scikit-learn · agramfort · Oct 4, 2014 · May 31, 2014 · Oct 4, 2014 · agramfort
diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
@@ -184,7 +184,7 @@ for another implementation::
     >>> clf = linear_model.Lasso(alpha = 0.1)
     >>> clf.fit([[0, 0], [1, 1]], [0, 1])
     Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,
-       normalize=False, positive=False, precompute='auto', random_state=None,
+       normalize=False, positive=False, precompute=False, random_state=None,
        selection='cyclic', tol=0.0001, warm_start=False)
     >>> clf.predict([[1, 1]])
     array([ 0.8])

diff --git a/doc/tutorial/statistical_inference/supervised_learning.rst b/doc/tutorial/statistical_inference/supervised_learning.rst
@@ -327,7 +327,7 @@ application of Occam's razor: *prefer simpler models*.
     >>> regr.alpha = best_alpha
     >>> regr.fit(diabetes_X_train, diabetes_y_train)
     Lasso(alpha=0.025118864315095794, copy_X=True, fit_intercept=True,
-       max_iter=1000, normalize=False, positive=False, precompute='auto',
+       max_iter=1000, normalize=False, positive=False, precompute=False,
        random_state=None, selection='cyclic', tol=0.0001, warm_start=False)
     >>> print(regr.coef_)
     [   0.         -212.43764548  517.19478111  313.77959962 -160.8303982    -0.

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
@@ -132,6 +132,13 @@ API changes summary
       but previous versions accidentally returned only the positive
       probability. Fixed by Will Lamond and `Lars Buitinck`_.
 
+    - Change default value of precompute in :class:`ElasticNet` and :class:`Lasso`
+      to False. Setting precompute to "auto" was found to be slower when
+      n_samples > n_features since the computation of the Gram matrix is
+      computationally expensive and outweighs the benefit of fitting the Gram
+      for just one alpha.
+      ``precompute="auto"`` is now deprecated and will be removed in 0.18
+      By `Manoj Kumar`_.
 
 .. _changes_0_15_2:
 

diff --git a/sklearn/linear_model/coordinate_descent.py b/sklearn/linear_model/coordinate_descent.py
@@ -15,7 +15,7 @@
 from .base import LinearModel, _pre_fit
 from ..base import RegressorMixin
 from .base import center_data, sparse_center_data
-from ..utils import check_array
+from ..utils import check_array, check_X_y
 from ..utils.validation import check_random_state
 from ..cross_validation import _check_cv as check_cv
 from ..externals.joblib import Parallel, delayed
@@ -604,6 +604,8 @@ class ElasticNet(LinearModel, RegressorMixin):
         calculations. If set to ``'auto'`` let us decide. The Gram
         matrix can also be passed as argument. For sparse input
         this option is always ``True`` to preserve sparsity.
+        WARNING : The ``'auto'`` option is deprecated and will
+        be removed in 0.18.
 
     max_iter : int, optional
         The maximum number of iterations
@@ -665,7 +667,7 @@ class ElasticNet(LinearModel, RegressorMixin):
     path = staticmethod(enet_path)
 
     def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,
-                 normalize=False, precompute='auto', max_iter=1000,
+                 normalize=False, precompute=False, max_iter=1000,
                  copy_X=True, tol=1e-4, warm_start=False, positive=False,
                  random_state=None, selection='cyclic'):
         self.alpha = alpha
@@ -708,10 +710,16 @@ def fit(self, X, y):
             warnings.warn("With alpha=0, this algorithm does not converge "
                           "well. You are advised to use the LinearRegression "
                           "estimator", stacklevel=2)
-        X = check_array(X, 'csc', dtype=np.float64, order='F', copy=self.copy_X
-                        and self.fit_intercept)
-        # From now on X can be touched inplace
-        y = np.asarray(y, dtype=np.float64)
+
+        if self.precompute == 'auto':
+            warnings.warn("Setting precompute to 'auto', was found to be "
+                          "slower even when n_samples > n_features. Hence "
+                          "it will be removed in 0.18.",
+                          DeprecationWarning, stacklevel=2)
+
+        X, y = check_X_y(X, y, accept_sparse='csc', dtype=np.float64,
+                         order='F', copy=self.copy_X and self.fit_intercept,
+                         multi_output=True)
 
         X, y, X_mean, y_mean, X_std, precompute, Xy = \
             _pre_fit(X, y, None, self.precompute, self.normalize,
@@ -830,6 +838,8 @@ class Lasso(ElasticNet):
         calculations. If set to ``'auto'`` let us decide. The Gram
         matrix can also be passed as argument. For sparse input
         this option is always ``True`` to preserve sparsity.
+        WARNING : The ``'auto'`` option is deprecated and will
+        be removed in 0.18.
 
     max_iter : int, optional
         The maximum number of iterations
@@ -880,7 +890,7 @@ class Lasso(ElasticNet):
     >>> clf = linear_model.Lasso(alpha=0.1)
     >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
     Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,
-       normalize=False, positive=False, precompute='auto', random_state=None,
+       normalize=False, positive=False, precompute=False, random_state=None,
        selection='cyclic', tol=0.0001, warm_start=False)
     >>> print(clf.coef_)
     [ 0.85  0.  ]
@@ -906,7 +916,7 @@ class Lasso(ElasticNet):
     path = staticmethod(enet_path)
 
     def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
-                 precompute='auto', copy_X=True, max_iter=1000,
+                 precompute=False, copy_X=True, max_iter=1000,
                  tol=1e-4, warm_start=False, positive=False,
                  random_state=None, selection='cyclic'):
         super(Lasso, self).__init__(
@@ -1207,6 +1217,7 @@ def fit(self, X, y):
         model.alpha = best_alpha
         model.l1_ratio = best_l1_ratio
         model.copy_X = copy_X
+        model.precompute = False
         model.fit(X, y)
         if not hasattr(self, 'l1_ratio'):
             del self.l1_ratio_

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -562,6 +562,18 @@ def test_random_descent():
     assert_raises(ValueError, clf_random.fit, X, y)
 
 
+def test_deprection_precompute_enet():
+    """
+    Test that setting precompute="auto" gives a Deprecation Warning.
+    """
+
+    X, y, _, _ = build_dataset(n_samples=20, n_features=10)
+    clf = ElasticNet(precompute="auto")
+    assert_warns(DeprecationWarning, clf.fit, X, y)
+    clf = Lasso(precompute="auto")
+    assert_warns(DeprecationWarning, clf.fit, X, y)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()