def _dim_two_gp(mean_shift: tuple[float,
float] = (0.0, 0.0)) -> GaussianProcess:
matern52 = tfp.math.psd_kernels.MaternFiveHalves(
amplitude=tf.cast(2.3, tf.float64),
length_scale=tf.cast(0.5, tf.float64))
return GaussianProcess(
[
lambda x: mean_shift[0] + branin(x),
lambda x: mean_shift[1] + quadratic(x)
],
[matern52, rbf()],
)
def _mo_test_model(
num_obj: int,
*kernel_amplitudes: float | TensorType | None) -> GaussianProcess:
means = [
quadratic, lambda x: tf.reduce_sum(x, axis=-1, keepdims=True),
quadratic
]
kernels = [
tfp.math.psd_kernels.ExponentiatedQuadratic(k_amp)
for k_amp in kernel_amplitudes
]
return GaussianProcess(means[:num_obj], kernels[:num_obj])
def test_expected_constrained_improvement_is_less_for_constrained_points() -> None:
class _Constraint(AcquisitionFunctionBuilder):
def prepare_acquisition_function(
self, datasets: Mapping[str, Dataset], models: Mapping[str, ProbabilisticModel]
) -> AcquisitionFunction:
return lambda x: tf.cast(x >= 0, x.dtype)
def two_global_minima(x: tf.Tensor) -> tf.Tensor:
return x ** 4 / 4 - x ** 2 / 2
initial_query_points = tf.constant([[-2.0], [0.0], [1.2]])
data = {"foo": Dataset(initial_query_points, two_global_minima(initial_query_points))}
models_ = {"foo": GaussianProcess([two_global_minima], [rbf()])}
eci = ExpectedConstrainedImprovement("foo", _Constraint()).prepare_acquisition_function(
data, models_
)
npt.assert_array_less(eci(tf.constant([-1.0])), eci(tf.constant([1.0])))