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
+from .dataset_utils import vocab, concat_data, generate_deep, generate_ml, generate_text, open_dict
 from .task_cohort import create_cohort
 
 
@@ -238,7 +238,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             if (len(lab)!=interv):
                 verif=False
         return verif
-
+    
 ###########################################################RAW##################################################################
 
     def _info_raw(self):
@@ -435,11 +435,9 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 
         df = pd.DataFrame.from_dict(dico, orient='index')
         for i, data in df.iterrows():
-            dyn_df,cond_df,demo=concat_data(data,self.config.name.replace(" ","_"),self.feat_cond,self.feat_proc,self.feat_out, self.feat_chart, self.feat_meds,self.feat_lab)
+            dyn_df,cond_df,demo=concat_data(data,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)
             dyn=dyn_df.copy()
             dyn.columns=dyn.columns.droplevel(0)
-            #cols=dyn.columns
-            #time=dyn.shape[0]
             concat_cols = [f"{col}_{t}" for t in range(dyn.shape[0]) for col in dyn.columns]
             demo['gender']=gen_encoder.transform(demo['gender'])
             demo['ethnicity']=eth_encoder.transform(demo['ethnicity'])
@@ -486,8 +484,7 @@ 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.config.name.replace(" ","_"), self.feat_cond,  self.feat_proc,  self.feat_out,  self.feat_chart, self.feat_meds,self.feat_lab)
-            
+            stat, demo, meds, chart, out, proc, lab, y = generate_deep(data, 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)
             if self.verif_dim_tensor(proc, out, chart, meds, lab):
                 if self.data_icu:
                     yield int(key), {
@@ -542,6 +539,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
     def _info(self):
         self.path = self.init_cohort()
         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)
         if (self.encoding == 'concat' or self.encoding =='aggreg'):
             return self._info_encoded()