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"""Compas Dataset""" |
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from typing import List |
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import datetime |
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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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_ORIGINAL_FEATURE_NAMES = [ |
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"id", |
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"name", |
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"first", |
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"last", |
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"compas_screening_date", |
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"sex", |
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"dob", |
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"age", |
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"age_cat", |
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"race", |
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"juv_fel_count", |
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"decile_score", |
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"juv_misd_count", |
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"juv_other_count", |
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"priors_count", |
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"days_b_screening_arrest", |
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"c_jail_in", |
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"c_jail_out", |
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"c_case_number", |
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"c_offense_date", |
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"c_arrest_date", |
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"c_days_from_compas", |
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"c_charge_degree", |
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"c_charge_desc", |
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"is_recid", |
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"r_case_number", |
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"r_charge_degree", |
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"r_days_from_arrest", |
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"r_offense_date", |
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"r_charge_desc", |
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"r_jail_in", |
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"r_jail_out", |
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"violent_recid", |
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"is_violent_recid", |
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"vr_case_number", |
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"vr_charge_degree", |
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"vr_offense_date", |
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"vr_charge_desc", |
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"type_of_assessment", |
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"decile_score", |
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"score_text", |
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"screening_date", |
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"v_type_of_assessment", |
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"v_decile_score", |
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"v_score_text", |
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"v_screening_date", |
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"in_custody", |
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"out_custody", |
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"priors_count", |
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"start", |
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"end", |
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"event", |
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"two_year_recid", |
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"two_year_recid" |
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] |
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_BASE_FEATURE_NAMES = [ |
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"sex", |
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"age", |
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"race", |
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"number_of_juvenile_fellonies", |
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"decile_score", |
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"number_of_juvenile_misdemeanors", |
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"number_of_other_juvenile_offenses", |
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"number_of_prior_offenses", |
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"days_before_screening_arrest", |
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"is_recidivous", |
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"days_of_recidividity_after_arrest", |
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"days_in_custody", |
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"is_violent_recidivous", |
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"violence_decile_score", |
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"two_year_recidivous", |
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] |
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DESCRIPTION = "COMPAS dataset for recidivism prediction." |
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_HOMEPAGE = "https://github.com/propublica/compas-analysis" |
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_URLS = ("https://huggingface.co/datasets/mstz/compas/raw/main/compas-scores-two-years-violent.csv") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/compas/raw/main/compas-scores-two-years-violent.csv", |
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} |
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features_types_per_config = { |
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"encoding": { |
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"feature": datasets.Value("string"), |
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"original_value": datasets.Value("string"), |
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"encoded_value": datasets.Value("int8"), |
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}, |
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"two-years-recidividity": { |
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"sex": datasets.Value("int64"), |
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"age": datasets.Value("int64"), |
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"race": datasets.Value("int64"), |
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"number_of_juvenile_fellonies": datasets.Value("int64"), |
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"decile_score": datasets.Value("int64"), |
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"number_of_juvenile_misdemeanors": datasets.Value("int64"), |
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"number_of_other_juvenile_offenses": datasets.Value("int64"), |
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"number_of_prior_offenses": datasets.Value("int64"), |
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"days_before_screening_arrest": datasets.Value("int64"), |
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"is_recidivous": datasets.Value("int64"), |
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"days_of_recidividity_after_arrest": datasets.Value("int64"), |
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"days_in_custody": datasets.Value("int64"), |
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"is_violent_recidivous": datasets.Value("int64"), |
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"violence_decile_score": datasets.Value("int64"), |
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"two_year_recidivous": datasets.ClassLabel(num_classes=2, names=("no", "yes")), |
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}, |
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"two-years-recidividity-no-race": { |
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"sex": datasets.Value("int64"), |
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"age": datasets.Value("int64"), |
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"number_of_juvenile_fellonies": datasets.Value("int64"), |
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"decile_score": datasets.Value("int64"), |
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"number_of_juvenile_misdemeanors": datasets.Value("int64"), |
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"number_of_other_juvenile_offenses": datasets.Value("int64"), |
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"number_of_prior_offenses": datasets.Value("int64"), |
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"days_before_screening_arrest": datasets.Value("int64"), |
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"is_recidivous": datasets.Value("int64"), |
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"days_of_recidividity_after_arrest": datasets.Value("int64"), |
