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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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DESCRIPTION = "House16 dataset from the OpenML repository." |
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_HOMEPAGE = "https://www.openml.org/search?type=data&sort=runs&id=722&status=active" |
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_URLS = ("https://www.openml.org/search?type=data&sort=runs&id=722&status=active") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/house16/raw/main/house_16H.csv" |
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} |
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features_types_per_config = { |
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"house16": { |
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"P1": datasets.Value("int64"), |
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"P5p1": datasets.Value("float64"), |
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"P6p2": datasets.Value("float64"), |
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"P11p4": datasets.Value("float64"), |
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"P14p9": datasets.Value("float64"), |
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"P15p1": datasets.Value("float64"), |
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"P15p3": datasets.Value("float64"), |
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"P16p2": datasets.Value("float64"), |
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"P18p2": datasets.Value("float64"), |
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"P27p4": datasets.Value("float64"), |
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"H2p2": datasets.Value("float64"), |
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"H8p2": datasets.Value("float64"), |
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"H10p1": datasets.Value("float64"), |
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"H13p1": datasets.Value("float64"), |
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"H18pA": datasets.Value("float64"), |
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"H40p4": datasets.Value("float64"), |
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"binaryClass": 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 House16Config(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(House16Config, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class House16(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "house16" |
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BUILDER_CONFIGS = [ |
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House16Config(name="house16", |
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description="House16 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) |
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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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