Delete Mimic4Dataset.py

Mimic4Dataset.py

@@ -1,1178 +0,0 @@
-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 numpy as np
-from tqdm import tqdm
-import yaml
-import time
-import torch
-
-
-_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.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset"
-_CITATION = "https://proceedings.mlr.press/v193/gupta22a.html"
-_URL = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline"
-_DATA_GEN = 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/data_generation_icu_modify.py'
-_DATA_GEN_HOSP= 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/data_generation_modify.py'
-_DAY_INT= 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/day_intervals_cohort_v22.py'
-_CONFIG_URLS = {'los' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/los.config',
-                'mortality' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/mortality.config',
-                'phenotype' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/phenotype.config',
-                'readmission' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/readmission.config'
-        }
-
-
-
-def check_config(task,config_file):
-    with open(config_file) as f:
-        config = yaml.safe_load(f)
-
-    if task=='Phenotype':
-        disease_label =  config['disease_label']
-    else :
-        disease_label = ""
-    time = config['timePrediction']
-    label = task
-    timeW = config['timeWindow']
-    include=int(timeW.split()[1])
-    bucket = config['timebucket']
-    radimp = config['radimp']
-    predW = config['predW']
-    disease_filter = config['disease_filter']
-    icu_no_icu = config['icu_no_icu']
-    groupingDiag = config['groupingDiag']
-
-    assert( icu_no_icu in ['ICU','Non-ICU' ], "Chossen data should be one of the following: ICU, Non-ICU")
-    data_icu = icu_no_icu=='ICU'
-    
-    if data_icu:
-        chart_flag = config['chart']
-        output_flag = config['output']
-        select_chart = config['select_chart']
-        lab_flag = False
-        select_lab = False
-    else:
-        lab_flag =config['lab']
-        select_lab = config['select_lab']
-        groupingMed = config['groupingMed']
-        groupingProc = config['groupingProc']
-        chart_flag = False
-        output_flag = False
-        select_chart = False
-
-
-    diag_flag= config['diagnosis']
-    proc_flag = config['proc']
-    meds_flag = config['meds']
-    select_diag= config['select_diag']
-    select_med= config['select_med']
-    select_proc= config['select_proc']
-    select_out = config['select_out']
-
-    outlier_removal=config['outlier_removal']
-    thresh=config['outlier']
-    left_thresh=config['left_outlier']
-    
-    if data_icu:
-        assert (isinstance(select_diag,bool) and isinstance(select_med,bool) and isinstance(select_proc,bool) and isinstance(select_out,bool) and isinstance(select_chart,bool), " select_diag, select_chart, select_med, select_proc, select_out should be boolean")
-        assert (isinstance(chart_flag,bool) and isinstance(output_flag,bool) and isinstance(diag_flag,bool) and isinstance(proc_flag,bool) and isinstance(meds_flag,bool), "chart_flag, output_flag, diag_flag, proc_flag, meds_flag should be boolean")
-    
-    else:
-        assert (isinstance(select_diag,bool) and isinstance(select_med,bool) and isinstance(select_proc,bool) and isinstance(select_out,bool) and isinstance(select_lab,bool), " select_diag, select_lab, select_med, select_proc, select_out should be boolean")
-        assert (isinstance(lab_flag,bool) and isinstance(diag_flag,bool) and isinstance(proc_flag,bool) and isinstance(meds_flag,bool), "lab_flag, diag_flag, proc_flag, meds_flag should be boolean")
-    
-    if task=='Phenotype':
-        if disease_label=='Heart Failure':
-            label='Readmission'
-            time=30
-            disease_label='I50'
-        elif disease_label=='CAD':
-            label='Readmission'
-            time=30
-            disease_label='I25'
-        elif disease_label=='CKD':
-            label='Readmission'
-            time=30
-            disease_label='N18'
-        elif disease_label=='COPD':
-            label='Readmission'
-            time=30
-            disease_label='J44'
-        else :
-            raise ValueError('Disease label not correct provide one in the list: Heart Failure, CAD, CKD, COPD')
-        predW=0
-        assert (timeW[0]=='Last' and include<=72 and include>=24, "Time window should be between Last 24 and Last 72")
-    
-    elif task=='Mortality':
-        time=0
-        label= 'Mortality'
-        assert (predW<=8 and predW>=2, "Prediction window should be between 2 and 8")
-        assert (timeW[0]=='Fisrt' and include<=72 and include>=24, "Time window should be between First 24 and First 72")
-    
