Update dataset_utils.py

dataset_utils.py

@@ -1,391 +1,580 @@
+import os
 import pandas as pd
+import datasets
+import sys
 import pickle
+import subprocess
+import shutil
+from urllib.request import urlretrieve
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+import yaml
 import numpy as np
-import torch
-
-
-def create_vocab(file,task):
-    with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
-        condVocab = pickle.load(fp)
-    condVocabDict={}
-    condVocabDict[0]=0
-    for val in range(len(condVocab)):
-        condVocabDict[condVocab[val]]= val+1    
-
-    return condVocabDict
-
-def gender_vocab():
-    genderVocabDict={}
-    genderVocabDict['<PAD>']=0
-    genderVocabDict['M']=1
-    genderVocabDict['F']=2
-
-    return genderVocabDict
-
-def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag):
-        condVocabDict={}
-        procVocabDict={}
-        medVocabDict={}
-        outVocabDict={}
-        chartVocabDict={}
-        labVocabDict={}
-        ethVocabDict={}
-        ageVocabDict={}
-        genderVocabDict={}
-        insVocabDict={}
-        
-        ethVocabDict=create_vocab('ethVocab',task)
-        with open('./data/dict/'+task+'/ethVocabDict', 'wb') as fp:
-            pickle.dump(ethVocabDict, fp)
-            
-        ageVocabDict=create_vocab('ageVocab',task)
-        with open('./data/dict/'+task+'/ageVocabDict', 'wb') as fp:
-            pickle.dump(ageVocabDict, fp)
-        
-        genderVocabDict=gender_vocab()
-        with open('./data/dict/'+task+'/genderVocabDict', 'wb') as fp:
-            pickle.dump(genderVocabDict, fp)
+from .dataset_utils import vocab, concat_data, generate_deep, generate_ml, generate_text, open_dict
+from .task_cohort import create_cohort
+
+
+
+_DESCRIPTION = """\
+Dataset for mimic4 data, by default for the Mortality task.
+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
+"""
+_BASE_URL = "https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main"
+_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"
+_CONFIG_URLS = {'los' : f"{_BASE_URL}/config/los.config",
+                'mortality' : f"{_BASE_URL}/config/mortality.config",
+                'phenotype' : f"{_BASE_URL}/config/phenotype.config",
+                'readmission' : f"{_BASE_URL}/config/readmission.config"
+        }
+
+
+class Mimic4DatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for Mimic4Dataset."""
+
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+
+class Mimic4Dataset(datasets.GeneratorBasedBuilder):
+    """Create Mimic4Dataset dataset from Mimic-IV data stored in user machine."""
+    VERSION = datasets.Version("1.0.0")
+
+    def __init__(self, **kwargs):
+        self.mimic_path = kwargs.pop("mimic_path", None)
+        self.encoding = kwargs.pop("encoding",'concat')
+        self.config_path = kwargs.pop("config_path",None)
+        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)
+
+        if self.encoding == 'concat':
+            self.concat = True
+        else:
+            self.concat = False
             
-        insVocabDict=create_vocab('insVocab',task)
-        with open('./data/dict/'+task+'/insVocabDict', 'wb') as fp:
-            pickle.dump(insVocabDict, fp)
+        super().__init__(**kwargs)
         
-        if diag_flag:
-            file='condVocab'
-            with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
-                condVocabDict = pickle.load(fp)
-        if proc_flag:
-            file='procVocab'
-            with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
-                procVocabDict = pickle.load(fp)
-        if med_flag:
-            file='medVocab'
-            with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
-                medVocabDict = pickle.load(fp)
-        if out_flag:
-            file='outVocab'
-            with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
-                outVocabDict = pickle.load(fp)
-        if chart_flag:
-            file='chartVocab'
-            with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
-                chartVocabDict = pickle.load(fp)
-        if lab_flag:
-            file='labsVocab'
-            with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
-                labVocabDict = pickle.load(fp)
         
