def test_compute_linear_truncated_kernel_no_batch(self):
x1 = torch.tensor([[1, 0.1, 0.2], [2, 0.3, 0.4]])
x2 = torch.tensor([[3, 0.5, 0.6], [4, 0.7, 0.8]])
t_1 = torch.tensor([[0.3584, 0.1856], [0.2976, 0.1584]])
for nu, fidelity_dims in itertools.product({0.5, 1.5, 2.5}, ([2], [1, 2])):
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=fidelity_dims, dimension=3, nu=nu
)
kernel.power = 1
n_fid = len(fidelity_dims)
if n_fid > 1:
active_dimsM = [0]
t_2 = torch.tensor([[0.4725, 0.2889], [0.4025, 0.2541]])
t_3 = torch.tensor([[0.1685, 0.0531], [0.1168, 0.0386]])
t = 1 + t_1 + t_2 + t_3
else:
active_dimsM = [0, 1]
t = 1 + t_1
matern_ker = MaternKernel(nu=nu, active_dims=active_dimsM)
matern_term = matern_ker(x1, x2).evaluate()
actual = t * matern_term
res = kernel(x1, x2).evaluate()
self.assertLess(torch.norm(res - actual), 1e-4)
# test diagonal mode
res_diag = kernel(x1, x2, diag=True)
self.assertLess(torch.norm(res_diag - actual.diag()), 1e-4)
# make sure that we error out if last_dim_is_batch=True
with self.assertRaises(NotImplementedError):
kernel(x1, x2, diag=True, last_dim_is_batch=True)
def test_compute_linear_truncated_kernel_no_batch(self):
x1 = torch.tensor([1, 0.1, 0.2, 2, 0.3, 0.4],
dtype=torch.float).view(2, 3)
x2 = torch.tensor([3, 0.5, 0.6, 4, 0.7, 0.8],
dtype=torch.float).view(2, 3)
t_1 = torch.tensor([0.3584, 0.1856, 0.2976, 0.1584],
dtype=torch.float).view(2, 2)
for nu in {0.5, 1.5, 2.5}:
for fidelity_dims in ([2], [1, 2]):
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=fidelity_dims, dimension=3, nu=nu)
kernel.power = 1
if len(fidelity_dims) > 1:
active_dimsM = [0]
t_2 = torch.tensor([0.4725, 0.2889, 0.4025, 0.2541],
dtype=torch.float).view(2, 2)
t_3 = torch.tensor([0.1685, 0.0531, 0.1168, 0.0386],
dtype=torch.float).view(2, 2)
t = 1 + t_1 + t_2 + t_3
else:
active_dimsM = [0, 1]
t = 1 + t_1
matern_ker = MaternKernel(nu=nu, active_dims=active_dimsM)
matern_term = matern_ker(x1, x2).evaluate()
actual = t * matern_term
res = kernel(x1, x2).evaluate()
self.assertLess(torch.norm(res - actual), 1e-4)
def test_compute_linear_truncated_kernel_with_batch(self):
x1 = torch.tensor([1, 0.1, 0.2, 3, 0.3, 0.4, 5, 0.5, 0.6, 7, 0.7, 0.8],
dtype=torch.float).view(2, 2, 3)
x2 = torch.tensor([2, 0.8, 0.7, 4, 0.6, 0.5, 6, 0.4, 0.3, 8, 0.2, 0.1],
dtype=torch.float).view(2, 2, 3)
t_1 = torch.tensor(
[0.2736, 0.44, 0.2304, 0.36, 0.3304, 0.3816, 0.1736, 0.1944],
dtype=torch.float,
).view(2, 2, 2)
batch_shape = torch.Size([2])
for nu in {0.5, 1.5, 2.5}:
for fidelity_dims in ([2], [1, 2]):
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=fidelity_dims,
dimension=3,
nu=nu,
batch_shape=batch_shape,
)
kernel.power = 1
