library packages

.venv/lib/python3.12/site-packages/sklearn/inspection/tests/test_partial_dependence.py

@@ -0,0 +1,929 @@
+"""
+Testing for the partial dependence module.
+"""
+
+import numpy as np
+import pytest
+
+import sklearn
+from sklearn.base import BaseEstimator, ClassifierMixin, clone, is_regressor
+from sklearn.cluster import KMeans
+from sklearn.compose import make_column_transformer
+from sklearn.datasets import load_iris, make_classification, make_regression
+from sklearn.dummy import DummyClassifier
+from sklearn.ensemble import (
+    GradientBoostingClassifier,
+    GradientBoostingRegressor,
+    HistGradientBoostingClassifier,
+    HistGradientBoostingRegressor,
+    RandomForestRegressor,
+)
+from sklearn.exceptions import NotFittedError
+from sklearn.inspection import partial_dependence
+from sklearn.inspection._partial_dependence import (
+    _grid_from_X,
+    _partial_dependence_brute,
+    _partial_dependence_recursion,
+)
+from sklearn.linear_model import LinearRegression, LogisticRegression, MultiTaskLasso
+from sklearn.metrics import r2_score
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import (
+    PolynomialFeatures,
+    RobustScaler,
+    StandardScaler,
+    scale,
+)
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.tree.tests.test_tree import assert_is_subtree
+from sklearn.utils._testing import assert_allclose, assert_array_equal
+from sklearn.utils.fixes import _IS_32BIT
+from sklearn.utils.validation import check_random_state
+
+# toy sample
+X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+y = [-1, -1, -1, 1, 1, 1]
+
+
+# (X, y), n_targets  <-- as expected in the output of partial_dep()
+binary_classification_data = (make_classification(n_samples=50, random_state=0), 1)
+multiclass_classification_data = (
+    make_classification(
+        n_samples=50, n_classes=3, n_clusters_per_class=1, random_state=0
+    ),
+    3,
+)
+regression_data = (make_regression(n_samples=50, random_state=0), 1)
+multioutput_regression_data = (
+    make_regression(n_samples=50, n_targets=2, random_state=0),
+    2,
+)
+
+# iris
+iris = load_iris()
+
+
+@pytest.mark.parametrize(
+    "Estimator, method, data",
+    [
+        (GradientBoostingClassifier, "auto", binary_classification_data),
+        (GradientBoostingClassifier, "auto", multiclass_classification_data),
+        (GradientBoostingClassifier, "brute", binary_classification_data),
+        (GradientBoostingClassifier, "brute", multiclass_classification_data),
+        (GradientBoostingRegressor, "auto", regression_data),
+        (GradientBoostingRegressor, "brute", regression_data),
+        (DecisionTreeRegressor, "brute", regression_data),
+        (LinearRegression, "brute", regression_data),
+        (LinearRegression, "brute", multioutput_regression_data),
+        (LogisticRegression, "brute", binary_classification_data),
+        (LogisticRegression, "brute", multiclass_classification_data),
+        (MultiTaskLasso, "brute", multioutput_regression_data),
+    ],
+)
+@pytest.mark.parametrize("grid_resolution", (5, 10))
+@pytest.mark.parametrize("features", ([1], [1, 2]))
+@pytest.mark.parametrize("kind", ("average", "individual", "both"))
+def test_output_shape(Estimator, method, data, grid_resolution, features, kind):
+    # Check that partial_dependence has consistent output shape for different
+    # kinds of estimators:
+    # - classifiers with binary and multiclass settings
+    # - regressors
+    # - multi-task regressors
+
+    est = Estimator()
+    if hasattr(est, "n_estimators"):
+        est.set_params(n_estimators=2)  # speed-up computations
+
+    # n_target corresponds to the number of classes (1 for binary classif) or
+    # the number of tasks / outputs in multi task settings. It's equal to 1 for
+    # classical regression_data.
+    (X, y), n_targets = data
+    n_instances = X.shape[0]
+
+    est.fit(X, y)
+    result = partial_dependence(
+        est,
+        X=X,
+        features=features,
+        method=method,
+        kind=kind,
+        grid_resolution=grid_resolution,
+    )
+    pdp, axes = result, result["grid_values"]
+
+    expected_pdp_shape = (n_targets, *[grid_resolution for _ in range(len(features))])
+    expected_ice_shape = (
+        n_targets,
+        n_instances,
+        *[grid_resolution for _ in range(len(features))],
+    )
+    if kind == "average":
+        assert pdp.average.shape == expected_pdp_shape
+    elif kind == "individual":
+        assert pdp.individual.shape == expected_ice_shape
+    else:  # 'both'
+        assert pdp.average.shape == expected_pdp_shape
+        assert pdp.individual.shape == expected_ice_shape
+
+    expected_axes_shape = (len(features), grid_resolution)
+    assert axes is not None
+    assert np.asarray(axes).shape == expected_axes_shape
+
+
+def test_grid_from_X():
+    # tests for _grid_from_X: sanity check for output, and for shapes.
+
+    # Make sure that the grid is a cartesian product of the input (it will use
+    # the unique values instead of the percentiles)
+    percentiles = (0.05, 0.95)
+    grid_resolution = 100
+    is_categorical = [False, False]
+    X = np.asarray([[1, 2], [3, 4]])
+    grid, axes = _grid_from_X(X, percentiles, is_categorical, grid_resolution)
+    assert_array_equal(grid, [[1, 2], [1, 4], [3, 2], [3, 4]])
+    assert_array_equal(axes, X.T)
+
+    # test shapes of returned objects depending on the number of unique values
+    # for a feature.
+    rng = np.random.RandomState(0)
+    grid_resolution = 15
+
+    # n_unique_values > grid_resolution
+    X = rng.normal(size=(20, 2))
+    grid, axes = _grid_from_X(
+        X, percentiles, is_categorical, grid_resolution=grid_resolution
+    )
+    assert grid.shape == (grid_resolution * grid_resolution, X.shape[1])
+    assert np.asarray(axes).shape == (2, grid_resolution)
+
+    # n_unique_values < grid_resolution, will use actual values
+    n_unique_values = 12
+    X[n_unique_values - 1 :, 0] = 12345
+    rng.shuffle(X)  # just to make sure the order is irrelevant
+    grid, axes = _grid_from_X(
+        X, percentiles, is_categorical, grid_resolution=grid_resolution
+    )
+    assert grid.shape == (n_unique_values * grid_resolution, X.shape[1])
+    # axes is a list of arrays of different shapes
+    assert axes[0].shape == (n_unique_values,)
+    assert axes[1].shape == (grid_resolution,)
+
+
+@pytest.mark.parametrize(
+    "grid_resolution",
+    [
+        2,  # since n_categories > 2, we should not use quantiles resampling
+        100,
+    ],
+)
+def test_grid_from_X_with_categorical(grid_resolution):
+    """Check that `_grid_from_X` always sample from categories and does not
+    depend from the percentiles.
+    """
+    pd = pytest.importorskip("pandas")
+    percentiles = (0.05, 0.95)
+    is_categorical = [True]
+    X = pd.DataFrame({"cat_feature": ["A", "B", "C", "A", "B", "D", "E"]})
+    grid, axes = _grid_from_X(
+        X, percentiles, is_categorical, grid_resolution=grid_resolution
+    )
+    assert grid.shape == (5, X.shape[1])
+    assert axes[0].shape == (5,)
+
+
+@pytest.mark.parametrize("grid_resolution", [3, 100])
+def test_grid_from_X_heterogeneous_type(grid_resolution):
+    """Check that `_grid_from_X` always sample from categories and does not
+    depend from the percentiles.
