def test_trust_region_for_unsuccessful_local_to_global_trust_region_reduced(
) -> None:
tr = TrustRegion(NegativeLowerConfidenceBound(0).using(OBJECTIVE))
dataset = Dataset(tf.constant([[0.1, 0.2], [-0.1, -0.2]]),
tf.constant([[0.4], [0.5]]))
lower_bound = tf.constant([-2.2, -1.0])
upper_bound = tf.constant([1.3, 3.3])
search_space = Box(lower_bound, upper_bound)
eps = 0.5 * (search_space.upper - search_space.lower) / 10
previous_y_min = dataset.observations[0]
is_global = False
acquisition_space = Box(dataset.query_points[0] - eps,
dataset.query_points[0] + eps)
previous_state = TrustRegion.State(acquisition_space, eps, previous_y_min,
is_global)
_, current_state = tr.acquire(search_space, {OBJECTIVE: dataset},
{OBJECTIVE: QuadraticMeanAndRBFKernel()},
previous_state)
npt.assert_array_less(
current_state.eps,
previous_state.eps) # current TR smaller than previous
assert current_state.is_global
npt.assert_array_almost_equal(current_state.acquisition_space.lower,
lower_bound)
def test_reducers_on_lcb(reducer):
m = 6
beta = tf.convert_to_tensor(1.96, dtype=tf.float64)
model = QuadraticMeanAndRBFKernel()
acqs = [NegativeLowerConfidenceBound(beta).using("foo") for _ in range(m)]
acq = reducer.type_class(*acqs)
acq_fn = acq.prepare_acquisition_function({"foo": reducer.dataset}, {"foo": model})
individual_lcb = [-lower_confidence_bound(model, beta, reducer.query_point) for _ in range(m)]
expected = reducer.raw_reduce_op(individual_lcb)
desired = acq_fn(reducer.query_point)
np.testing.assert_array_almost_equal(expected, desired)
def test_trust_region_for_default_state() -> None:
tr = TrustRegion(NegativeLowerConfidenceBound(0))
dataset = Dataset(tf.constant([[0.1, 0.2]]), tf.constant([[0.012]]))
lower_bound = tf.constant([-2.2, -1.0])
upper_bound = tf.constant([1.3, 3.3])
search_space = Box(lower_bound, upper_bound)
query_point, state = tr.acquire_single(search_space, dataset,
QuadraticMeanAndRBFKernel(), None)
npt.assert_array_almost_equal(query_point, tf.constant([[0.0, 0.0]]), 5)
npt.assert_array_almost_equal(state.acquisition_space.lower, lower_bound)
npt.assert_array_almost_equal(state.acquisition_space.upper, upper_bound)
npt.assert_array_almost_equal(state.y_min, [0.012])
assert state.is_global
def test_async_greedy_raises_for_non_greedy_function() -> None:
non_greedy_function_builder = NegativeLowerConfidenceBound()
with pytest.raises(NotImplementedError):
# we are deliberately passing in wrong object
# hence type ignore
AsynchronousGreedy(non_greedy_function_builder) # type: ignore
class _Midpoint(AcquisitionRule[TensorType, Box]):
def acquire(
self,
search_space: Box,
models: Mapping[str, ProbabilisticModel],
datasets: Optional[Mapping[str, Dataset]] = None,
) -> TensorType:
return (search_space.upper[None] + search_space.lower[None]) / 2
@pytest.mark.parametrize(
"rule, expected_query_point",
[
(EfficientGlobalOptimization(NegativeLowerConfidenceBound(0)), [[0.0, 0.0]]),
(_Midpoint(), [[-0.45, 1.15]]),
],
)
def test_trust_region_for_default_state(
rule: AcquisitionRule[TensorType, Box], expected_query_point: TensorType
) -> None:
tr = TrustRegion(rule)
dataset = Dataset(tf.constant([[0.1, 0.2]]), tf.constant([[0.012]]))
lower_bound = tf.constant([-2.2, -1.0])
upper_bound = tf.constant([1.3, 3.3])
search_space = Box(lower_bound, upper_bound)
state, query_point = tr.acquire_single(
search_space, QuadraticMeanAndRBFKernel(), dataset=dataset
)(None)
