| """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}} |
| }""" |
|
|
| |
| urls_per_split = { |
| "hungary": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.hungarian.data"}, |
| } |
| features_types_per_config = { |
| "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"), |
| "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} |
|
|
| _ENCODING_DICS = { |
| "type_of_chest_pain": { |
| 1: "typical angina", |
| 2: "atypical angina", |
| 3: "non-anginal pain", |
| 4: "asymptomatic" |
| } |
| } |
|
|
| 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): |
| |
| DEFAULT_CONFIG = "hungary" |
| BUILDER_CONFIGS = [ |
| 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]["train"]}) |
| ] |
| |
| def _generate_examples(self, filepath: str): |
| data = pandas.read_csv(filepath, header=None) |
| data.columns = _BASE_FEATURE_NAMES |
| data = self.preprocess(data, self.config.name) |
|
|
| for row_id, row in data.iterrows(): |
| data_row = dict(row) |
|
|
| yield row_id, data_row |
|
|
| def preprocess(self, data, config): |
| for feature in _ENCODING_DICS: |
| encoding_function = partial(self.encode, feature) |
| data.loc[:, feature] = data[feature].apply(encoding_function) |
|
|
| data[["age"]].applymap(int) |
|
|
| data.drop("slope_of_peak_exercise", axis="columns", inplace=True) |
| data.drop("number_of_major_vessels_colored_by_flourosopy", axis="columns", inplace=True) |
| data.drop("thal", axis="columns", inplace=True) |
| data = data[data.serum_cholesterol != "?"] |
|
|
| data = data.infer_objects() |
|
|
| data = data[data.resting_blood_pressure != "?"] |
| data = data[data.fasting_blood_sugar != "?"] |
| data = data[data.rest_electrocardiographic_type != "?"] |
| data = data[data.maximum_heart_rate != "?"] |
| data = data[data.has_exercise_induced_angina != "?"] |
|
|
| data = data.astype({"is_male": bool, "has_exercise_induced_angina": bool, |
| "serum_cholesterol": float, "maximum_heart_rate": float, |
| "resting_blood_pressure": float, "fasting_blood_sugar": float}) |
|
|
| return data |
|
|
| def encode(self, feature, value): |
| if feature in _ENCODING_DICS: |
| return _ENCODING_DICS[feature][value] |
| raise ValueError(f"Unknown feature: {feature}") |
|
|
