Update diamonds.py

diamonds.py

@@ -69,8 +69,20 @@ features_types_per_config = {
         "observation_point_on_axis_y": datasets.Value("float32"),
         "observation_point_on_axis_z": datasets.Value("float32"),
         "cut": datasets.ClassLabel(num_classes=5, names=("Fair", "Good", "Very Good", "Premium", "Ideal"))
-    }
-    
+    },
+
+    "cut_binary": {
+        "carat": datasets.Value("float32"),
+        "color": datasets.Value("string"),
+        "clarity": datasets.Value("float32"),
+        "depth": datasets.Value("float32"),
+        "table": datasets.Value("float32"),
+        "price": datasets.Value("float32"),
+        "observation_point_on_axis_x": datasets.Value("float32"),
+        "observation_point_on_axis_y": datasets.Value("float32"),
+        "observation_point_on_axis_z": datasets.Value("float32"),
+        "cut": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
+    },
 }
 features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
 
@@ -85,10 +97,9 @@ class Diamond(datasets.GeneratorBasedBuilder):
     # dataset versions
     DEFAULT_CONFIG = "cut"
     BUILDER_CONFIGS = [
-        DiamondConfig(name="encoding",
-                      description="Encoding dictionaries for discrete features."),
-        DiamondConfig(name="cut",
-                      description="5-ary classification, predict the cut quality of the diamond."),
+        DiamondConfig(name="encoding", description="Encoding dictionaries for discrete features."),
+        DiamondConfig(name="cut", description="5-ary classification, predict the cut quality of the diamond."),
+        DiamondConfig(name="cut_binary", description="Binary classification."),
     ]
 
 
@@ -118,18 +129,20 @@ class Diamond(datasets.GeneratorBasedBuilder):
             yield row_id, data_row
 
     def preprocess(self, data: pandas.DataFrame, config: str = "cut") -> pandas.DataFrame:
-        data.loc[:, "clarity"] = data.clarity.apply(lambda x: x.replace("b", "").replace("'", ""))
-        data.loc[:, "cut"] = data.cut.apply(lambda x: x.replace("b", "").replace("'", ""))
-        data.loc[:, "color"] = data.color.astype(str)
-        data.loc[:, "color"] = data.color.apply(lambda x: x[2]).replace("\"", "")
+        data["clarity"] = data.clarity.apply(lambda x: x.replace("b", "").replace("'", ""))
+        data["cut"] = data.cut.apply(lambda x: x.replace("b", "").replace("'", ""))
+        data["color"] = data.color.astype(str)
+        data["color"] = data.color.apply(lambda x: x[2]).replace("\"", "")
 
         for feature in _ENCODING_DICS:
             encoding_function = partial(self.encode, feature)
-            data.loc[:, feature] = data[feature].apply(encoding_function)
+            data[feature] = data[feature].apply(encoding_function)
          
         data.columns = _BASE_FEATURE_NAMES
-        data = data.drop_duplicates(subset=["carat", "color", "clarity", "depth", "table",
-                                            "price", "cut"])
+        data = data.drop_duplicates(subset=["carat", "color", "clarity", "depth", "table", "price", "cut"])
+
+        if self.config.name == "cut_binary":
+            data.cut = data.cut.apply(lambda x: 0 if x <= 2 else 1)            
         
         
         return data[list(features_types_per_config["cut"].keys())]