def test_qMS_init(self):
d = 2
q = 1
num_data = 3
q_batch_sizes = [1, 1, 1]
num_fantasies = [2, 2, 1]
t_batch_size = [2]
for dtype in (torch.float, torch.double):
bounds = torch.tensor([[0], [1]], device=self.device, dtype=dtype)
bounds = bounds.repeat(1, d)
train_X = torch.rand(num_data, d, device=self.device, dtype=dtype)
train_Y = torch.rand(num_data, 1, device=self.device, dtype=dtype)
model = SingleTaskGP(train_X, train_Y)
# exactly one of samplers or num_fantasies
with self.assertRaises(UnsupportedError):
qMultiStepLookahead(
model=model,
batch_sizes=q_batch_sizes,
valfunc_cls=[qExpectedImprovement] * 4,
valfunc_argfacs=[make_best_f] * 4,
inner_mc_samples=[2] * 4,
)
# cannot use qMS as its own valfunc_cls
with self.assertRaises(UnsupportedError):
qMultiStepLookahead(
model=model,
batch_sizes=q_batch_sizes,
valfunc_cls=[qMultiStepLookahead] * 4,
valfunc_argfacs=[make_best_f] * 4,
num_fantasies=num_fantasies,
inner_mc_samples=[2] * 4,
)
# construct using samplers
samplers = [
SobolQMCNormalSampler(num_samples=nf,
resample=False,
collapse_batch_dims=True)
for nf in num_fantasies
]
qMS = qMultiStepLookahead(
model=model,
batch_sizes=q_batch_sizes,
valfunc_cls=[qExpectedImprovement] * 4,
valfunc_argfacs=[make_best_f] * 4,
inner_mc_samples=[2] * 4,
samplers=samplers,
)
self.assertEqual(qMS.num_fantasies, num_fantasies)
# use default valfunc_cls, valfun_argfacs, inner_mc_samples
qMS = qMultiStepLookahead(
model=model,
batch_sizes=q_batch_sizes,
samplers=samplers,
)
self.assertEqual(len(qMS._valfunc_cls), 4)
self.assertEqual(len(qMS.inner_samplers), 4)
self.assertEqual(len(qMS._valfunc_argfacs), 4)
# _construct_inner_samplers error catching tests below
# AnalyticAcquisitionFunction with MCAcquisitionObjective
with self.assertRaises(UnsupportedError):
qMultiStepLookahead(
model=model,
objective=IdentityMCObjective(),
batch_sizes=q_batch_sizes,
valfunc_cls=[ExpectedImprovement] * 4,
valfunc_argfacs=[make_best_f] * 4,
num_fantasies=num_fantasies,
)
# AnalyticAcquisitionFunction and q > 1
with self.assertRaises(UnsupportedError):
qMultiStepLookahead(
model=model,
batch_sizes=[2, 2, 2],
valfunc_cls=[ExpectedImprovement] * 4,
valfunc_argfacs=[make_best_f] * 4,
num_fantasies=num_fantasies,
inner_mc_samples=[2] * 4,
)
# AnalyticAcquisitionFunction and inner_mc_samples
with self.assertWarns(Warning):
qMultiStepLookahead(
model=model,
batch_sizes=q_batch_sizes,
valfunc_cls=[ExpectedImprovement] * 4,
valfunc_argfacs=[make_best_f] * 4,
num_fantasies=num_fantasies,
inner_mc_samples=[2] * 4,
)
# MCAcquisitionFunction and non MCAcquisitionObjective
with self.assertRaises(UnsupportedError):
qMultiStepLookahead(
model=model,
objective=ScalarizedObjective(weights=torch.tensor([1.0])),
batch_sizes=[2, 2, 2],
valfunc_cls=[qExpectedImprovement] * 4,
valfunc_argfacs=[make_best_f] * 4,
num_fantasies=num_fantasies,
inner_mc_samples=[2] * 4,
)
# test warmstarting
qMS = qMultiStepLookahead(
model=model,
batch_sizes=q_batch_sizes,
samplers=samplers,
)
q_prime = qMS.get_augmented_q_batch_size(q)
eval_X = torch.rand(t_batch_size + [q_prime, d])
warmstarted_X = warmstart_multistep(
acq_function=qMS,
bounds=bounds,
num_restarts=5,
raw_samples=10,
