def multi_source_optimizer():
mock_acquisition_optimizer = mock.create_autospec(AcquisitionOptimizer)
mock_acquisition_optimizer.optimize.return_value = (np.array([[0.]]), None)
space = ParameterSpace(
[ContinuousParameter('x', 0, 1),
InformationSourceParameter(2)])
return MultiSourceAcquisitionOptimizer(mock_acquisition_optimizer, space)
def test_multi_source_batch_experimental_design():
objective, space = multi_fidelity_forrester_function()
# Create initial data
random_design = RandomDesign(space)
x_init = random_design.get_samples(10)
intiial_results = objective.evaluate(x_init)
y_init = np.array([res.Y for res in intiial_results])
# Create multi source acquisition optimizer
acquisition_optimizer = GradientAcquisitionOptimizer(space)
multi_source_acquisition_optimizer = MultiSourceAcquisitionOptimizer(
acquisition_optimizer, space)
# Create GP model
gpy_model = GPy.models.GPRegression(x_init, y_init)
model = GPyModelWrapper(gpy_model)
# Create acquisition
acquisition = ModelVariance(model)
# Create batch candidate point calculator
batch_candidate_point_calculator = GreedyBatchPointCalculator(
model, acquisition, multi_source_acquisition_optimizer, batch_size=5)
initial_loop_state = LoopState(intiial_results)
loop = OuterLoop(batch_candidate_point_calculator,
FixedIntervalUpdater(model, 1), initial_loop_state)
loop.run_loop(objective, 10)
assert loop.loop_state.X.shape[0] == 60
def test_multi_source_sequential_with_source_context():
# Check that we can fix a non-information source parameter with context
mock_acquisition = mock.create_autospec(Acquisition)
mock_acquisition.has_gradients = False
mock_acquisition.evaluate = lambda x: np.sum(x**2, axis=1)[:, None]
space = ParameterSpace(
[ContinuousParameter("x", 0, 1), ContinuousParameter("y", 0, 1), InformationSourceParameter(2)]
)
acquisition_optimizer = GradientAcquisitionOptimizer(space)
multi_source_acquisition_optimizer = MultiSourceAcquisitionOptimizer(acquisition_optimizer, space)
loop_state_mock = mock.create_autospec(LoopState)
seq = SequentialPointCalculator(mock_acquisition, multi_source_acquisition_optimizer)
next_points = seq.compute_next_points(loop_state_mock, context={"source": 1.0})
# "SequentialPointCalculator" should only ever return 1 value
assert len(next_points) == 1
# Context value should be what we set
assert np.isclose(next_points[0, 1], 1.0)