def initialize(self):
parameter_space = ParameterSpace([
ContinuousParameter("x%d" % index, bounds[0], bounds[1])
for index, bounds in enumerate(self.problem.bounds)
] + [InformationSourceParameter(len(self.problem.fidelities))])
# Obtain initial sample
design = LatinDesign(parameter_space)
initial_parameters = design.get_samples(self.initial_sample_count)
initial_response = self._evaluate_batch(initial_parameters)
kernels = [GPy.kern.RBF(1)] * len(self.problem.fidelities)
kernel = emukit.multi_fidelity.kernels.LinearMultiFidelityKernel(kernels)
model = GPyLinearMultiFidelityModel(
initial_parameters, initial_response,
kernel, n_fidelities = len(self.problem.fidelities)
)
model = GPyMultiOutputWrapper(model, len(self.problem.fidelities))
acquisition = NegativeLowerConfidenceBound(model)
self.loop = BayesianOptimizationLoop(
model = model, space = parameter_space,
acquisition = acquisition, batch_size = self.batch_size
)
def initialize(self):
parameter_space = ParameterSpace([
ContinuousParameter("x%d" % index, bounds[0], bounds[1])
for index, bounds in enumerate(self.problem.bounds)
])
# Obtain initial sample
design = LatinDesign(parameter_space)
initial_parameters = design.get_samples(self.initial_sample_count)
initial_response = self._evaluate_batch(initial_parameters)
kernel = None # GPy.kern.RBF(1)
model = GPy.models.GPRegression(initial_parameters, initial_response, kernel)
model = GPyModelWrapper(model)
acquisition = NegativeLowerConfidenceBound(model)
self.loop = BayesianOptimizationLoop(
model = model, space = parameter_space,
acquisition = acquisition, batch_size = self.batch_size
)
def negative_lower_confidence_bound_acquisition(gpy_model):
return NegativeLowerConfidenceBound(gpy_model)
model = RandomForest(X_init=X_init, Y_init=Y_init)
with_gradients = False
elif args.model_type == "dngo":
model = DNGO(X_init=X_init, Y_init=Y_init)
with_gradients = False
elif args.model_type == "gp":
model = BOGP(X_init=X_init, Y_init=Y_init)
if args.acquisition_type == "ei":
acquisition = ExpectedImprovement(model)
elif args.acquisition_type == "pi":
acquisition = ProbabilityOfImprovement(model)
elif args.acquisition_type == "nlcb":
acquisition = NegativeLowerConfidenceBound(model)
elif args.acquisition_type == "logei":
acquisition = LogExpectedImprovement(model)
elif args.acquisition_type == "entropy_search":
model = BOGP(X_init=X_init, Y_init=Y_init)
acquisition = EntropySearch(model, space=space)
# if with_gradients:
# acquisition_optimizer = AcquisitionOptimizer(space)
# else:
acquisition_optimizer = DirectOptimizer(space)
candidate_point_calculator = Sequential(acquisition, acquisition_optimizer)
bo = BayesianOptimizationLoop(model=model, space=space, X_init=X_init, Y_init=Y_init, acquisition=acquisition,
def acquisition():
rng = np.random.RandomState(42)
x_init = rng.rand(5, 2)
y_init = rng.rand(5, 1)
model = GPRegression(x_init, y_init, RBF(2))
return NegativeLowerConfidenceBound(GPyModelWrapper(model))