-        return len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict
-
-def open_dict(task,cond, proc, out, chart, lab, med):
-    if cond:
-        with open("./data/dict/"+task+"/condVocab", 'rb') as fp:
-            condDict = pickle.load(fp)
-    else:
-        condDict = None
-    if proc:
-        with open("./data/dict/"+task+"/procVocab", 'rb') as fp:
-            procDict = pickle.load(fp)
-    else:
-        procDict = None
-    if out:
-        with open("./data/dict/"+task+"/outVocab", 'rb') as fp:
-            outDict = pickle.load(fp)
-    else:
-        outDict = None
-    if chart:
-        with open("./data/dict/"+task+"/chartVocab", 'rb') as fp:
-            chartDict = pickle.load(fp)
-    elif lab:
-        with open("./data/dict/"+task+"/labsVocab", 'rb') as fp:
-            chartDict = pickle.load(fp)
-    else:
-        chartDict = None
-    if med:
-        with open("./data/dict/"+task+"/medVocab", 'rb') as fp:
-            medDict = pickle.load(fp)
-    else:
-        medDict = None
+    BUILDER_CONFIGS = [
+        Mimic4DatasetConfig(
+            name="Phenotype",
+            version=VERSION,
+            description="Dataset for mimic4 Phenotype task"
+        ),
+        Mimic4DatasetConfig(
+            name="Readmission",
+            version=VERSION,
+            description="Dataset for mimic4 Readmission task"
+        ),
+        Mimic4DatasetConfig(
+            name="Length of Stay",
+            version=VERSION,
+            description="Dataset for mimic4 Length of Stay task"
+        ),
+        Mimic4DatasetConfig(
+            name="Mortality",
+            version=VERSION,
+            description="Dataset for mimic4 Mortality task"
+        ),
+    ]
     
