def test_condition_invalid_type(mock_next_point_calculator, mock_updater,
mock_user_function):
invalid_n_iter = 10.0
loop = OuterLoop(mock_next_point_calculator, mock_updater)
with pytest.raises(ValueError):
loop.run_loop(mock_user_function, invalid_n_iter)
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_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_default_condition(mock_next_point_calculator, mock_updater, mock_user_function):
n_iter = 10
loop = OuterLoop(mock_next_point_calculator, mock_updater)
loop.run_loop(mock_user_function, n_iter)
assert (loop.loop_state.iteration == n_iter)
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_default_condition(mock_next_point_calculator, mock_updater,
mock_user_function):
n_iter = 10
loop = OuterLoop(mock_next_point_calculator, mock_updater)
loop.run_loop(mock_user_function, n_iter)
assert (loop.loop_state.iteration == n_iter)
def test_accept_non_wrapped_function(mock_next_point_calculator, mock_updater):
stopping_condition = mock.create_autospec(StoppingCondition)
stopping_condition.should_stop.side_effect = [False, False, True]
user_function = lambda x: np.array([[0]])
loop = OuterLoop(mock_next_point_calculator, mock_updater)
loop.run_loop(user_function, stopping_condition)
assert (loop.loop_state.iteration == 2)
assert (np.array_equal(loop.loop_state.X, np.array([[0], [0]])))
def test_outer_loop(mock_next_point_calculator, mock_updater, mock_user_function):
""" Example of automatic outer loop """
stopping_condition = mock.create_autospec(StoppingCondition)
stopping_condition.should_stop.side_effect = [False, False, True]
loop = OuterLoop(mock_next_point_calculator, mock_updater)
loop.run_loop(mock_user_function, stopping_condition)
assert (loop.loop_state.iteration == 2)
assert (np.array_equal(loop.loop_state.X, np.array([[0], [0]])))
def test_outer_loop(mock_next_point_calculator, mock_updater,
mock_user_function):
""" Example of automatic outer loop """
stopping_condition = mock.create_autospec(StoppingCondition)
stopping_condition.should_stop.side_effect = [False, False, True]
loop = OuterLoop(mock_next_point_calculator, mock_updater)
loop.run_loop(mock_user_function, stopping_condition)
assert (loop.loop_state.iteration == 2)
assert (np.array_equal(loop.loop_state.X, np.array([[0], [0]])))
def test_accept_non_wrapped_function(mock_next_point_calculator, mock_updater):
stopping_condition = mock.create_autospec(StoppingCondition)
stopping_condition.should_stop.side_effect = [False, False, True]
def user_function(x):
return np.array([[0]])
loop = OuterLoop(mock_next_point_calculator, mock_updater)
loop.run_loop(user_function, stopping_condition)
assert (loop.loop_state.iteration == 2)
assert (np.array_equal(loop.loop_state.X, np.array([[0], [0]])))
def test_outer_loop_model_update(mock_next_point_calculator,
mock_user_function):
""" Checks the model has the correct number of data points """
class MockModelUpdater(ModelUpdater):
def __init__(self, model):
self.model = model
def update(self, loop_state):
self.model.set_data(loop_state.X, loop_state.Y)
class MockModel(IModel):
@property
def X(self):
return self._X
@property
def Y(self):
return self._Y
def predict(self, x):
pass
def set_data(self, x, y):
self._X = x
self._Y = y
def optimize(self):
pass
mock_model = MockModel()
model_updater = MockModelUpdater(mock_model)
loop = OuterLoop(mock_next_point_calculator, model_updater)
loop.run_loop(mock_user_function, 2)
# Check update was last called with a loop state with all the collected data points
assert mock_model.X.shape[0] == 2
assert mock_model.Y.shape[0] == 2
def test_outer_loop_model_update(mock_next_point_calculator, mock_user_function):
""" Checks the model has the correct number of data points """
class MockModelUpdater(ModelUpdater):
def __init__(self, model):
self.model = model
def update(self, loop_state):
self.model.set_data(loop_state.X, loop_state.Y)
class MockModel(IModel):
@property
def X(self):
return self._X
@property
def Y(self):
return self._Y
def predict(self, x):
pass
def set_data(self, x, y):
self._X = x
self._Y = y
def optimize(self):
pass
mock_model = MockModel()
model_updater = MockModelUpdater(mock_model)
loop = OuterLoop(mock_next_point_calculator, model_updater)
loop.run_loop(mock_user_function, 2)
# Check update was last called with a loop state with all the collected data points
assert mock_model.X.shape[0] == 2
assert mock_model.Y.shape[0] == 2
def test_condition_invalid_type(mock_next_point_calculator, mock_updater, mock_user_function):
invalid_n_iter = 10.0
loop = OuterLoop(mock_next_point_calculator, mock_updater)
with pytest.raises(ValueError):
loop.run_loop(mock_user_function, invalid_n_iter)
