"""Heart"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
    "age"
    "is_male"
    "type_of_chest_pain"
    "resting_blood_pressure"
    "serum_cholesterol"
    "fasting_blood_sugar"
    "rest_electrocardiographic_type"
    "maximum_heart_rate"
    "has_exercise_induced_angina"
    "depression_induced_by_exercise"
    "slope_of_peak_exercise"
    "number_of_major_vessels_colored_by_flourosopy"
    "thal"
    "has_hearth_disease"
]

DESCRIPTION = "Heart dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Heart"
_URLS = ("https://huggingface.co/datasets/mstz/heart/raw/heart.csv")
_CITATION = """
@misc{misc_heart_disease_45,
  author       = {Janosi,Andras, Steinbrunn,William, Pfisterer,Matthias, Detrano,Robert & M.D.,M.D.},
  title        = {{Heart Disease}},
  year         = {1988},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C52P4X}}
}"""

# Dataset info
urls_per_split = {
    "cleveland": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.cleveland.data"}
    "hungary": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.hungarian.data"}
    "switzerland": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.switzerland.data"}
    "va": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.va.data"}
}
features_types_per_config = {
    "cleveland": {
        "age": datasets.Value("int8")
        "is_male": datasets.Value("bool")
        "type_of_chest_pain": datasets.Value("string")
        "resting_blood_pressure": datasets.Value("float32")
        "serum_cholesterol": datasets.Value("float32")
        "fasting_blood_sugar": datasets.Value("float32")
        "rest_electrocardiographic_type": datasets.Value("string")
        "maximum_heart_rate": datasets.Value("float32")
        "has_exercise_induced_angina": datasets.Value("bool")
        "depression_induced_by_exercise": datasets.Value("float32")
        "slope_of_peak_exercise": datasets.Value("float32")
        "number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16")
        "thal": datasets.Value("float32")
        "has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
    },
    "va": {
        "age": datasets.Value("int8")
        "is_male": datasets.Value("bool")
        "type_of_chest_pain": datasets.Value("string")
        "resting_blood_pressure": datasets.Value("float32")
        "serum_cholesterol": datasets.Value("float32")
        "fasting_blood_sugar": datasets.Value("float32")
        "rest_electrocardiographic_type": datasets.Value("string")
        "maximum_heart_rate": datasets.Value("float32")
        "has_exercise_induced_angina": datasets.Value("bool")
        "depression_induced_by_exercise": datasets.Value("float32")
        "slope_of_peak_exercise": datasets.Value("float32")
        "number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16")
        "thal": datasets.Value("float32")
        "has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
    },
    "switzerland": {
        "age": datasets.Value("int8")
        "is_male": datasets.Value("bool")
        "type_of_chest_pain": datasets.Value("string")
        "resting_blood_pressure": datasets.Value("float32")
        "serum_cholesterol": datasets.Value("float32")
        "fasting_blood_sugar": datasets.Value("float32")
        "rest_electrocardiographic_type": datasets.Value("string")
        "maximum_heart_rate": datasets.Value("float32")
        "has_exercise_induced_angina": datasets.Value("bool")
        "depression_induced_by_exercise": datasets.Value("float32")
        "slope_of_peak_exercise": datasets.Value("float32")
        "number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16")
        "thal": datasets.Value("float32")
        "has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
    },
    "hungary": {
        "age": datasets.Value("int8")
        "is_male": datasets.Value("bool")
        "type_of_chest_pain": datasets.Value("string")
        "resting_blood_pressure": datasets.Value("float32")
        "serum_cholesterol": datasets.Value("float32")
        "fasting_blood_sugar": datasets.Value("float32")
        "rest_electrocardiographic_type": datasets.Value("string")
        "maximum_heart_rate": datasets.Value("float32")
        "has_exercise_induced_angina": datasets.Value("bool")
        "depression_induced_by_exercise": datasets.Value("float32")
        "slope_of_peak_exercise": datasets.Value("float32")
        "number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16")
        "thal": datasets.Value("float32")
        "has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
    },
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


class HeartConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(HeartConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Heart(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "cleveland"
    BUILDER_CONFIGS = [
        HeartConfig(name="cleveland",
                    description="Heart for binary classification, dataset."),
        HeartConfig(name="va",
                    description="Heart for binary classification, va dataset."),
        HeartConfig(name="switzerland",
                    description="Heart for binary classification, switzerland dataset."),
        HeartConfig(name="hungary",
                    description="Heart for binary classification, hungary dataset.")
    ]


    def _info(self):
        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
                                    features=features_per_config[self.config.name])

        return info
    
    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        downloads = dl_manager.download_and_extract(urls_per_split)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads[self.config.name][self.config.name]["train"]})
        ]
    
    def _generate_examples(self, filepath: str):
        data = pandas.read_csv(filepath)
        data.columns = _BASE_FEATURE_NAMES
        data = self.preprocess(data, config=self.config.name)

        for row_id, row in data.iterrows():
            data_row = dict(row)

            yield row_id, data_row