def _get_model_and_training_data(input_transform=None, **tkwargs):
train_X, train_Y = _get_random_mt_data(**tkwargs)
model = MultiTaskGP(train_X,
train_Y,
task_feature=1,
input_transform=input_transform)
return model.to(**tkwargs), train_X, train_Y
def _get_fixed_prior_model(**tkwargs):
train_X, train_Y = _get_random_mt_data(**tkwargs)
sd_prior = GammaPrior(2.0, 0.15)
sd_prior._event_shape = torch.Size([2])
model = MultiTaskGP(train_X,
train_Y,
task_feature=1,
prior=LKJCovariancePrior(2, 0.6, sd_prior))
return model.to(**tkwargs)
def _get_given_covar_module_model(**tkwargs):
train_X, train_Y = _get_random_mt_data(**tkwargs)
model = MultiTaskGP(
train_X,
train_Y,
task_feature=1,
covar_module=RBFKernel(lengthscale_prior=LogNormalPrior(0.0, 1.0)),
)
return model.to(**tkwargs)
def _get_model_single_output(**tkwargs):
train_X, train_Y = _get_random_mt_data(**tkwargs)
model = MultiTaskGP(train_X, train_Y, task_feature=1, output_tasks=[1])
return model.to(**tkwargs)
def _get_model(**tkwargs):
train_X, train_Y = _get_random_mt_data(**tkwargs)
model = MultiTaskGP(train_X, train_Y, task_feature=1)
return model.to(**tkwargs)
def _get_model_single_output(**tkwargs):
train_X, train_Y = _get_random_mt_data(**tkwargs)
model = MultiTaskGP(train_X, train_Y, task_feature=1, output_tasks=[1])
return model.to(**tkwargs)
def _get_model(**tkwargs):
train_X, train_Y = _get_random_mt_data(**tkwargs)
model = MultiTaskGP(train_X, train_Y, task_feature=1)
return model.to(**tkwargs)