| | """TicTacToe""" |
| |
|
| | from typing import List |
| |
|
| | import datasets |
| |
|
| | import pandas |
| |
|
| |
|
| | VERSION = datasets.Version("1.0.0") |
| | _BASE_FEATURE_NAMES = [ |
| | "top_left_square", |
| | "top_middle_square", |
| | "top_right_square", |
| | "middle_left_square", |
| | "middle_middle_square", |
| | "middle_right_square", |
| | "bottom_left_square", |
| | "bottom_middle_square", |
| | "bottom_right_square", |
| | "x_wins" |
| | ] |
| |
|
| | DESCRIPTION = "TicTacToe dataset from the UCI ML repository." |
| | _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/TicTacToe" |
| | _URLS = ("https://archive.ics.uci.edu/ml/datasets/TicTacToe") |
| | _CITATION = """ |
| | @misc{misc_tic-tac-toe_endgame_101, |
| | author = {Aha,David}, |
| | title = {{Tic-Tac-Toe Endgame}}, |
| | year = {1991}, |
| | howpublished = {UCI Machine Learning Repository}, |
| | note = {{DOI}: \\url{10.24432/C5688J}} |
| | }""" |
| |
|
| | |
| | urls_per_split = { |
| | "train": "https://huggingface.co/datasets/mstz/tic_tac_toe/raw/main/tic-tac-toe.data" |
| | } |
| | features_types_per_config = { |
| | "tic_tac_toe": { |
| | "top_left_square": datasets.Value("string"), |
| | "top_middle_square": datasets.Value("string"), |
| | "top_right_square": datasets.Value("string"), |
| | "middle_left_square": datasets.Value("string"), |
| | "middle_middle_square": datasets.Value("string"), |
| | "middle_right_square": datasets.Value("string"), |
| | "bottom_left_square": datasets.Value("string"), |
| | "bottom_middle_square": datasets.Value("string"), |
| | "bottom_right_square": datasets.Value("string"), |
| | "x_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 TicTacToeConfig(datasets.BuilderConfig): |
| | def __init__(self, **kwargs): |
| | super(TicTacToeConfig, self).__init__(version=VERSION, **kwargs) |
| | self.features = features_per_config[kwargs["name"]] |
| |
|
| |
|
| | class TicTacToe(datasets.GeneratorBasedBuilder): |
| | |
| | DEFAULT_CONFIG = "tic_tac_toe" |
| | BUILDER_CONFIGS = [ |
| | TicTacToeConfig(name="tic_tac_toe", |
| | description="TicTacToe 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, header=None) |
| | data.columns = _BASE_FEATURE_NAMES |
| | data.loc[:, "x_wins"] = data.x_wins.apply(lambda x: 1 if x == "positive" else 0) |
| |
|
| | for row_id, row in data.iterrows(): |
| | data_row = dict(row) |
| |
|
| | yield row_id, data_row |
| |
|
