updated to datasets 4.*

README.md

@@ -1,24 +1,35 @@
 ---
-language:
-- en
+configs:
+- config_name: two-years-recidividity
+  data_files:
+  - path: two-years-recidividity/train.csv
+    split: train
+  default: true
+- config_name: two-years-recidividity-no-race
+  data_files:
+  - path: two-years-recidividity-no-race/train.csv
+    split: train
+  default: false
+- config_name: priors-prediction
+  data_files:
+  - path: priors-prediction/train.csv
+    split: train
+  default: false
+- config_name: priors-prediction-no-race
+  data_files:
+  - path: priors-prediction-no-race/train.csv
+    split: train
+  default: false
+language: en
+license: cc
+pretty_name: Compas
+size_categories: 1M<n<10M
 tags:
-- compas
 - tabular_classification
 - binary_classification
-- UCI
-pretty_name: Compas
-size_categories:
-- 1K<n<10K
+- multiclass_classification
 task_categories:
 - tabular-classification
-configs:
-- encoding
-- two-years-recidividity
-- two-years-recidividity-no-race
-- priors-prediction
-- priors-prediction-no-race
-- race
-license: cc
 ---
 # Compas
 The [Compas dataset](https://github.com/propublica/compas-analysis) for recidivism prediction.

compas.py

@@ -1,349 +0,0 @@
-"""Compas Dataset"""
-
-from typing import List
-from functools import partial
-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 = [
-    "is_male", 
-    "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_custody",
-    "is_violent_recidivous",
-    "violence_decile_score",
-    "two_year_recidivous",
-]
-_ENCODING_DICS = {
-    "is_male": {
-        "Male": 1,
-        "Female": 0
-    },
-    "race": {
-        "Caucasian": 0,
-        "African-American": 1,
-        "Hispanic": 2,
-        "Asian": 3,
-        "Other": 4,
-        "Native American": 5,
-    }
-}
-
-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 = {
-    "encoding": {
-        "feature": datasets.Value("string"),
-        "original_value": datasets.Value("string"),
-        "encoded_value":  datasets.Value("int8"),
-    },
-
-    "two-years-recidividity": {
-        "is_male": datasets.Value("bool"),
-        "age": datasets.Value("int64"),
-        "race": datasets.Value("string"),
-        "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_prior_offenses": datasets.Value("int64"),
-        "days_before_screening_arrest": datasets.Value("int64"),
-        "is_recidivous": datasets.Value("bool"),
-        "days_in_custody": datasets.Value("int64"),
-        "is_violent_recidivous": datasets.Value("bool"),
-        "violence_decile_score": datasets.Value("int64"),
-        "two_year_recidivous": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
-    },
-    
-    "two-years-recidividity-no-race": {
-        "is_male": datasets.Value("bool"),
-        "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_prior_offenses": datasets.Value("int64"),
-        "days_before_screening_arrest": datasets.Value("int64"),
-        "is_recidivous": datasets.Value("bool"),
-        "days_in_custody": datasets.Value("int64"),
-        "is_violent_recidivous": datasets.Value("bool"),
-        "violence_decile_score": datasets.Value("int64"),
-        "two_year_recidivous": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
-    },
-    
-    "priors-prediction": {
-        "is_male": datasets.Value("bool"),
-        "age": datasets.Value("int64"),
-        "race": datasets.Value("string"),
-        "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"),
-        "days_before_screening_arrest": datasets.Value("int64"),
-        "is_recidivous": datasets.Value("bool"),
-        "days_in_custody": datasets.Value("int64"),
-        "is_violent_recidivous": datasets.Value("bool"),
-        "violence_decile_score": datasets.Value("int64"),
-        "two_year_recidivous": datasets.Value("int64"),
-        "number_of_prior_offenses": datasets.Value("int64")
-    },
-    
-    "priors-prediction-no-race": {
-        "is_male": datasets.Value("bool"),
-        "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"),
-        "days_before_screening_arrest": datasets.Value("int64"),
-        "is_recidivous": datasets.Value("bool"),
-        "days_in_custody": datasets.Value("int64"),
-        "is_violent_recidivous": datasets.Value("bool"),
-        "violence_decile_score": datasets.Value("int64"),
-        "two_year_recidivous": datasets.Value("int64"),
-        "number_of_prior_offenses": datasets.Value("int64"),
-    },
-
-    "race": {
-        "is_male": datasets.Value("bool"),
-        "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"),
-        "days_before_screening_arrest": datasets.Value("int64"),
-        "is_recidivous": datasets.Value("bool"),
-        "days_in_custody": datasets.Value("int64"),
-        "is_violent_recidivous": datasets.Value("bool"),
-        "violence_decile_score": datasets.Value("int64"),
-        "two_year_recidivous": datasets.Value("int64"),
-        "number_of_prior_offenses": datasets.Value("int64"),
-        "race": datasets.ClassLabel(num_classes=6, names=("Caucasian", "African-American",
-                                                          "Hispanic", "Asian", "Other", "Native American")),
-    }
-}
-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="race",
-                    description="Multiclass classification, predict `race` out of other features."),
-        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."),
-        CompasConfig(name="encoding",
-                    description="Encoding dictionaries for discrete labels."),
-    ]
-
-
-    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):
-        if self.config.name == "encoding":
-            data = self.encoding_dics()
-        else:
-            data = pandas.read_csv(filepath)
-            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 = "income") -> pandas.DataFrame:
-        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)
-        data.drop("r_days_from_arrest", axis="columns", inplace=True)
-        
-
-        # handle nan values
-        data.loc[data.days_b_screening_arrest.isna(), "days_b_screening_arrest"] = -1
-        data["days_b_screening_arrest"] = data.days_b_screening_arrest.astype(int)
-
-        # 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)
-
-        data = data[["sex",
-                     "age",
-                     "race",
-                     "juv_fel_count",
-                     "decile_score",
-                     "juv_misd_count",
-                     "juv_other_count",
-                     "priors_count",
-                     "days_b_screening_arrest",
-                     "is_recid",
-                     "days_in_custody",
-                     "is_violent_recid",
-                     "v_decile_score",
-                     "two_year_recid"]]
-
-        data.columns = _BASE_FEATURE_NAMES
-
-        for feature in _ENCODING_DICS:
-            if feature == "race":
-                if config != "race":
-                    continue
-            encoding_function = partial(self.encode, feature)
-            data.loc[:, feature] = data[feature].apply(encoding_function)
-        data.loc[:, "is_recidivous"] = data["is_recidivous"].apply(bool)
-        data.loc[:, "is_violent_recidivous"] = data["is_violent_recidivous"].apply(bool)
-        data = data.astype({"is_recidivous": "bool", "is_violent_recidivous": "bool"})
-                
-        return data[list(features_types_per_config[config].keys())]
-       
-    def encode(self, feature, value):
-        if feature in _ENCODING_DICS:
-            return _ENCODING_DICS[feature][value]
-        raise ValueError(f"Unknown feature: {feature}")
-
-    def encoding_dics(self):
-        data = [pandas.DataFrame([(feature, original, encoded) for original, encoded in d.items()])
-                for feature, d in _ENCODING_DICS.items()]
-        data = pandas.concat(data, axis="rows").reset_index()
-        data.drop("index", axis="columns", inplace=True)
-        data.columns = ["feature", "original_value", "encoded_value"]
-
-        return data