Datasets:
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from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Contraceptive dataset from the UCI repository."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/30/contraceptive+method+choice"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/30/contraceptive+method+choice")
_CITATION = """
@misc{misc_contraceptive_method_choice_30,
author = {Lim,Tjen-Sien},
title = {{Contraceptive Method Choice}},
year = {1997},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C59W2D}}
}"""
# Dataset info
_BASE_FEATURE_NAMES = [
"age_of_wife",
"education_level_of_wife",
"education_level_of_husband",
"number_of_born_children",
"is_wife_muslim",
"is_wife_a_housekeeper",
"job_of_husband",
"standard_of_living",
"is_media_exposure_negative",
"use_contraceptives"
]
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/contraceptive/raw/main/cmc.data"
}
features_types_per_config = {
"contraceptive": {
"age_of_wife": datasets.Value("int8"),
"education_level_of_wife": datasets.Value("int8"),
"education_level_of_husband": datasets.Value("int8"),
"number_of_born_children": datasets.Value("int8"),
"is_wife_muslim": datasets.Value("bool"),
"is_wife_a_housekeeper": datasets.Value("bool"),
"job_of_husband": datasets.Value("string"),
"standard_of_living": datasets.Value("int8"),
"is_media_exposure_negative": datasets.Value("bool"),
"use_contraceptives": 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}
_ENCODING_DICS = {
"use_contraceptives": {
1: 0,
2: 1,
3: 1
}
}
class ContraceptiveConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(ContraceptiveConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Contraceptive(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "contraceptive"
BUILDER_CONFIGS = [
ContraceptiveConfig(name="contraceptive",
description="Contraceptive 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 = self.preprocess(data)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
data.columns = _BASE_FEATURE_NAMES
for feature in _ENCODING_DICS:
encoding_function = partial(self.encode, feature)
data.loc[:, feature] = data[feature].apply(encoding_function)
return data
def encode(self, feature, value):
if feature in _ENCODING_DICS:
return _ENCODING_DICS[feature][value]
raise ValueError(f"Unknown feature: {feature}")
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