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"""Heart""" |
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from typing import List |
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from functools import partial |
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import datasets |
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import pandas |
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VERSION = datasets.Version("1.0.0") |
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_BASE_FEATURE_NAMES = [ |
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"age", |
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"is_male", |
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"type_of_chest_pain", |
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"resting_blood_pressure", |
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"serum_cholesterol", |
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"fasting_blood_sugar", |
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"rest_electrocardiographic_type", |
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"maximum_heart_rate", |
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"has_exercise_induced_angina", |
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"depression_induced_by_exercise", |
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"slope_of_peak_exercise", |
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"number_of_major_vessels_colored_by_flourosopy", |
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"thal", |
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"has_hearth_disease" |
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] |
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DESCRIPTION = "Heart dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Heart" |
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_URLS = ("https://huggingface.co/datasets/mstz/heart/raw/heart.csv") |
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_CITATION = """ |
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@misc{misc_heart_disease_45, |
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author = {Janosi,Andras, Steinbrunn,William, Pfisterer,Matthias, Detrano,Robert & M.D.,M.D.}, |
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title = {{Heart Disease}}, |
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year = {1988}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C52P4X}} |
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}""" |
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urls_per_split = { |
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"hungary": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.hungarian.data"}, |
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} |
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features_types_per_config = { |
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"hungary": { |
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"age": datasets.Value("int8"), |
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"is_male": datasets.Value("bool"), |
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"type_of_chest_pain": datasets.Value("string"), |
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"resting_blood_pressure": datasets.Value("float32"), |
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"serum_cholesterol": datasets.Value("float32"), |
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"fasting_blood_sugar": datasets.Value("float32"), |
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"rest_electrocardiographic_type": datasets.Value("string"), |
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"maximum_heart_rate": datasets.Value("float32"), |
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"has_exercise_induced_angina": datasets.Value("bool"), |
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"depression_induced_by_exercise": datasets.Value("float32"), |
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"has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes")) |
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}, |
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} |
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
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_ENCODING_DICS = { |
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"type_of_chest_pain": { |
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1: "typical angina", |
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2: "atypical angina", |
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3: "non-anginal pain", |
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4: "asymptomatic" |
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} |
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} |
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class HeartConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(HeartConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Heart(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "hungary" |
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BUILDER_CONFIGS = [ |
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HeartConfig(name="hungary", |
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description="Heart for binary classification, hungary dataset.") |
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] |
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def _info(self): |
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
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features=features_per_config[self.config.name]) |
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return info |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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downloads = dl_manager.download_and_extract(urls_per_split) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads[self.config.name]["train"]}) |
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] |
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def _generate_examples(self, filepath: str): |
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data = pandas.read_csv(filepath, header=None) |
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data.columns = _BASE_FEATURE_NAMES |
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data = self.preprocess(data, self.config.name) |
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for row_id, row in data.iterrows(): |
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data_row = dict(row) |
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yield row_id, data_row |
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def preprocess(self, data, config): |
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for feature in _ENCODING_DICS: |
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encoding_function = partial(self.encode, feature) |
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data.loc[:, feature] = data[feature].apply(encoding_function) |
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data[["age"]].applymap(int) |
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data.drop("slope_of_peak_exercise", axis="columns", inplace=True) |
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data.drop("number_of_major_vessels_colored_by_flourosopy", axis="columns", inplace=True) |
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data.drop("thal", axis="columns", inplace=True) |
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data = data[data.serum_cholesterol != "?"] |
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data = data.infer_objects() |
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data = data[data.resting_blood_pressure != "?"] |
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data = data[data.fasting_blood_sugar != "?"] |
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data = data[data.rest_electrocardiographic_type != "?"] |
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data = data[data.maximum_heart_rate != "?"] |
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data = data[data.has_exercise_induced_angina != "?"] |
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data = data.astype({"is_male": bool, "has_exercise_induced_angina": bool, |
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"serum_cholesterol": float, "maximum_heart_rate": float, |
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"resting_blood_pressure": float, "fasting_blood_sugar": float}) |
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return data |
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def encode(self, feature, value): |
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if feature in _ENCODING_DICS: |
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return _ENCODING_DICS[feature][value] |
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raise ValueError(f"Unknown feature: {feature}") |
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