def test_gen_batch_initial_conditions_warning(self):
for dtype in (torch.float, torch.double):
bounds = torch.tensor([[0, 0], [1, 1]],
device=self.device,
dtype=dtype)
samples = torch.zeros(10, 1, 2, device=self.device, dtype=dtype)
with ExitStack() as es:
ws = es.enter_context(warnings.catch_warnings(record=True))
es.enter_context(settings.debug(True))
es.enter_context(
mock.patch(
"botorch.optim.initializers.draw_sobol_samples",
return_value=samples,
))
batch_initial_conditions = gen_batch_initial_conditions(
acq_function=MockAcquisitionFunction(),
bounds=bounds,
q=1,
num_restarts=2,
raw_samples=10,
options={"seed": 1234},
)
self.assertEqual(len(ws), 1)
self.assertTrue(
any(
issubclass(w.category, BadInitialCandidatesWarning)
for w in ws))
self.assertTrue(
torch.equal(
batch_initial_conditions,
torch.zeros(2, 1, 2, device=self.device, dtype=dtype),
))
def test_gen_batch_initial_conditions_highdim(self):
d = 120
bounds = torch.stack([torch.zeros(d), torch.ones(d)])
for dtype in (torch.float, torch.double):
bounds = bounds.to(device=self.device, dtype=dtype)
for nonnegative in (True, False):
for seed in (None, 1234):
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,
options={
"nonnegative": nonnegative,
"eta": 0.01,
"alpha": 0.1,
"seed": seed,
},
)
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)
def test_gen_batch_initial_conditions(self):
for dtype in (torch.float, torch.double):
bounds = torch.tensor([[0, 0], [1, 1]],
device=self.device,
dtype=dtype)
for nonnegative in (True, False):
for seed in (None, 1234):
batch_initial_conditions = gen_batch_initial_conditions(
acq_function=MockAcquisitionFunction(),
bounds=bounds,
q=1,
num_restarts=2,
raw_samples=10,
options={
"nonnegative": nonnegative,
"eta": 0.01,
"alpha": 0.1,
"seed": seed,
},
)
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)
def test_gen_batch_initial_conditions_simple_warning(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
bounds = torch.tensor([[0, 0], [1, 1]], device=device, dtype=dtype)
with warnings.catch_warnings(record=True) as ws:
with mock.patch(
"botorch.optim.optimize.draw_sobol_samples",
return_value=torch.zeros(10,
1,
2,
device=device,
dtype=dtype),
):
batch_initial_conditions = gen_batch_initial_conditions(
acq_function=MockAcquisitionFunction(),
bounds=bounds,
q=1,
num_restarts=2,
raw_samples=10,
)
self.assertEqual(len(ws), 1)
self.assertTrue(
issubclass(ws[-1].category,
BadInitialCandidatesWarning))
self.assertTrue(
torch.equal(
batch_initial_conditions,
torch.zeros(2, 1, 2, device=device, dtype=dtype),
))
def test_gen_batch_initial_conditions_simple_warning(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
bounds = torch.tensor([[0, 0], [1, 1]], device=device, dtype=dtype)
with warnings.catch_warnings(record=True) as ws:
with mock.patch(
"botorch.optim.optimize.draw_sobol_samples",
return_value=torch.zeros(10, 1, 2, device=device, dtype=dtype),
):
batch_initial_conditions = gen_batch_initial_conditions(
acq_function=MockAcquisitionFunction(),
bounds=bounds,
q=1,
num_restarts=2,
raw_samples=10,
)
self.assertEqual(len(ws), 1)
self.assertTrue(
issubclass(ws[-1].category, BadInitialCandidatesWarning)
)
self.assertTrue(
torch.equal(
batch_initial_conditions,
torch.zeros(2, 1, 2, device=device, dtype=dtype),
)
)
def test_gen_batch_initial_conditions(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
bounds = torch.tensor([[0, 0], [1, 1]], device=device, dtype=dtype)
for nonnegative in (True, False):
batch_initial_conditions = gen_batch_initial_conditions(
acq_function=MockAcquisitionFunction(),
bounds=bounds,
q=1,
num_restarts=2,
raw_samples=10,
options={"nonnegative": nonnegative, "eta": 0.01, "alpha": 0.1},
)
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)