Datasets:
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"""Compas Dataset"""
from typing import List
import datetime
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
"id",
"name",
"first",
"last",
"compas_screening_date",
"sex",
"dob",
"age",
"age_cat",
"race",
"juv_fel_count",
"decile_score",
"juv_misd_count",
"juv_other_count",
"priors_count",
"days_b_screening_arrest",
"c_jail_in",
"c_jail_out",
"c_case_number",
"c_offense_date",
"c_arrest_date",
"c_days_from_compas",
"c_charge_degree",
"c_charge_desc",
"is_recid",
"r_case_number",
"r_charge_degree",
"r_days_from_arrest",
"r_offense_date",
"r_charge_desc",
"r_jail_in",
"r_jail_out",
"violent_recid",
"is_violent_recid",
"vr_case_number",
"vr_charge_degree",
"vr_offense_date",
"vr_charge_desc",
"type_of_assessment",
"decile_score",
"score_text",
"screening_date",
"v_type_of_assessment",
"v_decile_score",
"v_score_text",
"v_screening_date",
"in_custody",
"out_custody",
"priors_count",
"start",
"end",
"event",
"two_year_recid",
"two_year_recid"
]
_BASE_FEATURE_NAMES = [
"sex",
"age",
"race",
"number_of_juvenile_fellonies",
"decile_score",
"number_of_juvenile_misdemeanors",
"number_of_other_juvenile_offenses",
"number_of_prior_offenses",
"days_before_screening_arrest",
"is_recidivous",
# "days_in_jail_before_recidividity",
"days_of_recidividity_after_arrest",
"is_violent_recidivous",
"violence_decile_score",
"two_year_recidivous",
"days_in_custody",
]
DESCRIPTION = "COMPAS dataset for recidivism prediction."
_HOMEPAGE = "https://github.com/propublica/compas-analysis"
_URLS = ("https://huggingface.co/datasets/mstz/compas/raw/main/compas-scores-two-years-violent.csv")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/compas/raw/main/compas-scores-two-years-violent.csv",
}
features_types_per_config = {
"two-years-recidividity": {
"sex": datasets.Value("int64"),
"age": datasets.Value("int64"),
"race": datasets.Value("int64"),
"number_of_juvenile_fellonies": datasets.Value("int64"),
"decile_score": datasets.Value("int64"),
"number_of_juvenile_misdemeanors": datasets.Value("int64"),
"number_of_other_juvenile_offenses": datasets.Value("int64"),
"number_of_priors_offenses": datasets.Value("int64"),
"days_before_screening_arrest": datasets.Value("int64"),
"is_recidivous": datasets.Value("int64"),
"days_of_recidividity_after_arrest": datasets.Value("int64"),
"days_in_jail_before_recidividity": datasets.Value("int64"),
"days_in_custody": datasets.Value("int64"),
"is_violent_recidivous": datasets.Value("int64"),
"violence_decile_score": datasets.Value("int64"),
"priors_count": datasets.Value("int64"),
"two_year_recidivous": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
},
"two-years-recidividity-no-race": {
"sex": datasets.Value("int64"),
"age": datasets.Value("int64"),
"number_of_juvenile_fellonies": datasets.Value("int64"),
"decile_score": datasets.Value("int64"),
"number_of_juvenile_misdemeanors": datasets.Value("int64"),
"number_of_other_juvenile_offenses": datasets.Value("int64"),
"number_of_priors_offenses": datasets.Value("int64"),
"days_before_screening_arrest": datasets.Value("int64"),
"is_recidivous": datasets.Value("int64"),
"days_of_recidividity_after_arrest": datasets.Value("int64"),
"days_in_jail_before_recidividity": datasets.Value("int64"),
"days_in_custody": datasets.Value("int64"),
"is_violent_recidivous": datasets.Value("int64"),
"violence_decile_score": datasets.Value("int64"),
"priors_count": datasets.Value("int64"),
"two_year_recidivous": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
},
"priors-prediction": {
"sex": datasets.Value("int64"),
"age": datasets.Value("int64"),
"race": datasets.Value("int64"),
"number_of_juvenile_fellonies": datasets.Value("int64"),
"decile_score": datasets.Value("int64"),
"number_of_juvenile_misdemeanors": datasets.Value("int64"),
