def create_per(sigma2, lengthscale, period_length):
per = PeriodicKernel()
per.lengthscale = lengthscale
per.period_length = period_length
kernel = ScaleKernel(per)
kernel.outputscale = sigma2
return kernel
def test_computes_periodic_function(self):
a = torch.tensor([4, 2, 8], dtype=torch.float).view(3, 1)
b = torch.tensor([0, 2], dtype=torch.float).view(2, 1)
lengthscale = 2
period = 3
kernel = PeriodicKernel().initialize(log_lengthscale=math.log(lengthscale), log_period_length=math.log(period))
kernel.eval()
actual = torch.zeros(3, 2)
for i in range(3):
for j in range(2):
val = 2 * torch.pow(torch.sin(math.pi * (a[i] - b[j]) / 3), 2) / lengthscale
actual[i, j] = torch.exp(-val).item()
res = kernel(a, b).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
def test_batch(self):
a = torch.tensor([[4, 2, 8], [1, 2, 3]],
dtype=torch.float).view(2, 3, 1)
b = torch.tensor([[0, 2], [-1, 2]], dtype=torch.float).view(2, 2, 1)
period = torch.tensor(1, dtype=torch.float).view(1, 1, 1)
lengthscale = torch.tensor(2, dtype=torch.float).view(1, 1, 1)
kernel = PeriodicKernel().initialize(lengthscale=lengthscale,
period_length=period)
kernel.eval()
actual = torch.zeros(2, 3, 2)
for k in range(2):
actual[k] = kernel(a[k], b[k]).evaluate()
res = kernel(a, b).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
def test_computes_periodic_function(self):
a = torch.Tensor([4, 2, 8]).view(3, 1)
b = torch.Tensor([0, 2]).view(2, 1)
lengthscale = 2
period = 1
kernel = PeriodicKernel().initialize(log_lengthscale=math.log(lengthscale), log_period_length=math.log(period))
kernel.eval()
actual = torch.zeros(3, 2)
for i in range(3):
for j in range(2):
val = 2 * torch.pow(torch.sin(math.pi * (a[i] - b[j])), 2) / lengthscale
actual[i, j] = torch.exp(-val)[0]
res = kernel(Variable(a), Variable(b)).evaluate().data
self.assertLess(torch.norm(res - actual), 1e-5)
def test_is_pd(self):
# ensures 1d input is positive definite with additional jitter
x = torch.randn(100).reshape(-1, 1)
kernel = PeriodicKernel()
with torch.no_grad():
K = kernel(x, x).evaluate() + 1e-4 * torch.eye(len(x))
eig = torch.symeig(K)[0]
self.assertTrue((eig > 0.0).all().item())
def test_batch_separate(self):
a = torch.tensor([[4, 2, 8], [1, 2, 3]], dtype=torch.float).view(2, 3, 1)
b = torch.tensor([[0, 2], [-1, 2]], dtype=torch.float).view(2, 2, 1)
period = torch.tensor([1, 2], dtype=torch.float).view(2, 1, 1)
lengthscale = torch.tensor([2, 1], dtype=torch.float).view(2, 1, 1)
kernel = PeriodicKernel(batch_shape=torch.Size([2])).initialize(lengthscale=lengthscale, period_length=period)
kernel.eval()
actual = torch.zeros(2, 3, 2)
for k in range(2):
for i in range(3):
for j in range(2):
val = 2 * torch.pow(torch.sin(math.pi * (a[k, i] - b[k, j]) / period[k]), 2) / lengthscale[k]
actual[k, i, j] = torch.exp(-val).item()
res = kernel(a, b).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
def test_multidimensional_inputs(self):
# test taken from issue #835
# ensures multidimensional input results in a positive definite kernel matrix, with additional jitter
x = torch.randn(1000, 2)
kernel = PeriodicKernel()
with torch.no_grad():
K = kernel(x, x).evaluate() + 1e-4 * torch.eye(len(x))
eig = torch.symeig(K)[0]
self.assertTrue((eig > 0.0).all().item())
def test_batch_separate(self):
a = torch.tensor([[4, 2, 8], [1, 2, 3]],
dtype=torch.float).view(2, 3, 1)
b = torch.tensor([[0, 2], [-1, 2]], dtype=torch.float).view(2, 2, 1)
period = torch.tensor([1, 2], dtype=torch.float).view(2, 1, 1)
lengthscale = torch.tensor([2, 1], dtype=torch.float).view(2, 1, 1)
kernel = PeriodicKernel(batch_shape=torch.Size([2])).initialize(
lengthscale=lengthscale, period_length=period)
kernel.eval()
actual = torch.zeros(2, 3, 2)
for k in range(2):
diff = a[k].unsqueeze(1) - b[k].unsqueeze(0)
diff = diff * math.pi / period[k].unsqueeze(-1)
actual[k] = diff.sin().pow(2).sum(dim=-1).div(
lengthscale[k]).mul(-2.0).exp_()
res = kernel(a, b).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
def __init__(self,
mean_name='constant',
kernel_name='RBF',
grid_bounds=[(-1, 1), (-1, 1)],
grid_size=100,
num_samples=1000):
self.mean = mean_name
self.kernel = kernel_name
self.num_samples = num_samples
self.grid_bounds = grid_bounds
self.grid_size = grid_size
self.x_dim = 2 # x and y dim are fixed for this dataset.
