def test_joint_optimize(
self,
mock_get_best_candidates,
mock_gen_candidates,
mock_gen_batch_initial_conditions,
cuda=False,
):
q = 3
num_restarts = 2
raw_samples = 10
options = {}
mock_acq_function = MockAcquisitionFunction()
tkwargs = {
"device": torch.device("cuda") if cuda else torch.device("cpu")
}
cnt = 1
for dtype in (torch.float, torch.double):
tkwargs["dtype"] = dtype
mock_gen_batch_initial_conditions.return_value = torch.zeros(
num_restarts, q, 3, **tkwargs)
mock_gen_candidates.return_value = torch.cat([
i * torch.ones(1, q, 3, **tkwargs) for i in range(num_restarts)
],
dim=0)
mock_get_best_candidates.return_value = torch.ones(
1, q, 3, **tkwargs)
expected_candidates = mock_get_best_candidates.return_value
bounds = torch.stack(
[torch.zeros(3, **tkwargs), 4 * torch.ones(3, **tkwargs)])
candidates = joint_optimize(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
)
self.assertTrue(torch.equal(candidates, expected_candidates))
candidates = joint_optimize(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
return_best_only=False,
batch_initial_conditions=torch.zeros(num_restarts, q, 3,
**tkwargs),
)
self.assertTrue(
torch.equal(candidates, mock_gen_candidates.return_value[0]))
self.assertEqual(mock_gen_batch_initial_conditions.call_count, cnt)
cnt += 1
def test_joint_optimize(
self,
mock_get_best_candidates,
mock_gen_candidates,
mock_gen_batch_initial_conditions,
cuda=False,
):
q = 3
num_restarts = 2
raw_samples = 10
options = {}
mock_acq_function = MockAcquisitionFunction()
tkwargs = {"device": torch.device("cuda") if cuda else torch.device("cpu")}
for dtype in (torch.float, torch.double):
tkwargs["dtype"] = dtype
mock_gen_batch_initial_conditions.return_value = torch.zeros(
num_restarts, q, 3, **tkwargs
)
mock_gen_candidates.return_value = torch.cat(
[i * torch.ones(1, q, 3, **tkwargs) for i in range(num_restarts)], dim=0
)
mock_get_best_candidates.return_value = torch.ones(1, q, 3, **tkwargs)
expected_candidates = mock_get_best_candidates.return_value
bounds = torch.stack(
[torch.zeros(3, **tkwargs), 4 * torch.ones(3, **tkwargs)]
)
candidates = joint_optimize(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
)
self.assertTrue(torch.equal(candidates, expected_candidates))
def test_joint_optimize(self, mock_optimize_acqf):
kwargs = {
**self.shared_kwargs,
"return_best_only": True,
"batch_initial_conditions": None,
}
with warnings.catch_warnings(record=True) as ws:
candidates, acq_values = joint_optimize(**kwargs)
self.assertTrue(any(issubclass(w.category, DeprecationWarning) for w in ws))
self.assertTrue(
any("joint_optimize is deprecated" in str(w.message) for w in ws)
)
mock_optimize_acqf.assert_called_once_with(**kwargs, sequential=False)
self.assertIsNone(candidates)
self.assertIsNone(acq_values)