+    """
+    pd = pytest.importorskip("pandas")
+    percentiles = (0.05, 0.95)
+    is_categorical = [True, False]
+    X = pd.DataFrame(
+        {
+            "cat": ["A", "B", "C", "A", "B", "D", "E", "A", "B", "D"],
+            "num": [1, 1, 1, 2, 5, 6, 6, 6, 6, 8],
+        }
+    )
+    nunique = X.nunique()
+
+    grid, axes = _grid_from_X(
+        X, percentiles, is_categorical, grid_resolution=grid_resolution
+    )
+    if grid_resolution == 3:
+        assert grid.shape == (15, 2)
+        assert axes[0].shape[0] == nunique["num"]
+        assert axes[1].shape[0] == grid_resolution
+    else:
+        assert grid.shape == (25, 2)
+        assert axes[0].shape[0] == nunique["cat"]
+        assert axes[1].shape[0] == nunique["cat"]
+
+
+@pytest.mark.parametrize(
+    "grid_resolution, percentiles, err_msg",
+    [
+        (2, (0, 0.0001), "percentiles are too close"),
+        (100, (1, 2, 3, 4), "'percentiles' must be a sequence of 2 elements"),
+        (100, 12345, "'percentiles' must be a sequence of 2 elements"),
+        (100, (-1, 0.95), r"'percentiles' values must be in \[0, 1\]"),
+        (100, (0.05, 2), r"'percentiles' values must be in \[0, 1\]"),
+        (100, (0.9, 0.1), r"percentiles\[0\] must be strictly less than"),
+        (1, (0.05, 0.95), "'grid_resolution' must be strictly greater than 1"),
+    ],
+)
+def test_grid_from_X_error(grid_resolution, percentiles, err_msg):
+    X = np.asarray([[1, 2], [3, 4]])
+    is_categorical = [False]
+    with pytest.raises(ValueError, match=err_msg):
+        _grid_from_X(X, percentiles, is_categorical, grid_resolution)
+
+
+@pytest.mark.parametrize("target_feature", range(5))
+@pytest.mark.parametrize(
+    "est, method",
+    [
+        (LinearRegression(), "brute"),
+        (GradientBoostingRegressor(random_state=0), "brute"),
+        (GradientBoostingRegressor(random_state=0), "recursion"),
+        (HistGradientBoostingRegressor(random_state=0), "brute"),
+        (HistGradientBoostingRegressor(random_state=0), "recursion"),
+    ],
+)
+def test_partial_dependence_helpers(est, method, target_feature):
+    # Check that what is returned by _partial_dependence_brute or
+    # _partial_dependence_recursion is equivalent to manually setting a target
+    # feature to a given value, and computing the average prediction over all
+    # samples.
+    # This also checks that the brute and recursion methods give the same
+    # output.
+    # Note that even on the trainset, the brute and the recursion methods
+    # aren't always strictly equivalent, in particular when the slow method
+    # generates unrealistic samples that have low mass in the joint
+    # distribution of the input features, and when some of the features are
+    # dependent. Hence the high tolerance on the checks.
+
+    X, y = make_regression(random_state=0, n_features=5, n_informative=5)
+    # The 'init' estimator for GBDT (here the average prediction) isn't taken
+    # into account with the recursion method, for technical reasons. We set
+    # the mean to 0 to that this 'bug' doesn't have any effect.
+    y = y - y.mean()
+    est.fit(X, y)
+
+    # target feature will be set to .5 and then to 123
+    features = np.array([target_feature], dtype=np.intp)
+    grid = np.array([[0.5], [123]])
+
+    if method == "brute":
+        pdp, predictions = _partial_dependence_brute(
+            est, grid, features, X, response_method="auto"
+        )
+    else:
+        pdp = _partial_dependence_recursion(est, grid, features)
+
+    mean_predictions = []
+    for val in (0.5, 123):
+        X_ = X.copy()
+        X_[:, target_feature] = val
+        mean_predictions.append(est.predict(X_).mean())
+
+    pdp = pdp[0]  # (shape is (1, 2) so make it (2,))
+
+    # allow for greater margin for error with recursion method
+    rtol = 1e-1 if method == "recursion" else 1e-3
+    assert np.allclose(pdp, mean_predictions, rtol=rtol)
+
+
+@pytest.mark.parametrize("seed", range(1))
+def test_recursion_decision_tree_vs_forest_and_gbdt(seed):
+    # Make sure that the recursion method gives the same results on a
+    # DecisionTreeRegressor and a GradientBoostingRegressor or a
+    # RandomForestRegressor with 1 tree and equivalent parameters.
+
+    rng = np.random.RandomState(seed)
+
+    # Purely random dataset to avoid correlated features
+    n_samples = 1000
+    n_features = 5
+    X = rng.randn(n_samples, n_features)
+    y = rng.randn(n_samples) * 10
+
+    # The 'init' estimator for GBDT (here the average prediction) isn't taken
+    # into account with the recursion method, for technical reasons. We set
+    # the mean to 0 to that this 'bug' doesn't have any effect.
+    y = y - y.mean()
+
+    # set max_depth not too high to avoid splits with same gain but different
+    # features
+    max_depth = 5
+
+    tree_seed = 0
+    forest = RandomForestRegressor(
+        n_estimators=1,
+        max_features=None,
+        bootstrap=False,
+        max_depth=max_depth,
+        random_state=tree_seed,
+    )
+    # The forest will use ensemble.base._set_random_states to set the
+    # random_state of the tree sub-estimator. We simulate this here to have
+    # equivalent estimators.
