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"""Monk Dataset""" |
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from typing import List |
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from functools import partial |
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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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_ORIGINAL_FEATURE_NAMES = [ |
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"is_monk", |
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"head_shape", |
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"body_shape", |
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"is_smiling", |
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"holding", |
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"jacket_color", |
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"has_tie", |
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"ID" |
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] |
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_BASE_FEATURE_NAMES = [ |
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"head_shape", |
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"body_shape", |
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"is_smiling", |
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"holding", |
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"jacket_color", |
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"has_tie", |
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"is_monk" |
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] |
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_ENCODING_DICS = { |
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"head_shape": { |
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0: "round", |
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1: "square", |
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2: "octagon", |
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}, |
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"body_shape": { |
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0: "round", |
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1: "square", |
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2: "octagon", |
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}, |
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"holding": { |
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0: "sword", |
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1: "baloon", |
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2: "flag", |
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}, |
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"jacket_color": { |
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0: "red", |
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1: "yellow", |
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2: "green", |
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3: "blue" |
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}, |
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"is_smiling": { |
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1: True, |
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0: False |
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}, |
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"has_tie": { |
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1: True, |
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0: False |
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} |
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} |
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DESCRIPTION = "Monk quality dataset." |
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_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/70/monk+s+problems" |
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_URLS = ("https://archive-beta.ics.uci.edu/dataset/70/monk+s+problems") |
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_CITATION = """ |
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@misc{misc_monk's_problems_70, |
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author = {Wnek,J.}, |
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title = {{MONK's Problems}}, |
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year = {1992}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5R30R}} |
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}""" |
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urls_per_split = { |
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"monks1": { |
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"train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-1.train", |
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"test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-1.test" |
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}, |
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"monks2": { |
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"train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-2.train", |
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"test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-2.test" |
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}, |
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"monks3": { |
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"train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-3.train", |
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"test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-3.test" |
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} |
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} |
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features_types_per_config = { |
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"monks1": { |
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"head_shape": datasets.Value("string"), |
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"body_shape": datasets.Value("string"), |
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"is_smiling": datasets.Value("bool"), |
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"holding": datasets.Value("string"), |
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"jacket_color": datasets.Value("string"), |
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"has_tie": datasets.Value("bool"), |
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"is_monk": datasets.ClassLabel(num_classes=2) |
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}, |
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"monks2": { |
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"head_shape": datasets.Value("string"), |
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"body_shape": datasets.Value("string"), |
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"is_smiling": datasets.Value("bool"), |
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"holding": datasets.Value("string"), |
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"jacket_color": datasets.Value("string"), |
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"has_tie": datasets.Value("bool"), |
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"is_monk": datasets.ClassLabel(num_classes=2) |
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}, |
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"monks3": { |
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"head_shape": datasets.Value("string"), |
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"body_shape": datasets.Value("string"), |
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"is_smiling": datasets.Value("bool"), |
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"holding": datasets.Value("string"), |
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"jacket_color": datasets.Value("string"), |
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"has_tie": datasets.Value("bool"), |
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"is_monk": datasets.ClassLabel(num_classes=2) |
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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 MonkConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(MonkConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Monk(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "monks1" |
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BUILDER_CONFIGS = [ |
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MonkConfig(name="monks1", |
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description="Monk 1 problem."), |
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MonkConfig(name="monks2", |
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description="Monk 2 problem."), |
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MonkConfig(name="monks3", |
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description="Monk 3 problem.") |
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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[self.config.name]["train"]}), |
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] |
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def _generate_examples(self, filepath: str): |
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data = pandas.read_csv(filepath, header=None, sep=" ") |
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data = self.preprocess(data, config=self.config.name) |
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print(data.head()) |
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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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def preprocess(self, data: pandas.DataFrame, config: str = "monks1") -> pandas.DataFrame: |
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data.columns = _BASE_FEATURE_NAMES |
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data.drop("ID", axis="columns", inplace=True) |
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data.loc[:, "has_tie"] = data.has_tie.apply(bool) |
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data.loc[:, "is_smiling"] = data.is_smiling.apply(bool) |
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for feature in _ENCODING_DICS: |
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encoding_function = partial(self.encode, feature) |
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data.loc[:, feature] = data[feature].apply(encoding_function) |
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return data[list(features_types_per_config[config].keys())] |
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def encode(self, feature, value): |
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if feature in _ENCODING_DICS: |
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return _ENCODING_DICS[feature][value] |
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raise ValueError(f"Unknown feature: {feature}") |
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