Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -767,8 +767,8 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 ###########################################################ENCODED##################################################################
        
     def _info_encoded(self,X_encoded):
-        columns = {col: self.map_dtype(X_encoded[col].dtype) for col in X_encoded.columns}
-        features = datasets.Features(columns)
+        keys=list(set(key for item in X_encoded.values() for key in item.keys()))
+        features = datasets.Features({key: self.map_dtype(X_encoded[key].dtype) for key in keys})
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=features,
@@ -790,7 +790,8 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         for i, row in df.iterrows():
             yield i, row.to_dict()
 #############################################################################################################################
-    def _info_deep(self,X_deep):
+    def _info_deep(self,X_deep_dict):
+        keys= list(X_deep_dict.keys())
         columns = {col: self.map_dtype(X_deep[col].dtype) for col in X_deep.columns}
         features = datasets.Features(columns)
         return datasets.DatasetInfo(
@@ -804,15 +805,34 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         data_dir = "./data/dict/"+self.config.name.replace(" ","_")
 
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir+'/X_train_deep.csv'}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir+'/X_val_deep.csv'}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir+'/X_test_deep.csv'}),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir+'/X_train_deep.pkl'}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir+'/X_val_deep.pkl'}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir+'/X_test_deep.pkl'}),
             ]
     
     def _generate_examples_deep(self, filepath):
-        df = pd.read_csv(filepath, header=0)
-        for i, row in df.iterrows():
-            yield i, row.to_dict()
+        with open(filepath, 'rb') as fp:
+            dico = pickle.load(fp)
+            for key, data in dico.items():
+                proc_features = data['proc']
+                chart_features = data['chart']
+                meds_features = data['meds']
+                out_features = data['out']
+                cond_features = data['stat']
+                demo= data['demo']
+                label = data['y']
+                lab=data['lab']
+
+                yield int(key), {
+                    'proc': proc_features,
+                    'chart': chart_features,
+                    'meds': meds_features,
+                    'out': out_features,
+                    'stat': cond_features,
+                    'demo': demo,
+                    'lab': lab,
+                    'y': label
+                    }
     
 #############################################################################################################################
     def _info(self):
@@ -827,14 +847,19 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             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)
             return self._info_encoded(X_train_encoded)
+        
         elif self.encoding == 'deep' :
             X_train_deep = generate_split_deep(self.path+'/train_data.pkl',self.config.name,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,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,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)
-            X_val_deep.to_csv(self.path+"/X_val_deep.csv", index=False)
+            with open(self.path+"/X_train_deep.pkl", 'wb') as f:
+                pickle.dump(X_train_deep, f)
+            with open(self.path+"/X_test_deep.pkl", 'wb') as f:
+                pickle.dump(X_test_deep, f)
+            with open(self.path+"/X_val_deep.pkl", 'wb') as f:
+                pickle.dump(X_val_deep, f)
+
             return self._info_deep(X_train_deep)
         else:
             return self._info_raw()