def test_l1_hessian():
npt.assert_array_equal(regs.L1().hessian(np.array([1, 2])),
np.array([[0, 0], [0, 0]]))
def test_l1_gradient(beta, expected):
npt.assert_array_equal(regs.L1().gradient(beta), expected)
def test_l1_gradient_raises_near_zero(beta):
with pytest.raises(ValueError):
regs.L1().gradient(beta)
def test_l1_function(beta, expected):
assert regs.L1().f(beta) == expected
def test_elastic_net_gradient_zero_weight_is_l1():
beta = np.array([1, 2, 3])
npt.assert_array_equal(
regs.ElasticNet(weight=1).gradient(beta),
regs.L1().gradient(beta))
def test_elastic_net_function_zero_weight_is_l1():
beta = np.array([1, 2, 3])
assert regs.ElasticNet(weight=1).f(beta) == regs.L1().f(beta)
def test_l1_proximal_operator(beta, expected):
npt.assert_array_equal(regs.L1().proximal_operator(beta, 1), expected)
def test_l1_hessian_raises():
with pytest.raises(ValueError):
regs.L1().hessian(np.array([0, 0, 0]))
