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"""Blood""" |
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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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"months_since_last_donation", |
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"total_donation", |
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"total_blood_donated_in_cc", |
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"months_since_last_donation", |
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"has_donated_last_month" |
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] |
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DESCRIPTION = "Blood dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Blood" |
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_URLS = ("https://huggingface.co/datasets/mstz/blood/raw/blood.csv") |
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_CITATION = """ |
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@misc{misc_blood_transfusion_service_center_176, |
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author = {Yeh,I-Cheng}, |
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title = {{Blood Transfusion Service Center}}, |
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year = {2008}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5GS39}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/blood/raw/main/transfusion.data" |
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} |
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features_types_per_config = { |
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"blood": { |
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"months_since_last_donation": datasets.Value("int64"), |
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"total_donation": datasets.Value("int64"), |
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"total_blood_donated_in_cc": datasets.Value("int64"), |
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"months_since_last_donation": datasets.Value("int64"), |
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"has_donated_last_month": 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 BloodConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(BloodConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Blood(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "blood" |
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BUILDER_CONFIGS = [ |
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BloodConfig(name="blood", |
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description="Blood for binary classification.") |
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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["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 = data.astype({ |
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"months_since_last_donation": "int64", |
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"total_donation": "int64", |
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"total_blood_donated_in_cc": "int64", |
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"months_since_last_donation": "int64", |
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"has_donated_last_month": "int64" |
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}) |
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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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