-    elif task=='Length_of_Stay':
-        label= 'Length of Stay'
-        assert (timeW[0]=='Fisrt' and include<=72 and include>=24, "Time window should be between Fisrt 24 and Fisrt 72")
-        assert (time<=10 and time>=1, "Length of stay should be between 1 and 10")
-        predW=0
-    
-    elif task=='Readmission':
-        label= 'Readmission'
-        assert (timeW[0]=='Last' and include<=72 and include>=24, "Time window should be between Last 24 and Last 72")
-        assert (time<=150 and time>=10 and time%10==0, "Readmission window should be between 10 and 150 with a step of 10")
-        predW=0
-    
-    else:
-        raise ValueError('Task not correct')
-    
-    assert( disease_filter in ['Heart Failure','COPD','CKD','CAD',""], "Disease filter should be one of the following: Heart Failure, COPD, CKD, CAD or empty")
-    assert( groupingDiag in ['Convert ICD-9 to ICD-10 and group ICD-10 codes','Keep both ICD-9 and ICD-10 codes','Convert ICD-9 to ICD-10 codes'], "Grouping ICD should be one of the following: Convert ICD-9 to ICD-10 and group ICD-10 codes, Keep both ICD-9 and ICD-10 codes, Convert ICD-9 to ICD-10 codes")
-    assert (bucket<=6 and bucket>=1 and isinstance(bucket, int), "Time bucket should be between 1 and 6 and an integer")
-    assert (radimp in ['No Imputation', 'forward fill and mean','forward fill and median'], "imputation should be one of the following: No Imputation, forward fill and mean, forward fill and median")
-    if chart_flag:
-        assert (left_thresh>=0 and left_thresh<=10 and isinstance(left_thresh, int), "Left outlier threshold should be between 0 and 10 and an integer")
-        assert (thresh>=90 and thresh<=99 and isinstance(thresh, int), "Outlier threshold should be between 90 and 99 and an integer")
-        assert (outlier_removal in ['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'], "Outlier removal should be one of the following: No outlier detection, Impute Outlier (default:98), Remove outliers (default:98)")
-    if lab_flag:
-        assert (left_thresh>=0 and left_thresh<=10 and isinstance(left_thresh, int), "Left outlier threshold should be between 0 and 10 and an integer")
-        assert (thresh>=90 and thresh<=99 and isinstance(thresh, int), "Outlier threshold should be between 90 and 99 and an integer")
-        assert (outlier_removal in ['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'], "Outlier removal should be one of the following: No outlier detection, Impute Outlier (default:98), Remove outliers (default:98)")
-        assert (groupingProc in ['ICD-9 and ICD-10','ICD-10'], "Grouping procedure should be one of the following: ICD-9 and ICD-10, ICD-10")
-        assert (groupingMed in ['Yes','No'], "Do you want to group Medication codes to use Non propietary names? : Grouping medication should be one of the following: Yes, No")
-
-    return label, time, disease_label, predW
-
-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)
-            
-        insVocabDict=create_vocab('insVocab',task)
-        with open('./data/dict/'+task+'/insVocabDict', 'wb') as fp:
-            pickle.dump(insVocabDict, fp)
-        
-        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 concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab):
-    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):
-        #get all conds
-        with open("./data/dict/"+task+"/condVocab", 'rb') as fp:
-            conDict = pickle.load(fp)
-        conds=pd.DataFrame(conDict,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
-
-    ##########PROC#########
-    if (feat_proc):
-        with open("./data/dict/"+task+"/procVocab", 'rb') as fp:
-            procDic = pickle.load(fp)
-
-        if proc :
-            feat=proc.keys()
-            proc_val=[proc[key] for key in feat]
-            procedures=pd.DataFrame(procDic,columns=['PROC'])
-            features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
-            features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
-            procs=pd.DataFrame(columns=feat)
-            for p,v in zip(feat,proc_val):
-                procs[p]=v
-            procs.columns=pd.MultiIndex.from_product([["PROC"], procs.columns])
-            proc_df = pd.concat([features,procs],ignore_index=True).fillna(0)
-        else:
-            procedures=pd.DataFrame(procDic,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):
-        with open("./data/dict/"+task+"/outVocab", 'rb') as fp:
-            outDic = pickle.load(fp)
-
-        if out :
-            feat=out.keys()
-            out_val=[out[key] for key in feat]
-            outputs=pd.DataFrame(outDic,columns=['OUT'])
-            features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
-            features.columns=pd.MultiIndex.from_product([["OUT"], features.columns])
