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README.md

@@ -5,7 +5,7 @@ tags:
 - breast
 - tabular_classification
 - binary_classification
-pretty_name: Bank
+pretty_name: Breast
 size_categories:
 - 100<n<1K
 task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
@@ -14,4 +14,21 @@ configs:
 - cancer
 ---
 # Breast cancer
-The [Breast cancer dataset](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29) is cool.
+The [Breast cancer dataset](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+
+# Configurations and tasks
+- `cancer` Binary classification task to classify the cell clump as cancerous or not.
+
+# Features
+| **Name**                      |**Type**|**Description**             |
+|-------------------------------|--------|----------------------------|
+|`clump_thickness`              |`int8`  |Thickness of the clump      |
+|`uniformity_of_cell_size`      |`int8`  |Uniformity of cell size     |
+|`uniformity_of_cell_shape`     |`int8`  |Uniformity of cell shape    |
+|`marginal_adhesion`            |`int8`  |Marginal adhesion           |
+|`single_epithelial_cell_size`  |`int8`  |single_epithelial_cell_size |
+|`bare_nuclei`                  |`int8`  |bare_nuclei                 |
+|`bland_chromatin`              |`int8`  |bland_chromatin             |
+|`normal_nucleoli`              |`int8`  |normal_nucleoli             |
+|`mitoses`                      |`int8`  |mitoses                     |
+|`is_cancer`                    |`int8`  |Is the clump cancer         |

breast.py

@@ -55,12 +55,6 @@ urls_per_split = {
     "train": "https://huggingface.co/datasets/mstz/breast/raw/main/breast-cancer-wisconsin.data",
 }
 features_types_per_config = {
-    "encoding": {
-        "feature": datasets.Value("string"),
-        "original_value": datasets.Value("string"),
-        "encoded_value":  datasets.Value("int8"),
-    },
-
     "cancer": {
         "clump_thickness": datasets.Value("int8"),
         "uniformity_of_cell_size": datasets.Value("int8"),
@@ -93,10 +87,7 @@ class Breast(datasets.GeneratorBasedBuilder):
     ]
 
 
-    def _info(self):
-        if self.config.name not in features_per_config:
-            raise ValueError(f"Unknown configuration: {self.config.name}")
-        
+    def _info(self):       
         info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
                                     features=features_per_config[self.config.name])
 
@@ -110,19 +101,22 @@ class Breast(datasets.GeneratorBasedBuilder):
         ]
     
     def _generate_examples(self, filepath: str):
-        data = pandas.read_csv(filepath, header=None)
-        data.columns=_ORIGINAL_FEATURE_NAMES
-        data = self.preprocess(data, config=self.config.name)
+        if self.config.name == "cancer":
+            data = pandas.read_csv(filepath, header=None)
+            data.columns=_ORIGINAL_FEATURE_NAMES
 
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
+            data = self.preprocess(data, config=self.config.name)
 
-            yield row_id, data_row
+            for row_id, row in data.iterrows():
+                data_row = dict(row)
+
+                yield row_id, data_row
+        else:
+            raise ValueError(f"Unknown config: {self.config.name}")
 
     def preprocess(self, data: pandas.DataFrame, config: str = "cancer") -> pandas.DataFrame:
         data.drop("id", axis="columns", inplace=True)
 
-        print(data.dtypes)
         data = data[data.bare_nuclei != "?"]
         for c in data.columns:
             data.loc[:, c] = data[c].astype(int)