-    return condDict, procDict, outDict, chartDict, medDict
-
-def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
-    meds=data['Med']
-    proc = data['Proc']
-    out = data['Out']
-    chart = data['Chart']
-    cond= data['Cond']['fids']
-
-    cond_df=pd.DataFrame()
-    proc_df=pd.DataFrame()
-    out_df=pd.DataFrame()
-    chart_df=pd.DataFrame()
-    meds_df=pd.DataFrame()
-
-    #demographic
-    demo=pd.DataFrame(columns=['Age','gender','ethnicity','label','insurance'])
-    new_row = {'Age': data['age'], 'gender': data['gender'], 'ethnicity': data['ethnicity'], 'label': data['label'], 'insurance': data['insurance']}
-    demo = demo.append(new_row, ignore_index=True)
-
-    ##########COND#########
-    if (feat_cond):
-        conds=pd.DataFrame(condDict,columns=['COND'])
-        features=pd.DataFrame(np.zeros([1,len(conds)]),columns=conds['COND'])
-
-        #onehot encode
-        if(cond ==[]):
-            cond_df=pd.DataFrame(np.zeros([1,len(features)]),columns=features['COND'])
-            cond_df=cond_df.fillna(0)
-        else:
-            cond_df=pd.DataFrame(cond,columns=['COND'])
-            cond_df['val']=1
-            cond_df=(cond_df.drop_duplicates()).pivot(columns='COND',values='val').reset_index(drop=True)
-            cond_df=cond_df.fillna(0)
-            oneh = cond_df.sum().to_frame().T
-            combined_df = pd.concat([features,oneh],ignore_index=True).fillna(0)
-            combined_oneh=combined_df.sum().to_frame().T
-            cond_df=combined_oneh
-            for c in cond_df.columns :
-                if c not in features: 
-                    cond_df=cond_df.drop(columns=[c])
-
-    ##########PROC#########
-    if (feat_proc):
-        if proc :
-            feat=proc.keys()
-            proc_val=[proc[key] for key in feat]
-            procedures=pd.DataFrame(procDict,columns=['PROC'])
-            features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
-            procs=pd.DataFrame(columns=feat)
-            for p,v in zip(feat,proc_val):
-                procs[p]=v
-            features=features.drop(columns=procs.columns.to_list())
-            proc_df = pd.concat([features,procs],axis=1).fillna(0)
-            proc_df.columns=pd.MultiIndex.from_product([["PROC"], proc_df.columns])
-        else:
-            procedures=pd.DataFrame(procDict,columns=['PROC'])
-            features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
-            features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
-            proc_df=features.fillna(0)
-
-    ##########OUT#########
-    if (feat_out):
-        if out :
-            feat=out.keys()
-            out_val=[out[key] for key in feat]
-            outputs=pd.DataFrame(outDict,columns=['OUT'])
-            features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
-            outs=pd.DataFrame(columns=feat)
-            for o,v in zip(feat,out_val):
-                outs[o]=v
-            features=features.drop(columns=outs.columns.to_list())
-            out_df = pd.concat([features,outs],axis=1).fillna(0)
-            out_df.columns=pd.MultiIndex.from_product([["OUT"], out_df.columns])
-        else:
-            outputs=pd.DataFrame(outDict,columns=['OUT'])
-            features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
-            features.columns=pd.MultiIndex.from_product([["OUT"], features.columns])
-            out_df=features.fillna(0)
-
-    ##########CHART#########
-    if (feat_chart):
-        if chart:
-            charts=chart['val']
-            feat=charts.keys()
-            chart_val=[charts[key] for key in feat]
-            charts=pd.DataFrame(chartDict,columns=['CHART'])
-            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
-            chart=pd.DataFrame(columns=feat)
-            for c,v in zip(feat,chart_val):
-                chart[c]=v
-            features=features.drop(columns=chart.columns.to_list())
-            chart_df = pd.concat([features,chart],axis=1).fillna(0)
-            chart_df.columns=pd.MultiIndex.from_product([["CHART"], chart_df.columns])
+    DEFAULT_CONFIG_NAME = "Mortality"
+        
+    def init_cohort(self):
+        if self.config_path==None:
+            if self.config.name == 'Phenotype' : self.config_path = _CONFIG_URLS['phenotype'] 