+    equiv_random_state = check_random_state(tree_seed).randint(np.iinfo(np.int32).max)
+    gbdt = GradientBoostingRegressor(
+        n_estimators=1,
+        learning_rate=1,
+        criterion="squared_error",
+        max_depth=max_depth,
+        random_state=equiv_random_state,
+    )
+    tree = DecisionTreeRegressor(max_depth=max_depth, random_state=equiv_random_state)
+
+    forest.fit(X, y)
+    gbdt.fit(X, y)
+    tree.fit(X, y)
+
+    # sanity check: if the trees aren't the same, the PD values won't be equal
+    try:
+        assert_is_subtree(tree.tree_, gbdt[0, 0].tree_)
+        assert_is_subtree(tree.tree_, forest[0].tree_)
+    except AssertionError:
+        # For some reason the trees aren't exactly equal on 32bits, so the PDs
+        # cannot be equal either. See
+        # https://github.com/scikit-learn/scikit-learn/issues/8853
+        assert _IS_32BIT, "this should only fail on 32 bit platforms"
+        return
+
+    grid = rng.randn(50).reshape(-1, 1)
+    for f in range(n_features):
+        features = np.array([f], dtype=np.intp)
+
+        pdp_forest = _partial_dependence_recursion(forest, grid, features)
+        pdp_gbdt = _partial_dependence_recursion(gbdt, grid, features)
+        pdp_tree = _partial_dependence_recursion(tree, grid, features)
+
+        np.testing.assert_allclose(pdp_gbdt, pdp_tree)
+        np.testing.assert_allclose(pdp_forest, pdp_tree)
+
+
+@pytest.mark.parametrize(
+    "est",
+    (
+        GradientBoostingClassifier(random_state=0),
+        HistGradientBoostingClassifier(random_state=0),
+    ),
+)
+@pytest.mark.parametrize("target_feature", (0, 1, 2, 3, 4, 5))
+def test_recursion_decision_function(est, target_feature):
+    # Make sure the recursion method (implicitly uses decision_function) has
+    # the same result as using brute method with
+    # response_method=decision_function
+
+    X, y = make_classification(n_classes=2, n_clusters_per_class=1, random_state=1)
+    assert np.mean(y) == 0.5  # make sure the init estimator predicts 0 anyway
+
+    est.fit(X, y)
+
+    preds_1 = partial_dependence(
+        est,
+        X,
+        [target_feature],
+        response_method="decision_function",
+        method="recursion",
+        kind="average",
+    )
+    preds_2 = partial_dependence(
+        est,
+        X,
+        [target_feature],
+        response_method="decision_function",
+        method="brute",
+        kind="average",
+    )
+
+    assert_allclose(preds_1["average"], preds_2["average"], atol=1e-7)
+
+
+@pytest.mark.parametrize(
+    "est",
+    (
+        LinearRegression(),
+        GradientBoostingRegressor(random_state=0),
+        HistGradientBoostingRegressor(
+            random_state=0, min_samples_leaf=1, max_leaf_nodes=None, max_iter=1
+        ),
+        DecisionTreeRegressor(random_state=0),
+    ),
+)
+@pytest.mark.parametrize("power", (1, 2))
+def test_partial_dependence_easy_target(est, power):
+    # If the target y only depends on one feature in an obvious way (linear or
+    # quadratic) then the partial dependence for that feature should reflect
+    # it.
+    # We here fit a linear regression_data model (with polynomial features if
+    # needed) and compute r_squared to check that the partial dependence
+    # correctly reflects the target.
+
+    rng = np.random.RandomState(0)
+    n_samples = 200
+    target_variable = 2
+    X = rng.normal(size=(n_samples, 5))
+    y = X[:, target_variable] ** power
+
+    est.fit(X, y)
+
+    pdp = partial_dependence(
+        est, features=[target_variable], X=X, grid_resolution=1000, kind="average"
+    )
+
+    new_X = pdp["grid_values"][0].reshape(-1, 1)
+    new_y = pdp["average"][0]
+    # add polynomial features if needed
+    new_X = PolynomialFeatures(degree=power).fit_transform(new_X)
+
+    lr = LinearRegression().fit(new_X, new_y)
+    r2 = r2_score(new_y, lr.predict(new_X))
+
+    assert r2 > 0.99
+
+
+@pytest.mark.parametrize(
+    "Estimator",
+    (
+        sklearn.tree.DecisionTreeClassifier,
+        sklearn.tree.ExtraTreeClassifier,
+        sklearn.ensemble.ExtraTreesClassifier,
+        sklearn.neighbors.KNeighborsClassifier,
+        sklearn.neighbors.RadiusNeighborsClassifier,
+        sklearn.ensemble.RandomForestClassifier,
+    ),
+)
+def test_multiclass_multioutput(Estimator):
+    # Make sure error is raised for multiclass-multioutput classifiers
+
+    # make multiclass-multioutput dataset
+    X, y = make_classification(n_classes=3, n_clusters_per_class=1, random_state=0)
+    y = np.array([y, y]).T
+
+    est = Estimator()
+    est.fit(X, y)
+
+    with pytest.raises(
+        ValueError, match="Multiclass-multioutput estimators are not supported"
+    ):
+        partial_dependence(est, X, [0])
+
+
+class NoPredictProbaNoDecisionFunction(ClassifierMixin, BaseEstimator):
+    def fit(self, X, y):
+        # simulate that we have some classes
+        self.classes_ = [0, 1]
+        return self
+
+
+@pytest.mark.filterwarnings("ignore:A Bunch will be returned")
+@pytest.mark.parametrize(
+    "estimator, params, err_msg",
+    [
+        (
+            KMeans(random_state=0, n_init="auto"),
+            {"features": [0]},
+            "'estimator' must be a fitted regressor or classifier",
+        ),
+        (
+            LinearRegression(),
+            {"features": [0], "response_method": "predict_proba"},
+            "The response_method parameter is ignored for regressors",
+        ),
+        (
+            GradientBoostingClassifier(random_state=0),
+            {
+                "features": [0],
+                "response_method": "predict_proba",
+                "method": "recursion",
+            },
+            "'recursion' method, the response_method must be 'decision_function'",
+        ),
+        (
+            GradientBoostingClassifier(random_state=0),
+            {"features": [0], "response_method": "predict_proba", "method": "auto"},
+            "'recursion' method, the response_method must be 'decision_function'",
+        ),
+        (
+            LinearRegression(),
+            {"features": [0], "method": "recursion", "kind": "individual"},
+            "The 'recursion' method only applies when 'kind' is set to 'average'",
+        ),
+        (
+            LinearRegression(),
+            {"features": [0], "method": "recursion", "kind": "both"},
+            "The 'recursion' method only applies when 'kind' is set to 'average'",
+        ),
+        (
+            LinearRegression(),
+            {"features": [0], "method": "recursion"},
+            "Only the following estimators support the 'recursion' method:",
+        ),
+    ],
+)
+def test_partial_dependence_error(estimator, params, err_msg):
+    X, y = make_classification(random_state=0)
+    estimator.fit(X, y)
+
+    with pytest.raises(ValueError, match=err_msg):
+        partial_dependence(estimator, X, **params)
+
+
+@pytest.mark.parametrize(
+    "estimator", [LinearRegression(), GradientBoostingClassifier(random_state=0)]
+)
+@pytest.mark.parametrize("features", [-1, 10000])
+def test_partial_dependence_unknown_feature_indices(estimator, features):
+    X, y = make_classification(random_state=0)
+    estimator.fit(X, y)
+
+    err_msg = "all features must be in"
+    with pytest.raises(ValueError, match=err_msg):
+        partial_dependence(estimator, X, [features])
+
+
+@pytest.mark.parametrize(
+    "estimator", [LinearRegression(), GradientBoostingClassifier(random_state=0)]
+)
+def test_partial_dependence_unknown_feature_string(estimator):
+    pd = pytest.importorskip("pandas")
+    X, y = make_classification(random_state=0)
+    df = pd.DataFrame(X)
+    estimator.fit(df, y)
+
+    features = ["random"]
+    err_msg = "A given column is not a column of the dataframe"
+    with pytest.raises(ValueError, match=err_msg):
+        partial_dependence(estimator, df, features)
+
+
+@pytest.mark.parametrize(
+    "estimator", [LinearRegression(), GradientBoostingClassifier(random_state=0)]
+)
+def test_partial_dependence_X_list(estimator):
+    # check that array-like objects are accepted
+    X, y = make_classification(random_state=0)
+    estimator.fit(X, y)
+    partial_dependence(estimator, list(X), [0], kind="average")
+
+
+def test_warning_recursion_non_constant_init():
+    # make sure that passing a non-constant init parameter to a GBDT and using
+    # recursion method yields a warning.
