def testSobolGeneratorDedupe(self):
generator = SobolGenerator(seed=0, deduplicate=True)
n_tunable = fixed_param_index = 3
bounds = self._create_bounds(n_tunable=n_tunable, n_fixed=1)
generated_points, weights = generator.gen(
n=2,
bounds=bounds,
linear_constraints=(
np.array([[1, 1, 0, 0], [0, 1, 1, 0]]),
np.array([1, 1]),
),
fixed_features={fixed_param_index: 1},
rounding_func=lambda x: x,
)
self.assertEqual(len(generated_points), 2)
generated_points, weights = generator.gen(
n=1,
bounds=bounds,
linear_constraints=(
np.array([[1, 1, 0, 0], [0, 1, 1, 0]]),
np.array([1, 1]),
),
fixed_features={fixed_param_index: 1},
rounding_func=lambda x: x,
)
self.assertEqual(len(generated_points), 1)
self.assertIsNotNone(generator._get_state().get("generated_points"))
def testSobolGeneratorAllTunable(self):
generator = SobolGenerator(seed=0)
bounds = self._create_bounds(n_tunable=3, n_fixed=0)
generated_points, weights = generator.gen(n=3, bounds=bounds)
self.assertEqual(np.shape(generated_points), (3, 3))
np_bounds = np.array(bounds)
self.assertTrue(np.alltrue(generated_points >= np_bounds[:, 0]))
self.assertTrue(np.alltrue(generated_points <= np_bounds[:, 1]))
self.assertTrue(np.all(weights == 1.0))
self.assertEqual(generator._get_state(), {"init_position": 3})
def testSobolGeneratorAllTunable(self):
generator = SobolGenerator(seed=0)
bounds = self._create_bounds(n_tunable=3, n_fixed=0)
generated_points, weights = generator.gen(n=3, bounds=bounds)
expected_points = np.array([
[0.63552922, 0.17165081, 0.85513169],
[0.92333341, 0.75570321, 0.72268772],
[0.21601909, 0.48894, 0.11520141],
])
self.assertTrue(
np.shape(expected_points) == np.shape(generated_points))
self.assertTrue(np.allclose(expected_points, generated_points))
self.assertTrue(np.all(weights == 1.0))
self.assertEqual(generator._get_state(), {"init_position": 3})