def test_squared_loss_lipschitz(n_samples=4, n_features=2, random_state=42):
rng = np.random.RandomState(random_state)
for scaling in np.logspace(-3, 3, num=7):
X = rng.randn(n_samples, n_features) * scaling
y = rng.randn(n_samples)
n_features = X.shape[1]
L = spectral_norm_squared(X)
_check_lipschitz_continuous(lambda w: _squared_loss_grad(X, y, w),
n_features, L)
def test_squared_loss_lipschitz(n_samples=4, n_features=2, random_state=42):
rng = np.random.RandomState(random_state)
for scaling in np.logspace(-3, 3, num=7):
X = rng.randn(n_samples, n_features) * scaling
y = rng.randn(n_samples)
n_features = X.shape[1]
L = spectral_norm_squared(X)
_check_lipschitz_continuous(lambda w: _squared_loss_grad(
X, y, w), n_features, L)
def test_same_energy_calculus_pure_lasso():
rng = check_random_state(42)
X, y, w, mask = _make_data(rng=rng, masked=True)
# check funcvals
f1 = _squared_loss(X, y, w)
f2 = _squared_loss_and_spatial_grad(X, y, w.ravel(), mask, 0.)
assert f1 == f2
# check derivatives
g1 = _squared_loss_grad(X, y, w)
g2 = _squared_loss_and_spatial_grad_derivative(X, y, w.ravel(), mask, 0.)
np.testing.assert_array_equal(g1, g2)
def test_same_energy_calculus_pure_lasso():
rng = check_random_state(42)
X, y, w, mask = _make_data(rng=rng, masked=True)
# check funcvals
f1 = _squared_loss(X, y, w)
f2 = _squared_loss_and_spatial_grad(X, y, w.ravel(), mask, 0.)
assert_equal(f1, f2)
# check derivatives
g1 = _squared_loss_grad(X, y, w)
g2 = _squared_loss_and_spatial_grad_derivative(X, y, w.ravel(), mask, 0.)
np.testing.assert_array_equal(g1, g2)
