uscensus.py

"""Uscensus Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {}
_BASE_FEATURE_NAMES = [
	"caseid",
	"dAge",
	"dAncstry1",
	"dAncstry2",
	"iAvail",
	"iCitizen",
	"iClass",
	"dDepart",
	"iDisabl1",
	"iDisabl2",
	"iEnglish",
	"iFeb55",
	"iFertil",
	"dHispanic",
	"dHour89",
	"dHours",
	"iImmigr",
	"dIncome1",
	"dIncome2",
	"dIncome3",
	"dIncome4",
	"dIncome5",
	"dIncome6",
	"dIncome7",
	"dIncome8",
	"dIndustry",
	"iKorean",
	"iLang1",
	"iLooking",
	"iMarital",
	"iMay75880",
	"iMeans",
	"iMilitary",
	"iMobility",
	"iMobillim",
	"dOccup",
	"iOthrserv",
	"iPerscare",
	"dPOB",
	"dPoverty",
	"dPwgt1",
	"iRagechld",
	"dRearning",
	"iRelat1",
	"iRelat2",
	"iRemplpar",
	"iRiders",
	"iRlabor",
	"iRownchld",
	"dRpincome",
	"iRPOB",
	"iRrelchld",
	"iRspouse",
	"iRvetserv",
	"iSchool",
	"iSept80",
	"iSex",
	"iSubfam1",
	"iSubfam2",
	"iTmpabsnt",
	"dTravtime",
	"iVietnam",
	"dWeek89",
	"iWork89",
	"iWorklwk",
	"iWWII",
	"iYearsch",
	"iYearwrk",
	"dYrsserv",
]

DESCRIPTION = "Uscensus dataset."
_HOMEPAGE = ""
_URLS = ("")
_CITATION = """"""

# Dataset info
urls_per_split = {
	"train": "https://huggingface.co/datasets/mstz/uscensus/resolve/main/uscensus1990.csv"
}
features_types_per_config = {
	"uscensus": {
		"dAge": datasets.Value("int64"),
		"dAncstry1": datasets.Value("int64"),
		"dAncstry2": datasets.Value("int64"),
		"iAvail": datasets.Value("int64"),
		"iCitizen": datasets.Value("int64"),
		"iClass": datasets.Value("int64"),
		"dDepart": datasets.Value("int64"),
		"iDisabl1": datasets.Value("int64"),
		"iDisabl2": datasets.Value("int64"),
		"iEnglish": datasets.Value("int64"),
		"iFeb55": datasets.Value("int64"),
		"iFertil": datasets.Value("int64"),
		"dHispanic": datasets.Value("int64"),
		"dHour89": datasets.Value("int64"),
		"dHours": datasets.Value("int64"),
		"iImmigr": datasets.Value("int64"),
		"dIncome1": datasets.Value("int64"),
		"dIncome2": datasets.Value("int64"),
		"dIncome3": datasets.Value("int64"),
		"dIncome4": datasets.Value("int64"),
		"dIncome5": datasets.Value("int64"),
		"dIncome6": datasets.Value("int64"),
		"dIncome7": datasets.Value("int64"),
		"dIncome8": datasets.Value("int64"),
		"dIndustry": datasets.Value("int64"),
		"iKorean": datasets.Value("int64"),
		"iLang1": datasets.Value("int64"),
		"iLooking": datasets.Value("int64"),
		"iMarital": datasets.Value("int64"),
		"iMay75880": datasets.Value("int64"),
		"iMeans": datasets.Value("int64"),
		"iMilitary": datasets.Value("int64"),
		"iMobility": datasets.Value("int64"),
		"iMobillim": datasets.Value("int64"),
		"dOccup": datasets.Value("int64"),
		"iOthrserv": datasets.Value("int64"),
		"iPerscare": datasets.Value("int64"),
		"dPOB": datasets.Value("int64"),
		"dPoverty": datasets.Value("int64"),
		"dPwgt1": datasets.Value("int64"),
		"iRagechld": datasets.Value("int64"),
		"dRearning": datasets.Value("int64"),
		"iRelat1": datasets.Value("int64"),
		"iRelat2": datasets.Value("int64"),
		"iRemplpar": datasets.Value("int64"),
		"iRiders": datasets.Value("int64"),
		"iRlabor": datasets.Value("int64"),
		"iRownchld": datasets.Value("int64"),
		"dRpincome": datasets.Value("int64"),
		"iRPOB": datasets.Value("int64"),
		"iRrelchld": datasets.Value("int64"),
		"iRspouse": datasets.Value("int64"),
		"iRvetserv": datasets.Value("int64"),
		"iSchool": datasets.Value("int64"),
		"iSept80": datasets.Value("int64"),
		"iSex": datasets.Value("int64"),
		"iSubfam1": datasets.Value("int64"),
		"iSubfam2": datasets.Value("int64"),
		"iTmpabsnt": datasets.Value("int64"),
		"dTravtime": datasets.Value("int64"),
		"iVietnam": datasets.Value("int64"),
		"dWeek89": datasets.Value("int64"),
		"iWork89": datasets.Value("int64"),
		"iWorklwk": datasets.Value("int64"),
		"iWWII": datasets.Value("int64"),
		"iYearsch": datasets.Value("int64"),
		"iYearwrk": datasets.Value("int64"),
		"dYrsserv": datasets.ClassLabel(num_classes=3)
	}
}

features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


class UscensusConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
		super(UscensusConfig, self).__init__(version=VERSION, **kwargs)
		self.features = features_per_config[kwargs["name"]]


class Uscensus(datasets.GeneratorBasedBuilder):
	# dataset versions
	DEFAULT_CONFIG = "uscensus"
	BUILDER_CONFIGS = [UscensusConfig(name="uscensus", description="Uscensus 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) -> pandas.DataFrame:
		data.columns = _BASE_FEATURE_NAMES

		for feature in _ENCODING_DICS:
			encoding_function = partial(self.encode, feature)
			data[feature] = data[feature].apply(encoding_function)
				
		return data[list(features_types_per_config[self.config.name].keys())]

	def encode(self, feature, value):
		if feature in _ENCODING_DICS:
			return _ENCODING_DICS[feature][value]
		raise ValueError(f"Unknown feature: {feature}")