Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -9,8 +9,8 @@ from urllib.request import urlretrieve
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 import yaml
-from .dataset_utils import create_vocab,gender_vocab,vocab, concat_data, generate_deep, generate_ml
-from .task_cohort import create_cohort, check_config_file
+from .dataset_utils import vocab, concat_data, generate_deep, generate_ml
+from .task_cohort import create_cohort
 
 
 
@@ -103,7 +103,11 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         version = self.mimic_path.split('/')[-1]
         mimic_folder= self.mimic_path.split('/')[-2]
         mimic_complete_path='/'+mimic_folder+'/'+version
-        
+        print('mimic_complete_path : ',mimic_complete_path)
+        print('mimic_folder : ',mimic_folder)
+        print('self.mimic_path : ',self.mimic_path)
+        print('version : ',version)
+
         current_directory = os.getcwd()
         if os.path.exists(os.path.dirname(current_directory)+'/MIMIC-IV-Data-Pipeline-main'):
             dir =os.path.dirname(current_directory) 
@@ -111,6 +115,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         else:
             #move to parent directory of mimic data
             dir = self.mimic_path.replace(mimic_complete_path,'')
+            print('dir : ',dir)
             if dir[-1]!='/':
                 dir=dir+'/'
             elif dir=='':
@@ -138,9 +143,9 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             file_path, head = urlretrieve(self.config_path,c)
         else :
             file_path = self.config_path
-
         if not os.path.exists('./config'):
             os.makedirs('config')
+        
         #save config file in config folder
         self.conf='./config/'+file_path.split('/')[-1]
         if not os.path.exists(self.conf):
@@ -211,7 +216,26 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             pickle.dump(test_dic, f)
 
         return dict_dir
-  
+    
+    def verif_dim_tensor(self, proc, out, chart, meds, lab):
+        verif=True
+        if self.feat_proc:
+            if (len(proc)<(self.timeW//self.bucket)):
+                verif=False
+        if self.feat_out:
+            if (len(out)<(self.timeW//self.bucket)):
+                verif=False
+        if self.feat_chart:
+            if (len(chart)<(self.timeW//self.bucket)):
+                verif=False
+        if self.feat_meds:
+            if (len(meds)<(self.timeW//self.bucket)):
+                verif=False
+        if self.feat_lab:
+            if (len(lab)<(self.timeW//self.bucket)):
+                verif=False
+        return verif
+
 ###########################################################RAW##################################################################
 
     def _info_raw(self):
@@ -407,11 +431,9 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             dico = pickle.load(fp)
 
         df = pd.DataFrame.from_dict(dico, orient='index')
-        task=self.config.name.replace(" ","_")
-        
         for i, data in df.iterrows():
             concat_cols=[]
-            dyn_df,cond_df,demo=concat_data(data,task,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)
             dyn=dyn_df.copy()
             dyn.columns=dyn.columns.droplevel(0)
             cols=dyn.columns
@@ -426,14 +448,16 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             demo=demo.drop(['label'],axis=1)
             X= generate_ml(dyn_df,cond_df,demo,concat_cols,self.concat)
             X=X.values.tolist()[0]
+
             size_concat = self.size_cond+ self.size_proc * ((self.timeW//self.bucket)+1) + self.size_meds* ((self.timeW//self.bucket)+1)+ self.size_out* ((self.timeW//self.bucket)+1)+ self.size_chart* ((self.timeW//self.bucket)+1)+ self.size_lab* ((self.timeW//self.bucket)+1) + 4
             size_aggreg = self.size_cond+ self.size_proc + self.size_meds+ self.size_out+ self.size_chart+ self.size_lab + 4
-            print(len(X),size_concat,(self.timeW//self.bucket))
+            
             if (len(X)==size_concat or len(X)==size_aggreg):
                 yield int(i), {
                     "label": label,
                     "features": X,
                 }
+
 ######################################################DEEP###############################################################
     def _info_deep(self):
         features = datasets.Features(
@@ -459,27 +483,11 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
     def _generate_examples_deep(self, filepath):
         with open(filepath, 'rb') as fp:
             dico = pickle.load(fp)
-        task=self.config.name.replace(" ","_")
+
         for key, data in dico.items():   
-            stat, demo, meds, chart, out, proc, lab, y = generate_deep(data, task, self.feat_cond,  self.feat_proc,  self.feat_out,  self.feat_chart, self.feat_meds,self.feat_lab)
-
-            verri=True
-            if self.feat_proc:
-                if (len(proc)<(self.timeW//self.bucket)):
-                    verri=False
-            if self.feat_out:
-                if (len(out)<(self.timeW//self.bucket)):
-                    verri=False
-            if self.feat_chart:
-                if (len(chart)<(self.timeW//self.bucket)):
-                    verri=False
-            if self.feat_meds:
-                if (len(meds)<(self.timeW//self.bucket)):
-                    verri=False
-            if self.feat_lab:
-                if (len(lab)<(self.timeW//self.bucket)):
-                    verri=False
-            if verri:
+            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)
+            
+            if self.verif_dim_tensor(proc, out, chart, meds, lab):
                 if self.data_icu:
                     yield int(key), {
                         'label': y,
@@ -505,7 +513,6 @@ 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)
-    
         if (self.encoding == 'concat' or self.encoding =='aggreg'):
             return self._info_encoded()
         
@@ -517,21 +524,20 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
 
 
     def _split_generators(self, dl_manager):
-        csv_dir = "./data/dict/"+self.config.name.replace(" ","_")
+        data_dir = "./data/dict/"+self.config.name.replace(" ","_")
         if self.val_size > 0 :
             return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": csv_dir+'/train_data.pkl'}),
-                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": csv_dir+'/val_data.pkl'}),
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": csv_dir+'/test_data.pkl'}),
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir+'/train_data.pkl'}),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir+'/val_data.pkl'}),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir+'/test_data.pkl'}),
             ]
         else : 
             return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": csv_dir+'/train_data.pkl'}),
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": csv_dir+'/test_data.pkl'}),
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir+'/train_data.pkl'}),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir+'/test_data.pkl'}),
             ]
         
     def _generate_examples(self, filepath):
-        
         if (self.encoding == 'concat' or self.encoding == 'aggreg'):
             yield from self._generate_examples_encoded(filepath)