+            if self.config.name == 'Readmission' : self.config_path = _CONFIG_URLS['readmission'] 
+            if self.config.name == 'Length of Stay' : self.config_path = _CONFIG_URLS['los'] 
+            if self.config.name == 'Mortality' : self.config_path = _CONFIG_URLS['mortality']
+        
+        version = self.mimic_path.split('/')[-1]
+        mimic_folder= self.mimic_path.split('/')[-2]
+        mimic_complete_path='/'+mimic_folder+'/'+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) 
+            os.chdir(dir)
         else:
-            charts=pd.DataFrame(chartDict,columns=['CHART'])
-            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
-            features.columns=pd.MultiIndex.from_product([["CHART"], features.columns])
-            chart_df=features.fillna(0)
-        ##########LAB#########
-    
-    if (feat_lab):
-        if chart:
-            charts=chart['val']
-            feat=charts.keys()
-            chart_val=[charts[key] for key in feat]
-            charts=pd.DataFrame(chartDict,columns=['LAB'])
-            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
-            chart=pd.DataFrame(columns=feat)
-            for c,v in zip(feat,chart_val):
-                chart[c]=v
-            features=features.drop(columns=chart.columns.to_list())
-            chart.columns=pd.MultiIndex.from_product([["LAB"], chart.columns])
-            chart_df = pd.concat([features,chart],axis=1).fillna(0)
-            chart_df.columns=pd.MultiIndex.from_product([["LAB"], chart_df.columns])
+            #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=='':
+                dir="./"
+            parent_dir = os.path.dirname(self.mimic_path)
+            os.chdir(parent_dir)
+
+        #####################clone git repo if doesnt exists
+        repo_url='https://github.com/healthylaife/MIMIC-IV-Data-Pipeline'
+        if os.path.exists('MIMIC-IV-Data-Pipeline-main'):
+            path_bench = './MIMIC-IV-Data-Pipeline-main'
         else:
-            charts=pd.DataFrame(chartDict,columns=['LAB'])
-            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
-            features.columns=pd.MultiIndex.from_product([["LAB"], features.columns])
-            chart_df=features.fillna(0)
-    
-    ###MEDS
-    if (feat_meds):
-        if meds:
-            feat=meds['signal'].keys()
-            med_val=[meds['amount'][key] for key in feat]
-            meds=pd.DataFrame(medDict,columns=['MEDS'])
-            features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
-            med=pd.DataFrame(columns=feat)
-            for m,v in zip(feat,med_val):
-                med[m]=v
-            features=features.drop(columns=med.columns.to_list())
-            meds_df = pd.concat([features,med],axis=1).fillna(0)
-            meds_df.columns=pd.MultiIndex.from_product([["MEDS"], meds_df.columns])
+            path_bench ='./MIMIC-IV-Data-Pipeline-main'
+            subprocess.run(["git", "clone", repo_url, path_bench])
+            os.makedirs(path_bench+'/'+'mimic-iv')
+            shutil.move(version,path_bench+'/'+'mimic-iv')
+
+        os.chdir(path_bench)
+        self.mimic_path = './'+'mimic-iv'+'/'+version
+
+        ####################Get configurations param
+        #download config file if not custom
+        if self.config_path[0:4] == 'http':
+            c = self.config_path.split('/')[-1]
+            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):
+            shutil.move(file_path,'./config')
+        with open(self.conf) as f:
+            config = yaml.safe_load(f)
+
+            
+        timeW = config['timeWindow']
+        self.timeW=int(timeW.split()[1])
+        self.bucket = config['timebucket']
+        self.predW = config['predW']
+
+        self.data_icu = config['icu_no_icu']=='ICU'
+        
+        if self.data_icu:
+            self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out, self.feat_lab = config['diagnosis'], config['chart'], config['proc'],  config['meds'], config['output'], False
         else:
-            meds=pd.DataFrame(medDict,columns=['MEDS'])
-            features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
-            features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns])
-            meds_df=features.fillna(0)
+            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
 
