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MNT Postpone conversion of RidgeCV's alphas out of __init__ #21506

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Merged
merged 3 commits into from
Nov 3, 2021
Merged

MNT Postpone conversion of RidgeCV's alphas out of __init__ #21506

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2594eb0
Adapt _scale_alpha_inplace to ensure broadcasting
Oct 29, 2021
e9a7cd3
TST Assert correct alpha conversion for `_BaseRidgeCV`
Nov 3, 2043
8b01748
Postpone the conversion of `alphas` as np.ndarray
Nov 3, 2043
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6 changes: 4 additions & 2 deletions sklearn/linear_model/_ridge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1483,7 +1483,7 @@ def __init__(
is_clf=False,
alpha_per_target=False,
):
self.alphas = np.asarray(alphas)
self.alphas = alphas
self.fit_intercept = fit_intercept
self.normalize = normalize
self.scoring = scoring
Expand Down Expand Up @@ -1842,6 +1842,8 @@ def fit(self, X, y, sample_weight=None):
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)

self.alphas = np.asarray(self.alphas)

if np.any(self.alphas <= 0):
raise ValueError(
"alphas must be strictly positive. Got {} containing some "
Expand Down Expand Up @@ -1977,7 +1979,7 @@ def __init__(
store_cv_values=False,
alpha_per_target=False,
):
self.alphas = np.asarray(alphas)
self.alphas = alphas
self.fit_intercept = fit_intercept
self.normalize = normalize
self.scoring = scoring
Expand Down
7 changes: 5 additions & 2 deletions sklearn/linear_model/tests/test_coordinate_descent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -351,7 +351,7 @@ def test_lasso_cv_positive_constraint():

def _scale_alpha_inplace(estimator, n_samples):
"""Rescale the parameter alpha from when the estimator is evoked with
normalize set to True to when it is evoked in a Pipeline with normalize set
normalize set to True as if it were evoked in a Pipeline with normalize set
to False and with a StandardScaler.
"""
if ("alpha" not in estimator.get_params()) and (
Expand All @@ -360,7 +360,10 @@ def _scale_alpha_inplace(estimator, n_samples):
return

if isinstance(estimator, (RidgeCV, RidgeClassifierCV)):
alphas = estimator.alphas * n_samples
# alphas is not validated at this point and can be a list.
# We convert it to a np.ndarray to make sure broadcasting
# is used.
alphas = np.asarray(estimator.alphas) * n_samples
return estimator.set_params(alphas=alphas)
if isinstance(estimator, (Lasso, LassoLars, MultiTaskLasso)):
alpha = estimator.alpha * np.sqrt(n_samples)
Expand Down
21 changes: 21 additions & 0 deletions sklearn/linear_model/tests/test_ridge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1110,6 +1110,27 @@ def test_ridge_classifier_cv_store_cv_values(scoring):
assert r.cv_values_.shape == (n_samples, n_targets, n_alphas)


@pytest.mark.parametrize("Estimator", [RidgeCV, RidgeClassifierCV])
def test_ridgecv_alphas_conversion(Estimator):
rng = np.random.RandomState(0)
alphas = (0.1, 1.0, 10.0)

n_samples, n_features = 5, 5
if Estimator is RidgeCV:
y = rng.randn(n_samples)
else:
y = rng.randint(0, 2, n_samples)
X = rng.randn(n_samples, n_features)

ridge_est = Estimator(alphas=alphas)
assert (
ridge_est.alphas is alphas
), f"`alphas` was mutated in `{Estimator.__name__}.__init__`"

ridge_est.fit(X, y)
assert_array_equal(ridge_est.alphas, np.asarray(alphas))


def test_ridgecv_sample_weight():
rng = np.random.RandomState(0)
alphas = (0.1, 1.0, 10.0)
Expand Down