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"""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}}
}"""
# Dataset info
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):
# dataset versions
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[["x_wins"]].applymap(lambda x: 1 if x == "positive" else 0)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
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