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_ego(search_space: SearchSpace, expected_minimum: tf.Tensor) -> None:
ego = EfficientGlobalOptimization(
NegativeLowerConfidenceBound(0).using(OBJECTIVE))
dataset = Dataset(tf.zeros([0, 2]), tf.zeros([0, 1]))
query_point, _ = ego.acquire(search_space, {OBJECTIVE: dataset},
{OBJECTIVE: QuadraticMeanAndRBFKernel()})
npt.assert_array_almost_equal(query_point, expected_minimum, decimal=5)
def test_trust_region_for_unsuccessful_global_to_local_trust_region_unchanged(
) -> 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 = True
acquisition_space = search_space
previous_state = TrustRegion.State(acquisition_space, eps, previous_y_min,
is_global)
query_point, current_state = tr.acquire(
search_space, {OBJECTIVE: dataset},
{OBJECTIVE: QuadraticWithUnitVariance()}, previous_state)
npt.assert_array_almost_equal(current_state.eps, previous_state.eps)
assert not current_state.is_global
npt.assert_array_less(lower_bound, current_state.acquisition_space.lower)
npt.assert_array_less(current_state.acquisition_space.upper, upper_bound)
assert query_point[0] in current_state.acquisition_space
def test_ego(search_space: SearchSpace, expected_minimum: tf.Tensor) -> None:
ego = EfficientGlobalOptimization(
NegativeLowerConfidenceBound(0).using(OBJECTIVE))
dataset = Dataset(tf.constant([[]]), tf.constant([[]]))
query_point, _ = ego.acquire(search_space, {OBJECTIVE: dataset},
{OBJECTIVE: QuadraticWithUnitVariance()})
npt.assert_array_almost_equal(query_point, expected_minimum, decimal=5)
def test_negative_lower_confidence_bound_builder_builds_negative_lower_confidence_bound() -> None:
model = QuadraticMeanAndRBFKernel()
beta = 1.96
acq_fn = NegativeLowerConfidenceBound(beta).prepare_acquisition_function(
Dataset(tf.zeros([0, 1]), tf.zeros([0, 1])), model
)
query_at = tf.linspace([-10], [10], 100)
expected = -lower_confidence_bound(model, beta, query_at)
npt.assert_array_almost_equal(acq_fn(query_at), expected)
def test_negative_lower_confidence_bound_builder_builds_negative_lower_confidence_bound(
) -> None:
model = QuadraticWithUnitVariance()
beta = 1.96
acq_fn = NegativeLowerConfidenceBound(beta).prepare_acquisition_function(
Dataset(tf.constant([[]]), tf.constant([[]])), model)
query_at = tf.constant([[-3.], [-2.], [-1.], [0.], [1.], [2.], [3.]])
expected = -lower_confidence_bound(model, beta, query_at)
npt.assert_array_almost_equal(acq_fn(query_at), expected)
def test_locally_penalized_expected_improvement_raises_when_called_with_invalid_base(
) -> None:
search_space = Box([0, 0], [1, 1])
base_builder = NegativeLowerConfidenceBound()
with pytest.raises(ValueError):
LocalPenalizationAcquisitionFunction(
search_space,
base_acquisition_function_builder=base_builder # type: ignore
)
def test_reducers_on_lcb(reducer):
m = 6
beta = tf.convert_to_tensor(1.96, dtype=tf.float64)
model = QuadraticWithUnitVariance()
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).using(OBJECTIVE))
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(search_space, {OBJECTIVE: dataset},
{OBJECTIVE: 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
expected = tf.reduce_mean(tf.reduce_max(tf.maximum(
min_predictive_mean_at_known_points - mvn_samples, 0.0
), axis=-1), axis=0)
# fmt: on
builder = BatchMonteCarloExpectedImprovement(10_000)
acq = builder.prepare_acquisition_function(mk_dataset([[0.3], [0.5]], [[0.09], [0.25]]), model)
npt.assert_allclose(acq(xs), expected, rtol=0.05)
@pytest.mark.parametrize(
"function, function_repr",
[
(ExpectedImprovement(), "ExpectedImprovement()"),
(NegativeLowerConfidenceBound(1.96), "NegativeLowerConfidenceBound(1.96)"),
(NegativePredictiveMean(), "NegativePredictiveMean()"),
(ProbabilityOfFeasibility(0.5), "ProbabilityOfFeasibility(0.5)"),
(ExpectedHypervolumeImprovement(), "ExpectedHypervolumeImprovement()"),
(
BatchMonteCarloExpectedImprovement(10_000),
f"BatchMonteCarloExpectedImprovement(10000, jitter={DEFAULTS.JITTER})",
),
],
)
def test_single_model_acquisition_function_builder_reprs(function, function_repr) -> None:
assert repr(function) == function_repr
assert repr(function.using("TAG")) == f"{function_repr} using tag 'TAG'"
assert (
repr(ExpectedConstrainedImprovement("TAG", function.using("TAG"), 0.0))
== f"ExpectedConstrainedImprovement('TAG', {function_repr} using tag 'TAG', 0.0)"