def test_sparse_logreg_all_positive_labels_warning(self):
b = np.array([0., 1.0, 0.5])
A = np.zeros((3, 3))
with captured_output() as out:
prob = blitzml.SparseLogisticRegressionProblem(A, b)
message = out[0]
self.assertIn("Warning", message)
def test_sparse_logreg_bad_label_too_small(self):
b = np.array([-1., 0., -2.])
A = np.zeros((3, 3))
with captured_output() as out:
prob = blitzml.SparseLogisticRegressionProblem(A, b)
message = out[0]
self.assertIn("Warning", message)
def test_sparse_logreg_bad_initial_conditions(self): n = 7 d = 3 A = np.arange(n * d).reshape(n, d) b = normalize_labels(np.arange(n), True) prob = blitzml.SparseLogisticRegressionProblem(A, b) lammax = prob.compute_max_l1_penalty() weights0 = -1 * np.arange(d) lam = 0.02 * lammax sol = prob.solve(lam, initial_weights=weights0, stopping_tolerance=1e-6) self.assertEqual(is_solution(sol, A, b, lam), True)
def test_sparse_logreg_good_initial_conditions(self):
n = 9
d = 21
np.random.seed(0)
A = np.random.randn(n, d)
b = normalize_labels(np.random.randn(n), True)
prob = blitzml.SparseLogisticRegressionProblem(A, b)
lammax = prob.compute_max_l1_penalty()
lam = 0.03 * lammax
sol0 = prob.solve(lam, stopping_tolerance=1e-4, max_time=1.0)
sol = prob.solve(lam, initial_weights=sol0.weights, max_time=-1.0)
self.assertEqual(is_solution(sol, A, b, lam), True)
def make_prob():
prob = blitzml.SparseLogisticRegressionProblem(A, b)
