elicitation_strategy=random_preference_elicitation)
utility = Utility(func=utility_func,
gradient=utility_gradient,
parameter_distribution=utility_param_distribution,
affine=True)
# --- Sampling policy
sampling_policy_name = 'uEI'
learn_preferences = False
if sampling_policy_name is 'uEI':
model = MultiOutputGP(output_dim=m,
exact_feval=[True] * m,
fixed_hyps=False) # Model (Multi-output GP)
acquisition_optimizer = AcquisitionOptimizer(
space=space,
model=model,
utility=utility,
optimizer='lbfgs',
include_baseline_points=True)
acquisition = uEI_affine(model,
space,
optimizer=acquisition_optimizer,
utility=utility)
evaluator = GPyOpt.core.evaluators.Sequential(acquisition)
sampling_policy = AcquisitionFunction(model, space, acquisition,
evaluator)
if learn_preferences:
dynamic_utility_parameter_distribution = True
else:
dynamic_utility_parameter_distribution = False
sampling_policy_name = 'uEI_prior'
elif sampling_policy_name is 'uTS':
def suggest_sample(self, number_of_samples=1):
"""
Returns a suggested next point to evaluate.
"""
# Get current evaluations
X_evaluated, Y_evaluated = self.model.get_XY()
self.X_aux = X_evaluated
self.Y_aux = Y_evaluated
# Auxiliary Bayesian optimization model to run ParEGO
weight = np.random.dirichlet(
np.ones(len(self.Y_aux))
) # self._sample_posterior_weight_for_chebyshev_scalarization()
self.Y_aux = np.reshape(self.Y_aux,
(len(self.Y_aux), self.Y_aux[0].shape[0]))
scalarized_fX = chebyshev_scalarization(self.Y_aux, weight)
scalarized_fX = np.reshape(scalarized_fX, (len(scalarized_fX), 1))
aux_model = MultiOutputGP(output_dim=1,
exact_feval=[True],
fixed_hyps=False)
def aux_utility_func(y, parameter):
return np.dot(parameter, y)
def aux_utility_gradient(y, parameter):
return parameter
aux_utility_parameter_support = np.ones((1, 1))
aux_utility_parameter_prob_distribution = np.ones((1, ))
aux_utility_param_distribution = UtilityDistribution(
support=aux_utility_parameter_support,
prob_dist=aux_utility_parameter_prob_distribution,
utility_func=aux_utility_func)
aux_utility = Utility(
func=aux_utility_func,
gradient=aux_utility_gradient,
parameter_distribution=aux_utility_param_distribution,
affine=True)
aux_acquisition_optimizer = AcquisitionOptimizer(
space=self.space,
model=aux_model,
utility=aux_utility,
optimizer=self.optimizer,
include_baseline_points=True)
aux_acquisition = uEI_affine(aux_model,
self.space,
optimizer=aux_acquisition_optimizer,
utility=aux_utility)
aux_evaluator = GPyOpt.core.evaluators.Sequential(aux_acquisition)
aux_sampling_policy = AcquisitionFunction(aux_model, self.space,
aux_acquisition,
aux_evaluator)
bopl = BOPL(aux_model,
self.space,
sampling_policy=aux_sampling_policy,
utility=aux_utility,
X_init=self.X_aux,
Y_init=[scalarized_fX],
dynamic_utility_parameter_distribution=False)
suggested_sample = bopl.suggest_next_points_to_evaluate()
return suggested_sample
parameter_distribution=utility_param_distribution,
expectation=expectation_utility)
# --- Sampling policy
sampling_policy_name = 'Random'
if sampling_policy_name is 'uEI':
# Model (Multi-output GP)
model = MultiOutputGP(output_dim=m,
exact_feval=[True] * m,
fixed_hyps=False)
# Sampling policy
acquisition_optimizer = AcquisitionOptimizer(
space=space,
model=model,
utility=utility,
expectation_utility=expectation_utility,
optimizer='lbfgs',
inner_optimizer='lbfgs')
acquisition = uEI(model,
space,
optimizer=acquisition_optimizer,
utility=utility)
evaluator = GPyOpt.core.evaluators.Sequential(acquisition)
sampling_policy = AcquisitionFunction(model, space, acquisition,
evaluator)
elif sampling_policy_name is 'TS':
model = MultiOutputGP(output_dim=m,
exact_feval=[True] * m,
fixed_hyps=False) # Model (Multi-output GP)