Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -110,7 +110,7 @@ def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag):
         return ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict 
 
 
-def concat_data(data,task,feat_cond=False,feat_proc=False,feat_out=False,feat_chart=False,feat_meds=False):
+def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
     meds=data['Med']
     proc = data['Proc']
     out = data['Out']
@@ -244,86 +244,44 @@ def concat_data(data,task,feat_cond=False,feat_proc=False,feat_out=False,feat_ch
     dyn_df = pd.concat([meds_df,proc_df,out_df,chart_df], axis=1)
     return dyn_df,cond_df,demo
 
-def getXY_deep(X_df,task,feat_cond,feat_chart,feat_proc, feat_meds, feat_out):
+def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
+    meds, chart, out, proc, lab =[],[],[],[],[]
     eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
-    dyn_df=[]
-    meds=torch.zeros(size=(0,0))
-    chart=torch.zeros(size=(0,0))
-    proc=torch.zeros(size=(0,0))
-    out=torch.zeros(size=(0,0))
-    lab=torch.zeros(size=(0,0))
-    stat_df=torch.zeros(size=(1,0))
-    demo_df=torch.zeros(size=(1,0))
-    y_df=[]
-
-    for index,sample in tqdm(X_df.iterrows(),desc='Encoding Splits Data for '+task+' task'):
-        dyn,stat,demo=concat_data(sample,task,feat_cond,feat_chart,feat_proc, feat_meds, feat_out)
-        dyn_k=dyn.copy()
-        keys=dyn_k.columns.levels[0]
-        if index==0:
-            for i in range(len(keys)):
-                dyn_df.append(torch.zeros(size=(1,0)))
-        y=demo['label']
-        
-        y_df.append(int(y))
-
-        for key in range(len(keys)):
-            dyn_temp=dyn[keys[key]]
-            dyn_temp=dyn_temp.to_numpy()
-            dyn_temp=torch.tensor(dyn_temp)
-            dyn_temp=dyn_temp.unsqueeze(0)
-            dyn_temp=torch.tensor(dyn_temp)
-            dyn_temp=dyn_temp.type(torch.LongTensor)
-            
-            if key<len(dyn_df):
-                if dyn_df[key].nelement():
-                    dyn_df[key]=torch.cat((dyn_df[key],dyn_temp),0)
-                else:
-                    dyn_df[key]=dyn_temp
-        
-        stat=stat.to_numpy()
-        stat=torch.tensor(stat)
-        if stat_df[0].nelement():
-            stat_df=torch.cat((stat_df,stat),0)
-        else:
-            stat_df=stat
-        demo=demo.drop(['label'],axis=1)
-        demo["gender"].replace(gender_vocab, inplace=True)
-        demo["ethnicity"].replace(eth_vocab, inplace=True)
-        demo["insurance"].replace(ins_vocab, inplace=True)
-        demo["Age"].replace(age_vocab, inplace=True)
-        demo=demo[["gender","ethnicity","insurance","Age"]]
-        demo=demo.values
-        demo=torch.tensor(demo)
-        if demo_df[0].nelement():
-            demo_df=torch.cat((demo_df,demo),0)
-        else:
-            demo_df=demo
+    dyn_df,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds)
+    keys=dyn_df.columns.levels[0]
+    dyn = dict.fromkeys(keys)
     
+    for key in range(len(keys)):
+        dyn_temp=dyn_df[keys[key]]
+        dyn_temp=dyn_temp.to_numpy()
+        dyn[key]=dyn_temp
     
     for k in range(len(keys)):
-        if key<len(dyn_df):
-            if keys[k]=='MEDS':
-                meds=dyn_df[k]
-            if keys[k]=='CHART':
-                chart=dyn_df[k]
-            if keys[k]=='OUT':
-                out=dyn_df[k]
-            if keys[k]=='PROC':
-                proc=dyn_df[k]
-            if keys[k]=='LAB':
-                lab=dyn_df[k]
-        
-    stat_df=torch.tensor(stat_df)
-    stat_df=stat_df.type(torch.LongTensor)
+        if keys[k]=='MEDS':
+            meds=dyn[k]
+        if keys[k]=='CHART':
+            chart=dyn[k]
+        if keys[k]=='OUT':
+            out=dyn[k]
+        if keys[k]=='PROC':
+            proc=dyn[k]
+        if keys[k]=='LAB':
+            lab=dyn[k]
+    
+    stat=cond_df
+    stat=stat.to_numpy()
 
-    demo_df=torch.tensor(demo_df)
-    demo_df=demo_df.type(torch.LongTensor)
+    y = demo['label']
+    
+    demo["gender"].replace(gender_vocab, inplace=True)
+    demo["ethnicity"].replace(eth_vocab, inplace=True)
+    demo["insurance"].replace(ins_vocab, inplace=True)
+    demo["Age"].replace(age_vocab, inplace=True)
+    demo=demo[["gender","ethnicity","insurance","Age"]]
+    demo=demo.values
     
-    y_df=torch.tensor(y_df)
-    y_df=y_df.type(torch.LongTensor)
-         
-    return (meds,chart,out,proc,lab ,stat_df, demo_df, y_df ) 
+    return stat, demo, meds, chart, out, proc, lab, y
+
 
 def getXY(dyn,stat,demo,concat_cols,concat):
     X_df=pd.DataFrame()
@@ -398,21 +356,14 @@ def generate_split(path,task,concat,feat_cond,feat_chart,feat_proc, feat_meds, f
 def generate_split_deep(path,task,feat_cond,feat_chart,feat_proc, feat_meds, feat_out):
     with open(path, 'rb') as fp:
         dico = pickle.load(fp)
-    df = pd.DataFrame.from_dict(dico, orient='index')
-    X_df=pd.DataFrame()
+    X = pd.DataFrame.from_dict(dico, orient='index')
+    X_dict = {}
     taskf=task.replace(" ","_")
-    meds,chart,out,proc,lab ,stat_df, demo_df, y_df  = getXY_deep(df,taskf,feat_cond,feat_chart,feat_proc, feat_meds, feat_out)
-    X_df=pd.concat([X_df,meds],axis=1)
-    X_df=pd.concat([X_df,chart],axis=1)
-    X_df=pd.concat([X_df,out],axis=1)
-    X_df=pd.concat([X_df,proc],axis=1)
-    X_df=pd.concat([X_df,lab],axis=1)
-    X_df=pd.concat([X_df,stat_df],axis=1)
-    X_df=pd.concat([X_df,demo_df],axis=1)
-    X_df=pd.concat([X_df,y_df],axis=1)
-    X_df=X_df.fillna(0)
-    X_df = encoding(X_df)
-    return X_df
+    for hid, data in tqdm(X.iterrows(),desc='Encoding Splits Data for '+task+' task'):
+        stat, demo, meds, chart, out, proc, lab, y = getXY_deep(data, taskf, feat_cond, feat_proc, feat_out, feat_chart,feat_meds)
+        X_dict[hid] = {'stat': stat, 'demo': demo, 'meds': meds, 'chart': chart, 'out': out, 'proc': proc, 'lab': lab, 'y': y}
+        
+    return X_dict
 
 class Mimic4DatasetConfig(datasets.BuilderConfig):
     """BuilderConfig for Mimic4Dataset."""
@@ -860,6 +811,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
                 pickle.dump(X_val_deep, f)
 
             return self._info_deep(X_train_deep)
+        
         else:
             return self._info_raw()