| """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}} |
| } |
| """ |
|
|
| |
| 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"), |
| |
| "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): |
| |
| 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}") |
|
|
