Datasets:
Tasks:
Tabular Classification
Languages:
English
File size: 4,846 Bytes
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"""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}")
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