def test_get_NEI_with_chebyshev_and_missing_Ys_error(self):
model = MultiObjectiveBotorchModel()
x = torch.zeros(2, 2)
weights = torch.ones(2)
with self.assertRaisesRegex(
ValueError, "Chebyshev Scalarization requires Ys argument"):
get_NEI(
model=model,
X_observed=x,
objective_weights=weights,
chebyshev_scalarization=True,
)
def ei_or_nei(
model: Union[ALEBOGP, ModelListGP],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]],
X_observed: Tensor,
X_pending: Optional[Tensor],
q: int,
noiseless: bool,
) -> AcquisitionFunction:
"""Use analytic EI if appropriate, otherwise Monte Carlo NEI.
Analytic EI can be used if: Single outcome, no constraints, no pending
points, not batch, and no noise.
Args:
model: GP.
objective_weights: Weights on each outcome for the objective.
outcome_constraints: Outcome constraints.
X_observed: Observed points for NEI.
X_pending: Pending points.
q: Batch size.
noiseless: True if evaluations are noiseless.
Returns: An AcquisitionFunction, either analytic EI or MC NEI.
"""
if (
len(objective_weights) == 1
and outcome_constraints is None
and X_pending is None
and q == 1
and noiseless
):
maximize = objective_weights[0] > 0
if maximize:
best_f = model.train_targets.max()
else:
best_f = model.train_targets.min()
return ExpectedImprovement(model=model, best_f=best_f, maximize=maximize)
else:
with gpytorch.settings.max_cholesky_size(2000):
acq = get_NEI(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
X_pending=X_pending,
)
return acq
