def test_single_task_batch_cv(self, cuda=False):
n = 10
for batch_shape in (torch.Size([]), torch.Size([2])):
for num_outputs in (1, 2):
for double in (False, True):
tkwargs = {
"device":
torch.device("cuda") if cuda else torch.device("cpu"),
"dtype":
torch.double if double else torch.float,
}
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)
noiseless_cv_folds = gen_loo_cv_folds(train_X=train_X,
train_Y=train_Y)
# Test SingleTaskGP
cv_results = batch_cross_validation(
model_cls=SingleTaskGP,
mll_cls=ExactMarginalLogLikelihood,
cv_folds=noiseless_cv_folds,
fit_args={"options": {
"maxiter": 1
}},
)
expected_shape = batch_shape + torch.Size(
[n, 1, num_outputs])
self.assertEqual(cv_results.posterior.mean.shape,
expected_shape)
self.assertEqual(cv_results.observed_Y.shape,
expected_shape)
# Test FixedNoiseGP
noisy_cv_folds = gen_loo_cv_folds(train_X=train_X,
train_Y=train_Y,
train_Yvar=train_Yvar)
cv_results = batch_cross_validation(
model_cls=FixedNoiseGP,
mll_cls=ExactMarginalLogLikelihood,
cv_folds=noisy_cv_folds,
fit_args={"options": {
"maxiter": 1
}},
)
self.assertEqual(cv_results.posterior.mean.shape,
expected_shape)
self.assertEqual(cv_results.observed_Y.shape,
expected_shape)
self.assertEqual(cv_results.observed_Y.shape,
expected_shape)
def test_single_task_batch_cv(self, cuda=False):
n = 10
for batch_shape in (torch.Size([]), torch.Size([2])):
for num_outputs in (1, 2):
for double in (False, True):
tkwargs = {
"device": torch.device("cuda") if cuda else torch.device("cpu"),
"dtype": torch.double if double else torch.float,
}
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)
noiseless_cv_folds = gen_loo_cv_folds(
train_X=train_X, train_Y=train_Y
)
# Test SingleTaskGP
cv_results = batch_cross_validation(
model_cls=SingleTaskGP,
mll_cls=ExactMarginalLogLikelihood,
cv_folds=noiseless_cv_folds,
fit_args={"options": {"maxiter": 1}},
)
expected_shape = batch_shape + torch.Size([n, 1, num_outputs])
self.assertEqual(cv_results.posterior.mean.shape, expected_shape)
self.assertEqual(cv_results.observed_Y.shape, expected_shape)
# Test FixedNoiseGP
noisy_cv_folds = gen_loo_cv_folds(
train_X=train_X, train_Y=train_Y, train_Yvar=train_Yvar
)
cv_results = batch_cross_validation(
model_cls=FixedNoiseGP,
mll_cls=ExactMarginalLogLikelihood,
cv_folds=noisy_cv_folds,
fit_args={"options": {"maxiter": 1}},
)
self.assertEqual(cv_results.posterior.mean.shape, expected_shape)
self.assertEqual(cv_results.observed_Y.shape, expected_shape)
self.assertEqual(cv_results.observed_Y.shape, expected_shape)
def test_single_task_batch_cv(self):
n = 10
for batch_shape, num_outputs, dtype in itertools.product(
(torch.Size(), torch.Size([2])), (1, 2),
(torch.float, torch.double)):
tkwargs = {"device": self.device, "dtype": dtype}
train_X, train_Y = _get_random_data(batch_shape=batch_shape,
num_outputs=num_outputs,
n=n,
**tkwargs)
if num_outputs == 1:
train_Y = train_Y.squeeze(-1)
train_Yvar = torch.full_like(train_Y, 0.01)
noiseless_cv_folds = gen_loo_cv_folds(train_X=train_X,
train_Y=train_Y)
# check shapes
expected_shape_train_X = batch_shape + torch.Size(
[n, n - 1, train_X.shape[-1]])
expected_shape_test_X = batch_shape + torch.Size(
[n, 1, train_X.shape[-1]])
self.assertEqual(noiseless_cv_folds.train_X.shape,
expected_shape_train_X)
self.assertEqual(noiseless_cv_folds.test_X.shape,
expected_shape_test_X)
expected_shape_train_Y = batch_shape + torch.Size(
[n, n - 1, num_outputs])
expected_shape_test_Y = batch_shape + torch.Size(
[n, 1, num_outputs])
self.assertEqual(noiseless_cv_folds.train_Y.shape,
expected_shape_train_Y)
self.assertEqual(noiseless_cv_folds.test_Y.shape,
expected_shape_test_Y)
self.assertIsNone(noiseless_cv_folds.train_Yvar)
self.assertIsNone(noiseless_cv_folds.test_Yvar)
# Test SingleTaskGP
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=OptimizationWarning)
cv_results = batch_cross_validation(
model_cls=SingleTaskGP,
mll_cls=ExactMarginalLogLikelihood,
cv_folds=noiseless_cv_folds,
fit_args={"options": {
"maxiter": 1
}},
)
expected_shape = batch_shape + torch.Size([n, 1, num_outputs])
self.assertEqual(cv_results.posterior.mean.shape, expected_shape)
self.assertEqual(cv_results.observed_Y.shape, expected_shape)
# Test FixedNoiseGP
noisy_cv_folds = gen_loo_cv_folds(train_X=train_X,
train_Y=train_Y,
train_Yvar=train_Yvar)
# check shapes
self.assertEqual(noisy_cv_folds.train_X.shape,
expected_shape_train_X)
self.assertEqual(noisy_cv_folds.test_X.shape,
expected_shape_test_X)
self.assertEqual(noisy_cv_folds.train_Y.shape,
expected_shape_train_Y)
self.assertEqual(noisy_cv_folds.test_Y.shape,
expected_shape_test_Y)
self.assertEqual(noisy_cv_folds.train_Yvar.shape,
expected_shape_train_Y)
self.assertEqual(noisy_cv_folds.test_Yvar.shape,
expected_shape_test_Y)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=OptimizationWarning)
cv_results = batch_cross_validation(
model_cls=FixedNoiseGP,
mll_cls=ExactMarginalLogLikelihood,
cv_folds=noisy_cv_folds,
fit_args={"options": {
"maxiter": 1
}},
)
self.assertEqual(cv_results.posterior.mean.shape, expected_shape)
self.assertEqual(cv_results.observed_Y.shape, expected_shape)
self.assertEqual(cv_results.observed_Y.shape, expected_shape)