-    dyn_df = pd.concat([meds_df,proc_df,out_df,chart_df], axis=1)
-    return dyn_df,cond_df,demo
+        
+        #####################downloads modules from hub
+        if not os.path.exists('./icd10.txt'):
+            file_path, head = urlretrieve(_ICD_CODE, "icd10.txt")
+            shutil.move(file_path, './')    
+                
+        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')
 
+        if not os.path.exists('./model/data_generation_modify.py'):
+            file_path, head = urlretrieve(_DATA_GEN_HOSP, "data_generation_modify.py")
+            shutil.move(file_path, './model')
+
+        if not os.path.exists('./preprocessing/day_intervals_preproc/day_intervals_cohort_v22.py'):
+            file_path, head = urlretrieve(_DAY_INT, "day_intervals_cohort_v22.py")
+            shutil.move(file_path, './preprocessing/day_intervals_preproc')
+            
+        data_dir = "./data/dict/"+self.config.name.replace(" ","_")+"/dataDic"
+        sys.path.append(path_bench)
+        config = self.config_path.split('/')[-1]
 
+        #####################create task cohort
+        if self.generate_cohort:
+            create_cohort(self.config.name.replace(" ","_"),self.mimic_path,config)
 
-def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
-    meds = []
-    charts = []
-    proc = []
-    out = []
-    lab = []
-    stat = []
-    demo = []
+        #####################Split data into train, test and val
+        with open(data_dir, 'rb') as fp:
+            dataDic = pickle.load(fp)
+        data = pd.DataFrame.from_dict(dataDic)
+
+        dict_dir = "./data/dict/"+self.config.name.replace(" ","_")
+        
+        data=data.T
+        train_data, test_data = train_test_split(data, test_size=self.test_size, random_state=42)
+        if self.val_size > 0 :
+            train_data, val_data = train_test_split(train_data, test_size=self.val_size, random_state=42)
+            val_dic = val_data.to_dict('index')
+            val_path = dict_dir+'/val_data.pkl'
+            with open(val_path, 'wb') as f:
+                pickle.dump(val_dic, f)
+        
+        train_dic = train_data.to_dict('index')
+        test_dic = test_data.to_dict('index')
+    
+        train_path = dict_dir+'/train_data.pkl'
+        test_path = dict_dir+'/test_data.pkl'
+        
+        with open(train_path, 'wb') as f:
+            pickle.dump(train_dic, f)
+        with open(test_path, 'wb') as f:
+            pickle.dump(test_dic, f)
+        return dict_dir
     
-    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,feat_lab,condDict, procDict, outDict, chartDict, medDict)
-    if feat_chart:
-        charts = dyn['CHART'].values
-    if feat_meds:
-        meds = dyn['MEDS'].values
-    if feat_proc:
-        proc = dyn['PROC'].values
-        print(proc)
-    if feat_out:
-        out = dyn['OUT'].values
-    if feat_lab:
-        lab = dyn['LAB'].values
-    if feat_cond:
-        stat=cond_df.values[0]
-    y = int(demo['label'])
+
+    def verif_dim_tensor(self, proc, out, chart, meds, lab):
+        interv = (self.timeW//self.bucket)
+        verif=True
+        if self.feat_proc:
+            if (len(proc)!= interv):
+                verif=False
+        if self.feat_out:
+            if (len(out)!=interv):
+                verif=False
+        if self.feat_chart:
+            if (len(chart)!=interv):
+                verif=False
+        if self.feat_meds:
+            if (len(meds)!=interv):
+                verif=False
+        if self.feat_lab:
+            if (len(lab)!=interv):
+                verif=False
+        return verif
     
