def test_deepgplayer_checks_constructor_args():
with pytest.raises(ValueError):
deepgp.Layer(kernels.RBFKernel(), input_dim=0)
with pytest.raises(ValueError):
deepgp.Layer(kernels.RBFKernel(), output_dim=0)
with pytest.raises(ValueError):
deepgp.Layer(kernels.RBFKernel(), grid_bound=0)
with pytest.raises(ValueError):
deepgp.Layer(kernels.RBFKernel(), grid_num=0)
def test_deepgp_can_compute_kl_regularization():
dgp = deepgp.DeepGP(
layers=(deepgp.Layer(kernels.RBFKernel()),
deepgp.Layer(kernels.RBFKernel())),
likelihood=deepgp.ExpPoisson(),
)
dgp(torch.randn(10, 1))
assert torch.is_tensor(dgp.kl_regularization)
assert dgp.kl_regularization > 0.0
def test_deepgp_can_compute_outputs():
dgp = deepgp.DeepGP(
layers=(deepgp.Layer(kernels.RBFKernel()),
deepgp.Layer(kernels.RBFKernel())),
likelihood=deepgp.ExpPoisson(),
)
loss = dgp(torch.randn(10, 1)).log_prob(torch.ones(10, 1)).sum()
loss.backward()
loss = dgp(torch.randn(10, 1)).log_prob(torch.ones(10, 1)).sum()
loss.backward()
dgp.eval()
dgp(torch.randn(10, 1))
def test_deepgp_can_compute_negative_elbo_loss():
dgp = deepgp.DeepGP(
layers=(deepgp.Layer(kernels.RBFKernel()),
deepgp.Layer(kernels.RBFKernel())),
likelihood=deepgp.ExpPoisson(),
)
elbo = dgp.negative_elbo(torch.randn(10, 1),
torch.ones((10, 1)),
num_data=10)
elbo.backward()
elbo = dgp.negative_elbo(torch.randn(10, 1),
torch.ones((10, 1)),
num_data=10)
elbo.backward()
assert torch.is_tensor(elbo)
def test_dgplayer_can_compute_predictions():
dgp = deepgp.Layer(kernels.RBFKernel(),
input_dim=2,
output_dim=3,
grid_num=4)
output = dgp(torch.rand(10, 2))
assert output.size() == (10, 3)
def test_deepgplayer_checks_input_dims():
dgp = deepgp.Layer(kernels.RBFKernel(),
input_dim=2,
output_dim=3,
grid_num=4)
with pytest.raises(ValueError):
dgp(torch.rand(10, 10))
def test_deepgplayer_can_turn_off_kl_regularization():
dgp = deepgp.Layer(kernels.RBFKernel(),
input_dim=2,
output_dim=3,
grid_num=4)
dgp(torch.rand(10, 2), compute_kl=False)
assert dgp.kl_regularization.item() == 0.0
def test_deepgp_can_backward_with_more_than_one_dim():
dgp = deepgp.DeepGP(
layers=(deepgp.Layer(kernels.RBFKernel(),
input_dim=2,
output_dim=1,
grid_num=4), ),
likelihood=deepgp.ExpPoisson(),
)
dgp.forward(torch.randn(10, 2)).mean.sum().backward()