Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -165,11 +165,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             self.feat_cond, self.feat_lab, self.feat_proc, self.feat_meds, self.feat_chart, self.feat_out = config['diagnosis'], config['lab'], config['proc'],  config['meds'], False, False
 
         
-        #####################downloads modules from hub
-        #if not os.path.exists('./icd10.txt'):
-        #    file_path, head = urlretrieve(_ICD_CODE, "icd10.txt")
-        #    shutil.move(file_path, './')    
-                
+        #####################downloads modules from hub   
         if not os.path.exists('./model/data_generation_icu_modify.py'):
             file_path, head = urlretrieve(_DATA_GEN, "data_generation_icu_modify.py")
             shutil.move(file_path, './model')
@@ -219,8 +215,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         return dict_dir
     
 
-    def verif_dim_tensor(self, proc, out, chart, meds, lab):
-        interv = (self.timeW//self.bucket)
+    def verif_dim_tensor(self, proc, out, chart, meds, lab,interv):
         verif=True
         if self.feat_proc:
             if (len(proc)!= interv):
@@ -435,7 +430,7 @@ 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,self.condDict, self.procDict, self.outDict, self.chartDict, self.medDict)
+            dyn_df,cond_df,demo=concat_data(data,self.interval,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)
             concat_cols = [f"{col}_{t}" for t in range(dyn.shape[0]) for col in dyn.columns]
@@ -447,8 +442,8 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             X= generate_ml(dyn_df,cond_df,demo,concat_cols,self.concat)
             X=X.values[0]
             
-            interv = (self.timeW//self.bucket)
-            size_concat = self.size_cond+ self.size_proc * interv + self.size_meds * interv+ self.size_out * interv+ self.size_chart *interv+ self.size_lab * interv + 4
+            
+            size_concat = self.size_cond+ self.size_proc * self.interval + self.size_meds * self.interval+ self.size_out * self.interval+ self.size_chart *self.interval+ self.size_lab * self.interval + 4
             size_aggreg = self.size_cond+ self.size_proc + self.size_meds+ self.size_out+ self.size_chart+ self.size_lab + 4
 
             if ((self.concat and len(X)==size_concat) or ((not self.concat) and len(X)==size_aggreg)):
@@ -484,8 +479,8 @@ 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,self.condDict, self.procDict, self.outDict, self.chartDict, self.medDict)
-            if self.verif_dim_tensor(proc, out, chart, meds, lab):
+            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)
+            if self.verif_dim_tensor(proc, out, chart, meds, lab, self.interval):
                 if self.data_icu:
                     yield int(key), {
                         'label': y,
@@ -538,6 +533,7 @@ 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)
         if (self.encoding == 'concat' or self.encoding =='aggreg'):