Datasets:
File size: 5,657 Bytes
ab3049c afcd003 ab3049c fb946a6 ab3049c f4c6751 ab3049c 8c81ba2 ab3049c a65a199 ab3049c 0dd6d6e ab3049c 809b7ad afcd003 ab3049c 9765a8d ab3049c 9ccfb28 ab3049c 6db76e1 ab3049c b4fdd7c de43b15 ab3049c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
"""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": {
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}}
}"""
# Dataset info
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):
# dataset versions
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:
print(data.head())
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]
print(data.head())
for feature in _ENCODING_DICS:
print(feature)
print(data[feature].unique())
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}")
|