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FIX RidgeCV works with multioutput and sample-weight non-default score #29877

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Merged
merged 2 commits into from
Sep 18, 2024
Merged

FIX RidgeCV works with multioutput and sample-weight non-default score #29877

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fd74bfb
FIX RidgeCV should works with multioutput and sample-weight with scor…
glemaitre Sep 17, 2024
3b3a034
TST/FIX do not ravel the predictions and true labels
glemaitre Sep 17, 2024
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7 changes: 1 addition & 6 deletions sklearn/linear_model/_ridge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2248,12 +2248,7 @@ def _score(self, *, predictions, y, n_y, scorer, score_params):
]
)
else:
_score = scorer(
identity_estimator,
predictions.ravel(),
y.ravel(),
**score_params,
)
_score = scorer(identity_estimator, predictions, y, **score_params)

return _score

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26 changes: 26 additions & 0 deletions sklearn/linear_model/tests/test_ridge.py
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Expand Up @@ -2252,6 +2252,32 @@ def test_ridge_cv_values_deprecated():
ridge.cv_values_


def test_ridge_cv_multioutput_sample_weight(global_random_seed):
"""Check that `RidgeCV` works properly with multioutput and sample_weight
when `scoring != None`.

We check the error reported by the RidgeCV is close to a naive LOO-CV using a
Ridge estimator.
"""
X, y = make_regression(n_targets=2, random_state=global_random_seed)
sample_weight = np.ones(shape=(X.shape[0],))

ridge_cv = RidgeCV(scoring="neg_mean_squared_error", store_cv_results=True)
ridge_cv.fit(X, y, sample_weight=sample_weight)

cv = LeaveOneOut()
ridge = Ridge(alpha=ridge_cv.alpha_)
y_pred_loo = np.squeeze(
[
ridge.fit(X[train], y[train], sample_weight=sample_weight[train]).predict(
X[test]
)
for train, test in cv.split(X)
]
)
assert_allclose(ridge_cv.best_score_, -mean_squared_error(y, y_pred_loo))


# Metadata Routing Tests
# ======================

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