def test_gen_batch_initial_conditions(self):
bounds = torch.stack([torch.zeros(2), torch.ones(2)])
mock_acqf = MockAcquisitionFunction()
mock_acqf.objective = lambda y: y.squeeze(-1)
for dtype in (torch.float, torch.double):
bounds = bounds.to(device=self.device, dtype=dtype)
mock_acqf.X_baseline = bounds # for testing sample_around_best
mock_acqf.model = MockModel(MockPosterior(mean=bounds[:, :1]))
for nonnegative, seed, init_batch_limit, ffs, sample_around_best in product(
[True, False], [None, 1234], [None, 1], [None, {0: 0.5}], [True, False]
):
with mock.patch.object(
MockAcquisitionFunction,
"__call__",
wraps=mock_acqf.__call__,
) as mock_acqf_call:
batch_initial_conditions = gen_batch_initial_conditions(
acq_function=mock_acqf,
bounds=bounds,
q=1,
num_restarts=2,
raw_samples=10,
fixed_features=ffs,
options={
"nonnegative": nonnegative,
"eta": 0.01,
"alpha": 0.1,
"seed": seed,
"init_batch_limit": init_batch_limit,
"sample_around_best": sample_around_best,
},
)
expected_shape = torch.Size([2, 1, 2])
self.assertEqual(batch_initial_conditions.shape, expected_shape)
self.assertEqual(batch_initial_conditions.device, bounds.device)
self.assertEqual(batch_initial_conditions.dtype, bounds.dtype)
batch_shape = (
torch.Size([])
if init_batch_limit is None
else torch.Size([init_batch_limit])
)
raw_samps = mock_acqf_call.call_args[0][0]
batch_shape = (
torch.Size([20 if sample_around_best else 10])
if init_batch_limit is None
else torch.Size([init_batch_limit])
)
expected_raw_samps_shape = batch_shape + torch.Size([1, 2])
self.assertEqual(raw_samps.shape, expected_raw_samps_shape)
if ffs is not None:
for idx, val in ffs.items():
self.assertTrue(
torch.all(batch_initial_conditions[..., idx] == val)
)
def test_gen_batch_initial_conditions_highdim(self):
d = 2200 # 2200 * 10 (q) > 21201 (sobol max dim)
bounds = torch.stack([torch.zeros(d), torch.ones(d)])
ffs_map = {i: random() for i in range(0, d, 2)}
mock_acqf = MockAcquisitionFunction()
mock_acqf.objective = lambda y: y.squeeze(-1)
for dtype in (torch.float, torch.double):
bounds = bounds.to(device=self.device, dtype=dtype)
mock_acqf.X_baseline = bounds # for testing sample_around_best
mock_acqf.model = MockModel(MockPosterior(mean=bounds[:, :1]))
for nonnegative, seed, ffs, sample_around_best in product(
[True, False], [None, 1234], [None, ffs_map], [True, False]):
with warnings.catch_warnings(
record=True) as ws, settings.debug(True):
batch_initial_conditions = gen_batch_initial_conditions(
acq_function=MockAcquisitionFunction(),
bounds=bounds,
q=10,
num_restarts=1,
raw_samples=2,
fixed_features=ffs,
options={
"nonnegative": nonnegative,
"eta": 0.01,
"alpha": 0.1,
"seed": seed,
"sample_around_best": sample_around_best,
},
)
self.assertTrue(
any(
issubclass(w.category, SamplingWarning)
for w in ws))
expected_shape = torch.Size([1, 10, d])
self.assertEqual(batch_initial_conditions.shape,
expected_shape)
self.assertEqual(batch_initial_conditions.device,
bounds.device)
self.assertEqual(batch_initial_conditions.dtype, bounds.dtype)
if ffs is not None:
for idx, val in ffs.items():
self.assertTrue(
torch.all(batch_initial_conditions[...,
idx] == val))
