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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 = "Pol 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/pol/raw/main/pol.csv" |
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} |
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features_types_per_config = { |
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"pol": { |
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"f1": datasets.Value("int64"), |
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"f2": datasets.Value("int64"), |
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"f3": datasets.Value("int64"), |
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"f4": datasets.Value("int64"), |
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"f5": datasets.Value("int64"), |
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"f6": datasets.Value("int64"), |
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"f7": datasets.Value("int64"), |
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"f8": datasets.Value("int64"), |
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"f9": datasets.Value("int64"), |
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"f10": datasets.Value("int64"), |
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"f11": datasets.Value("int64"), |
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"f12": datasets.Value("int64"), |
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"f13": datasets.Value("int64"), |
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"f14": datasets.Value("int64"), |
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"f15": datasets.Value("int64"), |
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"f16": datasets.Value("int64"), |
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"f17": datasets.Value("int64"), |
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"f18": datasets.Value("int64"), |
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"f19": datasets.Value("int64"), |
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"f20": datasets.Value("int64"), |
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"f21": datasets.Value("int64"), |
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"f22": datasets.Value("int64"), |
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"f23": datasets.Value("int64"), |
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"f24": datasets.Value("int64"), |
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"f25": datasets.Value("int64"), |
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"f26": datasets.Value("int64"), |
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"f27": datasets.Value("int64"), |
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"f28": datasets.Value("int64"), |
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"f29": datasets.Value("int64"), |
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"f30": datasets.Value("int64"), |
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"f31": datasets.Value("int64"), |
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"f32": datasets.Value("int64"), |
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"f33": datasets.Value("int64"), |
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"f34": datasets.Value("int64"), |
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"f35": datasets.Value("int64"), |
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"f36": datasets.Value("int64"), |
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"f37": datasets.Value("int64"), |
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"f38": datasets.Value("int64"), |
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"f39": datasets.Value("int64"), |
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"f40": datasets.Value("int64"), |
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"f41": datasets.Value("int64"), |
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"f42": datasets.Value("int64"), |
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"f43": datasets.Value("int64"), |
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"f44": datasets.Value("int64"), |
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"f45": datasets.Value("int64"), |
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"f46": datasets.Value("int64"), |
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"f47": datasets.Value("int64"), |
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"f48": datasets.Value("int64"), |
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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 ElectricityConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(ElectricityConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Electricity(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "pol" |
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BUILDER_CONFIGS = [ |
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ElectricityConfig(name="pol", |
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description="Electricity 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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