def test_tvl1_from_gradient(size=5, n_samples=10, random_state=42):
rng = np.random.RandomState(random_state)
shape = [size] * 3
n_voxels = np.prod(shape)
X = rng.randn(n_samples, n_voxels)
y = rng.randn(n_samples)
w = rng.randn(*shape)
mask = np.ones_like(w).astype(np.bool)
for alpha in [0., 1e-1, 1e-3]:
for l1_ratio in [0., .5, 1.]:
gradid = _gradient_id(w, l1_ratio=l1_ratio)
assert_equal(
_tvl1_objective(X, y,
w.copy().ravel(), alpha, l1_ratio, mask),
_squared_loss(X, y, w.copy().ravel(), compute_grad=False) +
alpha * _tvl1_objective_from_gradient(gradid))
def test_tvl1_from_gradient(size=5, n_samples=10, random_state=42):
rng = np.random.RandomState(random_state)
shape = [size] * 3
n_voxels = np.prod(shape)
X = rng.randn(n_samples, n_voxels)
y = rng.randn(n_samples)
w = rng.randn(*shape)
mask = np.ones_like(w).astype(np.bool)
for alpha in [0., 1e-1, 1e-3]:
for l1_ratio in [0., .5, 1.]:
gradid = _gradient_id(w, l1_ratio=l1_ratio)
assert_equal(_tvl1_objective(
X, y, w.copy().ravel(), alpha, l1_ratio, mask),
_squared_loss(X, y, w.copy().ravel(),
compute_grad=False
) + alpha * _tvl1_objective_from_gradient(
gradid))
def test_tvl1_objective_raises_value_error_if_invalid_loss():
pytest.raises(ValueError, lambda loss: _tvl1_objective(
None, None, None, None, None, None, loss=loss),
"invalidloss")
def test_tvl1_objective_raises_value_error_if_invalid_loss():
assert_raises(ValueError, lambda loss: _tvl1_objective(
None, None, None, None, None, None, loss=loss),
"invalidloss")
