def test_use_model_list(self):
self.assertFalse(
use_model_list(Xs=self.Xs, botorch_model_class=SingleTaskGP))
self.assertFalse( # Batched multi-output case.
use_model_list(Xs=self.Xs * 2, botorch_model_class=SingleTaskGP))
self.assertTrue(
use_model_list(Xs=self.Xs + self.Xs2,
botorch_model_class=SingleTaskGP))
def _autoset_surrogate(
self,
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
task_features: List[int],
fidelity_features: List[int],
metric_names: List[str],
) -> None:
"""Sets a default surrogate on this model if one was not explicitly
provided.
"""
# To determine whether to use `ListSurrogate`, we need to check for
# the batched multi-output case, so we first see which model would
# be chosen given the Yvars and the properties of data.
botorch_model_class = choose_model_class(
Yvars=Yvars,
task_features=task_features,
fidelity_features=fidelity_features,
)
if use_model_list(Xs=Xs, botorch_model_class=botorch_model_class):
# If using `ListSurrogate` / `ModelListGP`, pick submodels for each
# outcome.
botorch_submodel_class_per_outcome = {
metric_name: choose_model_class(
Yvars=[Yvar],
task_features=task_features,
fidelity_features=fidelity_features,
)
for Yvar, metric_name in zip(Yvars, metric_names)
}
self._surrogate = ListSurrogate(
botorch_submodel_class_per_outcome=
botorch_submodel_class_per_outcome,
**self.surrogate_options,
)
else:
# Using regular `Surrogate`, so botorch model picked at the beginning
# of the function is the one we should use.
self._surrogate = Surrogate(
botorch_model_class=botorch_model_class,
**self.surrogate_options)
