def integrated_expected_improvement_acquisition(gpy_model_mcmc):
return IntegratedHyperParameterAcquisition(gpy_model_mcmc,
ExpectedImprovement, 10)
def integrated_probability_of_improvement_acquisition(gpy_model_mcmc):
return IntegratedHyperParameterAcquisition(gpy_model_mcmc,
ProbabilityOfImprovement, 10)
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", np.log(s_min), np.log(s_max))
]) # optimize s on a log scale
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)