def test_get_next_by_random_search(self, patch):
def side_effect(size):
return [ConfigurationMock()] * size
patch.side_effect = side_effect
smbo = SMAC(self.scenario, rng=1).solver
rval = smbo._get_next_by_random_search(10, False)
self.assertEqual(len(rval), 10)
for i in range(10):
self.assertIsInstance(rval[i][1], ConfigurationMock)
self.assertEqual(rval[i][1].origin, 'Random Search')
self.assertEqual(rval[i][0], 0)
def test_choose_next_empty_X(self):
smbo = SMAC(self.scenario, rng=1).solver
smbo.acquisition_func._compute = mock.Mock(spec=RandomForestWithInstances)
smbo._get_next_by_random_search = mock.Mock(spec=smbo._get_next_by_random_search)
smbo._get_next_by_random_search.return_value = [[0, 0], [0, 1], [0, 2]]
X = np.zeros((0, 2))
Y = np.zeros((0, 1))
x = smbo.choose_next(X, Y)
self.assertEqual(x, [0, 1, 2])
self.assertEqual(smbo._get_next_by_random_search.call_count, 1)
self.assertEqual(smbo.acquisition_func._compute.call_count, 0)
def test_get_next_by_random_search_sorted(self, patch_sample, patch_ei,
patch_impute):
values = (10, 1, 9, 2, 8, 3, 7, 4, 6, 5)
patch_sample.return_value = [ConfigurationMock(i) for i in values]
patch_ei.return_value = np.array([[_] for _ in values], dtype=float)
patch_impute.side_effect = lambda l: values
smbo = SMAC(self.scenario, rng=1).solver
rval = smbo._get_next_by_random_search(10, True)
self.assertEqual(len(rval), 10)
for i in range(10):
self.assertIsInstance(rval[i][1], ConfigurationMock)
self.assertEqual(rval[i][1].value, 10 - i)
self.assertEqual(rval[i][0], 10 - i)
self.assertEqual(rval[i][1].origin, 'Random Search (sorted)')
# Check that config.get_array works as desired and imputation is used
# in between
np.testing.assert_allclose(patch_ei.call_args[0][0],
np.array(values, dtype=float))
