def test_trust_region_raises_for_missing_datasets_key(
datasets: dict[str, Dataset],
models: dict[str, ProbabilisticModel]) -> None:
search_space = Box([-1], [1])
rule = TrustRegion()
with pytest.raises(KeyError):
rule.acquire(search_space, datasets, models, None)
def test_trust_region_raises_for_missing_datasets_key(
datasets: Dict[str, Dataset],
models: Dict[str, ProbabilisticModel]) -> None:
search_space = one_dimensional_range(-1, 1)
rule = TrustRegion()
with pytest.raises(KeyError):
rule.acquire(search_space, datasets, models, None)
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_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_trust_region_for_successful_local_to_global_trust_region_increased(
rule: AcquisitionRule[TensorType, Box]
) -> None:
tr = TrustRegion(rule)
dataset = Dataset(tf.constant([[0.1, 0.2], [-0.1, -0.2]]), tf.constant([[0.4], [0.3]]))
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: QuadraticMeanAndRBFKernel()},
datasets={OBJECTIVE: dataset},
)(previous_state)
assert current_state is not None
npt.assert_array_less(previous_state.eps, current_state.eps) # current TR larger than previous
assert current_state.is_global
npt.assert_array_almost_equal(current_state.acquisition_space.lower, lower_bound)
npt.assert_array_almost_equal(current_state.acquisition_space.upper, upper_bound)
def test_trust_region_successful_global_to_global_trust_region_unchanged(
rule: AcquisitionRule[TensorType, Box], expected_query_point: TensorType
) -> None:
tr = TrustRegion(rule)
dataset = Dataset(tf.constant([[0.1, 0.2], [-0.1, -0.2]]), tf.constant([[0.4], [0.3]]))
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
previous_state = TrustRegion.State(search_space, eps, previous_y_min, is_global)
current_state, query_point = tr.acquire(
search_space,
{OBJECTIVE: QuadraticMeanAndRBFKernel()},
datasets={OBJECTIVE: dataset},
)(previous_state)
assert current_state is not None
npt.assert_array_almost_equal(current_state.eps, previous_state.eps)
assert current_state.is_global
npt.assert_array_almost_equal(query_point, expected_query_point, 5)
npt.assert_array_almost_equal(current_state.acquisition_space.lower, lower_bound)
npt.assert_array_almost_equal(current_state.acquisition_space.upper, upper_bound)
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