Update abalone.py

abalone.py

@@ -58,6 +58,17 @@ features_types_per_config = {
         "viscera_weight": datasets.Value("float64"),
         "shell_weight": datasets.Value("float64"),
         "number_of_rings": datasets.Value("int8")
+    },
+    "binary": {
+        "sex": datasets.Value("string"),
+        "length": datasets.Value("float64"),
+        "diameter": datasets.Value("float64"),
+        "height": datasets.Value("float64"),
+        "whole_weight": datasets.Value("float64"),
+        "shucked_weight": datasets.Value("float64"),
+        "viscera_weight": datasets.Value("float64"),
+        "shell_weight": datasets.Value("float64"),
+        "is_old": datasets.ClassLabel(num_classes=2)
     }
 }
 features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
@@ -73,8 +84,8 @@ class Abalone(datasets.GeneratorBasedBuilder):
     # dataset versions
     DEFAULT_CONFIG = "abalone"
     BUILDER_CONFIGS = [
-        AbaloneConfig(name="abalone",
-                    description="Abalone for regression."),
+        AbaloneConfig(name="abalone", description="Abalone for regression."),
+        AbaloneConfig(name="binary", description="Abalone for binary classification."),
     ]
 
 
@@ -95,6 +106,10 @@ class Abalone(datasets.GeneratorBasedBuilder):
         data = pandas.read_csv(filepath, header=None)
         data.columns = _BASE_FEATURE_NAMES
 
+        if self.config.name == "binary":
+            data = data.rename(columns={"number_of_rings": "is_old"})
+            data["is_old"] = data["is_old"].apply(lambda x: 1 if x > 9 else 0)
+
         for row_id, row in data.iterrows():
             data_row = dict(row)