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)
def test_logistic_gradient_at_simple_points():
# Tests gradient of logistic data loss function in points near to zero.
# This is a not so hard test, just for detecting big errors
X, y, w, mask = create_graph_net_simulation_data(n_samples=10, size=4)
grad_weight = 1
# Add the intercept
w = np.append(w, 0)
func = lambda w: _logistic_data_loss_and_spatial_grad(
X, y, w, mask, grad_weight)
func_grad = lambda w: _logistic_data_loss_and_spatial_grad_derivative(
X, y, w, mask, grad_weight)
for i in range(0, w.size, 7):
point = np.zeros(*w.shape)
point[i] = 1
assert_almost_equal(sp.optimize.check_grad(func, func_grad, point),
0, decimal=3)
def test_logistic_gradient_at_simple_points():
# Tests gradient of logistic data loss function in points near to zero.
# This is a not so hard test, just for detecting big errors
X, y, w, mask = create_graph_net_simulation_data(n_samples=10, size=4)
grad_weight = 1
# Add the intercept
w = np.append(w, 0)
func = lambda w: _logistic_data_loss_and_spatial_grad(
X, y, w, mask, grad_weight)
func_grad = lambda w: _logistic_data_loss_and_spatial_grad_derivative(
X, y, w, mask, grad_weight)
for i in range(0, w.size, 7):
point = np.zeros(*w.shape)
point[i] = 1
assert_almost_equal(sp.optimize.check_grad(func, func_grad, point),
0, decimal=3)