+
+    gbc = GradientBoostingClassifier(init=DummyClassifier(), random_state=0)
+    gbc.fit(X, y)
+
+    with pytest.warns(
+        UserWarning, match="Using recursion method with a non-constant init predictor"
+    ):
+        partial_dependence(gbc, X, [0], method="recursion", kind="average")
+
+    with pytest.warns(
+        UserWarning, match="Using recursion method with a non-constant init predictor"
+    ):
+        partial_dependence(gbc, X, [0], method="recursion", kind="average")
+
+
+def test_partial_dependence_sample_weight_of_fitted_estimator():
+    # Test near perfect correlation between partial dependence and diagonal
+    # when sample weights emphasize y = x predictions
+    # non-regression test for #13193
+    # TODO: extend to HistGradientBoosting once sample_weight is supported
+    N = 1000
+    rng = np.random.RandomState(123456)
+    mask = rng.randint(2, size=N, dtype=bool)
+
+    x = rng.rand(N)
+    # set y = x on mask and y = -x outside
+    y = x.copy()
+    y[~mask] = -y[~mask]
+    X = np.c_[mask, x]
+    # sample weights to emphasize data points where y = x
+    sample_weight = np.ones(N)
+    sample_weight[mask] = 1000.0
+
+    clf = GradientBoostingRegressor(n_estimators=10, random_state=1)
+    clf.fit(X, y, sample_weight=sample_weight)
+
+    pdp = partial_dependence(clf, X, features=[1], kind="average")
+
+    assert np.corrcoef(pdp["average"], pdp["grid_values"])[0, 1] > 0.99
+
+
+def test_hist_gbdt_sw_not_supported():
+    # TODO: remove/fix when PDP supports HGBT with sample weights
+    clf = HistGradientBoostingRegressor(random_state=1)
+    clf.fit(X, y, sample_weight=np.ones(len(X)))
+
+    with pytest.raises(
+        NotImplementedError, match="does not support partial dependence"
+    ):
+        partial_dependence(clf, X, features=[1])
+
+
+def test_partial_dependence_pipeline():
+    # check that the partial dependence support pipeline
+    iris = load_iris()
+
+    scaler = StandardScaler()
+    clf = DummyClassifier(random_state=42)
+    pipe = make_pipeline(scaler, clf)
+
+    clf.fit(scaler.fit_transform(iris.data), iris.target)
+    pipe.fit(iris.data, iris.target)
+
+    features = 0
+    pdp_pipe = partial_dependence(
+        pipe, iris.data, features=[features], grid_resolution=10, kind="average"
+    )
+    pdp_clf = partial_dependence(
+        clf,
+        scaler.transform(iris.data),
+        features=[features],
+        grid_resolution=10,
+        kind="average",
+    )
+    assert_allclose(pdp_pipe["average"], pdp_clf["average"])
+    assert_allclose(
+        pdp_pipe["grid_values"][0],
+        pdp_clf["grid_values"][0] * scaler.scale_[features] + scaler.mean_[features],
+    )
+
+
+@pytest.mark.parametrize(
+    "estimator",
+    [
+        LogisticRegression(max_iter=1000, random_state=0),
+        GradientBoostingClassifier(random_state=0, n_estimators=5),
+    ],
+    ids=["estimator-brute", "estimator-recursion"],
+)
+@pytest.mark.parametrize(
+    "preprocessor",
+    [
+        None,
+        make_column_transformer(
+            (StandardScaler(), [iris.feature_names[i] for i in (0, 2)]),
+            (RobustScaler(), [iris.feature_names[i] for i in (1, 3)]),
+        ),
+        make_column_transformer(
+            (StandardScaler(), [iris.feature_names[i] for i in (0, 2)]),
+            remainder="passthrough",
+        ),
+    ],
+    ids=["None", "column-transformer", "column-transformer-passthrough"],
+)
+@pytest.mark.parametrize(
+    "features",
+    [[0, 2], [iris.feature_names[i] for i in (0, 2)]],
+    ids=["features-integer", "features-string"],
+)
+def test_partial_dependence_dataframe(estimator, preprocessor, features):
+    # check that the partial dependence support dataframe and pipeline
+    # including a column transformer
+    pd = pytest.importorskip("pandas")
+    df = pd.DataFrame(scale(iris.data), columns=iris.feature_names)
+
+    pipe = make_pipeline(preprocessor, estimator)
+    pipe.fit(df, iris.target)
+    pdp_pipe = partial_dependence(
+        pipe, df, features=features, grid_resolution=10, kind="average"
+    )
+
+    # the column transformer will reorder the column when transforming
+    # we mixed the index to be sure that we are computing the partial
+    # dependence of the right columns
+    if preprocessor is not None:
+        X_proc = clone(preprocessor).fit_transform(df)
+        features_clf = [0, 1]
+    else:
+        X_proc = df
+        features_clf = [0, 2]
+
+    clf = clone(estimator).fit(X_proc, iris.target)
+    pdp_clf = partial_dependence(
+        clf,
+        X_proc,
+        features=features_clf,
+        method="brute",
+        grid_resolution=10,
+        kind="average",
+    )
+
+    assert_allclose(pdp_pipe["average"], pdp_clf["average"])
+    if preprocessor is not None:
+        scaler = preprocessor.named_transformers_["standardscaler"]
+        assert_allclose(
+            pdp_pipe["grid_values"][1],
+            pdp_clf["grid_values"][1] * scaler.scale_[1] + scaler.mean_[1],
+        )
+    else:
+        assert_allclose(pdp_pipe["grid_values"][1], pdp_clf["grid_values"][1])
+
+
+@pytest.mark.parametrize(
+    "features, expected_pd_shape",
+    [
+        (0, (3, 10)),
+        (iris.feature_names[0], (3, 10)),
+        ([0, 2], (3, 10, 10)),
+        ([iris.feature_names[i] for i in (0, 2)], (3, 10, 10)),
+        ([True, False, True, False], (3, 10, 10)),
+    ],
+    ids=["scalar-int", "scalar-str", "list-int", "list-str", "mask"],
+)
+def test_partial_dependence_feature_type(features, expected_pd_shape):
+    # check all possible features type supported in PDP
+    pd = pytest.importorskip("pandas")
+    df = pd.DataFrame(iris.data, columns=iris.feature_names)
+
+    preprocessor = make_column_transformer(
+        (StandardScaler(), [iris.feature_names[i] for i in (0, 2)]),
+        (RobustScaler(), [iris.feature_names[i] for i in (1, 3)]),
+    )
+    pipe = make_pipeline(
+        preprocessor, LogisticRegression(max_iter=1000, random_state=0)
+    )
+    pipe.fit(df, iris.target)
+    pdp_pipe = partial_dependence(
+        pipe, df, features=features, grid_resolution=10, kind="average"
+    )
+    assert pdp_pipe["average"].shape == expected_pd_shape
+    assert len(pdp_pipe["grid_values"]) == len(pdp_pipe["average"].shape) - 1
+
+
+@pytest.mark.parametrize(
+    "estimator",
+    [
+        LinearRegression(),
+        LogisticRegression(),
+        GradientBoostingRegressor(),
+        GradientBoostingClassifier(),
+    ],
+)
+def test_partial_dependence_unfitted(estimator):
+    X = iris.data
+    preprocessor = make_column_transformer(
+        (StandardScaler(), [0, 2]), (RobustScaler(), [1, 3])
+    )
+    pipe = make_pipeline(preprocessor, estimator)
+    with pytest.raises(NotFittedError, match="is not fitted yet"):
+        partial_dependence(pipe, X, features=[0, 2], grid_resolution=10)
+    with pytest.raises(NotFittedError, match="is not fitted yet"):
+        partial_dependence(estimator, X, features=[0, 2], grid_resolution=10)
+
+
+@pytest.mark.parametrize(
+    "Estimator, data",
+    [
+        (LinearRegression, multioutput_regression_data),
+        (LogisticRegression, binary_classification_data),
+    ],
+)
+def test_kind_average_and_average_of_individual(Estimator, data):
+    est = Estimator()
+    (X, y), n_targets = data
+    est.fit(X, y)
+
+    pdp_avg = partial_dependence(est, X=X, features=[1, 2], kind="average")
+    pdp_ind = partial_dependence(est, X=X, features=[1, 2], kind="individual")
+    avg_ind = np.mean(pdp_ind["individual"], axis=1)
+    assert_allclose(avg_ind, pdp_avg["average"])
+
+
+@pytest.mark.parametrize(
+    "Estimator, data",
+    [
+        (LinearRegression, multioutput_regression_data),
+        (LogisticRegression, binary_classification_data),
+    ],
+)
+def test_partial_dependence_kind_individual_ignores_sample_weight(Estimator, data):
+    """Check that `sample_weight` does not have any effect on reported ICE."""
