From a6753f3ed38d25cec0af8ed95697f48eaacaed24 Mon Sep 17 00:00:00 2001
From: Alexandre Gramfort <alexandre.gramfort@m4x.org>
Date: Sun, 9 Jul 2017 13:21:45 +0200
Subject: [PATCH] [MRG+1] Ridgecv normalize (#9302)
* FIX : normalize was not passed to grid search in RidgeCV
* update what's new
---
doc/whats_new.rst | 5 ++++-
sklearn/linear_model/ridge.py | 3 ++-
sklearn/linear_model/tests/test_ridge.py | 11 +++++++++++
3 files changed, 17 insertions(+), 2 deletions(-)
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 0c5608d6b5..3c87d4174c 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -471,10 +471,13 @@ Bug fixes
by :user:`Andre Ambrosio Boechat <boechat107>`, :user:`Utkarsh Upadhyay
<musically-ut>`, and `Joel Nothman`_.
-
- Add ``data_home`` parameter to
:func:`sklearn.datasets.fetch_kddcup99` by `Loic Esteve`_.
+ - Fix inconsistent results between :class:`linear_model.RidgeCV`
+ and :class:`linear_model.Ridge` when using ``normalize=True``
+ by `Alexandre Gramfort`_.
+
API changes summary
-------------------
diff --git a/sklearn/linear_model/ridge.py b/sklearn/linear_model/ridge.py
index e0c7b6f188..caf2f9eed6 100644
--- a/sklearn/linear_model/ridge.py
+++ b/sklearn/linear_model/ridge.py
@@ -1119,7 +1119,8 @@ class _BaseRidgeCV(LinearModel):
raise ValueError("cv!=None and store_cv_values=True "
" are incompatible")
parameters = {'alpha': self.alphas}
- gs = GridSearchCV(Ridge(fit_intercept=self.fit_intercept),
+ gs = GridSearchCV(Ridge(fit_intercept=self.fit_intercept,
+ normalize=self.normalize),
parameters, cv=self.cv, scoring=self.scoring)
gs.fit(X, y, sample_weight=sample_weight)
estimator = gs.best_estimator_
diff --git a/sklearn/linear_model/tests/test_ridge.py b/sklearn/linear_model/tests/test_ridge.py
index 4879e02def..ee44da5d56 100644
--- a/sklearn/linear_model/tests/test_ridge.py
+++ b/sklearn/linear_model/tests/test_ridge.py
@@ -383,6 +383,16 @@ def _test_ridge_loo(filter_):
return ret
+def _test_ridge_cv_normalize(filter_):
+ ridge_cv = RidgeCV(normalize=True, cv=3)
+ ridge_cv.fit(filter_(10. * X_diabetes), y_diabetes)
+
+ gs = GridSearchCV(Ridge(normalize=True), cv=3,
+ param_grid={'alpha': ridge_cv.alphas})
+ gs.fit(filter_(10. * X_diabetes), y_diabetes)
+ assert_equal(gs.best_estimator_.alpha, ridge_cv.alpha_)
+
+
def _test_ridge_cv(filter_):
ridge_cv = RidgeCV()
ridge_cv.fit(filter_(X_diabetes), y_diabetes)
@@ -462,6 +472,7 @@ def check_dense_sparse(test_func):
def test_dense_sparse():
for test_func in (_test_ridge_loo,
_test_ridge_cv,
+ _test_ridge_cv_normalize,
_test_ridge_diabetes,
_test_multi_ridge_diabetes,
_test_ridge_classifiers,
--
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