-            outs=pd.DataFrame(columns=feat)
-            for o,v in zip(feat,out_val):
-                outs[o]=v
-            outs.columns=pd.MultiIndex.from_product([["OUT"], outs.columns])
-            out_df = pd.concat([features,outs],ignore_index=True).fillna(0)
-        else:
-            outputs=pd.DataFrame(outDic,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):
-        with open("./data/dict/"+task+"/chartVocab", 'rb') as fp:
-            chartDic = pickle.load(fp)
-
-        if chart:
-            charts=chart['val']
-            feat=charts.keys()
-            chart_val=[charts[key] for key in feat]
-            charts=pd.DataFrame(chartDic,columns=['CHART'])
-            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
-            features.columns=pd.MultiIndex.from_product([["CHART"], features.columns])
-            
-            chart=pd.DataFrame(columns=feat)
-            for c,v in zip(feat,chart_val):
-                chart[c]=v
-            chart.columns=pd.MultiIndex.from_product([["CHART"], chart.columns])
-            chart_df = pd.concat([features,chart],ignore_index=True).fillna(0)
-        else:
-            charts=pd.DataFrame(chartDic,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):
-        with open("./data/dict/"+task+"/labsVocab", 'rb') as fp:
-            chartDic = pickle.load(fp)
-
-        if chart:
-            charts=chart['val']
-            feat=charts.keys()
-            chart_val=[charts[key] for key in feat]
-            charts=pd.DataFrame(chartDic,columns=['LAB'])
-            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
-            features.columns=pd.MultiIndex.from_product([["LAB"], features.columns])
-            
-            chart=pd.DataFrame(columns=feat)
-            for c,v in zip(feat,chart_val):
-                chart[c]=v
-            chart.columns=pd.MultiIndex.from_product([["LAB"], chart.columns])
-            chart_df = pd.concat([features,chart],ignore_index=True).fillna(0)
-        else:
-            charts=pd.DataFrame(chartDic,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):
-        with open("./data/dict/"+task+"/medVocab", 'rb') as fp:
-                medDic = pickle.load(fp)
-
-        if meds:
-            feat=meds['signal'].keys()
-            med_val=[meds['amount'][key] for key in feat]
-            meds=pd.DataFrame(medDic,columns=['MEDS'])
-            features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
-            features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns])
-            
-            med=pd.DataFrame(columns=feat)
-            for m,v in zip(feat,med_val):
-                med[m]=v
-            med.columns=pd.MultiIndex.from_product([["MEDS"], med.columns])
-            meds_df = pd.concat([features,med],ignore_index=True).fillna(0)
-        else:
-            meds=pd.DataFrame(medDic,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)
-
-    dyn_df = pd.concat([meds_df,proc_df,out_df,chart_df], axis=1)
-    return dyn_df,cond_df,demo
-def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab):
-    stat_df = torch.zeros(size=(1,0))
-    demo_df = torch.zeros(size=(1,0))
-    meds = torch.zeros(size=(0,0))
-    charts = torch.zeros(size=(0,0))
-    proc = torch.zeros(size=(0,0))
-    out = torch.zeros(size=(0,0))
-    lab = torch.zeros(size=(0,0))
-    stat_df = torch.zeros(size=(1,0))
-    demo_df = torch.zeros(size=(1,0))
-    
-    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)
-
-    ###########""
-    if feat_chart:
-        charts = dyn['CHART']
-        charts=charts.to_numpy()
-        charts = torch.tensor(charts, dtype=torch.long)
-        charts = charts.tolist()
-        
-    if feat_meds:
-        meds = dyn['MEDS']
-        meds=meds.to_numpy()
-        meds = torch.tensor(meds, dtype=torch.long)
-        meds = meds.tolist()
-
-    if feat_proc:
-        proc = dyn['PROC']
-        proc=proc.to_numpy()
-        proc = torch.tensor(proc, dtype=torch.long)
-        proc = proc.tolist()
-
-    if feat_out:
-        out = dyn['OUT']
-        out=out.to_numpy()
-        out = torch.tensor(out, dtype=torch.long)
-        out = out.tolist()
-
-    if feat_lab:
-        lab = dyn['LAB']
-        lab=lab.to_numpy()
-        lab = torch.tensor(lab, dtype=torch.long)
-        lab = lab.tolist()
-
-####################""
-    
-    stat=cond_df
-    stat = stat.to_numpy()
-    stat = torch.tensor(stat)
-    if stat_df[0].nelement():
-        stat_df = torch.cat((stat_df,stat),0)
-    else:
-        stat_df = stat 
-
-    y = int(demo['label'])
-    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
-    demo = torch.tensor(demo)
-    if demo_df[0].nelement():
-        demo_df = torch.cat((demo_df,demo),0)
-    else:
-        demo_df = demo
