def probability_of_improvement_acquisition(gpy_model):
return ProbabilityOfImprovement(gpy_model)
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 ProbabilityOfImprovement(GPyModelWrapper(model))
boEI = BayesianOptimizationLoop(model=model_emukitEI,
space=parameter_space,
acquisition=ExpectedImprovement(
model=model_emukit,
jitter=Exploration_parameter),
batch_size=1)
boEIep_r = []
EIcurMax = 0
EIcurMin = float('inf')
model_gpyPI = GPRegression(X, Y) # Train and wrap the model in Emukit
model_emukitPI = GPyModelWrapper(model_gpyPI)
boPI = BayesianOptimizationLoop(model=model_emukitPI,
space=parameter_space,
acquisition=ProbabilityOfImprovement(
model=model_emukit,
jitter=Exploration_parameter),
batch_size=1)
boPIep_r = []
PIcurMax = 0
PIcurMin = float('inf')
for t in range(50): # in one episode
bo.run_loop(fun, 1)
a = bo.loop_state.X[-1]
ppo.ppoMax = max(ppo.ppoMax, fun(a))
ppoMin = min(ppoMin, ppo.curFun(a))
# r = -(env.maxVal-ppo.ppoMax)
r = (env.minVal - ppoMin)
r = float(r)
boPPOep_r.append(r)
elif args.model_type == "rf":
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)