def test_gen_candidates(self, gen_candidates=gen_candidates_scipy, options=None):
options = options or {}
options = {**options, "maxiter": 5}
for double in (True, False):
self._setUp(double=double)
acqfs = [
qExpectedImprovement(self.model, best_f=self.f_best),
qKnowledgeGradient(
self.model, num_fantasies=4, current_value=self.f_best
),
]
for acqf in acqfs:
ics = self.initial_conditions
if isinstance(acqf, qKnowledgeGradient):
ics = ics.repeat(5, 1)
candidates, _ = gen_candidates(
initial_conditions=ics,
acquisition_function=acqf,
lower_bounds=0,
upper_bounds=1,
options=options or {},
)
if isinstance(acqf, qKnowledgeGradient):
candidates = acqf.extract_candidates(candidates)
self.assertTrue(-EPS <= candidates <= 1 + EPS)
def prepare_acquisition_function(args, model_obj, train_x, train_y, bounds, step):
if args.num_steps > 500:
sampler = IIDNormalSampler(num_samples=256)
else:
sampler = SobolQMCNormalSampler(num_samples=256)
if args.acqf == "ei":
acqf = qExpectedImprovement(
model=model_obj, best_f=train_y.max(), sampler=sampler,
)
elif args.acqf == "ucb":
acqf = qUpperConfidenceBound(model=model_obj, beta=0.9 ** step)
elif args.acqf == "nei":
acqf = qNoisyExpectedImprovement(
model=model_obj, X_baseline=train_x, sampler=sampler
)
elif args.acqf == "kg":
acqf = qKnowledgeGradient(
model=model_obj,
sampler=sampler,
num_fantasies=None,
current_value=train_y.max(),
)
elif args.acqf == "mves":
candidate_set = torch.rand(10000, bounds.size(0), device=bounds.device)
candidate_set = bounds[..., 0] + (bounds[..., 1] - bounds[..., 0]) * candidate_set
acqf = qMaxValueEntropy(
model=model_obj, candidate_set=candidate_set, train_inputs=train_x,
)
return acqf
def _maximize_kg(self) -> None:
"""
maximizes the KG acquisition function and stores the resulting value and
the candidate
"""
acq_func = qKnowledgeGradient(
model=self.model, current_value=self.current_best_val
)
# acq_func = qExpectedImprovement(model=self.model, best_f=self.current_best_val)
# acq_func = ExpectedImprovement(model=self.model, best_f=self.current_best_val)
self.next_candidate, self.kg_value = optimize_acqf(
acq_func,
Tensor([[0], [1]]).repeat(1, self.dim),
q=1,
num_restarts=self.num_restarts,
raw_samples=self.raw_samples,
)