Mimic4Dataset.py

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']
    else:
        lab_flag =config['lab']
        select_lab = config['select_lab']
        groupingMed = config['groupingMed']
        groupingProc = config['groupingProc']


    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)
        charts = charts.unsqueeze(0)
        charts = torch.tensor(charts)
        charts = charts.type(torch.LongTensor)
        charts=charts.view(charts.shape[1],charts.shape[2])
        
    if feat_meds:
        meds = dyn['MEDS']
        meds=meds.to_numpy()
        meds = torch.tensor(meds)
        meds = meds.unsqueeze(0)
        meds = torch.tensor(meds)
        meds = meds.type(torch.LongTensor)
        meds=meds.view(meds.shape[1],meds.shape[2])

    if feat_proc:
        proc = dyn['PROC']
        proc=proc.to_numpy()
        proc = torch.tensor(proc)
        proc = proc.unsqueeze(0)
        proc = torch.tensor(proc)
        proc = proc.type(torch.LongTensor)
        proc=proc.view(proc.shape[1],proc.shape[2])

    if feat_out:
        out = dyn['OUT']
        out=out.to_numpy()
        out = torch.tensor(out)
        out = out.unsqueeze(0)
        out = torch.tensor(out)
        out = out.type(torch.LongTensor)
        out=out.view(out.shape[1],out.shape[2])

    if feat_lab:
        lab = dyn['LAB']
        lab=lab.to_numpy()
        lab = torch.tensor(lab)
        lab = lab.unsqueeze(0)
        lab = torch.tensor(lab)
        lab = lab.type(torch.LongTensor)
        lab=lab.view(lab.shape[1],lab.shape[2])

####################""
    

    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)
    demo_df = torch.tensor(demo_df)
    demo_df = demo_df.type(torch.LongTensor)
    y_df = torch.tensor(y)
    y_df = y_df.type(torch.LongTensor)

    return stat_df, demo_df, meds, charts, out, proc, lab, y_df

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+'/'
    version = mimic_path.split('/')[-1][0]
    start = time.time()
    #----------------------------------------------config----------------------------------------------------
    label, tim, disease_label, predW = check_config(task,config_path)

    timeW = config['timeWindow']
    include=int(timeW.split()[1])
    bucket = config['timebucket']
    radimp = config['radimp']

    diag_flag = config['diagnosis']
    out_flag = config['output']
    chart_flag = config['chart']
    proc_flag= config['proc']
    med_flag = config['meds']
    lab_flag = config['lab']

    disease_filter = config['disease_filter']
    icu_no_icu = config['icu_no_icu']
    groupingDiag = config['groupingDiag']
    groupingMed = config['groupingMed']
    groupingProc = config['groupingProc']

    select_diag= config['select_diag']
    select_med= config['select_med']
    select_proc=  config['select_proc']
    select_lab= config['select_lab']
    select_out= config['select_out']
    select_chart=  config['select_chart']

    # -------------------------------------------------------------------------------------------------------------

    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-------------------------------------------------------
    print(data_icu)
    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---------------------------------------------

    if data_icu:
        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:
        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.name==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)

        self.data_icu = config['icu_no_icu']=='ICU'
        print(self.data_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
        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

        #####################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)
       
        data=data.T
        train_data, test_data = train_test_split(data, test_size=self.test_size, random_state=42)
        train_data, val_data = train_test_split(train_data, test_size=self.val_size, random_state=42)
        
        dict_dir = "./data/dict/"+self.config.name.replace(" ","_")
        train_dic = train_data.to_dict('index')
        test_dic = test_data.to_dict('index')
        val_dic = val_data.to_dict('index')

        train_path = dict_dir+'/train_data.pkl'
        test_path = dict_dir+'/test_data.pkl'
        val_path = dict_dir+'/val_data.pkl'
        
        with open(train_path, 'wb') as f:
            pickle.dump(train_dic, f)
        with open(val_path, 'wb') as f:
            pickle.dump(val_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"),
                "COND" : datasets.Array2D(shape=(None, 1025), 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') ,
                "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)

            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,
                    }
            
    
#############################################################################################################################
    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(" ","_")

        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'}),
        ]

    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)