Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -10,7 +10,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 import yaml
 import numpy as np
-from .dataset_utils import vocab, concat_data, generate_deep, generate_ml, generate_text, open_dict
+from .dataset_utils import vocab, concat_data, generate_deep, generate_ml, generate_text
 from .task_cohort import create_cohort
 
 
@@ -479,7 +479,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             dico = pickle.load(fp)
 
         for key, data in dico.items():   
-            stat, demo, meds, chart, out, proc, lab, y = generate_deep(data,self.interval, self.config.name.replace(" ","_"), self.feat_cond,  self.feat_proc,  self.feat_out,  self.feat_chart, self.feat_meds,self.feat_lab,self.condDict, self.procDict, self.outDict, self.chartDict, self.medDict)
+            stat, demo, meds, chart, out, proc, lab, y = generate_deep(data,self.interval, self.config.name.replace(" ","_"), self.feat_cond,  self.feat_proc,  self.feat_out,  self.feat_chart, self.feat_meds,self.feat_lab,self.condDict, self.procDict, self.outDict, self.chartDict, self.medDict, self.eth_vocab,self.gender_vocab,self.age_vocab,self.ins_vocab)
             if self.verif_dim_tensor(proc, out, chart, meds, lab, self.interval):
                 if self.data_icu:
                     yield int(key), {
@@ -534,8 +534,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
     def _info(self):
         self.path = self.init_cohort()
         self.interval = (self.timeW//self.bucket)
-        self.size_cond, self.size_proc, self.size_meds, self.size_out, self.size_chart, self.size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(self.config.name.replace(" ","_"),self.feat_cond,self.feat_proc,self.feat_out,self.feat_chart,self.feat_meds,self.feat_lab)
-        self.condDict, self.procDict, self.outDict, self.chartDict, self.medDict = open_dict(self.config.name.replace(" ","_"),self.feat_cond,self.feat_proc,self.feat_out,self.feat_chart,self.feat_lab,self.feat_meds)
+        self.size_cond, self.size_proc, self.size_meds, self.size_out, self.size_chart, self.size_lab, self.eth_vocab,self.gender_vocab,self.age_vocab,self.ins_vocab,self.condDict,self.procDict,self.medDict,self.outDict,self.chartDict,self.labDict=vocab(self.config.name.replace(" ","_"),self.feat_cond,self.feat_proc,self.feat_out,self.feat_chart,self.feat_meds,self.feat_lab)
         if (self.encoding == 'concat' or self.encoding =='aggreg'):
             return self._info_encoded()