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@auguste-probabl
auguste-probabl committed Mar 4, 2025
1 parent c25836b commit eccc1e7
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Showing 2 changed files with 28 additions and 42 deletions.
6 changes: 4 additions & 2 deletions skore/src/skore/sklearn/_estimator/feature_importance_accessor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,11 +112,13 @@ def coefficients(self) -> pd.DataFrame:

@available_if(_check_has_feature_importances())
def mean_decrease_impurity(self):
"""Retrieve the mean decrease impurity of a forest model.
"""Retrieve the mean decrease impurity (MDI) of a tree-based model.
This method is available for estimators that expose a `feature_importances_`
attribute. See for example the
`sklearn.ensemble.GradientBoostingClassifier documentation <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier.feature_importances_>`_.
In particular, note that the MDI is computed at fit time, i.e. using the
training data.
Examples
--------
Expand All @@ -126,7 +128,7 @@ def mean_decrease_impurity(self):
>>> from skore import EstimatorReport
>>> X, y = make_classification(n_features=5, random_state=42)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
>>> forest = RandomForestClassifier(random_state=0)
>>> forest = RandomForestClassifier(n_estimators=5, random_state=0)
>>> report = EstimatorReport(
... forest,
... X_train=X_train,
Expand Down
64 changes: 24 additions & 40 deletions skore/tests/unit/sklearn/estimator/feature_importance/test_mean_decrease_impurity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,39 +10,17 @@
from skore import EstimatorReport


def test(classification_data):
X, y = classification_data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

forest = RandomForestClassifier(random_state=0)
report = EstimatorReport(
forest, X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test
)

result = report.feature_importance.mean_decrease_impurity()

assert result.shape == (5, 1)
assert result.index.tolist() == [
"Feature #0",
"Feature #1",
"Feature #2",
"Feature #3",
"Feature #4",
]
assert result.columns.tolist() == ["Mean decrease impurity"]


@pytest.mark.parametrize(
"data, estimator, expected_shape",
[
(
make_classification(n_features=5, random_state=42),
RandomForestClassifier(random_state=0),
RandomForestClassifier(n_estimators=2, random_state=0),
(5, 1),
),
(
make_classification(n_features=5, random_state=42),
RandomForestClassifier(random_state=0),
RandomForestClassifier(n_estimators=2, random_state=0),
(5, 1),
),
(
Expand All @@ -53,7 +31,7 @@ def test(classification_data):
n_informative=3,
random_state=42,
),
RandomForestClassifier(random_state=0),
RandomForestClassifier(n_estimators=2, random_state=0),
(5, 1),
),
(
Expand All @@ -64,17 +42,21 @@ def test(classification_data):
n_informative=3,
random_state=42,
),
make_pipeline(StandardScaler(), RandomForestClassifier(random_state=0)),
make_pipeline(
StandardScaler(), RandomForestClassifier(n_estimators=2, random_state=0)
),
(5, 1),
),
(
make_classification(n_features=5, random_state=42),
make_pipeline(StandardScaler(), RandomForestClassifier(random_state=0)),
make_pipeline(
StandardScaler(), RandomForestClassifier(n_estimators=2, random_state=0)
),
(5, 1),
),
(
make_regression(n_features=5, n_targets=3, random_state=42),
RandomForestRegressor(random_state=0),
RandomForestRegressor(n_estimators=2, random_state=0),
(5, 1),
),
],
Expand All @@ -101,8 +83,10 @@ def test_numpy_arrays(data, estimator, expected_shape):
@pytest.mark.parametrize(
"estimator",
[
RandomForestClassifier(random_state=0),
make_pipeline(StandardScaler(), RandomForestClassifier(random_state=0)),
RandomForestClassifier(n_estimators=2, random_state=0),
make_pipeline(
StandardScaler(), RandomForestClassifier(n_estimators=2, random_state=0)
),
],
)
def test_pandas_dataframe(estimator):
Expand Down Expand Up @@ -134,15 +118,15 @@ def _make_estimator_param(estimator):
[
_make_estimator_param(estimator)
for estimator in [
sklearn.ensemble.AdaBoostClassifier(),
sklearn.ensemble.AdaBoostRegressor(),
sklearn.ensemble.ExtraTreesClassifier(),
sklearn.ensemble.ExtraTreesRegressor(),
sklearn.ensemble.GradientBoostingClassifier(),
sklearn.ensemble.GradientBoostingRegressor(),
sklearn.ensemble.RandomForestClassifier(),
sklearn.ensemble.RandomForestRegressor(),
sklearn.ensemble.RandomTreesEmbedding(),
sklearn.ensemble.AdaBoostClassifier(n_estimators=2),
sklearn.ensemble.AdaBoostRegressor(n_estimators=2),
sklearn.ensemble.ExtraTreesClassifier(n_estimators=2),
sklearn.ensemble.ExtraTreesRegressor(n_estimators=2),
sklearn.ensemble.GradientBoostingClassifier(n_estimators=2),
sklearn.ensemble.GradientBoostingRegressor(n_estimators=2),
sklearn.ensemble.RandomForestClassifier(n_estimators=2),
sklearn.ensemble.RandomForestRegressor(n_estimators=2),
sklearn.ensemble.RandomTreesEmbedding(n_estimators=2),
sklearn.tree.DecisionTreeClassifier(),
sklearn.tree.DecisionTreeRegressor(),
sklearn.tree.ExtraTreeClassifier(),
Expand Down Expand Up @@ -188,7 +172,7 @@ def test_pipeline_with_transformer(regression_data):

model = make_pipeline(
PolynomialFeatures(degree=2, interaction_only=True),
RandomForestRegressor(random_state=0),
RandomForestRegressor(n_estimators=2, random_state=0),
)

report = EstimatorReport(
Expand Down

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