Upload breast.py

breast.py

@@ -10,26 +10,26 @@ import pandas
 VERSION = datasets.Version("1.0.0")
 _ORIGINAL_FEATURE_NAMES = [
     "id",
-    "clump thickness",
-    "uniformity of cell size",
-    "uniformity of cell shape",
-    "marginal adhesion",
-    "single epithelial cell size",
-    "bare nuclei",
-    "bland chromatin",
-    "normal nucleoli",
+    "clump_thickness",
+    "uniformity_of_cell_size",
+    "uniformity_of_cell_shape",
+    "marginal_adhesion",
+    "single_epithelial_cell_size",
+    "bare_nuclei",
+    "bland_chromatin",
+    "normal_nucleoli",
     "mitoses",
     "is_cancer"
 ]
 _BASE_FEATURE_NAMES = [
-    "clump thickness",
-    "uniformity of cell size",
-    "uniformity of cell shape",
-    "marginal adhesion",
-    "single epithelial cell size",
-    "bare nuclei",
-    "bland chromatin",
-    "normal nucleoli",
+    "clump_thickness",
+    "uniformity_of_cell_size",
+    "uniformity_of_cell_shape",
+    "marginal_adhesion",
+    "single_epithelial_cell_size",
+    "bare_nuclei",
+    "bland_chromatin",
+    "normal_nucleoli",
     "mitoses",
     "is_cancer"
 ]
@@ -62,14 +62,14 @@ features_types_per_config = {
     },
 
     "cancer": {
-        "clump thickness": datasets.Value("int8"),
-        "uniformity of cell size": datasets.Value("int8"),
-        "uniformity of cell shape": datasets.Value("int8"),
-        "marginal adhesion": datasets.Value("int8"),
-        "single epithelial cell size": datasets.Value("int8"),
-        "bare nuclei": datasets.Value("int8"),
-        "bland chromatin": datasets.Value("int8"),
-        "normal nucleoli": datasets.Value("int8"),
+        "clump_thickness": datasets.Value("int8"),
+        "uniformity_of_cell_size": datasets.Value("int8"),
+        "uniformity_of_cell_shape": datasets.Value("int8"),
+        "marginal_adhesion": datasets.Value("int8"),
+        "single_epithelial_cell_size": datasets.Value("int8"),
+        "bare_nuclei": datasets.Value("int8"),
+        "bland_chromatin": datasets.Value("int8"),
+        "normal_nucleoli": datasets.Value("int8"),
         "mitoses": datasets.Value("int8"),
         "is_cancer": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
     }
@@ -121,6 +121,8 @@ class Breast(datasets.GeneratorBasedBuilder):
 
     def preprocess(self, data: pandas.DataFrame, config: str = "cancer") -> pandas.DataFrame:
         data.drop("id", axis="columns", inplace=True)
+
+        data = data[~data.bare_nuclei.isna()]
         
         data.columns = _BASE_FEATURE_NAMES
         data.loc[:, "is_cancer"] = data.is_cancer.apply(lambda x: 0 if x == 2 else 1)