[MRG + 1] ElasticNetCV: raise ValueError if l1_ratio=0 (scikit-learn#…

…7591)

Raise ValueError if l1_ratio=0 in ElasticNetCV and alphas=None

sklearn/linear_model/coordinate_descent.py

@@ -49,10 +49,10 @@ def _alpha_grid(X, y, Xy=None, l1_ratio=1.0, fit_intercept=True,
  Xy = np.dot(X.T, y) that can be precomputed.
 
  l1_ratio : float
- The elastic net mixing parameter, with ``0 <= l1_ratio <= 1``.
- For ``l1_ratio = 0`` the penalty is an L2 penalty. ``For
- l1_ratio = 1`` it is an L1 penalty.  For ``0 < l1_ratio <
- 1``, the penalty is a combination of L1 and L2.
+ The elastic net mixing parameter, with ``0 < l1_ratio <= 1``.
+ For ``l1_ratio = 0`` the penalty is an L2 penalty. (currently not
+ supported) ``For l1_ratio = 1`` it is an L1 penalty. For
+ ``0 < l1_ratio <1``, the penalty is a combination of L1 and L2.
 
  eps : float, optional
  Length of the path. ``eps=1e-3`` means that
@@ -77,6 +77,11 @@ def _alpha_grid(X, y, Xy=None, l1_ratio=1.0, fit_intercept=True,
  copy_X : boolean, optional, default True
  If ``True``, X will be copied; else, it may be overwritten.
  """
+ if l1_ratio == 0:
+ raise ValueError("Automatic alpha grid generation is not supported for"
+ " l1_ratio=0. Please supply a grid by providing "
+ "your estimator with the appropriate `alphas=` "
+ "argument.")
  n_samples = len(y)
 
  sparse_center = False

sklearn/linear_model/tests/test_coordinate_descent.py

@@ -17,6 +17,7 @@
 from sklearn.utils.testing import assert_greater
 from sklearn.utils.testing import assert_raises
 from sklearn.utils.testing import assert_raises_regex
+from sklearn.utils.testing import assert_raise_message
 from sklearn.utils.testing import assert_warns
 from sklearn.utils.testing import assert_warns_message
 from sklearn.utils.testing import ignore_warnings
@@ -712,3 +713,35 @@ def test_enet_float_precision():
  assert_array_almost_equal(intercept[np.float32],
  intercept[np.float64],
  decimal=4)
+
+
+def test_enet_l1_ratio():
+ # Test that an error message is raised if an estimator that
+ # uses _alpha_grid is called with l1_ratio=0
+ msg = ("Automatic alpha grid generation is not supported for l1_ratio=0. "
+ "Please supply a grid by providing your estimator with the "
+ "appropriate `alphas=` argument.")
+ X = np.array([[1, 2, 4, 5, 8], [3, 5, 7, 7, 8]]).T
+ y = np.array([12, 10, 11, 21, 5])
+
+ assert_raise_message(ValueError, msg, ElasticNetCV(
+ l1_ratio=0, random_state=42).fit, X, y)
+ assert_raise_message(ValueError, msg, MultiTaskElasticNetCV(
+ l1_ratio=0, random_state=42).fit, X, y[:, None])
+
+ # Test that l1_ratio=0 is allowed if we supply a grid manually
+ alphas = [0.1, 10]
+ estkwds = {'alphas': alphas, 'random_state': 42}
+ est_desired = ElasticNetCV(l1_ratio=0.00001, **estkwds)
+ est = ElasticNetCV(l1_ratio=0, **estkwds)
+ with ignore_warnings():
+ est_desired.fit(X, y)
+ est.fit(X, y)
+ assert_array_almost_equal(est.coef_, est_desired.coef_, decimal=5)
+
+ est_desired = MultiTaskElasticNetCV(l1_ratio=0.00001, **estkwds)
+ est = MultiTaskElasticNetCV(l1_ratio=0, **estkwds)
+ with ignore_warnings():
+ est.fit(X, y[:, None])
+ est_desired.fit(X, y[:, None])
+ assert_array_almost_equal(est.coef_, est_desired.coef_, decimal=5)