full_optimizer=eval_X,
)
self.assertEqual(warmstarted_X.shape, torch.Size([5, q_prime, d]))
def test_qMS(self):
d = 2
q = 1
num_data = 3
q_batch_sizes = [1, 1, 1]
num_fantasies = [2, 2, 1]
t_batch_size = [2]
for dtype in (torch.float, torch.double):
bounds = torch.tensor([[0], [1]], device=self.device, dtype=dtype)
bounds = bounds.repeat(1, d)
train_X = torch.rand(num_data, d, device=self.device, dtype=dtype)
train_Y = torch.rand(num_data, 1, device=self.device, dtype=dtype)
model = SingleTaskGP(train_X, train_Y)
# default evaluation tests
qMS = qMultiStepLookahead(
model=model,
batch_sizes=[1, 1, 1],
num_fantasies=num_fantasies,
)
q_prime = qMS.get_augmented_q_batch_size(q)
eval_X = torch.rand(t_batch_size + [q_prime, d])
result = qMS(eval_X)
self.assertEqual(result.shape, torch.Size(t_batch_size))
qMS = qMultiStepLookahead(
model=model,
batch_sizes=q_batch_sizes,
valfunc_cls=[qExpectedImprovement] * 4,
valfunc_argfacs=[make_best_f] * 4,
num_fantasies=num_fantasies,
inner_mc_samples=[2] * 4,
)
result = qMS(eval_X)
self.assertEqual(result.shape, torch.Size(t_batch_size))
# get induced fantasy model, with collapse_fantasy_base_samples
fant_model = qMS.get_induced_fantasy_model(eval_X)
self.assertEqual(
fant_model.train_inputs[0].shape,
torch.Size(num_fantasies[::-1] + t_batch_size +
[num_data + sum(q_batch_sizes), d]),
)
# collapse fantasy base samples
qMS = qMultiStepLookahead(
model=model,
batch_sizes=q_batch_sizes,
valfunc_cls=[qExpectedImprovement] * 4,
valfunc_argfacs=[make_best_f] * 4,
num_fantasies=num_fantasies,
inner_mc_samples=[2] * 4,
collapse_fantasy_base_samples=False,
)
q_prime = qMS.get_augmented_q_batch_size(q)
eval_X = torch.rand(t_batch_size + [q_prime, d])
result = qMS(eval_X)
self.assertEqual(result.shape, torch.Size(t_batch_size))
self.assertEqual(qMS.samplers[0].batch_range, (-3, -2))
# get induced fantasy model, without collapse_fantasy_base_samples
fant_model = qMS.get_induced_fantasy_model(eval_X)
self.assertEqual(
fant_model.train_inputs[0].shape,
torch.Size(num_fantasies[::-1] + t_batch_size +
[num_data + sum(q_batch_sizes), d]),
)
# X_pending
X_pending = torch.rand(5, d)
qMS = qMultiStepLookahead(
model=model,
batch_sizes=q_batch_sizes,
valfunc_cls=[qExpectedImprovement] * 4,
valfunc_argfacs=[make_best_f] * 4,
num_fantasies=num_fantasies,
inner_mc_samples=[2] * 4,
X_pending=X_pending,
)
q_prime = qMS.get_augmented_q_batch_size(q)
eval_X = torch.rand(t_batch_size + [q_prime, d])
result = qMS(eval_X)
self.assertEqual(result.shape, torch.Size(t_batch_size))
# add dummy base_weights to samplers
samplers = [
SobolQMCNormalSampler(num_samples=nf,
resample=False,
collapse_batch_dims=True)
for nf in num_fantasies
]
for s in samplers:
s.base_weights = torch.ones(s.sample_shape[0],
1,
device=self.device,
dtype=dtype)
qMS = qMultiStepLookahead(
model=model,
batch_sizes=[1, 1, 1],
samplers=samplers,
)
q_prime = qMS.get_augmented_q_batch_size(q)
eval_X = torch.rand(t_batch_size + [q_prime, d])
result = qMS(eval_X)
self.assertEqual(result.shape, torch.Size(t_batch_size))
# extract candidates
cand = qMS.extract_candidates(eval_X)
self.assertEqual(cand.shape, torch.Size(t_batch_size + [q, d]))