"number_of_other_juvenile_offenses": datasets.Value("int64"),
"number_of_priors_offenses": datasets.Value("int64"),
"days_before_screening_arrest": datasets.Value("int64"),
"is_recidivous": datasets.Value("int64"),
"days_of_recidividity_after_arrest": datasets.Value("int64"),
"days_in_jail_before_recidividity": datasets.Value("int64"),
"days_in_custody": datasets.Value("int64"),
"is_violent_recidivous": datasets.Value("int64"),
"violence_decile_score": datasets.Value("int64"),
"two_year_recidivous": datasets.Value("int64"),
"priors_count": datasets.Value("int64")
},
"priors-prediction-no-race": {
"sex": datasets.Value("int64"),
"age": datasets.Value("int64"),
"number_of_juvenile_fellonies": datasets.Value("int64"),
"decile_score": datasets.Value("int64"),
"number_of_juvenile_misdemeanors": datasets.Value("int64"),
"number_of_other_juvenile_offenses": datasets.Value("int64"),
"number_of_priors_offenses": datasets.Value("int64"),
"days_before_screening_arrest": datasets.Value("int64"),
"is_recidivous": datasets.Value("int64"),
"days_of_recidividity_after_arrest": datasets.Value("int64"),
"days_in_jail_before_recidividity": datasets.Value("int64"),
"days_in_custody": datasets.Value("int64"),
"is_violent_recidivous": datasets.Value("int64"),
"violence_decile_score": datasets.Value("int64"),
"two_year_recidivous": datasets.Value("int64"),
"priors_count": datasets.Value("int64")
},
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class CompasConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(CompasConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Compas(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "two-years-recidividity"
BUILDER_CONFIGS = [
CompasConfig(name="two-years-recidividity",
description="Compas binary classification for two-year recidividity."),
CompasConfig(name="two-years-recidividity-no-race",
description="Compas binary classification for two-year recidividity. Race excluded from features."),
CompasConfig(name="priors-prediction",
description="Compas regression task for estimating number of prior offenses of defendant."),
CompasConfig(name="priors-prediction-no-race",
description="Compas regression task for estimating number of prior offenses of defendant. Race excluded from features."),
]
def _info(self):
if self.config.name not in features_per_config:
raise ValueError(f"Unknown configuration: {self.config.name}")
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)
print("preprocessing...")
data = self.preprocess(data, config=self.config.name)
print("done!")
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def preprocess(self, data: pandas.DataFrame, config: str = "income") -> pandas.DataFrame:
print("dropping columns...")
data.drop("id", axis="columns", inplace=True)
data.drop("name", axis="columns", inplace=True)
data.drop("first", axis="columns", inplace=True)
data.drop("last", axis="columns", inplace=True)
data.drop("dob", axis="columns", inplace=True)
data.drop("age_cat", axis="columns", inplace=True)
data.drop("c_offense_date", axis="columns", inplace=True)
data.drop("c_jail_in", axis="columns", inplace=True)
data.drop("c_jail_out", axis="columns", inplace=True)
data.drop("c_arrest_date", axis="columns", inplace=True)
data.drop("c_charge_degree", axis="columns", inplace=True)
data.drop("c_charge_desc", axis="columns", inplace=True)
data.drop("r_case_number", axis="columns", inplace=True)
data.drop("r_charge_degree", axis="columns", inplace=True)
data.drop("r_offense_date", axis="columns", inplace=True)
data.drop("r_charge_desc", axis="columns", inplace=True)
data.drop("violent_recid", axis="columns", inplace=True)
data.drop("vr_case_number", axis="columns", inplace=True)
data.drop("vr_charge_degree", axis="columns", inplace=True)
data.drop("vr_offense_date", axis="columns", inplace=True)
data.drop("vr_charge_desc", axis="columns", inplace=True)
