def test_every_iteration_model_updater_with_cost():
"""
Tests that the model updater can use a different attribute from loop_state as the training targets
"""
class MockModel(IModel):
def optimize(self):
pass
def set_data(self, X: np.ndarray, Y: np.ndarray):
self._X = X
self._Y = Y
@property
def X(self):
return self._X
@property
def Y(self):
return self._Y
mock_model = MockModel()
updater = FixedIntervalUpdater(mock_model, 1, lambda loop_state: loop_state.cost)
loop_state_mock = mock.create_autospec(LoopState)
loop_state_mock.iteration = 1
loop_state_mock.X.return_value(np.random.rand(5, 1))
loop_state_mock.cost.return_value(np.random.rand(5, 1))
cost = np.random.rand(5, 1)
loop_state_mock.cost.return_value(cost)
updater.update(loop_state_mock)
assert np.array_equiv(mock_model.X, cost)
def test_every_iteration_model_updater():
mock_model = mock.create_autospec(IModel)
mock_model.optimize.return_value(None)
updater = FixedIntervalUpdater(mock_model, 1)
loop_state_mock = mock.create_autospec(LoopState)
loop_state_mock.iteration = 1
loop_state_mock.X.return_value(np.random.rand(5, 1))
loop_state_mock.Y.return_value(np.random.rand(5, 1))
updater.update(loop_state_mock)
mock_model.optimize.assert_called_once()
def __init__(self, space: ParameterSpace,
X_init: np.ndarray, Y_init: np.ndarray, cost_init: np.ndarray,
s_min: float, s_max: float,
update_interval: int = 1,
num_eval_points: int = 2000,
marginalize_hypers: bool = True):
"""
Implements FAst Bayesian Optimization for LArge DataSets as described in:
Fast Bayesian hyperparameter optimization on large datasets
A. Klein and S. Falkner and S. Bartels and P. Hennig and F. Hutter
Electronic Journal of Statistics (2017)
:param space: input space where the optimization is carried out.
:param X_init: initial data points
:param Y_init: initial function values
:param cost_init: initial costs
:param s_min: smallest possible dataset size
:param s_max: highest possible dataset size
:param update_interval: number of iterations between optimization of model hyper-parameters. Defaults to 1.
:param num_eval_points: number of points to evaluate the acquisition function
:param marginalize_hypers: if true, marginalize over the GP hyperparameters
"""
l = space.parameters
l.extend([ContinuousParameter("s", s_min, s_max)])
extended_space = ParameterSpace(l)
model_objective = FabolasModel(X_init=X_init, Y_init=Y_init, s_min=s_min, s_max=s_max)
model_cost = FabolasModel(X_init=X_init, Y_init=cost_init[:, None], s_min=s_min, s_max=s_max)
if marginalize_hypers:
acquisition_generator = lambda model: ContinuousFidelityEntropySearch(model_objective, space=extended_space,
target_fidelity_index=len(
extended_space.parameters) - 1)
entropy_search = IntegratedHyperParameterAcquisition(model_objective, acquisition_generator)
else:
entropy_search = ContinuousFidelityEntropySearch(model_objective, space=extended_space,
target_fidelity_index=len(extended_space.parameters) - 1)
acquisition = acquisition_per_expected_cost(entropy_search, model_cost)
model_updater_objective = FixedIntervalUpdater(model_objective, update_interval)
model_updater_cost = FixedIntervalUpdater(model_cost, update_interval, lambda state: state.cost)
acquisition_optimizer = RandomSearchAcquisitionOptimizer(extended_space, num_eval_points=num_eval_points)
candidate_point_calculator = SequentialPointCalculator(acquisition, acquisition_optimizer)
loop_state = create_loop_state(model_objective.X, model_objective.Y, model_cost.Y)
super(CostSensitiveBayesianOptimizationLoop, self).__init__(candidate_point_calculator,
[model_updater_objective, model_updater_cost],
loop_state)
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 __init__(self,
model: VanillaBayesianQuadrature,
acquisition: Acquisition = None,
model_updater: ModelUpdater = None):
"""
The loop for vanilla Bayesian Quadrature
:param model: the vanilla Bayesian quadrature method
:param acquisition: The acquisition function that is be used to collect new points.
default, IntegralVarianceReduction
:param model_updater: Defines how and when the quadrature model is updated if new data arrives.
Defaults to updating hyper-parameters every iteration.
"""
self.model = model
if acquisition is None:
acquisition = IntegralVarianceReduction(self.model)
if model_updater is None:
model_updater = FixedIntervalUpdater(self.model, 1)
space = ParameterSpace(self.model.integral_bounds.
convert_to_list_of_continuous_parameters())
acquisition_optimizer = AcquisitionOptimizer(space)
candidate_point_calculator = SequentialPointCalculator(
acquisition, acquisition_optimizer)
loop_state = create_loop_state(self.model.X, self.model.Y)
super().__init__(candidate_point_calculator, model_updater, loop_state)
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