Mimic4Dataset.py

import csv
import json
import os
import pandas as pd
import datasets
import pickle
#import cohort
from .test import print_test
import subprocess

_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 : 
"""

_HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset"
_CITATION = "https://proceedings.mlr.press/v193/gupta22a.html"
_URL = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline"

class Mimic4DatasetConfig(datasets.BuilderConfig):
    """BuilderConfig for Mimic4Dataset."""

    def __init__(
        self,
        mimic_path,
        #config,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.mimic_path =mimic_path
        
        #self.config = config
        #cohort.task_cohort(self.task,self.mimic_path)

        
class Mimic4Dataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        Mimic4DatasetConfig(
            name="Phenotype",
            version=VERSION,
            description="Dataset for mimic4 Phenotype task",
            mimic_path = None
        ),
        Mimic4DatasetConfig(
            name="Readmission",
            version=VERSION,
            description="Dataset for mimic4 Readmission task",
            mimic_path = None
        ),
        Mimic4DatasetConfig(
            name="Length of Stay",
            version=VERSION,
            description="Dataset for mimic4 Length of Stay task",
            mimic_path = None
        ),
        Mimic4DatasetConfig(
            name="Mortality",
            version=VERSION,
            description="Dataset for mimic4 Mortality task",
            mimic_path = None
        ),
    ]

    DEFAULT_CONFIG_NAME = "Mortality"

    def _info(self):
        

        features = datasets.Features(
            {
                "label": datasets.ClassLabel(names=["0", "1"]),
                "gender": datasets.Value("string"),
                "ethnicity": 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()):
        repodir = os.getcwd()
        path_bench = repodir+'/MIMIC-IV-Data-Pipeline-main'
        repo_url='https://github.com/healthylaife/MIMIC-IV-Data-Pipeline'
        data_dir = path_bench + "/data/dataDic"
        if not os.path.exists(path_bench):
            #subprocess.run(["git", "clone", repo_url, path_bench])
            print('ok')
        
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir, "benchmark": path_bench}),
        ]


    def _generate_examples(self, filepath,benchmark):
        print_test('hello',benchmark)
        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']
            
            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,
                "age" : age,
                "COND" : cond_features,
                "PROC" : procs,
                "CHART" : charts,
                "OUT" : outs,
                "MEDS" : meds
            }