| | from typing import List |
| |
|
| | import datasets |
| |
|
| | import pandas |
| |
|
| |
|
| | VERSION = datasets.Version("1.0.0") |
| | _BASE_FEATURE_NAMES = [ |
| | "party", |
| | "vote_on_handicapped_infants_bill", |
| | "vote_on_water_project_cost_sharing_bill", |
| | "vote_on_adoption_of_the_budget_resolution_bill", |
| | "vote_on_physician_fee_freeze_bill", |
| | "vote_on_el_salvador_aid_bill", |
| | "vote_on_religious_groups_in_schools_bill", |
| | "vote_on_anti_satellite_test_ban_bill", |
| | "vote_on_aid_to_nicaraguan_contras_bill", |
| | "vote_on_mx_missile_bill", |
| | "vote_on_immigration_bill", |
| | "vote_on_synfuels_corporation_cutback_bill", |
| | "vote_on_education_spending_bill", |
| | "vote_on_superfund_right_to_sue_bill", |
| | "vote_on_crime_bill", |
| | "vote_on_duty_free_exports_bill", |
| | "vote_on_export_administration_act_south_africa_bill", |
| | ] |
| |
|
| | DESCRIPTION = "Congress dataset from the UCI ML repository." |
| | _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Congress" |
| | _URLS = ("https://archive-beta.ics.uci.edu/dataset/105/congressional+voting+records") |
| | _CITATION = """ |
| | @misc{misc_congressional_voting_records_105, |
| | title = {{Congressional Voting Records}}, |
| | year = {1987}, |
| | howpublished = {UCI Machine Learning Repository}, |
| | note = {{DOI}: \\url{10.24432/C5C01P}} |
| | }""" |
| |
|
| | |
| | urls_per_split = { |
| | "train": "https://huggingface.co/datasets/mstz/congress/raw/main/house-votes-84.data" |
| | } |
| | features_types_per_config = { |
| | "voting": { |
| | "vote_on_handicapped_infants_bill": datasets.Value("string"), |
| | "vote_on_water_project_cost_sharing_bill": datasets.Value("string"), |
| | "vote_on_adoption_of_the_budget_resolution_bill": datasets.Value("string"), |
| | "vote_on_physician_fee_freeze_bill": datasets.Value("string"), |
| | "vote_on_el_salvador_aid_bill": datasets.Value("string"), |
| | "vote_on_religious_groups_in_schools_bill": datasets.Value("string"), |
| | "vote_on_anti_satellite_test_ban_bill": datasets.Value("string"), |
| | "vote_on_aid_to_nicaraguan_contras_bill": datasets.Value("string"), |
| | "vote_on_mx_missile_bill": datasets.Value("string"), |
| | "vote_on_immigration_bill": datasets.Value("string"), |
| | "vote_on_synfuels_corporation_cutback_bill": datasets.Value("string"), |
| | "vote_on_education_spending_bill": datasets.Value("string"), |
| | "vote_on_superfund_right_to_sue_bill": datasets.Value("string"), |
| | "vote_on_crime_bill": datasets.Value("string"), |
| | "vote_on_duty_free_exports_bill": datasets.Value("string"), |
| | "vote_on_export_administration_act_south_africa_bill": datasets.Value("string"), |
| | "party": datasets.ClassLabel(num_classes=2, names=("democrat", "republican")), |
| | } |
| | } |
| | features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
| |
|
| |
|
| | class CongressConfig(datasets.BuilderConfig): |
| | def __init__(self, **kwargs): |
| | super(CongressConfig, self).__init__(version=VERSION, **kwargs) |
| | self.features = features_per_config[kwargs["name"]] |
| |
|
| |
|
| | class Congress(datasets.GeneratorBasedBuilder): |
| | |
| | DEFAULT_CONFIG = "voting" |
| | BUILDER_CONFIGS = [ |
| | CongressConfig(name="voting", |
| | description="Binary classification of politician, either democrat or republican.") |
| | ] |
| |
|
| |
|
| | 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, config=self.config.name) |
| |
|
| | for row_id, row in data.iterrows(): |
| | data_row = dict(row) |
| |
|
| | yield row_id, data_row |
| |
|
| |
|
| | def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
| | data.columns = _BASE_FEATURE_NAMES |
| | vote_dictionary = { |
| | "y": "pro", |
| | "n": "against", |
| | "?": "did_not_vote", |
| | } |
| | for feature in _BASE_FEATURE_NAMES[1:]: |
| | data.loc[:, feature] = data[feature].apply(lambda x: vote_dictionary[x]) |
| | data.loc[:, "party"] = data["party"].apply(lambda x: 0 if x == "democrat" else 1) |
| | data = data.astype({"party": "int8"}) |
| |
|
| | data = data[list(features_types_per_config[config].keys())] |
| |
|
| | return data |
| |
|