self.y_dim = 1
self.data = []
# create grid
grid = torch.zeros(grid_size, len(grid_bounds))
for i in range(len(grid_bounds)):
grid_diff = float(grid_bounds[i][1] -
grid_bounds[i][0]) / (grid_size - 2)
grid[:,
i] = torch.linspace(grid_bounds[i][0] - grid_diff,
grid_bounds[i][1] + grid_diff, grid_size)
x = gpytorch.utils.grid.create_data_from_grid(grid)
# initialize likelihood and model
likelihood = gpytorch.likelihoods.GaussianLikelihood()
mean_dict = {'constant': ConstantMean()}
kernel_dict = {
'RBF': RBFKernel(),
'cosine': CosineKernel(),
'linear': LinearKernel(),
'periodic': PeriodicKernel(),
'LCM': LCMKernel(base_kernels=[CosineKernel()], num_tasks=1),
'polynomial': PolynomialKernel(power=3),
'matern': MaternKernel()
}
# evaluate GP on prior distribution
with gpytorch.settings.prior_mode(True):
model = ExactGPModel(x,
None,
likelihood,
mean_module=mean_dict[self.mean],
kernel_module=gpytorch.kernels.GridKernel(
kernel_dict[self.kernel], grid=grid))
gp = model(x)
for i in range(num_samples):
y = gp.sample()
self.data.append(
(x, y.unsqueeze(1))) #+torch.randn(y.size())*0.2))
def __init__(self,
mean_list,
kernel_list,
num_points=100,
num_samples=1000,
amplitude_range=(-5., 5.)):
self.mean_list = mean_list
self.kernel_list = kernel_list
self.num_config = len(mean_list) * len(kernel_list)
self.num_samples = num_samples
self.num_points = num_points
self.x_dim = 1 # x and y dim are fixed for this dataset.
self.y_dim = 1
self.amplitude_range = amplitude_range
self.data = []
# initialize likelihood and model
x = torch.linspace(self.amplitude_range[0], self.amplitude_range[1],
num_points).unsqueeze(1)
likelihood = gpytorch.likelihoods.GaussianLikelihood()
mean_dict = {'constant': ConstantMean(), 'linear': LinearMean(1)}
kernel_dict = {
'RBF': RBFKernel(),
'cosine': CosineKernel(),
'linear': LinearKernel(),
'periodic': PeriodicKernel(period_length=0.5),
'LCM': LCMKernel(base_kernels=[CosineKernel()], num_tasks=1),
'polynomial': PolynomialKernel(power=2),
'matern': MaternKernel()
}
# create a different GP from each possible configuration
for mean in self.mean_list:
for kernel in self.kernel_list:
# evaluate GP on prior distribution
with gpytorch.settings.prior_mode(True):
model = ExactGPModel(x,
None,
likelihood,
mean_module=mean_dict[mean],
kernel_module=kernel_dict[kernel])
gp = model(x)
# sample from current configuration
for i in range(num_samples // self.num_config + 1):
y = gp.sample()
self.data.append(
(x, y.unsqueeze(1))) #+torch.randn(y.shape)*0))
def test_random_fourier_features(self):
# test kernel that is not Scale, RBF, or Matern
with self.assertRaises(NotImplementedError):
RandomFourierFeatures(
kernel=PeriodicKernel(),
input_dim=2,
num_rff_features=3,
)
# test batched kernel
with self.assertRaises(NotImplementedError):
RandomFourierFeatures(
kernel=RBFKernel(batch_shape=torch.Size([2])),
input_dim=2,
num_rff_features=3,
)
tkwargs = {"device": self.device}
for dtype in (torch.float, torch.double):
tkwargs["dtype"] = dtype