+    est = Estimator()
+    (X, y), n_targets = data
+    sample_weight = np.arange(X.shape[0])
+    est.fit(X, y)
+
+    pdp_nsw = partial_dependence(est, X=X, features=[1, 2], kind="individual")
+    pdp_sw = partial_dependence(
+        est, X=X, features=[1, 2], kind="individual", sample_weight=sample_weight
+    )
+    assert_allclose(pdp_nsw["individual"], pdp_sw["individual"])
+    assert_allclose(pdp_nsw["grid_values"], pdp_sw["grid_values"])
+
+
+@pytest.mark.parametrize(
+    "estimator",
+    [
+        LinearRegression(),
+        LogisticRegression(),
+        RandomForestRegressor(),
+        GradientBoostingClassifier(),
+    ],
+)
+@pytest.mark.parametrize("non_null_weight_idx", [0, 1, -1])
+def test_partial_dependence_non_null_weight_idx(estimator, non_null_weight_idx):
+    """Check that if we pass a `sample_weight` of zeros with only one index with
+    sample weight equals one, then the average `partial_dependence` with this
+    `sample_weight` is equal to the individual `partial_dependence` of the
+    corresponding index.
+    """
+    X, y = iris.data, iris.target
+    preprocessor = make_column_transformer(
+        (StandardScaler(), [0, 2]), (RobustScaler(), [1, 3])
+    )
+    pipe = make_pipeline(preprocessor, estimator).fit(X, y)
+
+    sample_weight = np.zeros_like(y)
+    sample_weight[non_null_weight_idx] = 1
+    pdp_sw = partial_dependence(
+        pipe,
+        X,
+        [2, 3],
+        kind="average",
+        sample_weight=sample_weight,
+        grid_resolution=10,
+    )
+    pdp_ind = partial_dependence(pipe, X, [2, 3], kind="individual", grid_resolution=10)
+    output_dim = 1 if is_regressor(pipe) else len(np.unique(y))
+    for i in range(output_dim):
+        assert_allclose(
+            pdp_ind["individual"][i][non_null_weight_idx],
+            pdp_sw["average"][i],
+        )
+
+
+@pytest.mark.parametrize(
+    "Estimator, data",
+    [
+        (LinearRegression, multioutput_regression_data),
+        (LogisticRegression, binary_classification_data),
+    ],
+)
+def test_partial_dependence_equivalence_equal_sample_weight(Estimator, data):
+    """Check that `sample_weight=None` is equivalent to having equal weights."""
+
+    est = Estimator()
+    (X, y), n_targets = data
+    est.fit(X, y)
+
+    sample_weight, params = None, {"X": X, "features": [1, 2], "kind": "average"}
+    pdp_sw_none = partial_dependence(est, **params, sample_weight=sample_weight)
+    sample_weight = np.ones(len(y))
+    pdp_sw_unit = partial_dependence(est, **params, sample_weight=sample_weight)
+    assert_allclose(pdp_sw_none["average"], pdp_sw_unit["average"])
+    sample_weight = 2 * np.ones(len(y))
+    pdp_sw_doubling = partial_dependence(est, **params, sample_weight=sample_weight)
+    assert_allclose(pdp_sw_none["average"], pdp_sw_doubling["average"])
+
+
+def test_partial_dependence_sample_weight_size_error():
+    """Check that we raise an error when the size of `sample_weight` is not
+    consistent with `X` and `y`.
+    """
+    est = LogisticRegression()
+    (X, y), n_targets = binary_classification_data
+    sample_weight = np.ones_like(y)
+    est.fit(X, y)
+
+    with pytest.raises(ValueError, match="sample_weight.shape =="):
+        partial_dependence(
+            est, X, features=[0], sample_weight=sample_weight[1:], grid_resolution=10
+        )
+
+
+def test_partial_dependence_sample_weight_with_recursion():
+    """Check that we raise an error when `sample_weight` is provided with
+    `"recursion"` method.
+    """
+    est = RandomForestRegressor()
+    (X, y), n_targets = regression_data
+    sample_weight = np.ones_like(y)
+    est.fit(X, y, sample_weight=sample_weight)
+
+    with pytest.raises(ValueError, match="'recursion' method can only be applied when"):
+        partial_dependence(
+            est, X, features=[0], method="recursion", sample_weight=sample_weight
+        )
+
+
+def test_mixed_type_categorical():
+    """Check that we raise a proper error when a column has mixed types and
+    the sorting of `np.unique` will fail."""
+    X = np.array(["A", "B", "C", np.nan], dtype=object).reshape(-1, 1)
+    y = np.array([0, 1, 0, 1])
+
+    from sklearn.preprocessing import OrdinalEncoder
+
+    clf = make_pipeline(
+        OrdinalEncoder(encoded_missing_value=-1),
+        LogisticRegression(),
+    ).fit(X, y)
+    with pytest.raises(ValueError, match="The column #0 contains mixed data types"):
+        partial_dependence(clf, X, features=[0])

.venv/lib/python3.12/site-packages/sklearn/inspection/tests/test_pd_utils.py

@@ -0,0 +1,47 @@
+import numpy as np
+import pytest
+
+from sklearn.inspection._pd_utils import _check_feature_names, _get_feature_index
+from sklearn.utils._testing import _convert_container
+
+
+@pytest.mark.parametrize(
+    "feature_names, array_type, expected_feature_names",
+    [
+        (None, "array", ["x0", "x1", "x2"]),
+        (None, "dataframe", ["a", "b", "c"]),
+        (np.array(["a", "b", "c"]), "array", ["a", "b", "c"]),
+    ],
+)
+def test_check_feature_names(feature_names, array_type, expected_feature_names):
+    X = np.random.randn(10, 3)
+    column_names = ["a", "b", "c"]
+    X = _convert_container(X, constructor_name=array_type, columns_name=column_names)
+    feature_names_validated = _check_feature_names(X, feature_names)
+    assert feature_names_validated == expected_feature_names
+
+
+def test_check_feature_names_error():
+    X = np.random.randn(10, 3)
+    feature_names = ["a", "b", "c", "a"]
+    msg = "feature_names should not contain duplicates."
