def __init__(self,
model,
constraint_model,
space,
optimizer=None,
cost_withGradients=None,
num_samples=10,
exploration_weight=40):
self.model = model
self.constraint_model = constraint_model
self.optimizer = optimizer
super(constrained_LCB, self).__init__(model, space, optimizer)
self.num_samples = num_samples
self.LCB = AcquisitionLCB(model, space, optimizer, cost_withGradients)
self.exploration_weight = exploration_weight
self.xall = np.empty((1, 13))
self.yall = np.empty((1, 1))
objective = GPyOpt.core.task.SingleObjective(func.f)
space = GPyOpt.Design_space(space=[{
'name': 'var_1',
'type': 'continuous',
'domain': (-5, 10)
}, {
'name': 'var_2',
'type': 'continuous',
'domain': (1, 15)
}])
model = GPyOpt.models.GPModel(optimize_restarts=5, verbose=False)
aquisition_optimizer = GPyOpt.optimization.AcquisitionOptimizer(space)
initial_design = GPyOpt.experiment_design.initial_design('random', space, 5)
acquisition = AcquisitionLCB(model, space, optimizer=aquisition_optimizer)
evaluator = GPyOpt.core.evaluators.Sequential(acquisition)
bo = GPyOpt.methods.ModularBayesianOptimization(model, space, objective,
acquisition, evaluator,
initial_design)
max_iter = 10
bo.run_optimization(max_iter=max_iter)
# bo.plot_acquisition()
bo.plot_convergence()