def test_batch_decoupled_sampler(self):
train_n = 6
dim = 2
transform = None
num_fantasies = 3
num_fant_X = 4
model = SingleTaskGP(
torch.rand(train_n, dim),
torch.rand(train_n, 1),
outcome_transform=transform,
)
fantasy_X = torch.rand(num_fant_X, 1, dim)
fantasy_model = model.fantasize(fantasy_X,
IIDNormalSampler(num_fantasies))
sample_shape = [5]
num_basis = 16
sampler = decoupled_sampler(model=fantasy_model,
sample_shape=sample_shape,
num_basis=num_basis)
num_test = 8
test_X = torch.rand(num_test, dim)
ds_samples = sampler(test_X)
self.assertEqual(
list(ds_samples.shape),
sample_shape + [num_fantasies, num_fant_X, num_test, 1],
)
def test_gen_value_function_initial_conditions(self):
num_fantasies = 2
num_solutions = 3
num_restarts = 4
raw_samples = 5
n_train = 6
dim = 2
dtype = torch.float
# run a thorough test with dtype float
train_X = torch.rand(n_train, dim, device=self.device, dtype=dtype)
train_Y = torch.rand(n_train, 1, device=self.device, dtype=dtype)
model = SingleTaskGP(train_X, train_Y)
fant_X = torch.rand(num_solutions,
1,
dim,
device=self.device,
dtype=dtype)
fantasy_model = model.fantasize(fant_X,
IIDNormalSampler(num_fantasies))
bounds = torch.tensor([[0, 0], [1, 1]],
device=self.device,
dtype=dtype)
value_function = PosteriorMean(fantasy_model)
# test option error
with self.assertRaises(ValueError):
gen_value_function_initial_conditions(
acq_function=value_function,
bounds=bounds,
num_restarts=num_restarts,
raw_samples=raw_samples,
current_model=model,
options={"frac_random": 2.0},
)
# test output shape
ics = gen_value_function_initial_conditions(
acq_function=value_function,
bounds=bounds,
num_restarts=num_restarts,
raw_samples=raw_samples,
current_model=model,
)
self.assertEqual(
ics.shape,
torch.Size([num_restarts, num_fantasies, num_solutions, 1, dim]))
# test bounds
self.assertTrue(torch.all(ics >= bounds[0]))
self.assertTrue(torch.all(ics <= bounds[1]))
# test dtype
self.assertEqual(dtype, ics.dtype)
# minimal test cases for when all raw samples are random, with dtype double
dtype = torch.double
n_train = 2
dim = 1
num_solutions = 1
train_X = torch.rand(n_train, dim, device=self.device, dtype=dtype)
train_Y = torch.rand(n_train, 1, device=self.device, dtype=dtype)
model = SingleTaskGP(train_X, train_Y)
fant_X = torch.rand(1, 1, dim, device=self.device, dtype=dtype)
fantasy_model = model.fantasize(fant_X,
IIDNormalSampler(num_fantasies))
bounds = torch.tensor([[0], [1]], device=self.device, dtype=dtype)
value_function = PosteriorMean(fantasy_model)
ics = gen_value_function_initial_conditions(
acq_function=value_function,
bounds=bounds,
num_restarts=1,
raw_samples=1,
current_model=model,
options={"frac_random": 0.99},
)
self.assertEqual(ics.shape,
torch.Size([1, num_fantasies, num_solutions, 1, dim]))
# test bounds
self.assertTrue(torch.all(ics >= bounds[0]))
self.assertTrue(torch.all(ics <= bounds[1]))
# test dtype
self.assertEqual(dtype, ics.dtype)
