def _get_model(self, batch_shape, num_outputs, **tkwargs):
train_x, train_y = _get_random_data(batch_shape=batch_shape,
num_outputs=num_outputs,
**tkwargs)
train_yvar = (0.1 + 0.1 * torch.rand_like(train_y))**2
model = HeteroskedasticSingleTaskGP(train_X=train_x,
train_Y=train_y,
train_Yvar=train_yvar)
mll = ExactMarginalLogLikelihood(model.likelihood, model).to(**tkwargs)
fit_gpytorch_model(mll, options={"maxiter": 1})
return model
def _get_model_and_data(self, batch_shape, num_outputs, **tkwargs):
train_X, train_Y = _get_random_data(batch_shape=batch_shape,
num_outputs=num_outputs,
**tkwargs)
train_Yvar = (0.1 + 0.1 * torch.rand_like(train_Y))**2
model_kwargs = {
"train_X": train_X,
"train_Y": train_Y,
"train_Yvar": train_Yvar,
}
model = HeteroskedasticSingleTaskGP(**model_kwargs)
return model, model_kwargs
def _get_model_and_data(
self, batch_shape, m, outcome_transform=None, input_transform=None, **tkwargs
):
with manual_seed(0):
train_X, train_Y = _get_random_data(batch_shape=batch_shape, m=m, **tkwargs)
train_Yvar = (0.1 + 0.1 * torch.rand_like(train_Y)) ** 2
model_kwargs = {
"train_X": train_X,
"train_Y": train_Y,
"train_Yvar": train_Yvar,
"input_transform": input_transform,
"outcome_transform": outcome_transform,
}
model = HeteroskedasticSingleTaskGP(**model_kwargs)
return model, model_kwargs
def _get_model_and_data(self,
batch_shape,
m,
outcome_transform=None,
**tkwargs):
with manual_seed(0):
train_X, train_Y = _get_random_data(batch_shape=batch_shape,
num_outputs=m,
**tkwargs)
train_Yvar = (0.1 + 0.1 * torch.rand_like(train_Y))**2
model_kwargs = {
"train_X": train_X,
"train_Y": train_Y,
"train_Yvar": train_Yvar,
}
if outcome_transform is not None:
model_kwargs["outcome_transform"] = outcome_transform
model = HeteroskedasticSingleTaskGP(**model_kwargs)
return model, model_kwargs