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 test_batch_monte_carlo_expected_improvement_raises_for_model_with_wrong_event_shape(
) -> None:
builder = BatchMonteCarloExpectedImprovement(100)
data = mk_dataset([(0.0, 0.0)], [(0.0, 0.0)])
matern52 = tfp.math.psd_kernels.MaternFiveHalves(
amplitude=tf.cast(2.3, tf.float64),
length_scale=tf.cast(0.5, tf.float64))
model = GaussianProcess([lambda x: branin(x), lambda x: quadratic(x)],
[matern52, rbf()])
with pytest.raises(TF_DEBUGGING_ERROR_TYPES):
builder.prepare_acquisition_function(data, model)
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])))
def __init__(self):
super().__init__([lambda x: 2 * x], [rbf()])
def __init__(self):
super().__init__([lambda x: tf.exp(-x)], [rbf()])
