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"""Chess""" |
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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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"bkblk", |
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"bknwy", |
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"bkon8", |
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"bkona", |
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"bkspr", |
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"bkxbq", |
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"bkxcr", |
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"bkxwp", |
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"blxwp", |
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"bxqsq", |
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"cntxt", |
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"dsopp", |
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"dwipd", |
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"hdchk", |
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"katri", |
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"mulch", |
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"qxmsq", |
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"r2ar8", |
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"reskd", |
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"reskr", |
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"rimmx", |
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"rkxwp", |
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"rxmsq", |
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"simpl", |
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"skach", |
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"skewr", |
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"skrxp", |
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"spcop", |
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"stlmt", |
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"thrsk", |
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"wkcti", |
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"wkna8", |
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"wknck", |
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"wkovl", |
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"wkpos", |
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"white_wins" |
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] |
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DESCRIPTION = "Chess dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Chess" |
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_URLS = ("https://huggingface.co/datasets/mstz/chess/raw/chess.csv") |
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_CITATION = """ |
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@misc{misc_chess_(king-rook_vs._king-pawn)_22, |
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title = {{Chess (King-Rook vs. King-Pawn)}}, |
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year = {1989}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5DK5C}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/chess/raw/main/kr-vs-kp.data" |
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} |
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features_types_per_config = { |
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"chess": { |
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"bkblk": datasets.Value("string"), |
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"bknwy": datasets.Value("string"), |
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"bkon8": datasets.Value("string"), |
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"bkona": datasets.Value("string"), |
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"bkspr": datasets.Value("string"), |
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"bkxbq": datasets.Value("string"), |
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"bkxcr": datasets.Value("string"), |
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"bkxwp": datasets.Value("string"), |
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"blxwp": datasets.Value("string"), |
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"bxqsq": datasets.Value("string"), |
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"cntxt": datasets.Value("string"), |
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"dsopp": datasets.Value("string"), |
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"dwipd": datasets.Value("string"), |
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"hdchk": datasets.Value("string"), |
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"katri": datasets.Value("string"), |
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"mulch": datasets.Value("string"), |
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"qxmsq": datasets.Value("string"), |
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"r2ar8": datasets.Value("string"), |
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"reskd": datasets.Value("string"), |
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"reskr": datasets.Value("string"), |
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"rimmx": datasets.Value("string"), |
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"rkxwp": datasets.Value("string"), |
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"rxmsq": datasets.Value("string"), |
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"simpl": datasets.Value("string"), |
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"skach": datasets.Value("string"), |
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"skewr": datasets.Value("string"), |
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"skrxp": datasets.Value("string"), |
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"spcop": datasets.Value("string"), |
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"stlmt": datasets.Value("string"), |
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"thrsk": datasets.Value("string"), |
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"wkcti": datasets.Value("string"), |
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"wkna8": datasets.Value("string"), |
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"wknck": datasets.Value("string"), |
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"wkovl": datasets.Value("string"), |
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"wkpos": datasets.Value("string"), |
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"white_wins": 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 ChessConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(ChessConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Chess(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "chess" |
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BUILDER_CONFIGS = [ |
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ChessConfig(name="chess", |
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description="Chess 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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