-    stat_df = torch.tensor(stat_df)
-    stat_df = stat_df.type(torch.LongTensor)
-    stat_df = stat_df.squeeze()
-    demo_df = torch.tensor(demo_df)
-    demo_df = demo_df.type(torch.LongTensor)
-    demo_df=demo_df.squeeze()
-    y_df = torch.tensor(y)
-    y_df = y_df.type(torch.LongTensor)
-
-    return stat_df, demo_df, meds, charts, out, proc, lab, y
-    
-def getXY(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()
-            else:
-                agg=dyn_temp.aggregate("max")
-                agg=agg.reset_index()
-
-            if dyn_df.empty:
-                dyn_df=agg
-            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:,:]
-        
-    X_df=pd.concat([dyn_df,stat],axis=1)
-    X_df=pd.concat([X_df,demo],axis=1)
-    return X_df    
-
-
-
-
-
-
-def task_cohort(task, mimic_path, config_path):
-    sys.path.append('./preprocessing/day_intervals_preproc')
-    sys.path.append('./utils')
-    sys.path.append('./preprocessing/hosp_module_preproc')
-    sys.path.append('./model')
-    import day_intervals_cohort_v22
-    import day_intervals_cohort
-    import feature_selection_icu
-    import data_generation_icu_modify
-    import data_generation_modify
-    import feature_selection_hosp
-
-    
-    root_dir = os.path.dirname(os.path.abspath('UserInterface.ipynb'))
-    config_path='./config/'+config_path
-    with open(config_path) as f:
-        config = yaml.safe_load(f)
-    version_path = mimic_path+'/'
-    print(version_path)
-    version = mimic_path.split('/')[-1][0]
-    start = time.time()
-    #----------------------------------------------config----------------------------------------------------
-    label, tim, disease_label, predW = check_config(task,config_path)
-    icu_no_icu = config['icu_no_icu']
-    timeW = config['timeWindow']
-    include=int(timeW.split()[1])
-    bucket = config['timebucket']
-    radimp = config['radimp']
-
-    diag_flag = config['diagnosis'] 
-    proc_flag= config['proc']
-    med_flag = config['meds']
-    disease_filter = config['disease_filter']
-    groupingDiag = config['groupingDiag']
-    select_diag= config['select_diag']
-    select_med= config['select_med']
-    select_proc=  config['select_proc']
-
-    if icu_no_icu=='ICU':
-        out_flag = config['output']
-        chart_flag = config['chart']
-        select_out= config['select_out']
-        select_chart=  config['select_chart']
-        lab_flag = False
-        select_lab = False
-    else:
-        lab_flag = config['lab']
-        groupingMed = config['groupingMed']
-        groupingProc = config['groupingProc']
-        select_lab= config['select_lab']
-        out_flag = False
-        chart_flag = False
-        select_out= False
-        select_chart=  False
-
-    # -------------------------------------------------------------------------------------------------------------
-
-    data_icu=icu_no_icu=="ICU"
-    data_mort=label=="Mortality"
-    data_admn=label=='Readmission'
-    data_los=label=='Length of Stay'
-
-    if (disease_filter=="Heart Failure"):
-        icd_code='I50'
-    elif (disease_filter=="CKD"):
-        icd_code='N18'
-    elif (disease_filter=="COPD"):
-        icd_code='J44'
-    elif (disease_filter=="CAD"):
-        icd_code='I25'
-    else:
-        icd_code='No Disease Filter'
-
-    #-----------------------------------------------EXTRACT MIMIC-----------------------------------------------------
-    if version == '2':
-        cohort_output = day_intervals_cohort_v22.extract_data(icu_no_icu,label,tim,icd_code, root_dir,version_path,disease_label)
-
-    elif version == '1':
-        cohort_output = day_intervals_cohort.extract_data(icu_no_icu,label,tim,icd_code, root_dir,version_path,disease_label)
-    #----------------------------------------------FEATURES-------------------------------------------------------
-
-    if data_icu :
-        feature_selection_icu.feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag)
-    else:
-        feature_selection_hosp.feature_nonicu(cohort_output, version_path,diag_flag,lab_flag,proc_flag,med_flag)
-    #----------------------------------------------GROUPING-------------------------------------------------------
-    if data_icu:
-        if diag_flag:
-            group_diag=groupingDiag
-        feature_selection_icu.preprocess_features_icu(cohort_output, diag_flag, group_diag,False,False,False,0,0)
-    
-    else:
-        if diag_flag:
-            group_diag=groupingDiag
-        if med_flag:
-            group_med=groupingMed
-        if proc_flag:
-            group_proc=groupingProc
-        feature_selection_hosp.preprocess_features_hosp(cohort_output, diag_flag,proc_flag,med_flag,False,group_diag,group_med,group_proc,False,False,0,0)