-    demo["gender"].replace(gender_vocab, inplace=True)
-    demo["ethnicity"].replace(eth_vocab, inplace=True)
-    demo["insurance"].replace(ins_vocab, inplace=True)
-    demo["Age"].replace(age_vocab, inplace=True)
-    demo=demo[["gender","ethnicity","insurance","Age"]]
-    demo = demo.values[0]
-    return stat, demo, meds, charts, out, proc, lab, y
-
-
-def generate_ml(dyn,stat,demo,concat_cols,concat):
-    X_df=pd.DataFrame()
-    if concat:
-        dyna=dyn.copy()
-        dyna.columns=dyna.columns.droplevel(0)
-        dyna=dyna.to_numpy()
-        dyna=np.nan_to_num(dyna, copy=False)
-        dyna=dyna.reshape(1,-1)
-        dyn_df=pd.DataFrame(data=dyna,columns=concat_cols)
-    else:
-        dyn_df=pd.DataFrame()
-        for key in dyn.columns.levels[0]:     
-            dyn_temp=dyn[key]
-            if ((key=="CHART") or (key=="MEDS")):
-                agg=dyn_temp.aggregate("mean")
-                agg=agg.reset_index()
+###########################################################RAW##################################################################
+
+    def _info_raw(self):
+        features = datasets.Features(
+            {
+                "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
+                "gender": datasets.Value("string"),
+                "ethnicity": datasets.Value("string"),
+                "insurance": datasets.Value("string"),
+                "age": datasets.Value("int32"),
+                "COND": datasets.Sequence(datasets.Value("string")),
+                "MEDS": {
+                            "signal": 
+                                {
+                                    "id": datasets.Sequence(datasets.Value("int32")),
+                                    "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
+                                }
+                            ,
+                            "rate": 
+                                {
+                                    "id": datasets.Sequence(datasets.Value("int32")),
+                                    "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
+                                }
+                            ,
+                            "amount": 
+                                {
+                                    "id": datasets.Sequence(datasets.Value("int32")),
+                                    "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
+                                }
+                            
+                        },
+                "PROC":  {
+                            "id": datasets.Sequence(datasets.Value("int32")),
+                            "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
+                                },
+                "CHART/LAB":
+                    {
+                        "signal" : {
+                            "id": datasets.Sequence(datasets.Value("int32")),
+                            "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
+                                },
+                        "val" : {
+                            "id": datasets.Sequence(datasets.Value("int32")),
+                            "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
+                                },
+                    },
+                "OUT":  {
+                            "id": datasets.Sequence(datasets.Value("int32")),
+                            "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
+                                },
+                
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _generate_examples_raw(self, filepath):
+        with open(filepath, 'rb') as fp:
+            dataDic = pickle.load(fp)
+        for hid, data in dataDic.items():
+            proc_features = data['Proc']
+            meds_features = data['Med']
+            out_features = data['Out']
+            cond_features = data['Cond']['fids']
+            eth= data['ethnicity']
+            age = data['age']
+            gender = data['gender']
+            label = data['label']
+            insurance=data['insurance']
+            
+            items = list(proc_features.keys())
+            values =[proc_features[i] for i in items ]
+            procs = {"id" : items,
+                  "value": values}
+            
+            items_outs = list(out_features.keys())
+            values_outs =[out_features[i] for i in items_outs ]
+            outs = {"id" : items_outs,
+                  "value": values_outs}
+
+            if self.data_icu:
+                chart_features = data['Chart']
+            else:
+                chart_features = data['Lab']
+
+            #chart signal
+            if ('signal' in chart_features):
+                items_chart_sig = list(chart_features['signal'].keys())
+                values_chart_sig =[chart_features['signal'][i] for i in items_chart_sig ]
+                chart_sig = {"id" : items_chart_sig,
+                        "value": values_chart_sig}
+            else:
+                chart_sig = {"id" : [],
+                        "value": []}
+            #chart val
+            if ('val' in chart_features):
+                items_chart_val = list(chart_features['val'].keys())
+                values_chart_val =[chart_features['val'][i] for i in items_chart_val ]
+                chart_val = {"id" : items_chart_val,
+                        "value": values_chart_val}
             else:
-                agg=dyn_temp.aggregate("max")
-                agg=agg.reset_index()
+                chart_val = {"id" : [],
+                        "value": []}
+                
+            charts = {"signal" : chart_sig,
+                    "val" : chart_val}
 
-            if dyn_df.empty:
-                dyn_df=agg
+            #meds signal
+            if ('signal' in meds_features):
+                items_meds_sig = list(meds_features['signal'].keys())
+                values_meds_sig =[meds_features['signal'][i] for i in items_meds_sig ]
+                meds_sig = {"id" : items_meds_sig,
+                    "value": values_meds_sig}
             else:
-                dyn_df=pd.concat([dyn_df,agg],axis=0)
-        dyn_df=dyn_df.T
-        dyn_df.columns = dyn_df.iloc[0]
-        dyn_df=dyn_df.iloc[1:,:]
+                meds_sig = {"id" : [],
+                    "value": []}
+            #meds rate
+            if ('rate' in meds_features):
+                items_meds_rate = list(meds_features['rate'].keys())
+                values_meds_rate =[meds_features['rate'][i] for i in items_meds_rate ]
+                meds_rate = {"id" : items_meds_rate,
+                        "value": values_meds_rate}
+            else:
+                meds_rate = {"id" : [],
+                        "value": []}
+            #meds amount
+            if ('amount' in meds_features):
+                items_meds_amount = list(meds_features['amount'].keys())
+                values_meds_amount =[meds_features['amount'][i] for i in items_meds_amount ]
+                meds_amount = {"id" : items_meds_amount,
+                        "value": values_meds_amount}
+            else:
+                meds_amount = {"id" : [],
+                        "value": []}
+            
+            meds = {"signal" : meds_sig,
+                    "rate" : meds_rate,
+                    "amount" : meds_amount}
+            
+
+            yield int(hid), {
+                "label" : label,
+                "gender" : gender,
+                "ethnicity" : eth,
+                "insurance" : insurance,
+                "age" : age,
+                "COND" : cond_features,
+                "PROC" : procs,
+                "CHART/LAB" : charts,
+                "OUT" : outs,
+                "MEDS" : meds
+            }
+
+
+
+###########################################################ENCODED##################################################################
         
