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"""Heart Failure Dataset""" |
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from typing import List |
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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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"has_anaemia", |
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"creatinine_phosphokinase_concentration_in_blood", |
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"has_diabetes", |
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"heart_ejection_fraction", |
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"has_high_blood_pressure", |
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"platelets_concentration_in_blood", |
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"serum_creatinine_concentration_in_blood", |
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"serum_sodium_concentration_in_blood", |
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"sex", |
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"is_smoker", |
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"days_in_study", |
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"is_dead" |
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] |
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DESCRIPTION = "Heart Failure dataset." |
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_HOMEPAGE = "https://www.kaggle.com/datasets/ulrikthygepedersen/heart_failures" |
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_URLS = ("https://www.kaggle.com/datasets/ulrikthygepedersen/heart_failures") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/heart_failure/raw/main/heart_failure_clinical_records_dataset.csv", |
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} |
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features_types_per_config = { |
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"death": { |
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"age": datasets.Value("int8"), |
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"has_anaemia": datasets.Value("bool"), |
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"creatinine_phosphokinase_concentration_in_blood": datasets.Value("float64"), |
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"has_diabetes": datasets.Value("bool"), |
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"heart_ejection_fraction": datasets.Value("float64"), |
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"has_high_blood_pressure": datasets.Value("bool"), |
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"platelets_concentration_in_blood": datasets.Value("float64"), |
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"serum_creatinine_concentration_in_blood": datasets.Value("float64"), |
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"serum_sodium_concentration_in_blood": datasets.Value("float64"), |
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"is_male": datasets.Value("bool"), |
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"is_smoker": datasets.Value("bool"), |
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"days_in_study": datasets.Value("int64"), |
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"is_dead": 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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class HeartFailureConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(HeartFailureConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class HeartFailure(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "death" |
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BUILDER_CONFIGS = [ |
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HeartFailureConfig(name="death", |
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description="Binary classification, predict if the patient dies.") |
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] |
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def _info(self): |
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if self.config.name not in features_per_config: |
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raise ValueError(f"Unknown configuration: {self.config.name}") |
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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["train"]}), |
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] |
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def _generate_examples(self, filepath: str): |
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data = pandas.read_csv(filepath) |
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data = self.preprocess(data, config=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: pandas.DataFrame, config: str = "death") -> pandas.DataFrame: |
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data.columns = _BASE_FEATURE_NAMES |
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data = data.rename(columns={"sex": "is_male"}) |
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data = data.astype({"has_anaemia": "bool", "has_diabetes": "bool", "has_high_blood_pressure": "bool", "is_male": "bool", |
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"is_smoker": "bool"}) |
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return data |
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