Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -201,7 +201,7 @@ def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag):
             with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
                 labVocabDict = pickle.load(fp)
         
-        return ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict 
+        return len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict
 
 def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
     meds=data['Med']
@@ -346,7 +346,7 @@ def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
     stat_df = torch.zeros(size=(1,0))
     demo_df = torch.zeros(size=(1,0))
     
-    eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
+    size_cond, size_proc, size_meds, size_out, size_chart, size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
     dyn,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds)
    
     keys=dyn.columns.levels[0]
@@ -946,11 +946,11 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
                 "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
                 "DEMO": datasets.Array2D(shape=(1, 4), dtype="int64"),
                 "COND" : datasets.Array2D(shape=(1, 1025), dtype='int64') ,
-                "MEDS" : datasets.Array2D(shape=(None, None), dtype='int64') ,
-                "PROC" : datasets.Array2D(shape=(None, None), dtype='int64') ,
-                "CHART" : datasets.Array2D(shape=(None, None), dtype='int64') ,
-                "OUT" : datasets.Array2D(shape=(None, None), dtype='int64') ,
-                "LAB" : datasets.Array2D(shape=(None, None), dtype='int64') ,
+                "MEDS" : datasets.Array2D(shape=(None, self.size_meds), dtype='int64') ,
+                "PROC" : datasets.Array2D(shape=(None, self.size_proc), dtype='int64') ,
+                "CHART" : datasets.Array2D(shape=(None, self.size_chart), dtype='int64') ,
+                "OUT" : datasets.Array2D(shape=(None, self.size_out), dtype='int64') ,
+                "LAB" : datasets.Array2D(shape=(None, self.size_lab), dtype='int64') ,
                 
             }
         )
@@ -984,7 +984,8 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 #############################################################################################################################
     def _info(self):
         self.path = self.create_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,False)
+    
         if self.encoding == 'onehot' :
             return self._info_encoded()