-    X_df=pd.concat([dyn_df,stat],axis=1)
-    X_df=pd.concat([X_df,demo],axis=1)
-    return X_df  
-
-
-def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, feat_out):
-    #Diagnosis
-    if feat_cond:
-        conds = data.get('Cond', {}).get('fids', [])
-        conds=[icd[icd['code'] == code]['description'].to_string(index=False) for code in conds if not icd[icd['code'] == code].empty]
-        cond_text = '; '.join(conds)
-        cond_text = f"The patient was diagnosed with {cond_text}." if cond_text else ''
-    else:
-        cond_text = ''
+    def _info_encoded(self):
+        features = datasets.Features(
+            {
+                "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
+                "features" : datasets.Sequence(datasets.Value("float32")),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _generate_examples_encoded(self, filepath):
+        path= './data/dict/'+self.config.name.replace(" ","_")+'/ethVocab'
+        with open(path, 'rb') as fp:
+            ethVocab = pickle.load(fp)
+
+        path= './data/dict/'+self.config.name.replace(" ","_")+'/insVocab'
+        with open(path, 'rb') as fp:
+            insVocab = pickle.load(fp)
+
+        genVocab = ['<PAD>', 'M', 'F']
+        gen_encoder = LabelEncoder()
+        eth_encoder = LabelEncoder()
+        ins_encoder = LabelEncoder()
+        gen_encoder.fit(genVocab)
+        eth_encoder.fit(ethVocab)
+        ins_encoder.fit(insVocab)
+        with open(filepath, 'rb') as fp:
+            dico = pickle.load(fp)
+
+        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=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]
+            demo['gender']=gen_encoder.transform(demo['gender'])
+            demo['ethnicity']=eth_encoder.transform(demo['ethnicity'])
+            demo['insurance']=ins_encoder.transform(demo['insurance'])
+            label = data['label']
+            demo=demo.drop(['label'],axis=1)
+            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_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)):
+                yield int(i), {
+                    "label": label,
+                    "features": X,
+                }
+
+######################################################DEEP###############################################################
+    def _info_deep(self):
+        features = datasets.Features(
+            {
+                "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
+                "DEMO": datasets.Sequence(datasets.Value("int64")),
+                "COND" : datasets.Sequence(datasets.Value("int64")),
+                "MEDS" : datasets.Array2D(shape=(None, self.size_meds), dtype='int64') ,
+                "PROC" : datasets.Array2D(shape=(None, self.size_proc), dtype='int64') ,
+                "CHART/LAB" : datasets.Array2D(shape=(None, self.size_chart), dtype='int64') ,
+                "OUT" : datasets.Array2D(shape=(None, self.size_out), dtype='int64') ,
+                
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    
+    def _generate_examples_deep(self, filepath):
+        with open(filepath, 'rb') as fp:
+            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):
+                if self.data_icu:
+                    yield int(key), {
+                        'label': y,
+                        'DEMO': demo,
+                        'COND': stat,
+                        'MEDS': meds,
+                        'PROC': proc,
+                        'CHART/LAB': chart,
+                        'OUT': out,
+                        }
+                else:
+                    yield int(key), {
+                        'label': y,
+                        'DEMO': demo,
+                        'COND': stat,
+                        'MEDS': meds,
+                        'PROC': proc,
+                        'CHART/LAB': lab,
+                        'OUT': out,
+                        }          
+ ######################################################text##############################################################
+    def _info_text(self):
+        features = datasets.Features(
+            {
+                "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
+                "text" : datasets.Value(dtype='string', id=None),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _generate_examples_text(self, filepath):
+        icd = pd.read_csv('icd10.txt',names=['code','description'],sep='\t')
+        items= pd.read_csv(self.mimic_path+'/icu/d_items.csv.gz',compression='gzip', header=0)
+        with open(filepath, 'rb') as fp:
+            dico = pickle.load(fp)
+            
+        for key, data in dico.items():
+            demo_text,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)
+                
+            yield int(key),{
+                'label' : data['label'],
+                'text': demo_text+cond_text+chart_text+meds_text+proc_text+out_text
+            }
+
+#############################################################################################################################
+    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()
+        
+        elif self.encoding == 'tensor' :
+            return self._info_deep()
+
+        elif self.encoding == 'text' :
+            return self._info_text()
             
