Upload adult.py

adult.py

@@ -65,7 +65,7 @@ __urls_per_split = {
     "train": "https://huggingface.co/datasets/mstz/adult/raw/adult_tr.csv", 
     "test": "https://huggingface.co/datasets/mstz/adult/raw/adult_ts.csv"
 }
-__features_per_config = {
+features_per_config = {
     "income": datasets.Features({"age": datasets.Value("int8"),
                                  "capital_gain": datasets.Value("float16"),
                                  "capital_loss": datasets.Value("float16"),
@@ -120,9 +120,7 @@ __features_per_config = {
 class AdultConfig(datasets.BuilderConfig):
     def __init__(self, **kwargs):
         super(AdultConfig, self).__init__(version=VERSION, **kwargs)
-        print(kwargs)
-        print(kwargs["name"])
-        self.features = __features_per_config[kwargs["name"]]
+        self.features = features_per_config[kwargs["name"]]
 
 
 class Adult(datasets.GeneratorBasedBuilder):
@@ -138,11 +136,11 @@ class Adult(datasets.GeneratorBasedBuilder):
     ]
 
     def _info(self):
-        if self.config_name not in __features_per_config:
+        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])
+                                    features=features_per_config[self.config_name])
 
         return info
     
@@ -174,18 +172,18 @@ class Adult(datasets.GeneratorBasedBuilder):
         return data
     
     def income_preprocessing(data: pandas.DataFrame) -> pandas.DataFrame:
-        data = data[__features_per_config["income"]]
+        data = data[features_per_config["income"]]
 
         return data
         
     def income_norace_preprocessing(data: pandas.DataFrame) -> pandas.DataFrame:
-        data = data[__features_per_config["income-no race"]]
+        data = data[features_per_config["income-no race"]]
 
         return data
     
     def race_preprocessing(data: pandas.DataFrame) -> pandas.DataFrame:
         data["over_threshold"] = df.threshold
-        data = data[__features_per_config["race"]]
+        data = data[features_per_config["race"]]
 
         return data