Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -887,37 +887,65 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 
 
 ###########################################################ENCODED##################################################################
-       
-    def _info_encoded(self):
-        X_train_encoded=generate_split(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_encoded=generate_split(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_encoded=generate_split(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_encoded.to_csv(self.path+"/X_train_encoded.csv", index=False)
-        X_test_encoded.to_csv(self.path+"/X_test_encoded.csv", index=False)
-        X_val_encoded.to_csv(self.path+"/X_val_encoded.csv", index=False)
-        columns = {col: self.map_dtype(X_train_encoded[col].dtype) for col in X_train_encoded.columns}
-        features = datasets.Features(columns)
+    def _info_encoded(self):
+        features = datasets.Features(
+            {
+                "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
+                "onehot" : datasets.Array2D(shape=(None,None), dtype="float32"),
+            }
+        )
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=features,
             homepage=_HOMEPAGE,
             citation=_CITATION,
         )
-    
-    def __split_generators_encoded(self):
-        data_dir = "./data/dict/"+self.config.name.replace(" ","_")
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir+'/X_train_encoded.csv'}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir+'/X_val_encoded.csv'}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir+'/X_test_encoded.csv'}),
-            ]
 
     def _generate_examples_encoded(self, filepath):
-        df = pd.read_csv(filepath, header=0)
-        for i, row in df.iterrows():
-            yield i, row.to_dict()
+        path= './data/dict/'+self.config.name.replace(" ","_")+'/ethVocab'
+        with open(path, 'rb') as fp:
+            ethVocab = pickle.load(fp)
+
+        path= './data/dict/'+self.config.name.replace(" ","_")+'/insVocab'
+        with open(path, 'rb') as fp:
+            insVocab = pickle.load(fp)
+
+        genVocab = ['<PAD>', 'M', 'F']
+        gen_encoder = LabelEncoder()
+        eth_encoder = LabelEncoder()
+        ins_encoder = LabelEncoder()
+        gen_encoder.fit(genVocab)
+        eth_encoder.fit(ethVocab)
+        ins_encoder.fit(insVocab)
+        with open(filepath, 'rb') as fp:
+            dico = pickle.load(fp)
+
+        df = pd.DataFrame.from_dict(dico, orient='index')
+        task=self.config.name.replace(" ","_")
+        if 'Custom' in task:
+            task = task.rsplit('_', 1)[0]
+        
+        for i, data in df.iterrows():
+            concat_cols=[]
+            dyn_df,cond_df,demo=concat_data(data,task,self.feat_cond,self.feat_chart,self.feat_proc, self.feat_meds, self.feat_out)
+            dyn=dyn_df.copy()
+            dyn.columns=dyn.columns.droplevel(0)
+            cols=dyn.columns
+            time=dyn.shape[0]
+            for t in range(time):
+                cols_t = [str(x) + "_"+str(t) for x in cols]
+                concat_cols.extend(cols_t)
+            demo['gender']=gen_encoder.transform(demo['gender'])
+            demo['ethnicity']=eth_encoder.transform(demo['ethnicity'])
+            demo['insurance']=ins_encoder.transform(demo['insurance'])
+            label = data['label']
+            demo=demo.drop(['label'],axis=1)
+            X= getXY(dyn_df,cond_df,demo,concat_cols,True)
+            yield int(i), {
+                "label": label,
+                "onehot": X,
+            }
 ######################################################DEEP###############################################################
     def _info_deep(self):
         features = datasets.Features(
@@ -974,7 +1002,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             return self._info_raw()
 
 
-    def _split_generators(self):
+    def _split_generators(self, dl_manager):
         csv_dir = "./data/dict/"+self.config.name.replace(" ","_")
 
         return [