from trieste.space import Box
from trieste.utils.objectives import (
branin,
BRANIN_GLOBAL_MINIMUM,
BRANIN_GLOBAL_ARGMIN,
mk_observer,
)
from tests.util.misc import random_seed
@gpflow.config.as_context(gpflow.config.Config(float=tf.float64))
@random_seed(1793)
@pytest.mark.parametrize('num_steps, acquisition_rule', [
(12, EfficientGlobalOptimization()),
(22, TrustRegion()),
(17, ThompsonSampling(500, 3)),
])
def test_optimizer_finds_minima_of_the_branin_function(
num_steps: int, acquisition_rule: AcquisitionRule) -> None:
search_space = Box(tf.constant([0.0, 0.0], tf.float64),
tf.constant([1.0, 1.0], tf.float64))
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)
from trieste.types import State, TensorType
@random_seed
@pytest.mark.parametrize(
"num_steps, reload_state, acquisition_rule_fn",
cast(
List[Tuple[int, bool,
Union[Callable[[], AcquisitionRule[TensorType, Box]],
Callable[[], AcquisitionRule[State[TensorType, Union[
AsynchronousRuleState, TrustRegion.State], ],
Box, ], ], ], ]],
[
(20, False, lambda: EfficientGlobalOptimization()),
(20, True, lambda: EfficientGlobalOptimization()),
(15, False, lambda: TrustRegion()),
(15, True, lambda: TrustRegion()),
(
10,
False,
lambda: EfficientGlobalOptimization(
LocalPenalizationAcquisitionFunction(
BRANIN_SEARCH_SPACE, ).using(OBJECTIVE),
num_query_points=3,
),
),
(
30,
False,
lambda: AsynchronousGreedy(
LocalPenalizationAcquisitionFunction(
