def test_iteration_end_event():
space = ParameterSpace([ContinuousParameter('x', 0, 1)])
def user_function(x):
return x
x_test = np.linspace(0, 1)[:, None]
y_test = user_function(x_test)
x_init = np.linspace(0, 1, 5)[:, None]
y_init = user_function(x_init)
gpy_model = GPy.models.GPRegression(x_init, y_init)
model = GPyModelWrapper(gpy_model)
mse = []
def compute_mse(self, loop_state):
mse.append(np.mean(np.square(model.predict(x_test)[0] - y_test)))
loop_state = create_loop_state(x_init, y_init)
acquisition = ModelVariance(model)
acquisition_optimizer = AcquisitionOptimizer(space)
candidate_point_calculator = SequentialPointCalculator(
acquisition, acquisition_optimizer)
model_updater = FixedIntervalUpdater(model)
loop = OuterLoop(candidate_point_calculator, model_updater, loop_state)
loop.iteration_end_event.append(compute_mse)
loop.run_loop(user_function, 5)
assert len(mse) == 5
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_loop():
n_iterations = 5
x_init = np.random.rand(5, 1)
y_init = np.random.rand(5, 1)
# Make GPy model
gpy_model = GPy.models.GPRegression(x_init, y_init)
model = GPyModelWrapper(gpy_model)
space = ParameterSpace([ContinuousParameter('x', 0, 1)])
acquisition = ModelVariance(model)
# Make loop and collect points
exp_design = ExperimentalDesignLoop(space, model, acquisition)
exp_design.run_loop(UserFunctionWrapper(f), FixedIterationsStoppingCondition(n_iterations))
# Check we got the correct number of points
assert exp_design.loop_state.X.shape[0] == 10
def model_variance_acquisition(gpy_model):
return ModelVariance(gpy_model)
def test_model_variance_with_gradients(model):
acquisition = ModelVariance(model)
acquisition_value, acquisition_gradients = acquisition.evaluate_with_gradients(np.zeros((1, 2)))
assert(np.isclose(acquisition_value, 0.5))
assert(np.all(np.isclose(acquisition_gradients, 2*np.ones((1, 2)))))
def test_model_variance(model):
acquisition = ModelVariance(model)
acquisition_value = acquisition.evaluate(np.zeros((1, 2)))
assert(np.isclose(acquisition_value, 0.5))