def fixed_noise_gp_model_constructor(
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor], # Maybe these should be optional where irrelevant?
task_features: List[int],
fidelity_features: List[int],
metric_names: List[str],
state_dict: Optional[Dict[str, Tensor]] = None,
refit_model: bool = True,
**kwargs: Any,
) -> Model:
gp = FixedNoiseGP(train_X=Xs[0], train_Y=Ys[0], train_Yvar=Yvars[0], **kwargs)
gp.to(Xs[0])
if state_dict is not None:
gp.load_state_dict(state_dict)
if state_dict is None or refit_model:
fit_gpytorch_model(ExactMarginalLogLikelihood(gp.likelihood, gp))
return gp
def fixed_noise_gp_model_constructor(
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
task_features: List[int],
fidelity_features: List[int],
metric_names: List[str],
state_dict: Optional[Dict[str, Tensor]] = None,
refit_model: bool = True,
**kwargs: Any,
) -> Model:
gp = FixedNoiseGP(train_X=Xs[0], train_Y=Ys[0], train_Yvar=Yvars[0], **kwargs)
gp.to(Xs[0])
if state_dict is not None:
# pyre-fixme[6]: Expected `OrderedDict[typing.Any, typing.Any]` for 1st
# param but got `Dict[str, Tensor]`.
gp.load_state_dict(state_dict)
if state_dict is None or refit_model:
fit_gpytorch_model(ExactMarginalLogLikelihood(gp.likelihood, gp))
return gp
def _get_noiseless_fantasy_model(model: FixedNoiseGP, batch_X_observed: Tensor,
Y_fantasized: Tensor) -> FixedNoiseGP:
r"""Construct a fantasy model from a fitted model and provided fantasies.
The fantasy model uses the hyperparameters from the original fitted model and
assumes the fantasies are noiseless.
Args:
model: a fitted FixedNoiseGP
batch_X_observed: A `b x n x d` tensor of inputs where `b` is the number of
fantasies.
Y_fantasized: A `b x n` tensor of fantasized targets where `b` is the number of
fantasies.
Returns:
The fantasy model.
"""
# initialize a copy of FixedNoiseGP on the original training inputs
# this makes FixedNoiseGP a non-batch GP, so that the same hyperparameters
# are used across all batches (by default, a GP with batched training data
# uses independent hyperparameters for each batch).
fantasy_model = FixedNoiseGP(
train_X=model.train_inputs[0],
train_Y=model.train_targets.unsqueeze(-1),
train_Yvar=model.likelihood.noise_covar.noise.unsqueeze(-1),
)
# update training inputs/targets to be batch mode fantasies
fantasy_model.set_train_data(inputs=batch_X_observed,
targets=Y_fantasized,
strict=False)
# use noiseless fantasies
fantasy_model.likelihood.noise_covar.noise = torch.full_like(
Y_fantasized, 1e-7)
# load hyperparameters from original model
state_dict = deepcopy(model.state_dict())
fantasy_model.load_state_dict(state_dict)
return fantasy_model
