feat(api): Allow to flatten index in reports

skore/src/skore/sklearn/_cross_validation/metrics_accessor.py

@@ -12,6 +12,7 @@
     RocCurveDisplay,
 )
 from skore.utils._accessor import _check_supported_ml_task
+from skore.utils._index import flatten_multi_index
 from skore.utils._parallel import Parallel, delayed
 from skore.utils._progress_bar import progress_decorator
 
@@ -46,9 +47,10 @@ def report_metrics(
         data_source="test",
         scoring=None,
         scoring_names=None,
-        pos_label=None,
         scoring_kwargs=None,
+        pos_label=None,
         aggregate=None,
+        flat_index=False,
     ):
         """Report a set of metrics for our estimator.
 
@@ -73,15 +75,18 @@ def report_metrics(
             Used to overwrite the default scoring names in the report. It should be of
             the same length as the `scoring` parameter.
 
-        pos_label : int, float, bool or str, default=None
-            The positive class.
-
         scoring_kwargs : dict, default=None
             The keyword arguments to pass to the scoring functions.
 
+        pos_label : int, float, bool or str, default=None
+            The positive class.
+
         aggregate : {"mean", "std"} or list of such str, default=None
             Function to aggregate the scores across the cross-validation splits.
 
+        flat_index : bool, default=False
+            Whether to flatten the `MultiIndex` columns.
+
         Returns
         -------
         pd.DataFrame
@@ -104,7 +109,7 @@ def report_metrics(
         Precision (↗︎)            0.94...  0.024...
         Recall (↗︎)               0.96...  0.027...
         """
-        return self._compute_metric_scores(
+        results = self._compute_metric_scores(
             report_metric_name="report_metrics",
             data_source=data_source,
             aggregate=aggregate,
@@ -113,6 +118,12 @@ def report_metrics(
             scoring_kwargs=scoring_kwargs,
             scoring_names=scoring_names,
         )
+        if flat_index:
+            if isinstance(results.columns, pd.MultiIndex):
+                results.columns = flatten_multi_index(results.columns)
+            if isinstance(results.index, pd.MultiIndex):
+                results.index = flatten_multi_index(results.index)
+        return results
 
     @progress_decorator(description="Compute metric for each split")
     def _compute_metric_scores(

skore/src/skore/sklearn/_estimator/metrics_accessor.py

@@ -16,6 +16,7 @@
     RocCurveDisplay,
 )
 from skore.utils._accessor import _check_supported_ml_task
+from skore.utils._index import flatten_multi_index
 
 
 class _MetricsAccessor(_BaseAccessor, DirNamesMixin):
@@ -48,8 +49,9 @@ def report_metrics(
         y=None,
         scoring=None,
         scoring_names=None,
-        pos_label=None,
         scoring_kwargs=None,
+        pos_label=None,
+        flat_index=False,
     ):
         """Report a set of metrics for our estimator.
 
@@ -83,11 +85,14 @@ def report_metrics(
             Used to overwrite the default scoring names in the report. It should be of
             the same length as the `scoring` parameter.
 
+        scoring_kwargs : dict, default=None
+            The keyword arguments to pass to the scoring functions.
+
         pos_label : int, float, bool or str, default=None
             The positive class.
 
-        scoring_kwargs : dict, default=None
-            The keyword arguments to pass to the scoring functions.
+        flat_index : bool, default=False
+            Whether to flatten the multiindex columns.
 
         Returns
         -------
@@ -326,7 +331,13 @@ def report_metrics(
                         names=name_index,
                     )
 
-        return pd.concat(scores, axis=0)
+        results = pd.concat(scores, axis=0)
+        if flat_index:
+            if isinstance(results.columns, pd.MultiIndex):
+                results.columns = flatten_multi_index(results.columns)
+            if isinstance(results.index, pd.MultiIndex):
+                results.index = flatten_multi_index(results.index)
+        return results
 
     def _compute_metric_scores(
         self,

skore/src/skore/utils/_index.py

@@ -0,0 +1,32 @@
+import pandas as pd
+
+
+def flatten_multi_index(index: pd.MultiIndex) -> pd.Index:
+    """Flatten a pandas MultiIndex into a single-level Index.
+
+    Flatten a pandas `MultiIndex` into a single-level Index by joining the levels
+    with underscores. Empty strings are skipped when joining.
+
+    Parameters
+    ----------
+    index : pandas.MultiIndex
+        The `MultiIndex` to flatten.
+
+    Returns
+    -------
+    pandas.Index
+        A flattened `Index` with non-empty levels joined by underscores.
+
+    Examples
+    --------
+    >>> import pandas as pd
+    >>> mi = pd.MultiIndex.from_tuples(
+    ...     [('a', ''), ('b', '2')], names=['letter', 'number']
+    ... )
+    >>> flatten_multi_index(mi)
+    Index(['a', 'b_2'], dtype='object')
+    """
+    if not isinstance(index, pd.MultiIndex):
+        raise ValueError("`index` must be a MultiIndex.")
+
+    return pd.Index(["_".join(filter(bool, map(str, values))) for values in index])

skore/tests/unit/sklearn/test_cross_validation.py

@@ -219,6 +219,31 @@ def test_cross_validation_report_pickle(tmp_path, binary_classification_data):
     joblib.dump(report, tmp_path / "report.joblib")
 
