contraceptive.py

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}")