Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -13,6 +13,13 @@ import numpy as np
 from .dataset_utils import vocab, concat_data, generate_deep, generate_ml, generate_text
 from .task_cohort import create_cohort
 
+################################################################################
+################################################################################
+##                                                                            ##
+##                   MIMIC IV DATASET GENERATION SCRIPT                       ##
+##                                                                            ##
+################################################################################
+################################################################################
 
 
 _DESCRIPTION = """\
@@ -21,7 +28,7 @@ Available tasks are: Mortality, Length of Stay, Readmission, Phenotype.
 The data is extracted from the mimic4 database using this pipeline: 'https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main'
 mimic path should have this form : "path/to/mimic4data/from/username/mimiciv/2.2"
 If you choose a Custom task provide a configuration file for the Time series.
-Currently working with Mimic-IV version 1 and 2
+Currently working with Mimic-IV ICU Data.
 """
 _BASE_URL = "https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main"
 _HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset"
@@ -29,7 +36,6 @@ _HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset"
 _CITATION = "https://proceedings.mlr.press/v193/gupta22a.html"
 _GIT_URL = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline"
 
-#_ICD_CODE = f"{_BASE_URL}/icd10.txt"
 _DATA_GEN = f"{_BASE_URL}/data_generation_icu_modify.py"
 _DATA_GEN_HOSP= f"{_BASE_URL}/data_generation_modify.py"
 _DAY_INT= f"{_BASE_URL}/day_intervals_cohort_v22.py"
@@ -61,7 +67,6 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         self.test_size = kwargs.pop("test_size",0.2)
         self.val_size = kwargs.pop("val_size",0.1)
         self.generate_cohort = kwargs.pop("generate_cohort",True)
-        self.param = kwargs.pop("param",0)
 
         if self.encoding == 'concat':
             self.concat = True
@@ -152,7 +157,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         with open(self.conf) as f:
             config = yaml.safe_load(f)
 
-            
+        #get config parameters for time series and features    
         timeW = config['timeWindow']
         self.timeW=int(timeW.split()[1])
         self.bucket = config['timebucket']
@@ -215,7 +220,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             pickle.dump(test_dic, f)
         return dict_dir
     
-
+    #verify if the dimension of the tensors corresponds to the time window
     def verif_dim_tensor(self, proc, out, chart, meds, lab,interv):
         verif=True
         if self.feat_proc:
@@ -433,15 +438,6 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         feat_tocsv=True
         for i, data in df.iterrows():
             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)
-            if feat_tocsv:
-                #save the features of the vector for analysis purposes if needed
-                feats = list(dyn_df.columns.droplevel(0))
-                feats.extend(list(cond_df.columns))
-                feats.extend(list(demo.columns))
-                df_feats = pd.DataFrame(columns=feats)
-                path = './data/dict/'+self.config.name.replace(" ","_")+'/features_'+self.encoding+'.csv'
-                df_feats.to_csv(path)
-                feat_tocsv=False
             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]
@@ -450,6 +446,18 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             demo['insurance']=ins_encoder.transform(demo['insurance'])
             label = data['label']
             demo=demo.drop(['label'],axis=1)
+            if feat_tocsv:
+                #save the features of the vector for analysis purposes if needed
+                if self.encoding == 'concat':
+                    feats = concat_cols
+                else:
+                    feats = list(dyn_df.columns.droplevel(0))
+                feats.extend(list(cond_df.columns))
+                feats.extend(list(demo.columns))
+                df_feats = pd.DataFrame(columns=feats)
+                path = './data/dict/'+self.config.name.replace(" ","_")+'/features_'+self.encoding+'.csv'
+                df_feats.to_csv(path)
+                feat_tocsv=False
             X= generate_ml(dyn_df,cond_df,demo,concat_cols,self.concat)
             X=X.values[0]
             
@@ -535,16 +543,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             
         for key, data in dico.items():
             cond_text,chart_text,meds_text,proc_text,out_text = generate_text(data,icd,items, self.feat_cond, self.feat_chart, self.feat_meds, self.feat_proc, self.feat_out)
-            if self.param==1:
-                text= cond_text+chart_text+meds_text+proc_text+out_text
-            elif self.param==2:
-                text= cond_text
-            elif self.param==3:
-                text=cond_text+ chart_text
-            elif self.param==4:
-                text=cond_text+ chart_text+meds_text
-            elif self.param==5:
-                text=cond_text+ chart_text+meds_text+proc_text
+            text= cond_text+chart_text+meds_text+proc_text+out_text
             yield int(key),{
                 'label' : data['label'],
                 'text': text