def _get_model(self, batch_shape, num_outputs, n, **tkwargs):
train_x, train_y = _get_random_data(
batch_shape=batch_shape, num_outputs=num_outputs, n=n, **tkwargs
)
train_yvar = torch.full_like(train_y, 0.01)
model = FixedNoiseGP(train_X=train_x, train_Y=train_y, train_Yvar=train_yvar)
return model.to(**tkwargs)
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 _get_model(self, batch_shape, num_outputs, n, **tkwargs):
train_x, train_y = _get_random_data(batch_shape=batch_shape,
num_outputs=num_outputs,
n=n,
**tkwargs)
train_yvar = torch.full_like(train_y, 0.01)
model = FixedNoiseGP(train_X=train_x,
train_Y=train_y,
train_Yvar=train_yvar)
return model.to(**tkwargs)
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
