Mimic4Dataset.py

import csv
import json
import os
import pandas as pd
import datasets
import sys
import pickle
import subprocess
import shutil
from urllib.request import urlretrieve


_DESCRIPTION = """\
Dataset for mimic4 data, by default for the Mortality task.
Available tasks are: Mortality, Length of Stay, Readmission, Phenotype, Mortality Custom, Length of Stay Custom, Readmission Custom, Phenotype Custom.
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'
_DAY_INT= 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/day_intervals_cohort_v22.py'
_COHORT = 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/cohort.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'
        }
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.config_path = kwargs.pop("config_path",None)
        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"
        ),
        Mimic4DatasetConfig(
            name="Phenotype Custom",
            version=VERSION,
            description="Dataset for mimic4 Custom Phenotype task"
        ),
        Mimic4DatasetConfig(
            name="Readmission Custom",
            version=VERSION,
            description="Dataset for mimic4 Custom Readmission task"
        ),
        Mimic4DatasetConfig(
            name="Length of Stay Custom",
            version=VERSION,
            description="Dataset for mimic4 Custom Length of Stay task"
        ),
        Mimic4DatasetConfig(
            name="Mortality Custom",
            version=VERSION,
            description="Dataset for mimic4 Custom Mortality task"
        ),
    ]

    DEFAULT_CONFIG_NAME = "Mortality"

    def _info(self):

        features = datasets.Features(
            {
                "label": datasets.ClassLabel(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":
                    {
                        "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 _split_generators(self, dl_manager: datasets.DownloadManager()):
        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']
        if self.config.name in ['Phenotype Custom','Readmission Custom','Length of Stay Custom','Mortality Custom'] and self.config.name==None:
            raise ValueError('Please select a config file')
        
        version = self.mimic_path.split('/')[-1]
        m = self.mimic_path.split('/')[-2]
        s='/'+m+'/'+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(s,'')
            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

        #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

        #create config folder
        if not os.path.exists('./config'):
            os.makedirs('config')
        #save config file in config folder
        conf='./config/'+file_path.split('/')[-1]

        if not os.path.exists(conf):
            shutil.move(file_path,'./config')

        #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('./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')
            
        file_path, head = urlretrieve(_COHORT, "cohort.py")
        if not os.path.exists('cohort.py'):
            shutil.move(file_path, './')
            
        data_dir = "./data/dict/"+self.config.name.replace(" ","_")+"/dataDic"
        sys.path.append(path_bench)
        config = self.config_path.split('/')[-1]

        script = 'python cohort.py '+ self.config.name.replace(" ","_") +" "+ self.mimic_path+ " "+path_bench+ " "+config
        print(script)
        if not os.path.exists(data_dir) :
            os.system(script)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir}),
        ]


    def _generate_examples(self, filepath):
        with open(filepath, 'rb') as fp:
            dataDic = pickle.load(fp)
        for hid, data in dataDic.items():
            proc_features = data['Proc']
            chart_features = data['Chart']
            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}

            #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" : charts,
                "OUT" : outs,
                "MEDS" : meds
            }