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"days_in_custody": datasets.Value("int64"), |
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"is_violent_recidivous": datasets.Value("int64"), |
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"violence_decile_score": datasets.Value("int64"), |
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"two_year_recidivous": datasets.ClassLabel(num_classes=2, names=("no", "yes")), |
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}, |
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"priors-prediction": { |
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"sex": datasets.Value("int64"), |
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"age": datasets.Value("int64"), |
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"race": datasets.Value("int64"), |
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"number_of_juvenile_fellonies": datasets.Value("int64"), |
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"decile_score": datasets.Value("int64"), |
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"number_of_juvenile_misdemeanors": datasets.Value("int64"), |
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"number_of_other_juvenile_offenses": datasets.Value("int64"), |
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"days_before_screening_arrest": datasets.Value("int64"), |
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"is_recidivous": datasets.Value("int64"), |
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"days_of_recidividity_after_arrest": datasets.Value("int64"), |
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"days_in_custody": datasets.Value("int64"), |
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"is_violent_recidivous": datasets.Value("int64"), |
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"violence_decile_score": datasets.Value("int64"), |
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"two_year_recidivous": datasets.Value("int64"), |
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"number_of_prior_offenses": datasets.Value("int64") |
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}, |
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"priors-prediction-no-race": { |
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"sex": datasets.Value("int64"), |
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"age": datasets.Value("int64"), |
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"number_of_juvenile_fellonies": datasets.Value("int64"), |
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"decile_score": datasets.Value("int64"), |
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"number_of_juvenile_misdemeanors": datasets.Value("int64"), |
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"number_of_other_juvenile_offenses": datasets.Value("int64"), |
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"days_before_screening_arrest": datasets.Value("int64"), |
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"is_recidivous": datasets.Value("int64"), |
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"days_of_recidividity_after_arrest": datasets.Value("int64"), |
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"days_in_custody": datasets.Value("int64"), |
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"is_violent_recidivous": datasets.Value("int64"), |
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"violence_decile_score": datasets.Value("int64"), |
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"two_year_recidivous": datasets.Value("int64"), |
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"number_of_prior_offenses": datasets.Value("int64"), |
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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 CompasConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(CompasConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Compas(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "two-years-recidividity" |
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BUILDER_CONFIGS = [ |
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CompasConfig(name="encoding", |
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description="Encoding dictionaries for discrete labels."), |
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CompasConfig(name="two-years-recidividity", |
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description="Compas binary classification for two-year recidividity."), |
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CompasConfig(name="two-years-recidividity-no-race", |
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description="Compas binary classification for two-year recidividity. Race excluded from features."), |
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CompasConfig(name="priors-prediction", |
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description="Compas regression task for estimating number of prior offenses of defendant."), |
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CompasConfig(name="priors-prediction-no-race", |
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description="Compas regression task for estimating number of prior offenses of defendant. Race excluded from features."), |
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] |
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def _info(self): |
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if self.config.name not in features_per_config: |
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raise ValueError(f"Unknown configuration: {self.config.name}") |
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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) |
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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 = "income") -> pandas.DataFrame: |
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data.drop("id", axis="columns", inplace=True) |
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data.drop("name", axis="columns", inplace=True) |
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data.drop("first", axis="columns", inplace=True) |
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data.drop("last", axis="columns", inplace=True) |
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data.drop("dob", axis="columns", inplace=True) |
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data.drop("age_cat", axis="columns", inplace=True) |
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data.drop("c_offense_date", axis="columns", inplace=True) |
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data.drop("c_jail_in", axis="columns", inplace=True) |
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data.drop("c_jail_out", axis="columns", inplace=True) |
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data.drop("c_arrest_date", axis="columns", inplace=True) |
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data.drop("c_charge_degree", axis="columns", inplace=True) |
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data.drop("c_charge_desc", axis="columns", inplace=True) |
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data.drop("r_case_number", axis="columns", inplace=True) |
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data.drop("r_charge_degree", axis="columns", inplace=True) |
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data.drop("r_offense_date", axis="columns", inplace=True) |
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data.drop("r_charge_desc", axis="columns", inplace=True) |
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data.drop("violent_recid", axis="columns", inplace=True) |
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data.drop("vr_case_number", axis="columns", inplace=True) |
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data.drop("vr_charge_degree", axis="columns", inplace=True) |
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data.drop("vr_offense_date", axis="columns", inplace=True) |
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data.drop("vr_charge_desc", axis="columns", inplace=True) |
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data.drop("type_of_assessment", axis="columns", inplace=True) |
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data.drop("score_text", axis="columns", inplace=True) |
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data.drop("v_score_text", axis="columns", inplace=True) |
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data.drop("v_screening_date", axis="columns", inplace=True) |
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data.drop("screening_date", axis="columns", inplace=True) |
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data.drop("start", axis="columns", inplace=True) |
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data.drop("end", axis="columns", inplace=True) |
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data.drop("event", axis="columns", inplace=True) |
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data.drop("two_year_recid.1", axis="columns", inplace=True) |
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data.drop("r_jail_in", axis="columns", inplace=True) |
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data.drop("r_jail_out", axis="columns", inplace=True) |
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data.drop("v_type_of_assessment", axis="columns", inplace=True) |