sklearn/linear_model/tests/test_logistic.py

@@ -588,24 +588,28 @@ def test_logistic_regression_sample_weights():
  # Test that passing sample_weight as ones is the same as
  # not passing them at all (default None)
  for solver in ['lbfgs', 'liblinear']:
- clf_sw_none = LR(solver=solver, fit_intercept=False)
+ clf_sw_none = LR(solver=solver, fit_intercept=False,
+ random_state=42)
  clf_sw_none.fit(X, y)
- clf_sw_ones = LR(solver=solver, fit_intercept=False)
+ clf_sw_ones = LR(solver=solver, fit_intercept=False,
+ random_state=42)
  clf_sw_ones.fit(X, y, sample_weight=np.ones(y.shape[0]))
  assert_array_almost_equal(
  clf_sw_none.coef_, clf_sw_ones.coef_, decimal=4)
 
  # Test that sample weights work the same with the lbfgs,
  # newton-cg, and 'sag' solvers
- clf_sw_lbfgs = LR(solver='lbfgs', fit_intercept=False)
+ clf_sw_lbfgs = LR(solver='lbfgs', fit_intercept=False, random_state=42)
  clf_sw_lbfgs.fit(X, y, sample_weight=sample_weight)
- clf_sw_n = LR(solver='newton-cg', fit_intercept=False)
+ clf_sw_n = LR(solver='newton-cg', fit_intercept=False, random_state=42)
  clf_sw_n.fit(X, y, sample_weight=sample_weight)
- clf_sw_sag = LR(solver='sag', fit_intercept=False, tol=1e-10)
+ clf_sw_sag = LR(solver='sag', fit_intercept=False, tol=1e-10,
+ random_state=42)
  # ignore convergence warning due to small dataset
  with ignore_warnings():
  clf_sw_sag.fit(X, y, sample_weight=sample_weight)
- clf_sw_liblinear = LR(solver='liblinear', fit_intercept=False)
+ clf_sw_liblinear = LR(solver='liblinear', fit_intercept=False,
+ random_state=42)
  clf_sw_liblinear.fit(X, y, sample_weight=sample_weight)
  assert_array_almost_equal(
  clf_sw_lbfgs.coef_, clf_sw_n.coef_, decimal=4)
@@ -619,9 +623,9 @@ def test_logistic_regression_sample_weights():
  # to be 2 for all instances of class 2
  for solver in ['lbfgs', 'liblinear']:
  clf_cw_12 = LR(solver=solver, fit_intercept=False,
- class_weight={0: 1, 1: 2})
+ class_weight={0: 1, 1: 2}, random_state=42)
  clf_cw_12.fit(X, y)
- clf_sw_12 = LR(solver=solver, fit_intercept=False)
+ clf_sw_12 = LR(solver=solver, fit_intercept=False, random_state=42)
  clf_sw_12.fit(X, y, sample_weight=sample_weight)
  assert_array_almost_equal(
  clf_cw_12.coef_, clf_sw_12.coef_, decimal=4)
@@ -630,19 +634,21 @@ def test_logistic_regression_sample_weights():
  # since the patched liblinear code is different.
  clf_cw = LogisticRegression(
  solver="liblinear", fit_intercept=False, class_weight={0: 1, 1: 2},
- penalty="l1", tol=1e-5)
+ penalty="l1", tol=1e-5, random_state=42)
  clf_cw.fit(X, y)
  clf_sw = LogisticRegression(
- solver="liblinear", fit_intercept=False, penalty="l1", tol=1e-5)
+ solver="liblinear", fit_intercept=False, penalty="l1", tol=1e-5,
+ random_state=42)
  clf_sw.fit(X, y, sample_weight)
  assert_array_almost_equal(clf_cw.coef_, clf_sw.coef_, decimal=4)
 
  clf_cw = LogisticRegression(
  solver="liblinear", fit_intercept=False, class_weight={0: 1, 1: 2},
- penalty="l2", dual=True)
+ penalty="l2", dual=True, random_state=42)
  clf_cw.fit(X, y)
  clf_sw = LogisticRegression(
- solver="liblinear", fit_intercept=False, penalty="l2", dual=True)
+ solver="liblinear", fit_intercept=False, penalty="l2", dual=True,
+ random_state=42)
  clf_sw.fit(X, y, sample_weight)
  assert_array_almost_equal(clf_cw.coef_, clf_sw.coef_, decimal=4)