data.drop("type_of_assessment", axis="columns", inplace=True)
data.drop("score_text", axis="columns", inplace=True)
data.drop("v_score_text", axis="columns", inplace=True)
data.drop("v_screening_date", axis="columns", inplace=True)
data.drop("screening_date", axis="columns", inplace=True)
data.drop("start", axis="columns", inplace=True)
data.drop("end", axis="columns", inplace=True)
data.drop("event", axis="columns", inplace=True)
data.drop("two_year_recid.1", axis="columns", inplace=True)
data.drop("r_jail_in", axis="columns", inplace=True)
data.drop("r_jail_out", axis="columns", inplace=True)
data.drop("v_type_of_assessment", axis="columns", inplace=True)
data.drop("compas_screening_date", axis="columns", inplace=True)
data.drop("decile_score.1", axis="columns", inplace=True)
data.drop("priors_count.1", axis="columns", inplace=True)
data.drop("c_case_number", axis="columns", inplace=True)
data.drop("c_days_from_compas", axis="columns", inplace=True)
# drop nan values
data = data[~data.days_b_screening_arrest.isna()]
data = data[~data.c_days_from_compas.isna()]
data = data[~data.r_days_from_arrest.isna()]
# transform columns into intervals
data = data[(~data.in_custody.isna()) & (~data.out_custody.isna())]
in_dates = data.in_custody.apply(datetime.date.fromisoformat)
out_dates = data.out_custody.apply(datetime.date.fromisoformat)
days_in_custody = [delta.days for delta in out_dates - in_dates]
data.loc[:, "days_in_custody"] = days_in_custody
data.drop("in_custody", axis="columns", inplace=True)
data.drop("out_custody", axis="columns", inplace=True)
print(data.columns)
data = data[["sex",
"age",
"race",
"juv_fel_count",
"decile_score",
"juv_misd_count",
"juv_other_count",
"priors_count",
"days_b_screening_arrest",
"is_recid",
"r_days_from_arrest",
"days_in_custody",
"is_violent_recid",
"v_decile_score",
"two_year_recid"]]
data.columns = _BASE_FEATURE_NAMES
# binarize features
data.loc[:, "sex"] = data.sex.apply(self.encode_sex)
if config == "two-years-recidividity":
return self.two_years_recidividity_preprocessing(data)
elif config == "two-years-recidividity-no-race":
return self.two_years_recidividity_no_race_preprocessing(data)
elif config == "priors-prediction":
return self.priors_prediction_preprocessing(data)
elif config == "priors-prediction-no-race":
return self.priors_prediction_no_race_preprocessing(data)
else:
raise ValueError(f"Unknown config: {config}")
def two_years_recidividity_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
# categorize features
data.loc[:, "race"] = data.race.apply(self.encode_race(race))
return data
def two_years_recidividity_no_race_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
# categorize features
data.drop("race", axis="columns", inplace=True)
return data
def priors_prediction_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
# categorize features
data.loc[:, "race"] = data.race.apply(self.encode_race(race))
return data
def priors_prediction_no_race_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
# categorize features
data.drop("race", axis="columns", inplace=True)
return data
def encode_race(self, race):
return self.race_encoding_dic()[race]
def decode_race(self, code):
return self.race_decoding_dic()[code]
def race_decoding_dic(self):
return {
0: "Caucasian",
1: "African-American",
2: "Hispanic",
3: "Asian",
4: "Other",
5: "Native American",
}
def race_encoding_dic(self):
return {
"Caucasian": 0,
"African-American": 1,
"Hispanic": 2,
"Asian": 3,
"Other": 4,
"Native American": 5,
}
def encode_sex(self, sex):
return self.sex_encoding_dic()[sex]
def decode_sex(self, code):
return self.sex_decoding_dic()[code]
def sex_encoding_dic(self):
return {
"Male": 0,
"Female": 1
}
def sex_decoding_dic(self):
return {
0: "Male",
1: "Female"
}
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