Upload compas.py

compas.py

@@ -93,6 +93,12 @@ urls_per_split = {
     "train": "https://huggingface.co/datasets/mstz/compas/raw/main/compas-scores-two-years-violent.csv",
 }
 features_types_per_config = {
+    "config": {
+        "feature": datasets.Value("string"),
+        "original_value": datasets.Value("string"),
+        "encoded_value":  datasets.Value("int8"),
+    },
+
     "two-years-recidividity": {
         "sex": datasets.Value("int64"),
         "age": datasets.Value("int64"),
@@ -176,6 +182,8 @@ class Compas(datasets.GeneratorBasedBuilder):
     # dataset versions
     DEFAULT_CONFIG = "two-years-recidividity"
     BUILDER_CONFIGS = [
+        CompasConfig(name="encoding",
+                    description="Encoding dictionaries for discrete labels."),
         CompasConfig(name="two-years-recidividity",
                     description="Compas binary classification for two-year recidividity."),
         CompasConfig(name="two-years-recidividity-no-race",
@@ -207,8 +215,6 @@ class Compas(datasets.GeneratorBasedBuilder):
         data = pandas.read_csv(filepath)
         data = self.preprocess(data, config=self.config.name)
 
-        print("data.columns")
-        print(data.columns)
         for row_id, row in data.iterrows():
             data_row = dict(row)
 
@@ -257,10 +263,14 @@ class Compas(datasets.GeneratorBasedBuilder):
         # drop nan values
         data = data[~data.days_b_screening_arrest.isna()]
         data.loc[:, "days_b_screening_arrest"] = data.days_b_screening_arrest.astype(int)
+        print("dropping from " + data.shape[0])
         data = data[~data.r_days_from_arrest.isna()]
+        print("dropped to" + data.shape[0])
 
         # transform columns into intervals
+        print("dropping from " + data.shape[0])
         data = data[(~data.in_custody.isna()) & (~data.out_custody.isna())]
+        print("dropped to" + data.shape[0])
         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]
@@ -284,16 +294,14 @@ class Compas(datasets.GeneratorBasedBuilder):
                      "v_decile_score",
                      "two_year_recid"]]
 
-        print("data.columns again")
-        print(data.columns)
         data.columns = _BASE_FEATURE_NAMES
-        print("data.columns again 1")
-        print(data.columns)
 
         # binarize features
         data.loc[:, "sex"] = data.sex.apply(self.encode_sex)
         
-        if config == "two-years-recidividity":
+        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)
@@ -304,6 +312,14 @@ class Compas(datasets.GeneratorBasedBuilder):
         else:
             raise ValueError(f"Unknown config: {config}")
 
+    def encoding_dictionaries(self):
+        race_dic, sex_dic = self.race_decoding_dic(), self.sex_decoding_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