def test_ModelListGP_single(self):
tkwargs = {"device": self.device, "dtype": torch.float}
train_x1, train_x2, train_y1, train_y2 = _get_random_data(n=10, **tkwargs)
model1 = SingleTaskGP(train_X=train_x1, train_Y=train_y1)
model = ModelListGP(model1)
model.to(**tkwargs)
test_x = torch.tensor([[0.25], [0.75]], **tkwargs)
posterior = model.posterior(test_x)
self.assertIsInstance(posterior, GPyTorchPosterior)
self.assertIsInstance(posterior.mvn, MultivariateNormal)
def test_ModelListGPSingle(self, cuda=False):
tkwargs = {
"device": torch.device("cuda") if cuda else torch.device("cpu"),
"dtype": torch.float,
}
train_x1, train_x2, train_y1, train_y2 = _get_random_data(n=10, **tkwargs)
model1 = SingleTaskGP(train_X=train_x1, train_Y=train_y1)
model = ModelListGP(gp_models=[model1])
model.to(**tkwargs)
test_x = (torch.tensor([0.25, 0.75]).type_as(model.train_targets[0]),)
posterior = model.posterior(test_x)
self.assertIsInstance(posterior, GPyTorchPosterior)
self.assertIsInstance(posterior.mvn, MultivariateNormal)
def test_ModelListGPSingle(self, cuda=False):
tkwargs = {
"device": torch.device("cuda") if cuda else torch.device("cpu"),
"dtype": torch.float,
}
train_x1, train_x2, train_y1, train_y2 = _get_random_data(n=10, **tkwargs)
model1 = SingleTaskGP(train_X=train_x1, train_Y=train_y1)
model = ModelListGP(gp_models=[model1])
model.to(**tkwargs)
test_x = (torch.tensor([0.25, 0.75]).type_as(model.train_targets[0]),)
posterior = model.posterior(test_x)
self.assertIsInstance(posterior, GPyTorchPosterior)
self.assertIsInstance(posterior.mvn, MultivariateNormal)
def _get_model(n, fixed_noise=False, use_octf=False, **tkwargs):
train_x1, train_y1 = _get_random_data(batch_shape=torch.Size(),
m=1,
n=10,
**tkwargs)
train_x2, train_y2 = _get_random_data(batch_shape=torch.Size(),
m=1,
n=11,
**tkwargs)
octfs = [Standardize(m=1), Standardize(m=1)] if use_octf else [None, None]
if fixed_noise:
train_y1_var = 0.1 + 0.1 * torch.rand_like(train_y1, **tkwargs)
train_y2_var = 0.1 + 0.1 * torch.rand_like(train_y2, **tkwargs)
model1 = FixedNoiseGP(
train_X=train_x1,
train_Y=train_y1,
train_Yvar=train_y1_var,
outcome_transform=octfs[0],
)
model2 = FixedNoiseGP(
train_X=train_x2,
train_Y=train_y2,
train_Yvar=train_y2_var,
outcome_transform=octfs[1],
)
else:
model1 = SingleTaskGP(train_X=train_x1,
train_Y=train_y1,
outcome_transform=octfs[0])
model2 = SingleTaskGP(train_X=train_x2,
train_Y=train_y2,
outcome_transform=octfs[1])
model = ModelListGP(model1, model2)
return model.to(**tkwargs)
def _get_model(n, fixed_noise=False, **tkwargs):
train_x1, train_x2, train_y1, train_y2 = _get_random_data(n=n, **tkwargs)
if fixed_noise:
train_y1_var = 0.1 + 0.1 * torch.rand_like(train_y1, **tkwargs)
train_y2_var = 0.1 + 0.1 * torch.rand_like(train_y2, **tkwargs)
model1 = FixedNoiseGP(train_X=train_x1,
train_Y=train_y1,
train_Yvar=train_y1_var)
model2 = FixedNoiseGP(train_X=train_x2,
train_Y=train_y2,
train_Yvar=train_y2_var)
else:
model1 = SingleTaskGP(train_X=train_x1, train_Y=train_y1)
model2 = SingleTaskGP(train_X=train_x2, train_Y=train_y2)
model = ModelListGP(model1, model2)
return model.to(**tkwargs)
def _get_model(n, **tkwargs):
train_x1, train_x2, train_y1, train_y2 = _get_random_data(n=n, **tkwargs)
model1 = SingleTaskGP(train_X=train_x1, train_Y=train_y1)
model2 = SingleTaskGP(train_X=train_x2, train_Y=train_y2)
model = ModelListGP(gp_models=[model1, model2])
return model.to(**tkwargs)
def _get_model(n, **tkwargs):
train_x1, train_x2, train_y1, train_y2 = _get_random_data(n=n, **tkwargs)
model1 = SingleTaskGP(train_X=train_x1, train_Y=train_y1)
model2 = SingleTaskGP(train_X=train_x2, train_Y=train_y2)
model = ModelListGP(gp_models=[model1, model2])
return model.to(**tkwargs)