-    #----------------------------------------------SUMMARY-------------------------------------------------------
-    if data_icu:
-        feature_selection_icu.generate_summary_icu(diag_flag,proc_flag,med_flag,out_flag,chart_flag)
-    else:
-        feature_selection_hosp.generate_summary_hosp(diag_flag,proc_flag,med_flag,lab_flag)
-    #----------------------------------------------FEATURE SELECTION---------------------------------------------
-
-    #----------------------------------------------FEATURE SELECTION---------------------------------------------
-     
-    if data_icu:
-        if select_chart or select_out or select_diag or select_med or select_proc:
-            if select_chart:
-                input('Please edit list of codes in ./data/summary/chart_features.csv to select the chart items to keep and press enter to continue')
-            if select_out:
-                input('Please edit list of codes in ./data/summary/out_features.csv to select the output items to keep and press enter to continue')
-            if select_diag:
-                input('Please edit list of codes in ./data/summary/diag_features.csv to select the diagnosis ids to keep and press enter to continue')
-            if select_med:
-                input('Please edit list of codes in ./data/summary/med_features.csv to select the meds items to keep and press enter to continue')
-            if select_proc:
-                input('Please edit list of codes in ./data/summary/proc_features.csv to select the procedures ids to keep and press enter to continue')
-        feature_selection_icu.features_selection_icu(cohort_output, diag_flag,proc_flag,med_flag,out_flag, chart_flag,select_diag,select_med,select_proc,select_out,select_chart)
-    else:
-        if select_diag or select_med or select_proc or select_lab:
-            if select_diag:
-                input('Please edit list of codes in ./data/summary/diag_features.csv to select the diagnosis ids to keep and press enter to continue')
-            if select_med:
-                input('Please edit list of codes in ./data/summary/med_features.csv to select the meds items to keep and press enter to continue')
-            if select_proc:
-                input('Please edit list of codes in ./data/summary/proc_features.csv to select the procedures ids to keep and press enter to continue')
-            if select_lab:
-                input('Please edit list of codes in ./data/summary/labs_features.csv to select the labs items to keep and press enter to continue')
-        feature_selection_hosp.features_selection_hosp(cohort_output, diag_flag,proc_flag,med_flag,lab_flag,select_diag,select_med,select_proc,select_lab)
-
-    #---------------------------------------CLEANING OF FEATURES-----------------------------------------------
-    thresh=0
-    if data_icu:
-        if chart_flag:
-            outlier_removal=config['outlier_removal']
-            clean_chart=outlier_removal!='No outlier detection'
-            impute_outlier_chart=outlier_removal=='Impute Outlier (default:98)'
-            thresh=config['outlier']
-            left_thresh=config['left_outlier']
-        feature_selection_icu.preprocess_features_icu(cohort_output, False, False,chart_flag,clean_chart,impute_outlier_chart,thresh,left_thresh)
-    else:
-        if lab_flag:
-            outlier_removal=config['outlier_removal']
-            clean_chart=outlier_removal!='No outlier detection'
-            impute_outlier_chart=outlier_removal=='Impute Outlier (default:98)'
-            thresh=config['outlier']
-            left_thresh=config['left_outlier']
-        feature_selection_hosp.preprocess_features_hosp(cohort_output, False,False, False,lab_flag,False,False,False,clean_chart,impute_outlier_chart,thresh,left_thresh)
-    # ---------------------------------------tim-Series Representation--------------------------------------------
-    if radimp == 'forward fill and mean' :
-        impute='Mean'
-    elif radimp =='forward fill and median':
-        impute = 'Median'
-    else :
-        impute = False
-    
-    if data_icu:
-        gen=data_generation_icu_modify.Generator(task,cohort_output,data_mort,data_admn,data_los,diag_flag,proc_flag,out_flag,chart_flag,med_flag,impute,include,bucket,predW)
-    else:
-        gen=data_generation_modify.Generator(cohort_output,data_mort,data_admn,data_los,diag_flag,lab_flag,proc_flag,med_flag,impute,include,bucket,predW)
-    
-    end = time.time()
-    print("Time elapsed : ", round((end - start)/60,2),"mins")
-    print("[============TASK COHORT SUCCESSFULLY CREATED============]")
-
-
-
-
- #############################################DATASET####################################################################   
-class Mimic4DatasetConfig(datasets.BuilderConfig):
-    """BuilderConfig for Mimic4Dataset."""
