def test_crossover_deterministic(crossover: BaseCrossover, rand_value: float,
expected_params: np.ndarray) -> None:
study = optuna.study.create_study()
search_space: Dict[str, BaseDistribution] = {
"x": FloatDistribution(1, 10),
"y": FloatDistribution(1, 10),
}
numerical_transform = _SearchSpaceTransform(search_space)
parent_params = np.array([[1.0, 2.0], [3.0, 4.0]])
if crossover.n_parents == 3:
parent_params = np.append(parent_params, [[5.0, 6.0]], axis=0)
def _rand(*args: Any, **kwargs: Any) -> Any:
if len(args) == 0:
return rand_value
return np.full(args[0], rand_value)
def _normal(*args: Any, **kwargs: Any) -> Any:
if kwargs.get("size") is None:
return rand_value
return np.full(kwargs.get("size"), rand_value) # type: ignore
rng = Mock()
rng.rand = Mock(side_effect=_rand)
rng.normal = Mock(side_effect=_normal)
child_params = crossover.crossover(parent_params, rng, study,
numerical_transform.bounds)
np.testing.assert_almost_equal(child_params, expected_params)
def test_crossover_numerical_distribution(crossover: BaseCrossover) -> None:
study = optuna.study.create_study()
rng = np.random.RandomState()
search_space = {"x": FloatDistribution(1, 10), "y": IntDistribution(1, 10)}
numerical_transform = _SearchSpaceTransform(search_space)
parent_params = np.array([[1.0, 2], [3.0, 4]])
if crossover.n_parents == 3:
parent_params = np.append(parent_params, [[5.0, 6]], axis=0)
child_params = crossover.crossover(parent_params, rng, study, numerical_transform.bounds)
assert child_params.ndim == 1
assert len(child_params) == len(search_space)
assert np.nan not in child_params
assert np.inf not in child_params
def test_crossover_duplicated_param_values(crossover: BaseCrossover) -> None:
param_values = [1.0, 2.0]
study = optuna.study.create_study()
rng = np.random.RandomState()
search_space = {"x": FloatDistribution(1, 10), "y": IntDistribution(1, 10)}
numerical_transform = _SearchSpaceTransform(search_space)
parent_params = np.array([param_values, param_values])
if crossover.n_parents == 3:
parent_params = np.append(parent_params, [param_values], axis=0)
child_params = crossover.crossover(parent_params, rng, study,
numerical_transform.bounds)
assert child_params.ndim == 1
np.testing.assert_almost_equal(child_params, param_values)