def test_lipschitz_constant_loss_logreg():
rng = check_random_state(42)
X, _, w, mask = _make_data(rng=rng, masked=True)
l1_ratio = 1.
alpha = .1
grad_weight = alpha * X.shape[0] * (1. - l1_ratio)
a = _logistic_derivative_lipschitz_constant(X, mask, grad_weight)
b = _logistic_loss_lipschitz_constant(X)
assert a == b
def test_lipschitz_constant_loss_logreg():
rng = check_random_state(42)
X, _, w, mask = _make_data(rng=rng, masked=True)
l1_ratio = 1.
alpha = .1
grad_weight = alpha * X.shape[0] * (1. - l1_ratio)
a = _logistic_derivative_lipschitz_constant(X, mask, grad_weight)
b = _logistic_loss_lipschitz_constant(X)
assert_equal(a, b)
def test_logistic_derivative_lipschitz_constant():
# Tests Lipschitz-continuity of of the derivative of logistic loss
rng = check_random_state(42)
grad_weight = 2.08e-1
lipschitz_constant = _logistic_derivative_lipschitz_constant(
X, mask, grad_weight)
for _ in range(20):
x_1 = rng.rand((w.shape[0] + 1)) * rng.randint(1000)
x_2 = rng.rand((w.shape[0] + 1)) * rng.randint(1000)
gradient_difference = linalg.norm(
_logistic_data_loss_and_spatial_grad_derivative(
X, y, x_1, mask, grad_weight) -
_logistic_data_loss_and_spatial_grad_derivative(
X, y, x_2, mask, grad_weight))
point_difference = linalg.norm(x_1 - x_2)
assert gradient_difference <= lipschitz_constant * point_difference
def test_logistic_derivative_lipschitz_constant():
# Tests Lipschitz-continuity of of the derivative of logistic loss
rng = check_random_state(42)
grad_weight = 2.08e-1
lipschitz_constant = _logistic_derivative_lipschitz_constant(
X, mask, grad_weight)
for _ in range(20):
x_1 = rng.rand((w.shape[0] + 1)) * rng.randint(1000)
x_2 = rng.rand((w.shape[0] + 1)) * rng.randint(1000)
gradient_difference = extmath.norm(
_logistic_data_loss_and_spatial_grad_derivative(
X, y, x_1, mask, grad_weight)
- _logistic_data_loss_and_spatial_grad_derivative(
X, y, x_2, mask, grad_weight))
point_difference = extmath.norm(x_1 - x_2)
assert_true(
gradient_difference <= lipschitz_constant * point_difference)