+    with pytest.raises(ValueError, match=msg):
+        _check_feature_names(X, feature_names)
+
+
+@pytest.mark.parametrize("fx, idx", [(0, 0), (1, 1), ("a", 0), ("b", 1), ("c", 2)])
+def test_get_feature_index(fx, idx):
+    feature_names = ["a", "b", "c"]
+    assert _get_feature_index(fx, feature_names) == idx
+
+
+@pytest.mark.parametrize(
+    "fx, feature_names, err_msg",
+    [
+        ("a", None, "Cannot plot partial dependence for feature 'a'"),
+        ("d", ["a", "b", "c"], "Feature 'd' not in feature_names"),
+    ],
+)
+def test_get_feature_names_error(fx, feature_names, err_msg):
+    with pytest.raises(ValueError, match=err_msg):
+        _get_feature_index(fx, feature_names)

.venv/lib/python3.12/site-packages/sklearn/inspection/tests/test_permutation_importance.py

@@ -0,0 +1,540 @@
+import numpy as np
+import pytest
+from joblib import parallel_backend
+from numpy.testing import assert_allclose
+
+from sklearn.compose import ColumnTransformer
+from sklearn.datasets import (
+    load_diabetes,
+    load_iris,
+    make_classification,
+    make_regression,
+)
+from sklearn.dummy import DummyClassifier
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.impute import SimpleImputer
+from sklearn.inspection import permutation_importance
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.metrics import (
+    get_scorer,
+    mean_squared_error,
+    r2_score,
+)
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import KBinsDiscretizer, OneHotEncoder, StandardScaler, scale
+from sklearn.utils._testing import _convert_container
+
+
+@pytest.mark.parametrize("n_jobs", [1, 2])
+@pytest.mark.parametrize("max_samples", [0.5, 1.0])
+@pytest.mark.parametrize("sample_weight", [None, "ones"])
+def test_permutation_importance_correlated_feature_regression(
+    n_jobs, max_samples, sample_weight
+):
+    # Make sure that feature highly correlated to the target have a higher
+    # importance
+    rng = np.random.RandomState(42)
+    n_repeats = 5
+
+    X, y = load_diabetes(return_X_y=True)
+    y_with_little_noise = (y + rng.normal(scale=0.001, size=y.shape[0])).reshape(-1, 1)
+
+    X = np.hstack([X, y_with_little_noise])
+
+    weights = np.ones_like(y) if sample_weight == "ones" else sample_weight
+    clf = RandomForestRegressor(n_estimators=10, random_state=42)
+    clf.fit(X, y)
+
+    result = permutation_importance(
+        clf,
+        X,
+        y,
+        sample_weight=weights,
+        n_repeats=n_repeats,
+        random_state=rng,
+        n_jobs=n_jobs,
+        max_samples=max_samples,
+    )
+
+    assert result.importances.shape == (X.shape[1], n_repeats)
+
+    # the correlated feature with y was added as the last column and should
+    # have the highest importance
+    assert np.all(result.importances_mean[-1] > result.importances_mean[:-1])
+
+
+@pytest.mark.parametrize("n_jobs", [1, 2])
+@pytest.mark.parametrize("max_samples", [0.5, 1.0])
+def test_permutation_importance_correlated_feature_regression_pandas(
+    n_jobs, max_samples
+):
+    pd = pytest.importorskip("pandas")
+
+    # Make sure that feature highly correlated to the target have a higher
+    # importance
+    rng = np.random.RandomState(42)
+    n_repeats = 5
+
+    dataset = load_iris()
+    X, y = dataset.data, dataset.target
+    y_with_little_noise = (y + rng.normal(scale=0.001, size=y.shape[0])).reshape(-1, 1)
+
+    # Adds feature correlated with y as the last column
+    X = pd.DataFrame(X, columns=dataset.feature_names)
+    X["correlated_feature"] = y_with_little_noise
+
+    clf = RandomForestClassifier(n_estimators=10, random_state=42)
+    clf.fit(X, y)
+
+    result = permutation_importance(
+        clf,
+        X,
+        y,
+        n_repeats=n_repeats,
+        random_state=rng,
+        n_jobs=n_jobs,
+        max_samples=max_samples,
+    )
+
+    assert result.importances.shape == (X.shape[1], n_repeats)
+
+    # the correlated feature with y was added as the last column and should
+    # have the highest importance
+    assert np.all(result.importances_mean[-1] > result.importances_mean[:-1])
+
+
+@pytest.mark.parametrize("n_jobs", [1, 2])
+@pytest.mark.parametrize("max_samples", [0.5, 1.0])
+def test_robustness_to_high_cardinality_noisy_feature(n_jobs, max_samples, seed=42):
+    # Permutation variable importance should not be affected by the high
+    # cardinality bias of traditional feature importances, especially when
+    # computed on a held-out test set:
+    rng = np.random.RandomState(seed)
+    n_repeats = 5
+    n_samples = 1000
+    n_classes = 5
+    n_informative_features = 2
+    n_noise_features = 1
+    n_features = n_informative_features + n_noise_features
+
+    # Generate a multiclass classification dataset and a set of informative
+    # binary features that can be used to predict some classes of y exactly
+    # while leaving some classes unexplained to make the problem harder.
+    classes = np.arange(n_classes)
+    y = rng.choice(classes, size=n_samples)
+    X = np.hstack([(y == c).reshape(-1, 1) for c in classes[:n_informative_features]])
+    X = X.astype(np.float32)
+
+    # Not all target classes are explained by the binary class indicator
+    # features:
+    assert n_informative_features < n_classes
+
+    # Add 10 other noisy features with high cardinality (numerical) values
+    # that can be used to overfit the training data.
+    X = np.concatenate([X, rng.randn(n_samples, n_noise_features)], axis=1)
+    assert X.shape == (n_samples, n_features)
+
+    # Split the dataset to be able to evaluate on a held-out test set. The
+    # Test size should be large enough for importance measurements to be
+    # stable:
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.5, random_state=rng
+    )
+    clf = RandomForestClassifier(n_estimators=5, random_state=rng)
+    clf.fit(X_train, y_train)
+
+    # Variable importances computed by impurity decrease on the tree node
+    # splits often use the noisy features in splits. This can give misleading
+    # impression that high cardinality noisy variables are the most important:
+    tree_importances = clf.feature_importances_
+    informative_tree_importances = tree_importances[:n_informative_features]
+    noisy_tree_importances = tree_importances[n_informative_features:]
+    assert informative_tree_importances.max() < noisy_tree_importances.min()
+
+    # Let's check that permutation-based feature importances do not have this
+    # problem.
