Datasets:
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"""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}")
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