def test__squared_loss_derivative_lipschitz_constant():
# Tests Lipschitz-continuity of the derivative of _squared_loss loss
# function
rng = check_random_state(42)
grad_weight = 2.08e-1
lipschitz_constant = _squared_loss_derivative_lipschitz_constant(
X, mask, grad_weight)
for _ in range(20):
x_1 = rng.rand(*w.shape) * rng.randint(1000)
x_2 = rng.rand(*w.shape) * rng.randint(1000)
gradient_difference = linalg.norm(
_squared_loss_and_spatial_grad_derivative(X, y, x_1, mask,
grad_weight) -
_squared_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__squared_loss_derivative_lipschitz_constant():
# Tests Lipschitz-continuity of the derivative of _squared_loss loss
# function
rng = check_random_state(42)
grad_weight = 2.08e-1
lipschitz_constant = _squared_loss_derivative_lipschitz_constant(
X, mask, grad_weight)
for _ in range(20):
x_1 = rng.rand(*w.shape) * rng.randint(1000)
x_2 = rng.rand(*w.shape) * rng.randint(1000)
gradient_difference = extmath.norm(
_squared_loss_and_spatial_grad_derivative(X, y, x_1, mask,
grad_weight)
- _squared_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_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)
def test__squared_loss_gradient_at_simple_points():
"""Tests gradient of 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
func = lambda w: _squared_loss_and_spatial_grad(X, y, w, mask,
grad_weight)
func_grad = lambda w: _squared_loss_and_spatial_grad_derivative(
X, y, w, mask, grad_weight)
for i in range(0, w.size, 2):
point = np.zeros(*w.shape)
point[i] = 1
assert_almost_equal(sp.optimize.check_grad(func, func_grad, point),
0, decimal=3)
def test__squared_loss_gradient_at_simple_points():
"""Tests gradient of 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
func = lambda w: _squared_loss_and_spatial_grad(X, y, w, mask, grad_weight)
func_grad = lambda w: _squared_loss_and_spatial_grad_derivative(
X, y, w, mask, grad_weight)
for i in range(0, w.size, 2):
point = np.zeros(*w.shape)
point[i] = 1
assert_almost_equal(sp.optimize.check_grad(func, func_grad, point),
0,
decimal=3)
