|
"""Monk Dataset""" |
|
|
|
from typing import List |
|
from functools import partial |
|
|
|
import datasets |
|
|
|
import pandas |
|
|
|
|
|
VERSION = datasets.Version("1.0.0") |
|
_ORIGINAL_FEATURE_NAMES = [ |
|
"empty", |
|
"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": { |
|
1: "round", |
|
2: "square", |
|
3: "octagon", |
|
}, |
|
"body_shape": { |
|
1: "round", |
|
2: "square", |
|
3: "octagon", |
|
}, |
|
"holding": { |
|
1: "sword", |
|
2: "baloon", |
|
3: "flag", |
|
}, |
|
"jacket_color": { |
|
1: "red", |
|
2: "yellow", |
|
3: "green", |
|
4: "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) |
|
}, |
|
"monks2": { |
|
"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) |
|
}, |
|
"monks3": { |
|
"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[self.config.name]["train"]}), |
|
] |
|
|
|
def _generate_examples(self, filepath: str): |
|
data = pandas.read_csv(filepath, header=None, sep=" ") |
|
data = self.preprocess(data, config=self.config.name) |
|
|
|
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 = _ORIGINAL_FEATURE_NAMES |
|
data.drop("ID", axis="columns", inplace=True) |
|
data.drop("empty", axis="columns", inplace=True) |
|
data.loc[:, "has_tie"] = data.has_tie.apply(bool) |
|
data.loc[:, "is_smiling"] = data.is_smiling.apply(bool) |
|
data = data[_BASE_FEATURE_NAMES] |
|
|
|
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}") |
|
|
