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"""TwoNorm""" |
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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 = "TwoNorm dataset from the OpenML repository." |
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_HOMEPAGE = "https://www.openml.org/search?type=data&status=active&id=1507" |
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_URLS = ("https://www.openml.org/search?type=data&status=active&id=1507") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/twonorm/raw/main/twonorm.csv" |
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} |
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features_types_per_config = { |
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"twonorm": { |
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"V1": datasets.Value("float64"), |
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"V2": datasets.Value("float64"), |
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"V3": datasets.Value("float64"), |
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"V4": datasets.Value("float64"), |
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"V5": datasets.Value("float64"), |
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"V6": datasets.Value("float64"), |
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"V7": datasets.Value("float64"), |
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"V8": datasets.Value("float64"), |
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"V9": datasets.Value("float64"), |
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"V10": datasets.Value("float64"), |
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"V11": datasets.Value("float64"), |
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"V12": datasets.Value("float64"), |
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"V13": datasets.Value("float64"), |
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"V14": datasets.Value("float64"), |
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"V15": datasets.Value("float64"), |
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"V16": datasets.Value("float64"), |
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"V17": datasets.Value("float64"), |
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"V18": datasets.Value("float64"), |
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"V19": datasets.Value("float64"), |
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"V20": datasets.Value("float64"), |
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"class": 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 TwoNormConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(TwoNormConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class TwoNorm(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "twonorm" |
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BUILDER_CONFIGS = [ |
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TwoNormConfig(name="twonorm", |
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description="TwoNorm 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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