# test init
# test ScaleKernel
base_kernel = RBFKernel(ard_num_dims=2)
kernel = ScaleKernel(base_kernel).to(**tkwargs)
rff = RandomFourierFeatures(
kernel=kernel,
input_dim=2,
num_rff_features=3,
)
self.assertTrue(torch.equal(rff.outputscale, kernel.outputscale))
# check that rff makes a copy
self.assertFalse(rff.outputscale is kernel.outputscale)
self.assertTrue(
torch.equal(rff.lengthscale, base_kernel.lengthscale))
# check that rff makes a copy
self.assertFalse(rff.lengthscale is kernel.lengthscale)
for sample_shape in [torch.Size(), torch.Size([5])]:
# test not ScaleKernel
rff = RandomFourierFeatures(
kernel=base_kernel,
input_dim=2,
num_rff_features=3,
sample_shape=sample_shape,
)
self.assertTrue(
torch.equal(rff.outputscale, torch.tensor(1, **tkwargs)))
self.assertTrue(
torch.equal(rff.lengthscale, base_kernel.lengthscale))
# check that rff makes a copy
self.assertFalse(rff.lengthscale is kernel.lengthscale)
self.assertEqual(rff.weights.shape,
torch.Size([*sample_shape, 2, 3]))
self.assertEqual(rff.bias.shape, torch.Size([*sample_shape,
3]))
self.assertTrue(
((rff.bias <= 2 * pi) & (rff.bias >= 0.0)).all())
# test forward
for sample_shape in [torch.Size(), torch.Size([7])]:
rff = RandomFourierFeatures(
kernel=kernel,
input_dim=2,
num_rff_features=3,
sample_shape=sample_shape,
)
for input_batch_shape in [torch.Size([]), torch.Size([5])]:
X = torch.rand(*input_batch_shape, *sample_shape, 1, 2,
**tkwargs)
Y = rff(X)
self.assertTrue(
Y.shape,
torch.Size([*input_batch_shape, *sample_shape, 1, 1]))
_constant = torch.sqrt(2 * rff.outputscale /
rff.weights.shape[-1])
_arg_to_cos = X / base_kernel.lengthscale @ rff.weights
_bias_expanded = rff.bias.unsqueeze(-2)
expected_Y = _constant * (torch.cos(_arg_to_cos +
_bias_expanded))
self.assertTrue(torch.allclose(Y, expected_Y))
# test get_weights
for sample_shape in [torch.Size(), torch.Size([5])]:
with mock.patch("torch.randn",
wraps=torch.randn) as mock_randn:
rff._get_weights(
base_kernel=base_kernel,
input_dim=2,
num_rff_features=3,
sample_shape=sample_shape,
)
mock_randn.assert_called_once_with(
*sample_shape,
2,
3,
dtype=base_kernel.lengthscale.dtype,
device=base_kernel.lengthscale.device,
)
# test get_weights with Matern kernel
with mock.patch(
"torch.randn",
wraps=torch.randn) as mock_randn, mock.patch(
"torch.distributions.Gamma",
wraps=torch.distributions.Gamma) as mock_gamma:
base_kernel = MaternKernel(ard_num_dims=2).to(**tkwargs)
rff._get_weights(
base_kernel=base_kernel,
input_dim=2,
num_rff_features=3,
sample_shape=sample_shape,
)
mock_randn.assert_called_once_with(
*sample_shape,
2,
3,
dtype=base_kernel.lengthscale.dtype,
device=base_kernel.lengthscale.device,
)
mock_gamma.assert_called_once_with(
base_kernel.nu,
base_kernel.nu,
)
def create_kernel_with_prior(self, period_length_prior):
return PeriodicKernel(period_length_prior=period_length_prior)
def create_kernel_ard(self, num_dims, **kwargs):
return PeriodicKernel(ard_num_dims=num_dims, **kwargs)
def create_kernel_no_ard(self, **kwargs):
return PeriodicKernel(**kwargs)