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data.drop("compas_screening_date", axis="columns", inplace=True) |
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data.drop("decile_score.1", axis="columns", inplace=True) |
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data.drop("priors_count.1", axis="columns", inplace=True) |
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data.drop("c_case_number", axis="columns", inplace=True) |
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data.drop("c_days_from_compas", axis="columns", inplace=True) |
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data = data[data.days_b_screening_arrest.isna(), "days_b_screening_arrest"] = -1 |
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data.loc[:, "days_b_screening_arrest"] = data.days_b_screening_arrest.astype(int) |
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print("dropping from " + str(data.shape[0])) |
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data = data[~data.r_days_from_arrest.isna()] |
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print("dropped to" + str(data.shape[0])) |
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print("dropping from " + str(data.shape[0])) |
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data = data[(~data.in_custody.isna()) & (~data.out_custody.isna())] |
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print("dropped to" + str(data.shape[0])) |
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in_dates = data.in_custody.apply(datetime.date.fromisoformat) |
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out_dates = data.out_custody.apply(datetime.date.fromisoformat) |
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days_in_custody = [delta.days for delta in out_dates - in_dates] |
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data.loc[:, "days_in_custody"] = days_in_custody |
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data.drop("in_custody", axis="columns", inplace=True) |
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data.drop("out_custody", axis="columns", inplace=True) |
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data = data[["sex", |
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"age", |
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"race", |
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"juv_fel_count", |
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"decile_score", |
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"juv_misd_count", |
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"juv_other_count", |
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"priors_count", |
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"days_b_screening_arrest", |
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"is_recid", |
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"r_days_from_arrest", |
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"days_in_custody", |
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"is_violent_recid", |
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"v_decile_score", |
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"two_year_recid"]] |
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data.columns = _BASE_FEATURE_NAMES |
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data.loc[:, "sex"] = data.sex.apply(self.encode_sex) |
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if config == "encoding": |
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return self.encoding_dictionaries() |
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elif config == "two-years-recidividity": |
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return self.two_years_recidividity_preprocessing(data) |
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elif config == "two-years-recidividity-no-race": |
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return self.two_years_recidividity_no_race_preprocessing(data) |
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elif config == "priors-prediction": |
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return self.priors_prediction_preprocessing(data) |
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elif config == "priors-prediction-no-race": |
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return self.priors_prediction_no_race_preprocessing(data) |
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else: |
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raise ValueError(f"Unknown config: {config}") |
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def encoding_dictionaries(self): |
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race_dic, sex_dic = self.race_decoding_dic(), self.sex_decoding_dic() |
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race_data = [("race", race, code) for race, code in race_dic.items()] |
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sex_data = [("sex", sex, code) for sex, code in sex_dic.items()] |
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data = pandas.DataFrame(race_data + sex_data, |
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columns=["feature", "original_value", "encoded_value"]) |
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return data |
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def two_years_recidividity_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame: |
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data.loc[:, "race"] = data.race.apply(self.encode_race) |
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data.loc[:, "days_of_recidividity_after_arrest"] = data.days_of_recidividity_after_arrest.astype(int) |
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return data |
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def two_years_recidividity_no_race_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame: |
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data.drop("race", axis="columns", inplace=True) |
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return data |
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def priors_prediction_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame: |
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data.loc[:, "race"] = data.race.apply(self.encode_race) |
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return data |
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def priors_prediction_no_race_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame: |
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data.drop("race", axis="columns", inplace=True) |
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return data |
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def encode_race(self, race): |
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return self.race_encoding_dic()[race] |
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def decode_race(self, code): |
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return self.race_decoding_dic()[code] |
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def race_decoding_dic(self): |
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return { |
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0: "Caucasian", |
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1: "African-American", |
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2: "Hispanic", |
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3: "Asian", |
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4: "Other", |
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5: "Native American", |
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} |
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def race_encoding_dic(self): |
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return { |
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"Caucasian": 0, |
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"African-American": 1, |
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"Hispanic": 2, |
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"Asian": 3, |
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"Other": 4, |
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"Native American": 5, |
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} |
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def encode_sex(self, sex): |
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return self.sex_encoding_dic()[sex] |
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def decode_sex(self, code): |
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return self.sex_decoding_dic()[code] |
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def sex_encoding_dic(self): |
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return { |
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"Male": 0, |
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"Female": 1 |
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} |
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def sex_decoding_dic(self): |
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return { |
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0: "Male", |
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1: "Female" |
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} |
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