Upload compas.py

compas.py

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+"""Compas Dataset"""
+
+from typing import List
+
+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_priors_offenses",
+    "days_before_screening_arrest",
+    "is_recidivous",
+    "days_of_recidividity_after_arrest",
+    "days_in_jail_before_recidividity",
+    "days_in_custody",
+    "is_violent_recidivous",
+    "violence_decile_score",
+    "time_in_custody",
+    "priors_count",
+    "two_year_recidivous"
+]
+DESCRIPTION = "COMPAS dataset for recidivism prediction."
+_HOMEPAGE = "https://github.com/propublica/compas-analysis"
+_URLS = ("https://huggingface.co/datasets/mstz/compas/raw/compas-scores-two-years-violent.csv")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/compas/raw/compas-scores-two-years-violent.csv",
+}
+features_types_per_config = {
+    "two-year-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"),
+        "time_in_custody": datasets.Value("int64"),
+        "priors_count": datasets.Value("int64"),
+        "two_year_recidivous": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
+    },
+    
+    "two-year-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"),
+        "time_in_custody": 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"),
+        "time_in_custody": 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"),
+        "time_in_custody": 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-year-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_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_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("vr_case_number", axis="columns", inplace=True)
+        data.drop("type_of_assessment", axis="columns", inplace=True)
+        data.drop("v_type_of_assessment", axis="columns", inplace=True)
+        data.drop("v_score_text", 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("compas_screening_date", axis="columns", inplace=True)
+        data.drop("dob", axis="columns", inplace=True)
+        data.drop("decile_score.1", axis="columns", inplace=True)
+        data.drop("priors_count.1", 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["days_in_custody"] = days_in_custody
+        data.drop("in_custody", axis="columns", inplace=True)
+        data.drop("out_custody", axis="columns", inplace=True)
+
+        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_jail_before_recidividity",
+                     "days_in_custody",
+                     "is_violent_recid",
+                     "v_decile_score",
+                     "time_in_custody",
+                     "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"
+        }