def test_space_net_alpha_grid_pure_spatial():
rng = check_random_state(42)
X = rng.randn(10, 100)
y = np.arange(X.shape[0])
for is_classif in [True, False]:
assert_false(np.any(np.isnan(_space_net_alpha_grid(
X, y, l1_ratio=0., logistic=is_classif))))
def test_space_net_alpha_grid(n_samples=4, n_features=3):
rng = check_random_state(42)
X = rng.randn(n_samples, n_features)
y = np.arange(n_samples)
for l1_ratio, is_classif in itertools.product([.5, 1.], [True, False]):
alpha_max = np.max(np.abs(np.dot(X.T, y))) / l1_ratio
np.testing.assert_almost_equal(_space_net_alpha_grid(
X, y, n_alphas=1, l1_ratio=l1_ratio,
logistic=is_classif), alpha_max)
for l1_ratio, is_classif in itertools.product([.5, 1.], [True, False]):
alpha_max = np.max(np.abs(np.dot(X.T, y))) / l1_ratio
for n_alphas in range(1, 10):
alphas = _space_net_alpha_grid(
X, y, n_alphas=n_alphas, l1_ratio=l1_ratio,
logistic=is_classif)
np.testing.assert_almost_equal(alphas.max(), alpha_max)
np.testing.assert_almost_equal(n_alphas, len(alphas))
def test_space_net_alpha_grid_same_as_sk():
try:
from sklearn.linear_model.coordinate_descent import _alpha_grid
iris = load_iris()
X = iris.data
y = iris.target
np.testing.assert_almost_equal(
_space_net_alpha_grid(X, y, n_alphas=5),
X.shape[0] * _alpha_grid(X, y, n_alphas=5, fit_intercept=False))
except ImportError:
raise SkipTest
def test_space_net_alpha_grid_same_as_sk():
try:
from sklearn.linear_model.coordinate_descent import _alpha_grid
iris = load_iris()
X = iris.data
y = iris.target
np.testing.assert_almost_equal(_space_net_alpha_grid(
X, y, n_alphas=5), X.shape[0] * _alpha_grid(X, y, n_alphas=5,
fit_intercept=False))
except ImportError:
raise SkipTest
