def randomize_objective_weights(objective_weights: Tensor,
**acquisition_function_kwargs: Any) -> Tensor:
"""Generate a random weighting based on acquisition function settings.
Args:
objective_weights: Base weights to multiply by random values..
**acquisition_function_kwargs: Kwargs containing weight generation algorithm
options.
Returns:
A normalized list of indices such that each index is between `0` and `d-1`.
"""
# Set distribution and sample weights.
distribution = acquisition_function_kwargs.get(
"random_scalarization_distribution", SIMPLEX)
dtype = objective_weights.dtype
device = objective_weights.device
if distribution == SIMPLEX:
random_weights = sample_simplex(len(objective_weights),
dtype=dtype,
device=device).squeeze()
elif distribution == HYPERSPHERE:
random_weights = torch.abs(
sample_hypersphere(len(objective_weights),
dtype=dtype,
device=device).squeeze())
# pyre-fixme[61]: `random_weights` may not be initialized here.
objective_weights = torch.mul(objective_weights, random_weights)
return objective_weights
def test_sample_hypersphere(self):
for d, n, qmc, seed, dtype in itertools.product(
(1, 2, 3), (2, 5), (False, True), (None, 1234), (torch.float, torch.double)
):
samples = sample_hypersphere(
d=d, n=n, qmc=qmc, seed=seed, device=self.device, dtype=dtype
)
self.assertEqual(samples.shape, torch.Size([n, d]))
self.assertTrue(torch.max((samples.pow(2).sum(dim=-1) - 1).abs()) < 1e-5)
self.assertEqual(samples.device.type, self.device.type)
self.assertEqual(samples.dtype, dtype)
def gen_pareto_front(self, n: int) -> Tensor:
r"""Generate `n` pareto optimal points.
The pareto points are randomly sampled from the hypersphere's
positive section.
"""
f_X = sample_hypersphere(
n=n,
d=self.num_objectives,
dtype=self.ref_point.dtype,
device=self.ref_point.device,
qmc=True,
).abs()
if self.negate:
f_X *= -1
return f_X
def sample_hypersphere_positive_quadrant(dim: int) -> Tensor:
"""Sample uniformly from the positive quadrant of a dim-sphere."""
return torch.abs(
botorch_sampling.sample_hypersphere(dim, dtype=torch.double).squeeze())
