def setUp(self):
np.random.seed(1)
self.A = np.random.uniform(0, 1, size=(20, 10))
self.b = np.random.uniform(0, 1, size=20)
prob = blitzml.LassoProblem(self.A, self.b)
self.lam = 0.02 * prob.compute_max_l1_penalty()
self.sol = prob.solve(self.lam)
def test_sparse_linear_lambda_max_basic(self):
A = np.array([[1, -2], [1, 0]]).T
b = np.array([3, -1])
prob = blitzml.LassoProblem(A, b)
lammax = prob.compute_max_l1_penalty()
self.assertEqual(lammax, 6.0)
lammax2 = prob.compute_max_l1_penalty(include_bias_term=False)
self.assertEqual(lammax2, 5.0)
def test_lasso_empty(self):
A = sp.csc_matrix((100, 1000))
b = np.ones(100)
with captured_output() as out:
prob = blitzml.LassoProblem(A, b)
self.assertEqual(prob.compute_max_l1_penalty(), 0.)
sol = prob.solve(0.)
self.assertEqual(np.linalg.norm(sol.weights), 0.)
self.assertEqual(sol.bias, 1.0)
self.assertEqual(sol.objective_value, 0.)
self.assertEqual(sol.duality_gap, 0.)
def test_lasso_bad_initial_conditions(self): n = 6 d = 4 A = np.arange(n * d).reshape(n, d) b = np.arange(n) prob = blitzml.LassoProblem(A, b) lammax = prob.compute_max_l1_penalty() weights0 = -100 * np.arange(d) lam = 0.02 * lammax sol = prob.solve(lam, initial_weights=weights0, stopping_tolerance=1e-5) self.assertEqual(is_solution(sol, A, b, lam), True)
def test_lasso_good_initial_conditions(self):
n = 7
d = 30
np.random.seed(0)
A = np.random.randn(n, d)
b = np.random.randn(n)
prob = blitzml.LassoProblem(A, b)
lammax = prob.compute_max_l1_penalty()
lam = 0.005 * lammax
sol0 = prob.solve(lam, stopping_tolerance=1e-5)
sol = prob.solve(lam, initial_weights=sol0.weights, max_time=-1.0)
self.assertEqual(is_solution(sol, A, b, lam), True)
def test_lasso_empty_cols(self):
np.random.seed(1)
A = np.random.randn(20, 30)
A[:, 0] = 0.
A[:, 10] = 0.
A[:, 29] = 0.
A_sparse = sp.csc_matrix(A)
b = np.random.randn(20)
prob = blitzml.LassoProblem(A_sparse, b)
lammax = prob.compute_max_l1_penalty()
lam = 0.05 * lammax
sol = prob.solve(lam, stopping_tolerance=1e-5)
self.assertEqual(is_solution(sol, A, b, lam), True)
def setUp(self):
A = np.array([[2]], dtype=np.float)
b = np.array([10], dtype=np.double)
with captured_output() as out:
self.prob = blitzml.LassoProblem(A, b)
def test_lasso_csr_matrix_warning(self):
self.A = sp.csr_matrix(self.A)
with captured_output() as out:
prob = blitzml.LassoProblem(self.A, self.b)
message = out[0]
self.assertTrue("Warning" in message)
def test_lasso_dense_copy_warning_F_contiguous(self):
self.A = np.asfortranarray(self.A)
with captured_output() as out:
prob = blitzml.LassoProblem(self.A, self.b)
message = out[0]
self.assertTrue("Warning" not in message)
def setUp(self):
A = np.arange(20).reshape(5, 4)
b = np.arange(5).astype(np.float64)
self.prob = blitzml.LassoProblem(A, b)
