def test_ego(search_space: SearchSpace, expected_minimum: tf.Tensor) -> None:
ego = EfficientGlobalOptimization(
NegativeLowerConfidenceBound(0).using(OBJECTIVE))
dataset = Dataset(tf.zeros([0, 2]), tf.zeros([0, 1]))
query_point, _ = ego.acquire(search_space, {OBJECTIVE: dataset},
{OBJECTIVE: QuadraticMeanAndRBFKernel()})
npt.assert_array_almost_equal(query_point, expected_minimum, decimal=5)
def test_efficient_global_optimization(optimizer: AcquisitionOptimizer[Box]) -> None:
class NegQuadratic(SingleModelAcquisitionBuilder):
def __init__(self) -> None:
self._updated = False
def prepare_acquisition_function(
self,
model: ProbabilisticModel,
dataset: Optional[Dataset] = None,
) -> AcquisitionFunction:
return lambda x: -quadratic(tf.squeeze(x, -2) - 1)
def update_acquisition_function(
self,
function: AcquisitionFunction,
model: ProbabilisticModel,
dataset: Optional[Dataset] = None,
) -> AcquisitionFunction:
self._updated = True
return function
function = NegQuadratic()
search_space = Box([-10], [10])
ego = EfficientGlobalOptimization(function, optimizer)
data, model = empty_dataset([1], [1]), QuadraticMeanAndRBFKernel(x_shift=1)
query_point = ego.acquire_single(search_space, model, dataset=data)
npt.assert_allclose(query_point, [[1]], rtol=1e-4)
assert not function._updated
query_point = ego.acquire(search_space, {OBJECTIVE: model})
npt.assert_allclose(query_point, [[1]], rtol=1e-4)
assert function._updated
def test_ego(search_space: SearchSpace, expected_minimum: tf.Tensor) -> None:
ego = EfficientGlobalOptimization(
NegativeLowerConfidenceBound(0).using(OBJECTIVE))
dataset = Dataset(tf.constant([[]]), tf.constant([[]]))
query_point, _ = ego.acquire(search_space, {OBJECTIVE: dataset},
{OBJECTIVE: QuadraticWithUnitVariance()})
npt.assert_array_almost_equal(query_point, expected_minimum, decimal=5)
def test_efficient_global_optimization(
optimizer: AcquisitionOptimizer[Box]) -> None:
class NegQuadratic(AcquisitionFunctionBuilder):
def prepare_acquisition_function(
self, datasets: Mapping[str, Dataset],
models: Mapping[str,
ProbabilisticModel]) -> AcquisitionFunction:
return lambda x: -quadratic(tf.squeeze(x, -2) - 1)
search_space = Box([-10], [10])
ego = EfficientGlobalOptimization(NegQuadratic(), optimizer)
data, model = empty_dataset([1], [1]), QuadraticMeanAndRBFKernel(x_shift=1)
query_point, _ = ego.acquire(search_space, {"": data}, {"": model})
npt.assert_allclose(query_point, [[1]], rtol=1e-4)
