = 'The procedures performed were: {}. '
-            proc_text= template.format('; '.join(feat_text))
+            template = 'The procedures performed were: {}.'
+            proc_text= template.format(';'.join(feat_text))
         else:
             proc_text=''
     else:
@@ -373,4 +389,3 @@ def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, feat
         out_text=''
 
     return cond_text,chart_text,meds_text,proc_text,out_text
-