feat(skore): Adapt RocCurveDisplay.frame() for data_source="both" (#2168)

For EstimatorReports

Closes #2147

Author: auguste-probabl

skore/src/skore/_sklearn/_plot/metrics/roc_curve.py

@@ -1127,6 +1127,9 @@ def frame(self, with_roc_auc: bool = False) -> DataFrame:
         else:  # self.report_type == "comparison-cross-validation"
             indexing_columns = ["estimator_name", "split"]
 
+        if self.data_source == "both":
+            indexing_columns += ["data_source"]
+
         if self.ml_task == "binary-classification":
             columns = indexing_columns + statistical_columns
         else:

skore/tests/unit/displays/roc_curve/test_estimator.py

@@ -344,10 +344,37 @@ def test_frame_multiclass_classification(
     assert df["label"].nunique() == len(estimator.classes_)
 
     if with_roc_auc:
-        for (_), group in df.groupby(["label"], observed=True):
+        for _, group in df.groupby(["label"], observed=True):
             assert group["roc_auc"].nunique() == 1
 
 
+@pytest.mark.parametrize("with_roc_auc", [False, True])
+def test_frame_multiclass_classification_data_source_both(
+    logistic_multiclass_classification_with_train_test, with_roc_auc
+):
+    """
+    Test the frame method with multiclass classification data and data_source="both".
+    """
+    estimator, X_train, X_test, y_train, y_test = (
+        logistic_multiclass_classification_with_train_test
+    )
+    report = EstimatorReport(
+        estimator, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test
+    )
+    df = report.metrics.roc(data_source="both").frame(with_roc_auc=with_roc_auc)
+    expected_index = ["data_source", "label"]
+    expected_columns = ["threshold", "fpr", "tpr"]
+    if with_roc_auc:
+        expected_columns.append("roc_auc")
+
+    check_frame_structure(df, expected_index, expected_columns)
+    assert df["label"].nunique() == len(estimator.classes_)
+
+    if with_roc_auc:
+        for _, group in df.groupby(["label"], observed=True):
+            assert group["roc_auc"].nunique() == 2
+
+
 def test_legend(
     pyplot,
     logistic_binary_classification_with_train_test,