Datasets:
Tasks:
Tabular Classification
Languages:
English
"""Ipums Dataset""" | |
from typing import List | |
from functools import partial | |
import datasets | |
import pandas | |
VERSION = datasets.Version("1.0.0") | |
_ENCODING_DICS = { | |
"class": { | |
"- 50000.": 0, | |
"50000+.": 1 | |
} | |
} | |
DESCRIPTION = "Ipums dataset." | |
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/127/ipums+census+database" | |
_URLS = ("https://archive-beta.ics.uci.edu/dataset/127/ipums+census+database") | |
_CITATION = """ | |
@misc{misc_ipums_census_database_127, | |
author = {Ruggles,Steven & Sobek,Matthew}, | |
title = {{IPUMS Census Database}}, | |
year = {1999}, | |
howpublished = {UCI Machine Learning Repository}, | |
note = {{DOI}: \\url{10.24432/C5BG63}} | |
} | |
""" | |
# Dataset info | |
urls_per_split = { | |
"train": "https://huggingface.co/datasets/mstz/ipums/resolve/main/ipums.csv" | |
} | |
features_types_per_config = { | |
"ipums": { | |
"age": datasets.Value("int64"), | |
"class_of_worker": datasets.Value("string"), | |
"detailed_industry_recode": datasets.Value("string"), | |
"detailed_occupation_recode": datasets.Value("string"), | |
"education": datasets.Value("string"), | |
"wage_per_hour": datasets.Value("int64"), | |
"enroll_in_edu_inst_last_wk": datasets.Value("string"), | |
"marital_stat": datasets.Value("string"), | |
"major_industry_code": datasets.Value("string"), | |
"major_occupation_code": datasets.Value("string"), | |
"race": datasets.Value("string"), | |
"hispanic_origin": datasets.Value("string"), | |
"sex": datasets.Value("string"), | |
"member_of_a_labor_union": datasets.Value("string"), | |
"reason_for_unemployment": datasets.Value("string"), | |
"full_or_part_time_employment_stat": datasets.Value("string"), | |
"capital_gains": datasets.Value("int64"), | |
"capital_losses": datasets.Value("int64"), | |
"dividends_from_stocks": datasets.Value("int64"), | |
"tax_filer_stat": datasets.Value("string"), | |
"region_of_previous_residence": datasets.Value("string"), | |
"state_of_previous_residence": datasets.Value("string"), | |
"detailed_household_and_family_stat": datasets.Value("string"), | |
"detailed_household_summary_in_household": datasets.Value("string"), | |
# "instance_weight": datasets.Value("int64"), | |
"migration_code_change_in_msa": datasets.Value("string"), | |
"migration_code_change_in_reg": datasets.Value("string"), | |
"migration_code_move_within_reg": datasets.Value("string"), | |
"live_in_this_house_1_year_ago": datasets.Value("string"), | |
"migration_prev_res_in_sunbelt": datasets.Value("string"), | |
"num_persons_worked_for_employer": datasets.Value("int64"), | |
"family_members_under_18": datasets.Value("string"), | |
"country_of_birth_father": datasets.Value("string"), | |
"country_of_birth_mother": datasets.Value("string"), | |
"country_of_birth_self": datasets.Value("string"), | |
"citizenship": datasets.Value("string"), | |
"own_business_or_self_employed": datasets.Value("string"), | |
"fill_inc_questionnaire_for_veteran_admin": datasets.Value("string"), | |
"veterans_benefits": datasets.Value("string"), | |
"weeks_worked_in_year": datasets.Value("int64"), | |
"year": datasets.Value("int64"), | |
"class": datasets.ClassLabel(num_classes=2) | |
} | |
} | |
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} | |
class IpumsConfig(datasets.BuilderConfig): | |
def __init__(self, **kwargs): | |
super(IpumsConfig, self).__init__(version=VERSION, **kwargs) | |
self.features = features_per_config[kwargs["name"]] | |
class Ipums(datasets.GeneratorBasedBuilder): | |
# dataset versions | |
DEFAULT_CONFIG = "ipums" | |
BUILDER_CONFIGS = [IpumsConfig(name="ipums", description="Ipums 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) | |
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: | |
for feature in _ENCODING_DICS: | |
encoding_function = partial(self.encode, feature) | |
data.loc[:, feature] = data[feature].apply(encoding_function) | |
data.drop("instance_weight", axis="columns", inplace=True) | |
data = data.rename(columns={"instance migration_code_change_in_msa": "migration_code_change_in_msa"}) | |
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}") | |