def test_thompson_sampling_raises_for_invalid_models_keys(
datasets: dict[str, Dataset],
models: dict[str, ProbabilisticModel]) -> None:
search_space = Box([-1], [1])
rule = ThompsonSampling(100, 10)
with pytest.raises(ValueError):
rule.acquire(search_space, datasets, models)
def test_thompson_sampling_raises_for_invalid_models_keys(
datasets: Dict[str, Dataset],
models: Dict[str, ProbabilisticModel]) -> None:
search_space = one_dimensional_range(-1, 1)
rule = ThompsonSampling(100, 10)
with pytest.raises(ValueError):
rule.acquire(search_space, datasets, models)
@random_seed
@pytest.mark.parametrize(
"num_steps, acquisition_rule",
[
(20, EfficientGlobalOptimization()),
(
15,
EfficientGlobalOptimization(
BatchMonteCarloExpectedImprovement(sample_size=500).using(OBJECTIVE),
num_query_points=2,
),
),
(15, TrustRegion()),
(17, ThompsonSampling(500, 3)),
],
)
def test_optimizer_finds_minima_of_the_branin_function(
num_steps: int, acquisition_rule: AcquisitionRule
) -> None:
search_space = Box([0, 0], [1, 1])
def build_model(data: Dataset) -> GaussianProcessRegression:
variance = tf.math.reduce_variance(data.observations)
kernel = gpflow.kernels.Matern52(variance, tf.constant([0.2, 0.2], tf.float64))
gpr = gpflow.models.GPR((data.query_points, data.observations), kernel, noise_variance=1e-5)
gpflow.utilities.set_trainable(gpr.likelihood, False)
return GaussianProcessRegression(gpr)
initial_query_points = search_space.sample(5)
def test_thompson_sampling_raises_for_no_points(num_search_space_samples,
num_query_points) -> None:
with pytest.raises(ValueError):
ThompsonSampling(num_search_space_samples, num_query_points)