Datasets:
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"""Chess"""
from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
"bkblk",
"bknwy",
"bkon8",
"bkona",
"bkspr",
"bkxbq",
"bkxcr",
"bkxwp",
"blxwp",
"bxqsq",
"cntxt",
"dsopp",
"dwipd",
"hdchk",
"katri",
"mulch",
"qxmsq",
"r2ar8",
"reskd",
"reskr",
"rimmx",
"rkxwp",
"rxmsq",
"simpl",
"skach",
"skewr",
"skrxp",
"spcop",
"stlmt",
"thrsk",
"wkcti",
"wkna8",
"wknck",
"wkovl",
"wkpos",
"white_wins"
]
DESCRIPTION = "Chess dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Chess"
_URLS = ("https://huggingface.co/datasets/mstz/chess/raw/chess.csv")
_CITATION = """
@misc{misc_chess_(king-rook_vs._king-pawn)_22,
title = {{Chess (King-Rook vs. King-Pawn)}},
year = {1989},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5DK5C}}
}"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/chess/raw/main/kr-vs-kp.data"
}
features_types_per_config = {
"chess": {
"bkblk": datasets.Value("string"),
"bknwy": datasets.Value("string"),
"bkon8": datasets.Value("string"),
"bkona": datasets.Value("string"),
"bkspr": datasets.Value("string"),
"bkxbq": datasets.Value("string"),
"bkxcr": datasets.Value("string"),
"bkxwp": datasets.Value("string"),
"blxwp": datasets.Value("string"),
"bxqsq": datasets.Value("string"),
"cntxt": datasets.Value("string"),
"dsopp": datasets.Value("string"),
"dwipd": datasets.Value("string"),
"hdchk": datasets.Value("string"),
"katri": datasets.Value("string"),
"mulch": datasets.Value("string"),
"qxmsq": datasets.Value("string"),
"r2ar8": datasets.Value("string"),
"reskd": datasets.Value("string"),
"reskr": datasets.Value("string"),
"rimmx": datasets.Value("string"),
"rkxwp": datasets.Value("string"),
"rxmsq": datasets.Value("string"),
"simpl": datasets.Value("string"),
"skach": datasets.Value("string"),
"skewr": datasets.Value("string"),
"skrxp": datasets.Value("string"),
"spcop": datasets.Value("string"),
"stlmt": datasets.Value("string"),
"thrsk": datasets.Value("string"),
"wkcti": datasets.Value("string"),
"wkna8": datasets.Value("string"),
"wknck": datasets.Value("string"),
"wkovl": datasets.Value("string"),
"wkpos": datasets.Value("string"),
"wtoeg": datasets.Value("string"),
"white_wins": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
}
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class ChessConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(ChessConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Chess(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "chess"
BUILDER_CONFIGS = [
ChessConfig(name="chess",
description="Chess for binary classification.")
]
def _info(self):
info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
features=features_per_config[self.config.name])
return info
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
downloads = dl_manager.download_and_extract(urls_per_split)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
]
def _generate_examples(self, filepath: str):
data = pandas.read_csv(filepath)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
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