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 len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict 
 
 
-def onehot(data,task,feat_cond=False,feat_proc=False,feat_out=False,feat_chart=False,feat_meds=False):
+def concat_data(data,task,feat_cond=False,feat_proc=False,feat_out=False,feat_chart=False,feat_meds=False):
     meds=data['Med']
     proc = data['Proc']
     out = data['Out']
@@ -257,7 +257,7 @@ def getXY_deep(X_df,task,feat_cond=True,feat_chart=True,feat_proc=True, feat_med
     y_df=[]
 
     for index,sample in tqdm(X_df.iterrows(),desc='Encoding Splits Data for '+task+' task'):
-        dyn,stat,demo=onehot(sample,task,feat_cond,feat_chart,feat_proc, feat_meds, feat_out)
+        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:
@@ -368,7 +368,7 @@ def encoding(X_data):
     X_data['insurance']=ins_encoder.transform(X_data['insurance'])
     return X_data
 
-def generate_split(path,task,concat,feat_cond=True,feat_chart=True,feat_proc=True, feat_meds=True, feat_out=False):
+def generate_split(path,task,concat,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')
@@ -377,7 +377,7 @@ def generate_split(path,task,concat,feat_cond=True,feat_chart=True,feat_proc=Tru
     for _, data in tqdm(df.iterrows(),desc='Encoding Splits Data for '+task+' task'):
         concat_cols=[]
         sample=data
-        dyn_df,cond_df,demo=onehot(sample,taskf,feat_cond,feat_chart,feat_proc, feat_meds, feat_out)
+        dyn_df,cond_df,demo=concat_data(sample,taskf,feat_cond,feat_chart,feat_proc, feat_meds, feat_out)
         dyn=dyn_df.copy()
         dyn.columns=dyn.columns.droplevel(0)
         cols=dyn.columns
@@ -395,6 +395,24 @@ def generate_split(path,task,concat,feat_cond=True,feat_chart=True,feat_proc=Tru
     X_df = encoding(X_df)
     return X_df
 
+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()
+    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
 
 class Mimic4DatasetConfig(datasets.BuilderConfig):
     """BuilderConfig for Mimic4Dataset."""
@@ -810,9 +828,9 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             X_val_encoded.to_csv(self.path+"/X_val_encoded.csv", index=False)
             return self._info_encoded(X_train_encoded)
         elif self.encoding == 'deep' :
-            X_train_deep = getXY_deep(self.path+'/train_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
-            X_test_deep = getXY_deep(self.path+'/test_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
-            X_val_deep = getXY_deep(self.path+'/val_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
+            X_train_deep = generate_split_deep(self.path+'/train_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
+            X_test_deep = generate_split_deep(self.path+'/test_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
+            X_val_deep = generate_split_deep(self.path+'/val_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
 
             X_train_deep.to_csv(self.path+"/X_train_deep.csv", index=False)
             X_test_deep.to_csv(self.path+"/X_test_deep.csv", index=False)