+    r = permutation_importance(
+        clf,
+        X_test,
+        y_test,
+        n_repeats=n_repeats,
+        random_state=rng,
+        n_jobs=n_jobs,
+        max_samples=max_samples,
+    )
+
+    assert r.importances.shape == (X.shape[1], n_repeats)
+
+    # Split the importances between informative and noisy features
+    informative_importances = r.importances_mean[:n_informative_features]
+    noisy_importances = r.importances_mean[n_informative_features:]
+
+    # Because we do not have a binary variable explaining each target classes,
+    # the RF model will have to use the random variable to make some
+    # (overfitting) splits (as max_depth is not set). Therefore the noisy
+    # variables will be non-zero but with small values oscillating around
+    # zero:
+    assert max(np.abs(noisy_importances)) > 1e-7
+    assert noisy_importances.max() < 0.05
+
+    # The binary features correlated with y should have a higher importance
+    # than the high cardinality noisy features.
+    # The maximum test accuracy is 2 / 5 == 0.4, each informative feature
+    # contributing approximately a bit more than 0.2 of accuracy.
+    assert informative_importances.min() > 0.15
+
+
+def test_permutation_importance_mixed_types():
+    rng = np.random.RandomState(42)
+    n_repeats = 4
+
+    # Last column is correlated with y
+    X = np.array([[1.0, 2.0, 3.0, np.nan], [2, 1, 2, 1]]).T
+    y = np.array([0, 1, 0, 1])
+
+    clf = make_pipeline(SimpleImputer(), LogisticRegression(solver="lbfgs"))
+    clf.fit(X, y)
+    result = permutation_importance(clf, X, y, n_repeats=n_repeats, random_state=rng)
+
+    assert result.importances.shape == (X.shape[1], n_repeats)
+
+    # the correlated feature with y is the last column and should
+    # have the highest importance
+    assert np.all(result.importances_mean[-1] > result.importances_mean[:-1])
+
+    # use another random state
+    rng = np.random.RandomState(0)
+    result2 = permutation_importance(clf, X, y, n_repeats=n_repeats, random_state=rng)
+    assert result2.importances.shape == (X.shape[1], n_repeats)
+
+    assert not np.allclose(result.importances, result2.importances)
+
+    # the correlated feature with y is the last column and should
+    # have the highest importance
+    assert np.all(result2.importances_mean[-1] > result2.importances_mean[:-1])
+
+
+def test_permutation_importance_mixed_types_pandas():
+    pd = pytest.importorskip("pandas")
+    rng = np.random.RandomState(42)
+    n_repeats = 5
+
+    # Last column is correlated with y
+    X = pd.DataFrame({"col1": [1.0, 2.0, 3.0, np.nan], "col2": ["a", "b", "a", "b"]})
+    y = np.array([0, 1, 0, 1])
+
+    num_preprocess = make_pipeline(SimpleImputer(), StandardScaler())
+    preprocess = ColumnTransformer(
+        [("num", num_preprocess, ["col1"]), ("cat", OneHotEncoder(), ["col2"])]
+    )
+    clf = make_pipeline(preprocess, LogisticRegression(solver="lbfgs"))
+    clf.fit(X, y)
+
+    result = permutation_importance(clf, X, y, n_repeats=n_repeats, random_state=rng)
+
+    assert result.importances.shape == (X.shape[1], n_repeats)
+    # the correlated feature with y is the last column and should
+    # have the highest importance
+    assert np.all(result.importances_mean[-1] > result.importances_mean[:-1])
+
+
+def test_permutation_importance_linear_regresssion():
+    X, y = make_regression(n_samples=500, n_features=10, random_state=0)
+
+    X = scale(X)
+    y = scale(y)
+
+    lr = LinearRegression().fit(X, y)
+
+    # this relationship can be computed in closed form
+    expected_importances = 2 * lr.coef_**2
+    results = permutation_importance(
+        lr, X, y, n_repeats=50, scoring="neg_mean_squared_error"
+    )
+    assert_allclose(
+        expected_importances, results.importances_mean, rtol=1e-1, atol=1e-6
+    )
+
+
+@pytest.mark.parametrize("max_samples", [500, 1.0])
+def test_permutation_importance_equivalence_sequential_parallel(max_samples):
+    # regression test to make sure that sequential and parallel calls will
+    # output the same results.
+    # Also tests that max_samples equal to number of samples is equivalent to 1.0
+    X, y = make_regression(n_samples=500, n_features=10, random_state=0)
+    lr = LinearRegression().fit(X, y)
+
+    importance_sequential = permutation_importance(
+        lr, X, y, n_repeats=5, random_state=0, n_jobs=1, max_samples=max_samples
+    )
+
+    # First check that the problem is structured enough and that the model is
+    # complex enough to not yield trivial, constant importances:
+    imp_min = importance_sequential["importances"].min()
+    imp_max = importance_sequential["importances"].max()
+    assert imp_max - imp_min > 0.3
+
+    # The actually check that parallelism does not impact the results
+    # either with shared memory (threading) or without isolated memory
+    # via process-based parallelism using the default backend
+    # ('loky' or 'multiprocessing') depending on the joblib version:
+
+    # process-based parallelism (by default):
+    importance_processes = permutation_importance(
+        lr, X, y, n_repeats=5, random_state=0, n_jobs=2
+    )
+    assert_allclose(
+        importance_processes["importances"], importance_sequential["importances"]
+    )
+
+    # thread-based parallelism:
+    with parallel_backend("threading"):
+        importance_threading = permutation_importance(
+            lr, X, y, n_repeats=5, random_state=0, n_jobs=2
+        )
+    assert_allclose(
+        importance_threading["importances"], importance_sequential["importances"]
+    )
+
+
+@pytest.mark.parametrize("n_jobs", [None, 1, 2])
+@pytest.mark.parametrize("max_samples", [0.5, 1.0])
+def test_permutation_importance_equivalence_array_dataframe(n_jobs, max_samples):
+    # This test checks that the column shuffling logic has the same behavior
+    # both a dataframe and a simple numpy array.
+    pd = pytest.importorskip("pandas")
+
+    # regression test to make sure that sequential and parallel calls will
+    # output the same results.
+    X, y = make_regression(n_samples=100, n_features=5, random_state=0)
+    X_df = pd.DataFrame(X)
+
+    # Add a categorical feature that is statistically linked to y:
+    binner = KBinsDiscretizer(n_bins=3, encode="ordinal")
+    cat_column = binner.fit_transform(y.reshape(-1, 1))
+
+    # Concatenate the extra column to the numpy array: integers will be
+    # cast to float values
+    X = np.hstack([X, cat_column])
+    assert X.dtype.kind == "f"
+
+    # Insert extra column as a non-numpy-native dtype (while keeping backward
+    # compat for old pandas versions):
+    if hasattr(pd, "Categorical"):
+        cat_column = pd.Categorical(cat_column.ravel())
+    else:
+        cat_column = cat_column.ravel()
+    new_col_idx = len(X_df.columns)
+    X_df[new_col_idx] = cat_column
+    assert X_df[new_col_idx].dtype == cat_column.dtype
+
+    # Stich an arbitrary index to the dataframe:
+    X_df.index = np.arange(len(X_df)).astype(str)
+
+    rf = RandomForestRegressor(n_estimators=5, max_depth=3, random_state=0)
+    rf.fit(X, y)
+
+    n_repeats = 3
+    importance_array = permutation_importance(
+        rf,
+        X,
+        y,
+        n_repeats=n_repeats,
+        random_state=0,
+        n_jobs=n_jobs,
+        max_samples=max_samples,
+    )
+
+    # First check that the problem is structured enough and that the model is
+    # complex enough to not yield trivial, constant importances:
+    imp_min = importance_array["importances"].min()
+    imp_max = importance_array["importances"].max()
+    assert imp_max - imp_min > 0.3
+
+    # Now check that importances computed on dataframe matche the values
+    # of those computed on the array with the same data.
