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FIX add best_score_ to Ridge*CV estimators #15655

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Merged
merged 9 commits into from
Dec 10, 2019
Merged

FIX add best_score_ to Ridge*CV estimators #15655

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4b141b1
ENH do not allocate local arrays in Ridge*CV of store_cv_vales is False
glemaitre Nov 18, 2019
74d81d6
add whats new
glemaitre Nov 18, 2019
832df73
FIX add best_score_ to Ridge*CV estimators
glemaitre Nov 18, 2019
32d46ee
DOC add attributes in docstring
glemaitre Nov 18, 2019
834862e
Merge remote-tracking branch 'origin/master' into is/best_score_ridge
glemaitre Dec 9, 2019
2884150
doc
glemaitre Dec 9, 2019
37ba6ec
fix docstring
glemaitre Dec 9, 2019
92ce5d0
check dtype for best_score_
glemaitre Dec 10, 2019
4dbf6cd
docstring
glemaitre Dec 10, 2019
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4 changes: 4 additions & 0 deletions doc/whats_new/v0.23.rst
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Expand Up @@ -73,6 +73,10 @@ Changelog
`store_cv_values` is `True`.
:pr:`15652` by :user:`Jérôme Dockès <jeromedockes>`.

- |Fix| add `best_score_` attribute to :class:`linear_model.RidgeCV` and
:class:`linear_model.RidgeClassifierCV`.
:pr:`15653` by :user:`Jérôme Dockès <jeromedockes>`.
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Maybe add yourself?

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It is fine :)

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it was originally @jeromedockes, I am just splitting the PR such that it is easy to review


:mod:`sklearn.preprocessing`
............................

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10 changes: 9 additions & 1 deletion sklearn/linear_model/_ridge.py
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Expand Up @@ -1576,6 +1576,7 @@ def fit(self, X, y, sample_weight=None):
store_cv_values=self.store_cv_values)
estimator.fit(X, y, sample_weight=sample_weight)
self.alpha_ = estimator.alpha_
self.best_score_ = estimator.best_score_
if self.store_cv_values:
self.cv_values_ = estimator.cv_values_
else:
Expand All @@ -1591,6 +1592,7 @@ def fit(self, X, y, sample_weight=None):
gs.fit(X, y, sample_weight=sample_weight)
estimator = gs.best_estimator_
self.alpha_ = gs.best_estimator_.alpha
self.best_score_ = gs.best_score_

self.coef_ = estimator.coef_
self.intercept_ = estimator.intercept_
Expand Down Expand Up @@ -1693,6 +1695,9 @@ class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
alpha_ : float
Estimated regularization parameter.

best_score_ : float
Mean cross-validated score of the estimator with the best alpha found.

Examples
--------
>>> from sklearn.datasets import load_diabetes
Expand Down Expand Up @@ -1795,7 +1800,10 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
``fit_intercept = False``.

alpha_ : float
Estimated regularization parameter
Estimated regularization parameter.

best_score_ : float
Mean cross-validated score of the estimator with the best alpha found.

classes_ : array of shape (n_classes,)
The classes labels.
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20 changes: 17 additions & 3 deletions sklearn/linear_model/tests/test_ridge.py
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Expand Up @@ -664,17 +664,31 @@ def _test_ridge_cv(filter_):

@pytest.mark.parametrize(
"ridge, make_dataset",
[(RidgeCV(), make_regression),
(RidgeClassifierCV(), make_classification)]
[(RidgeCV(store_cv_values=False), make_regression),
(RidgeClassifierCV(store_cv_values=False), make_classification)]
)
def test_ridge_gcv_cv_values_not_stored(ridge, make_dataset):
# Check that `cv_values_` is not stored when store_cv_values is False
X, y = make_dataset(n_samples=6, random_state=42)
ridge.set_params(store_cv_values=False)
ridge.fit(X, y)
assert not hasattr(ridge, "cv_values_")


@pytest.mark.parametrize(
"ridge, make_dataset",
[(RidgeCV(), make_regression),
(RidgeClassifierCV(), make_classification)]
)
@pytest.mark.parametrize("cv", [None, 3])
def test_ridge_best_score(ridge, make_dataset, cv):
# check that the best_score_ is store
X, y = make_dataset(n_samples=6, random_state=42)
ridge.set_params(store_cv_values=False, cv=cv)
ridge.fit(X, y)
assert hasattr(ridge, "best_score_")
assert isinstance(ridge.best_score_, float)


def _test_ridge_diabetes(filter_):
ridge = Ridge(fit_intercept=False)
ridge.fit(filter_(X_diabetes), y_diabetes)
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