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"""Congress""" |
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from typing import List |
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import datasets |
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import pandas |
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VERSION = datasets.Version("1.0.0") |
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_BASE_FEATURE_NAMES = [ |
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"party", |
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"pro_handicapped_infants", |
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"pro_water_project_cost_sharing", |
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"pro_adoption_of_the_budget_resolution", |
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"pro_physician_fee_freeze", |
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"pro_el_salvador_aid", |
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"pro_religious_groups_in_schools", |
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"pro_anti_satellite_test_ban", |
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"pro_aid_to_nicaraguan_contras", |
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"pro_mx_missile", |
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"pro_immigration", |
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"pro_synfuels_corporation_cutback", |
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"pro_education_spending", |
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"pro_superfund_right_to_sue", |
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"pro_crime", |
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"pro_duty_free_exports", |
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"pro_export_administration_act_south_africa", |
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] |
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DESCRIPTION = "Congress dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Congress" |
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_URLS = ("https://archive-beta.ics.uci.edu/dataset/105/congressional+voting+records") |
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_CITATION = """ |
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@misc{misc_congressional_voting_records_105, |
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title = {{Congressional Voting Records}}, |
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year = {1987}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5C01P}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/congress/raw/main/house-votes-84.data" |
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} |
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features_types_per_config = { |
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"voting": { |
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"pro_handicapped_infants": datasets.Value("bool"), |
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"pro_water_project_cost_sharing": datasets.Value("bool"), |
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"pro_adoption_of_the_budget_resolution": datasets.Value("bool"), |
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"pro_physician_fee_freeze": datasets.Value("bool"), |
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"pro_el_salvador_aid": datasets.Value("bool"), |
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"pro_religious_groups_in_schools": datasets.Value("bool"), |
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"pro_anti_satellite_test_ban": datasets.Value("bool"), |
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"pro_aid_to_nicaraguan_contras": datasets.Value("bool"), |
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"pro_mx_missile": datasets.Value("bool"), |
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"pro_immigration": datasets.Value("bool"), |
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"pro_synfuels_corporation_cutback": datasets.Value("bool"), |
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"pro_education_spending": datasets.Value("bool"), |
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"pro_superfund_right_to_sue": datasets.Value("bool"), |
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"pro_crime": datasets.Value("bool"), |
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"pro_duty_free_exports": datasets.Value("bool"), |
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"pro_export_administration_act_south_africa": datasets.Value("bool"), |
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"party": datasets.ClassLabel(num_classes=2, names=("democrat", "republican")), |
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} |
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} |
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
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class CongressConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(CongressConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Congress(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "voting" |
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BUILDER_CONFIGS = [ |
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CongressConfig(name="voting", |
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description="Binary classification of politician, either democrat or republican.") |
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] |
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def _info(self): |
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
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features=features_per_config[self.config.name]) |
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return info |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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downloads = dl_manager.download_and_extract(urls_per_split) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}) |
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] |
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def _generate_examples(self, filepath: str): |
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data = pandas.read_csv(filepath, header=None) |
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data = self.preprocess(data, config=self.config.name) |
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for row_id, row in data.iterrows(): |
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data_row = dict(row) |
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yield row_id, data_row |
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def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
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data.columns = _BASE_FEATURE_NAMES |
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vote_dictionary = { |
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"y": "pro", |
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"n": "against", |
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"?": "did_not_vote", |
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} |
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for feature in _BASE_FEATURE_NAMES[1:]: |
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data.loc[:, feature] = data[feature].apply(lambda x: vote_dictionary[x]) |
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data.loc[:, "party"] = data["party"].apply(lambda x: 0 if x == "democrat" else 1) |
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data = data.astype({"party": "int8"}) |
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data = data[list(features_types_per_config[config].keys())] |
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return data |
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