+    importance_dataframe = permutation_importance(
+        rf,
+        X_df,
+        y,
+        n_repeats=n_repeats,
+        random_state=0,
+        n_jobs=n_jobs,
+        max_samples=max_samples,
+    )
+    assert_allclose(
+        importance_array["importances"], importance_dataframe["importances"]
+    )
+
+
+@pytest.mark.parametrize("input_type", ["array", "dataframe"])
+def test_permutation_importance_large_memmaped_data(input_type):
+    # Smoke, non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/15810
+    n_samples, n_features = int(5e4), 4
+    X, y = make_classification(
+        n_samples=n_samples, n_features=n_features, random_state=0
+    )
+    assert X.nbytes > 1e6  # trigger joblib memmaping
+
+    X = _convert_container(X, input_type)
+    clf = DummyClassifier(strategy="prior").fit(X, y)
+
+    # Actual smoke test: should not raise any error:
+    n_repeats = 5
+    r = permutation_importance(clf, X, y, n_repeats=n_repeats, n_jobs=2)
+
+    # Auxiliary check: DummyClassifier is feature independent:
+    # permutating feature should not change the predictions
+    expected_importances = np.zeros((n_features, n_repeats))
+    assert_allclose(expected_importances, r.importances)
+
+
+def test_permutation_importance_sample_weight():
+    # Creating data with 2 features and 1000 samples, where the target
+    # variable is a linear combination of the two features, such that
+    # in half of the samples the impact of feature 1 is twice the impact of
+    # feature 2, and vice versa on the other half of the samples.
+    rng = np.random.RandomState(1)
+    n_samples = 1000
+    n_features = 2
+    n_half_samples = n_samples // 2
+    x = rng.normal(0.0, 0.001, (n_samples, n_features))
+    y = np.zeros(n_samples)
+    y[:n_half_samples] = 2 * x[:n_half_samples, 0] + x[:n_half_samples, 1]
+    y[n_half_samples:] = x[n_half_samples:, 0] + 2 * x[n_half_samples:, 1]
+
+    # Fitting linear regression with perfect prediction
+    lr = LinearRegression(fit_intercept=False)
+    lr.fit(x, y)
+
+    # When all samples are weighted with the same weights, the ratio of
+    # the two features importance should equal to 1 on expectation (when using
+    # mean absolutes error as the loss function).
+    pi = permutation_importance(
+        lr, x, y, random_state=1, scoring="neg_mean_absolute_error", n_repeats=200
+    )
+    x1_x2_imp_ratio_w_none = pi.importances_mean[0] / pi.importances_mean[1]
+    assert x1_x2_imp_ratio_w_none == pytest.approx(1, 0.01)
+
+    # When passing a vector of ones as the sample_weight, results should be
+    # the same as in the case that sample_weight=None.
+    w = np.ones(n_samples)
+    pi = permutation_importance(
+        lr,
+        x,
+        y,
+        random_state=1,
+        scoring="neg_mean_absolute_error",
+        n_repeats=200,
+        sample_weight=w,
+    )
+    x1_x2_imp_ratio_w_ones = pi.importances_mean[0] / pi.importances_mean[1]
+    assert x1_x2_imp_ratio_w_ones == pytest.approx(x1_x2_imp_ratio_w_none, 0.01)
+
+    # When the ratio between the weights of the first half of the samples and
+    # the second half of the samples approaches to infinity, the ratio of
+    # the two features importance should equal to 2 on expectation (when using
+    # mean absolutes error as the loss function).
+    w = np.hstack([np.repeat(10.0**10, n_half_samples), np.repeat(1.0, n_half_samples)])
+    lr.fit(x, y, w)
+    pi = permutation_importance(
+        lr,
+        x,
+        y,
+        random_state=1,
+        scoring="neg_mean_absolute_error",
+        n_repeats=200,
+        sample_weight=w,
+    )
+    x1_x2_imp_ratio_w = pi.importances_mean[0] / pi.importances_mean[1]
+    assert x1_x2_imp_ratio_w / x1_x2_imp_ratio_w_none == pytest.approx(2, 0.01)
+
+
+def test_permutation_importance_no_weights_scoring_function():
+    # Creating a scorer function that does not takes sample_weight
+    def my_scorer(estimator, X, y):
+        return 1
+
+    # Creating some data and estimator for the permutation test
+    x = np.array([[1, 2], [3, 4]])
+    y = np.array([1, 2])
+    w = np.array([1, 1])
+    lr = LinearRegression()
+    lr.fit(x, y)
+
+    # test that permutation_importance does not return error when
+    # sample_weight is None
+    try:
+        permutation_importance(lr, x, y, random_state=1, scoring=my_scorer, n_repeats=1)
+    except TypeError:
+        pytest.fail(
+            "permutation_test raised an error when using a scorer "
+            "function that does not accept sample_weight even though "
+            "sample_weight was None"
+        )
+
+    # test that permutation_importance raise exception when sample_weight is
+    # not None
+    with pytest.raises(TypeError):
+        permutation_importance(
+            lr, x, y, random_state=1, scoring=my_scorer, n_repeats=1, sample_weight=w
+        )
+
+
+@pytest.mark.parametrize(
+    "list_single_scorer, multi_scorer",
+    [
+        (["r2", "neg_mean_squared_error"], ["r2", "neg_mean_squared_error"]),
+        (
+            ["r2", "neg_mean_squared_error"],
+            {
+                "r2": get_scorer("r2"),
+                "neg_mean_squared_error": get_scorer("neg_mean_squared_error"),
+            },
+        ),
+        (
+            ["r2", "neg_mean_squared_error"],
+            lambda estimator, X, y: {
+                "r2": r2_score(y, estimator.predict(X)),
+                "neg_mean_squared_error": -mean_squared_error(y, estimator.predict(X)),
+            },
+        ),
+    ],
+)
+def test_permutation_importance_multi_metric(list_single_scorer, multi_scorer):
+    # Test permutation importance when scoring contains multiple scorers
+
+    # Creating some data and estimator for the permutation test
+    x, y = make_regression(n_samples=500, n_features=10, random_state=0)
+    lr = LinearRegression().fit(x, y)
+
+    multi_importance = permutation_importance(
+        lr, x, y, random_state=1, scoring=multi_scorer, n_repeats=2
+    )
+    assert set(multi_importance.keys()) == set(list_single_scorer)
+
+    for scorer in list_single_scorer:
+        multi_result = multi_importance[scorer]
+        single_result = permutation_importance(
+            lr, x, y, random_state=1, scoring=scorer, n_repeats=2
+        )
+
+        assert_allclose(multi_result.importances, single_result.importances)
+
+
+def test_permutation_importance_max_samples_error():
+    """Check that a proper error message is raised when `max_samples` is not
+    set to a valid input value.
+    """
+    X = np.array([(1.0, 2.0, 3.0, 4.0)]).T
+    y = np.array([0, 1, 0, 1])
+
+    clf = LogisticRegression()
+    clf.fit(X, y)
+
+    err_msg = r"max_samples must be <= n_samples"
+
+    with pytest.raises(ValueError, match=err_msg):
+        permutation_importance(clf, X, y, max_samples=5)