-
-    def __init__(
-        self,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        
-class Mimic4Dataset(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    def __init__(self, **kwargs):
-        self.mimic_path = kwargs.pop("mimic_path", None)
-        self.encoding = kwargs.pop("encoding",'raw')
-        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
-            
-        super().__init__(**kwargs)
-        
-        
-    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"
-        ),
-    ]
-    
-    DEFAULT_CONFIG_NAME = "Mortality"
-        
-    def create_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:
-            #move to parent directory of mimic data
-            dir = self.mimic_path.replace(mimic_complete_path,'')
-            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:
-            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.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.lab = config['diagnosis'], config['chart'], config['proc'],  config['meds'], config['output'], False
-            self.feat_lab = False
-        else:
-            self.feat_cond, self.feat_lab, self.feat_proc, self.feat_meds, self.feat_chart, self.out = config['diagnosis'], config['lab'], config['proc'],  config['meds'], False, False
-            self.feat_out = False
-            self.feat_chart = False
-        #####################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')
-
-        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:
-            task_cohort(self.config.name.replace(" ","_"),self.mimic_path,config)
-
-        #####################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
-  
-###########################################################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:
-                chart_val = {"id" : [],
-                        "value": []}
-                
-            charts = {"signal" : chart_sig,
-                    "val" : chart_val}
-
-            #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:
-                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##################################################################
-        
-    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')
-        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=dyn_df.copy()
-            dyn.columns=dyn.columns.droplevel(0)
-            cols=dyn.columns
-            time=dyn.shape[0]
-            for t in range(time):
-                cols_t = [str(x) + "_"+str(t) for x in cols]
-                concat_cols.extend(cols_t)
-            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= getXY(dyn_df,cond_df,demo,concat_cols,self.concat)
-            X=X.values.tolist()[0]
-            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.Array2D(shape=(None, 4), dtype='int64') ,
-                "DEMO": datasets.Sequence(datasets.Value("int64")),
-                "COND" : datasets.Sequence(datasets.Value("int64")),
-                #"COND" : datasets.Array2D(shape=(None, self.size_cond), dtype='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') ,
-                #"CHART/LAB" : datasets.Sequence(datasets.Sequence(datasets.Value("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)
-        task=self.config.name.replace(" ","_")
-        for key, data in dico.items():   
-            stat, demo, meds, chart, out, proc, lab, y = getXY_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:
-                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,
-                        }
-            else:
-                continue
-            
-    
-#############################################################################################################################
-    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,self.feat_lab)
-    
-        if self.encoding == 'concat' :
-            return self._info_encoded()
-
-        elif self.encoding == 'aggreg' :
-            return self._info_encoded()
-        
-        elif self.encoding == 'tensor' :
-            return self._info_deep()
-        
-        else:
-            return self._info_raw()
-
-
-    def _split_generators(self, dl_manager):
-        csv_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'}),
-            ]
-        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'}),
-            ]
-    def _generate_examples(self, filepath):
-        
-        if self.encoding == 'concat' :
-            yield from self._generate_examples_encoded(filepath)
-        
-        elif self.encoding == 'aggreg' :
-            yield from self._generate_examples_encoded(filepath)
-        
-        elif self.encoding == 'tensor' :
-            yield from self._generate_examples_deep(filepath)
-        else :
-            yield from self._generate_examples_raw(filepath)