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README.md

@@ -2,16 +2,36 @@
 language:
 - en
 tags:
-- diamonds
+- student performance
 - tabular_classification
-- binary_classification
-pretty_name: Diamonds
+- multiclass_classification
+pretty_name: Diamond
 size_categories:
-- 10K<n<100K
+- 1k<n<100K
 task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
 - tabular-classification
 configs:
+- encoding
 - cut
 ---
 # Diamonds
-The [Diamonds dataset](https://www.kaggle.com/datasets/ulrikthygepedersen/diamonds) is cool.
+The [Diamonds dataset](https://www.kaggle.com/datasets/ulrikthygepedersen/diamonds) from Kaggle.
+Dataset collecting diamonds properties.
+
+# Configurations and tasks
+- `encoding` Encoding of non-numerical variables.
+- `cut` Multiclass classification task, predict the cut quality of the diamond.
+
+# Features
+|**Feature**                        |**Description**|
+|-----------------------------------|---------------|
+|`carat`                            | `float32`     |
+|`color`                            | `string`      |
+|`clarity`                          | `float32`     |
+|`depth`                            | `float32`     |
+|`table`                            | `float32`     |
+|`price`                            | `float32`     |
+|`observation_point_on_axis_x`      | `float32`     |
+|`observation_point_on_axis_y`      | `float32`     |
+|`observation_point_on_axis_z`      | `float32`     |
+|`cut`                              | `int8`        |

diamonds.py

@@ -79,8 +79,10 @@ 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."),
+                      description="5-ary classification, predict the cut quality of the diamond."),
     ]
 
 
@@ -101,8 +103,14 @@ class Diamond(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_dics()
+        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)
@@ -110,25 +118,32 @@ 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])
 
-        data.loc[:, "clarity"] = data.clarity.apply(lambda x: x.replace("b", "").replace("'", ""))
-        encode_clarity = partial(self.encode, "clarity")
-        data.loc[:, "clarity"] = data.clarity.apply(encode_clarity)
-        
-        encode_cut = partial(self.encode, "cut")
-        data.loc[:, "cut"] = data.cut.apply(lambda x: x.replace("b", "").replace("'", ""))
-        data.loc[:, "cut"] = data.cut.apply(encode_cut)
+        for feature in _ENCODING_DICS:
+            encoding_function = partial(self.encode, feature)
+            data.loc[:, feature] = data[feature].apply(encoding_function)
          
         data.columns = _BASE_FEATURE_NAMES
         data = data.drop_duplicates(subset=["carat", "color", "clarity", "depth", "table",
                                             "price", "cut"])
         
-        if config == "cut":
-            return data[list(features_types_per_config["cut"].keys())]
-        else:
-            raise ValueError(f"Unknown config: {config}")              
+        
+        return data[list(features_types_per_config["cut"].keys())]
 
     def encode(self, feature, value):
-        return _ENCODING_DICS[feature][value]
+        if feature in _ENCODING_DICS:
+            return _ENCODING_DICS[feature][value]
+        raise ValueError(f"Unknown feature: {feature}")
+
+    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"]
+
+        return data