def test_random_search_acquisition_optimizer(simple_square_acquisition):
space = ParameterSpace([CategoricalParameter('x', OrdinalEncoding(np.arange(0, 100)))])
optimizer = RandomSearchAcquisitionOptimizer(space, 1000)
opt_x, opt_val = optimizer.optimize(simple_square_acquisition)
# ordinal encoding is as integers 1, 2, ...
assert_array_equal(opt_x, np.array([[1.]]))
assert_array_equal(opt_val, np.array([[0.]]))
def test_random_search_acquisition_optimizer_with_context(simple_square_acquisition):
space = ParameterSpace([CategoricalParameter('x', OrdinalEncoding(np.arange(0, 100))),
InformationSourceParameter(10)])
optimizer = RandomSearchAcquisitionOptimizer(space, 1000)
source_encoding = 1
opt_x, opt_val = optimizer.optimize(simple_square_acquisition, {'source': source_encoding})
assert_array_equal(opt_x, np.array([[1., source_encoding]]))
assert_array_equal(opt_val, np.array([[0. + source_encoding]]))
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)