 
+def test_cross_validation_report_flat_index(binary_classification_data):
+    """Check that the index is flattened when `flat_index` is True.
+
+    Since `pos_label` is None, then by default a MultiIndex would be returned.
+    Here, we force to have a single-index by passing `flat_index=True`.
+    """
+    estimator, X, y = binary_classification_data
+    report = CrossValidationReport(estimator, X=X, y=y, cv_splitter=2)
+    result = report.metrics.report_metrics(flat_index=True)
+    assert result.shape == (6, 2)
+    assert isinstance(result.index, pd.Index)
+    assert result.index.tolist() == [
+        "Precision (↗︎)_0",
+        "Precision (↗︎)_1",
+        "Recall (↗︎)_0",
+        "Recall (↗︎)_1",
+        "ROC AUC (↗︎)",
+        "Brier score (↘︎)",
+    ]
+    assert result.columns.tolist() == [
+        "RandomForestClassifier_Split #0",
+        "RandomForestClassifier_Split #1",
+    ]
+
+
 ########################################################################################
 # Check the plot methods
 ########################################################################################

skore/tests/unit/sklearn/test_estimator.py

@@ -347,6 +347,28 @@ def test_estimator_report_pickle(tmp_path, binary_classification_data):
     joblib.dump(report, tmp_path / "report.joblib")
 
 
+def test_estimator_report_flat_index(binary_classification_data):
+    """Check that the index is flattened when `flat_index` is True.
+
+    Since `pos_label` is None, then by default a MultiIndex would be returned.
+    Here, we force to have a single-index by passing `flat_index=True`.
+    """
+    estimator, X_test, y_test = binary_classification_data
+    report = EstimatorReport(estimator, X_test=X_test, y_test=y_test)
+    result = report.metrics.report_metrics(flat_index=True)
+    assert result.shape == (6, 1)
+    assert isinstance(result.index, pd.Index)
+    assert result.index.tolist() == [
+        "Precision (↗︎)_0",
+        "Precision (↗︎)_1",
+        "Recall (↗︎)_0",
+        "Recall (↗︎)_1",
+        "ROC AUC (↗︎)",
+        "Brier score (↘︎)",
+    ]
+    assert result.columns.tolist() == ["RandomForestClassifier"]
+
+
 ########################################################################################
 # Check the plot methods
 ########################################################################################

skore/tests/unit/utils/test_index.py

@@ -0,0 +1,45 @@
+import pandas as pd
+import pytest
+from skore.utils._index import flatten_multi_index
+
+
+@pytest.mark.parametrize(
+    "input_tuples, names, expected_values",
+    [
+        pytest.param(
+            [("a", 1), ("b", 2)], ["letter", "number"], ["a_1", "b_2"], id="basic"
+        ),
+        pytest.param(
+            [("a", 1, "x"), ("b", 2, "y")],
+            ["letter", "number", "symbol"],
+            ["a_1_x", "b_2_y"],
+            id="multiple_levels",
+        ),
+        pytest.param(
+            [("a", None), (None, 2)],
+            ["letter", "number"],
+            ["a_nan", "nan_2.0"],
+            id="none_values",
+        ),
+        pytest.param(
+            [("a@b", "1#2"), ("c&d", "3$4")],
+            ["letter", "number"],
+            ["a@b_1#2", "c&d_3$4"],
+            id="special_chars",
+        ),
+        pytest.param([], ["letter", "number"], [], id="empty"),
+    ],
+)
+def test_flatten_multi_index(input_tuples, names, expected_values):
+    """Test flatten_multi_index with various input cases."""
+    mi = pd.MultiIndex.from_tuples(input_tuples, names=names)
+    result = flatten_multi_index(mi)
+    expected = pd.Index(expected_values)
+    pd.testing.assert_index_equal(result, expected)
+
+
+def test_flatten_multi_index_invalid_input():
+    """Test that non-MultiIndex input raises ValueError."""
+    simple_index = pd.Index(["a", "b"])
+    with pytest.raises(ValueError, match="`index` must be a MultiIndex."):
+        flatten_multi_index(simple_index)