-    #chart
-    if feat_chart:
-        chart = data.get('Chart', {})
-        if chart:
-            charts = chart.get('val', {})
-            feat = charts.keys()
-            chart_val = [charts[key] for key in feat]
-            chart_mean = [round(np.mean(c), 3) for c in chart_val]
-            feat_text = [(items[items['itemid'] == f]['label']).to_string(index=False) for f in feat]
-            chart_text = '; '.join(f"{mean_val} for {feat_label}" for mean_val, feat_label in zip(chart_mean, feat_text))
-            chart_text = f"The chart events measured were: {chart_text}."
         else:
-            chart_text = ''
-    else:
-        chart_text = ''
+            return self._info_raw()
+
+    def _split_generators(self, dl_manager):
+        data_dir = "./data/dict/"+self.config.name.replace(" ","_")
+        if self.val_size > 0 :
+            return [
+                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": 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)
         
-    #meds
-    if feat_meds:
-        meds = data.get('Med', {})
-        if meds:
-            feat = meds['signal'].keys()
-            meds_val = [meds['amount'][key] for key in feat]
-            meds_mean = [round(np.mean(c), 3) for c in meds_val]
-            feat_text = [(items[items['itemid'] == f]['label']).to_string(index=False) for f in feat]
-            meds_text = '; '.join(f"{mean_val} of {feat_label}" for mean_val, feat_label in zip(meds_mean, feat_text))
-            meds_text = f"The mean amounts of medications administered during the episode were: {meds_text}."
-        else:
-            meds_text = ''
-    else:
-        meds_text = ''
-
-    #proc
-    if feat_proc:  
-        proc = data['Proc']
-        if proc:
-            feat=proc.keys()
-            feat_text = [(items[items['itemid']==f]['label']).to_string(index=False) for f in feat]
-            template = 'The procedures performed were: {}.'
-            proc_text= template.format(';'.join(feat_text))
-        else:
-            proc_text=''
-    else:
-        proc_text=''
-    
-    #out
-    if feat_out:  
-        out = data['Out']
-        if out:
-            feat=out.keys()
-            feat_text = [(items[items['itemid']==f]['label']).to_string(index=False) for f in feat]
-            template ='The outputs collected were: {}.'
-            out_text = template.format('; '.join(feat_text))
-        else:
-            out_text=''
-    else:
-        out_text=''
+        elif self.encoding == 'tensor' :
+            yield from self._generate_examples_deep(filepath)
 
-    return cond_text,chart_text,meds_text,proc_text,out_text
+        elif self.encoding == 'text' :
+            yield from self._generate_examples_text(filepath)
+            
+        else :
+            yield from self._generate_examples_raw(filepath)