def evaluate(self, X_actual: Tensor, bounds: Tensor, **kwargs: Any) -> Tensor:
r"""Evaluate qKnowledgeGradient on the candidate set `X_actual` by
solving the inner optimization problem.
Args:
X_actual: A `b x q x d` Tensor with `b` t-batches of `q` design points
each. Unlike `forward()`, this does not include solutions of the
inner optimization problem.
bounds: A `2 x d` tensor of lower and upper bounds for each column of
the solutions to the inner problem.
kwargs: Additional keyword arguments. This includes the options for
optimization of the inner problem, i.e. `num_restarts`, `raw_samples`,
an `options` dictionary to be passed on to the optimization helpers, and
a `scipy_options` dictionary to be passed to `scipy.minimize`.
Returns:
A Tensor of shape `b`. For t-batch b, the q-KG value of the design
`X_actual[b]` is averaged across the fantasy models.
NOTE: If `current_value` is not provided, then this is not the
true KG value of `X_actual[b]`.
"""
# construct the fantasy model of shape `num_fantasies x b`
fantasy_model = self.model.fantasize(
X=X_actual, sampler=self.sampler, observation_noise=True
)
# get the value function
value_function = _get_value_function(
model=fantasy_model, objective=self.objective, sampler=self.inner_sampler
)
# optimize the inner problem
from botorch.optim.initializers import gen_value_function_initial_conditions
from botorch.generation.gen import gen_candidates_scipy
initial_conditions = gen_value_function_initial_conditions(
acq_function=value_function,
bounds=bounds,
num_restarts=kwargs.get("num_restarts", 20),
raw_samples=kwargs.get("raw_samples", 1024),
current_model=self.model,
options={**kwargs.get("options", {}), **kwargs.get("scipy_options", {})},
)
_, values = gen_candidates_scipy(
initial_conditions=initial_conditions,
acquisition_function=value_function,
lower_bounds=bounds[0],
upper_bounds=bounds[1],
options=kwargs.get("scipy_options"),
)
# get the maximizer for each batch
values, _ = torch.max(values, dim=0)
if self.current_value is not None:
values = values - self.current_value
# return average over the fantasy samples
return values.mean(dim=0)
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)
