| 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 |
|
|
