| """Monk Dataset""" |
|
|
| from typing import List |
| from functools import partial |
|
|
| import datasets |
|
|
| import pandas |
|
|
|
|
| VERSION = datasets.Version("1.0.0") |
| _ORIGINAL_FEATURE_NAMES = [ |
| "is_monk", |
| "head_shape", |
| "body_shape", |
| "is_smiling", |
| "holding", |
| "jacket_color", |
| "has_tie", |
| "ID" |
| ] |
| _BASE_FEATURE_NAMES = [ |
| "head_shape", |
| "body_shape", |
| "is_smiling", |
| "holding", |
| "jacket_color", |
| "has_tie", |
| "is_monk" |
| ] |
|
|
| _ENCODING_DICS = { |
| "head_shape": { |
| 0: "round", |
| 1: "square", |
| 2: "octagon", |
| }, |
| "body_shape": { |
| 0: "round", |
| 1: "square", |
| 2: "octagon", |
| }, |
| "holding": { |
| 0: "sword", |
| 1: "baloon", |
| 2: "flag", |
| }, |
| "jacket_color": { |
| 0: "red", |
| 1: "yellow", |
| 2: "green", |
| 3: "blue" |
| }, |
| "is_smiling": { |
| 1: True, |
| 0: False |
| }, |
| "has_tie": { |
| 1: True, |
| 0: False |
| } |
| } |
|
|
| DESCRIPTION = "Monk quality dataset." |
| _HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/70/monk+s+problems" |
| _URLS = ("https://archive-beta.ics.uci.edu/dataset/70/monk+s+problems") |
| _CITATION = """ |
| @misc{misc_monk's_problems_70, |
| author = {Wnek,J.}, |
| title = {{MONK's Problems}}, |
| year = {1992}, |
| howpublished = {UCI Machine Learning Repository}, |
| note = {{DOI}: \\url{10.24432/C5R30R}} |
| }""" |
|
|
| |
| urls_per_split = { |
| "monks1": { |
| "train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-1.train", |
| "test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-1.test" |
| }, |
| "monks2": { |
| "train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-2.train", |
| "test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-2.test" |
| }, |
| "monks3": { |
| "train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-3.train", |
| "test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-3.test" |
| } |
| } |
| features_types_per_config = { |
| "monks1": { |
| "head_shape": datasets.Value("string"), |
| "body_shape": datasets.Value("string"), |
| "is_smiling": datasets.Value("bool"), |
| "holding": datasets.Value("string"), |
| "jacket_color": datasets.Value("string"), |
| "has_tie": datasets.Value("bool"), |
| "is_monk": datasets.ClassLabel(num_classes=2) |
| } |
| |
| } |
| features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
|
|
|
|
| class MonkConfig(datasets.BuilderConfig): |
| def __init__(self, **kwargs): |
| super(MonkConfig, self).__init__(version=VERSION, **kwargs) |
| self.features = features_per_config[kwargs["name"]] |
|
|
|
|
| class Monk(datasets.GeneratorBasedBuilder): |
| |
| DEFAULT_CONFIG = "monks1" |
| BUILDER_CONFIGS = [ |
| MonkConfig(name="monks1", |
| description="Monk 1 problem."), |
| MonkConfig(name="monks2", |
| description="Monk 2 problem."), |
| MonkConfig(name="monks3", |
| description="Monk 3 problem.") |
| ] |
|
|
|
|
| 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, sep=" ") |
| data = self.preprocess(data, config=self.config.name) |
| print(data.head()) |
|
|
| for row_id, row in data.iterrows(): |
| data_row = dict(row) |
|
|
| yield row_id, data_row |
|
|
| def preprocess(self, data: pandas.DataFrame, config: str = "monks1") -> pandas.DataFrame: |
| data.columns = _BASE_FEATURE_NAMES |
| data.drop("ID", axis="columns", inplace=True) |
| data.loc[:, "has_tie"] = data.has_tie.apply(bool) |
| data.loc[:, "is_smiling"] = data.is_smiling.apply(bool) |
|
|
| for feature in _ENCODING_DICS: |
| encoding_function = partial(self.encode, feature) |
| data.loc[:, feature] = data[feature].apply(encoding_function) |
| |
| return data[list(features_types_per_config[config].keys())] |
|
|
| def encode(self, feature, value): |
| if feature in _ENCODING_DICS: |
| return _ENCODING_DICS[feature][value] |
| raise ValueError(f"Unknown feature: {feature}") |
|
|
