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README.md

@@ -11,10 +11,39 @@ size_categories:
 task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
 - tabular-classification
 configs:
+- encoding
 - two-years-recidividity
 - two-years-recidividity-no-race
 - priors-prediction
 - priors-prediction-no-race
+- race
 ---
 # Compas
-The [Compas dataset](https://github.com/propublica/compas-analysis) is cool.
+The [Compas dataset](https://github.com/propublica/compas-analysis) for recidivism prediction.
+
+# Configurations and tasks
+- `encoding` Encoding of non-numerical variables.
+- `two-years-recidividity` Binary classification of violent recidivism.
+- `two-years-recidividity-no-race` As above, but the `race` feature is removed.
+- `priors-prediction` Regression on the number of prior offenses of defendant.
+- `priors-prediction-no-race` As above, but the `race` feature is removed.
+- `race` Multiclass classification, predict `race` out of other features.
+
+# Features
+
+|**Feature**                            |**Type**   |**Description**                        |
+|---------------------------------------|-----------|---------------------------------------|
+|`sex`                                  |`int64`    |                                       |
+|`age`                                  |`int64`    |                                       |
+|`race`                                 |`int64`    |                                       |
+|`number_of_juvenile_fellonies`         |`int64`    |                                       |
+|`decile_score`                         |`int64`    |Criminality score                      |
+|`number_of_juvenile_misdemeanors`      |`int64`    |                                       |
+|`number_of_other_juvenile_offenses`    |`int64`    |                                       |
+|`number_of_prior_offenses`             |`int64`    |                                       |
+|`days_before_screening_arrest`         |`int64`    |                                       |
+|`is_recidivous`                        |`int64`    |                                       |
+|`days_in_custody`                      |`int64`    |Days spent in custody                  |
+|`is_violent_recidivous`                |`int64`    |                                       |
+|`violence_decile_score`                |`int64`    |Criminality score for violent crimes   |
+|`two_years_recidivous`                 |`int64`    |                                       |

compas.py

@@ -1,6 +1,7 @@
 """Compas Dataset"""
 
 from typing import List
+from functools import partial
 import datetime
 
 import datasets
@@ -81,6 +82,20 @@ _BASE_FEATURE_NAMES = [
     "violence_decile_score",
     "two_year_recidivous",
 ]
+_ENCODING_DICS = {
+    "sex": {
+        "Male": 0,
+        "Female": 1
+    },
+    "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"
@@ -163,6 +178,24 @@ features_types_per_config = {
         "two_year_recidivous": datasets.Value("int64"),
         "number_of_prior_offenses": datasets.Value("int64"),
     },
+
+    "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"),
+        "days_before_screening_arrest": datasets.Value("int64"),
+        "is_recidivous": 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"),
+        "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}
 
@@ -207,8 +240,14 @@ class Compas(datasets.GeneratorBasedBuilder):
         ]
     
     def _generate_examples(self, filepath: str):
-        data = pandas.read_csv(filepath)
-        data = self.preprocess(data, config=self.config.name)
+        if self.config.name not in features_types_per_config:
+            raise ValueError(f"Unknown config: {self.config.name}")
+
+        if self.config.name == "encoding":
+            data = self.encoding_dicts()
+        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)
@@ -287,95 +326,20 @@ class Compas(datasets.GeneratorBasedBuilder):
 
         data.columns = _BASE_FEATURE_NAMES
 
-        # binarize features
-        data.loc[:, "sex"] = data.sex.apply(self.encode_sex)
-        
-        if config == "encoding":
-            return self.encoding_dictionaries()
-        elif 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}")
+        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)
+                
+        return data[list(features_types_per_config[config].keys())]
+
+    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"]
 
-    def encoding_dictionaries(self):
-        race_dic, sex_dic = self.race_encoding_dic(), self.sex_encoding_dic()
-        race_data = [("race", race, code) for race, code in race_dic.items()]
-        sex_data = [("sex", sex, code) for sex, code in sex_dic.items()]
-        data = pandas.DataFrame(race_data + sex_data,
-                                columns=["feature", "original_value", "encoded_value"])
-        
         return data
-
-    def two_years_recidividity_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
-        # categorize features
-        data.loc[:, "race"] = data.race.apply(self.encode_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)
-
-        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"
-        }