if len(fidelity_dims) > 1:
active_dimsM = [0]
t_2 = torch.tensor(
[
0.0527, 0.167, 0.0383, 0.1159, 0.1159, 0.167,
0.0383, 0.0527
],
dtype=torch.float,
).view(2, 2, 2)
t_3 = torch.tensor(
[
0.1944, 0.3816, 0.1736, 0.3304, 0.36, 0.44, 0.2304,
0.2736
],
dtype=torch.float,
).view(2, 2, 2)
t = 1 + t_1 + t_2 + t_3
else:
active_dimsM = [0, 1]
t = 1 + t_1
matern_ker = MaternKernel(nu=nu,
active_dims=active_dimsM,
batch_shape=batch_shape)
matern_term = matern_ker(x1, x2).evaluate()
actual = t * matern_term
res = kernel(x1, x2).evaluate()
self.assertLess(torch.norm(res - actual), 1e-4)
# test diagonal mode
res_diag = kernel(x1, x2, diag=True)
self.assertLess(
torch.norm(res_diag -
torch.diagonal(actual, dim1=-1, dim2=-2)),
1e-4,
)
# make sure that we error out if last_dim_is_batch=True
with self.assertRaises(NotImplementedError):
kernel(x1, x2, diag=True, last_dim_is_batch=True)
def test_compute_linear_truncated_kernel_with_batch(self):
x1 = torch.tensor(
[[[1.0, 0.1, 0.2], [3.0, 0.3, 0.4]], [[5.0, 0.5, 0.6], [7.0, 0.7, 0.8]]]
)
x2 = torch.tensor(
[[[2.0, 0.8, 0.7], [4.0, 0.6, 0.5]], [[6.0, 0.4, 0.3], [8.0, 0.2, 0.1]]]
)
t_1 = torch.tensor(
[[[0.2736, 0.4400], [0.2304, 0.3600]], [[0.3304, 0.3816], [0.1736, 0.1944]]]
)
batch_shape = torch.Size([2])
for nu, fidelity_dims in itertools.product({0.5, 1.5, 2.5}, ([2], [1, 2])):
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=fidelity_dims, dimension=3, nu=nu, batch_shape=batch_shape
)
kernel.power = 1
if len(fidelity_dims) > 1:
active_dimsM = [0]
t_2 = torch.tensor(
[
[[0.0527, 0.1670], [0.0383, 0.1159]],
[[0.1159, 0.1670], [0.0383, 0.0527]],
]
)
t_3 = torch.tensor(
[
[[0.1944, 0.3816], [0.1736, 0.3304]],
[[0.3600, 0.4400], [0.2304, 0.2736]],
]
)
t = 1 + t_1 + t_2 + t_3
else:
active_dimsM = [0, 1]
t = 1 + t_1
matern_ker = MaternKernel(
nu=nu, active_dims=active_dimsM, batch_shape=batch_shape
)
matern_term = matern_ker(x1, x2).evaluate()
actual = t * matern_term
res = kernel(x1, x2).evaluate()
self.assertLess(torch.norm(res - actual), 1e-4)
# test diagonal mode
res_diag = kernel(x1, x2, diag=True)
self.assertLess(
torch.norm(res_diag - torch.diagonal(actual, dim1=-1, dim2=-2)), 1e-4
)
# make sure that we error out if last_dim_is_batch=True
with self.assertRaises(NotImplementedError):
kernel(x1, x2, diag=True, last_dim_is_batch=True)
def test_compute_linear_truncated_kernel_with_batch(self):
x1 = torch.tensor([1, 0.1, 0.2, 3, 0.3, 0.4, 5, 0.5, 0.6, 7, 0.7, 0.8],
dtype=torch.float).view(2, 2, 3)
x2 = torch.tensor([2, 0.8, 0.7, 4, 0.6, 0.5, 6, 0.4, 0.3, 8, 0.2, 0.1],
dtype=torch.float).view(2, 2, 3)
t_1 = torch.tensor(
[0.2736, 0.44, 0.2304, 0.36, 0.3304, 0.3816, 0.1736, 0.1944],
dtype=torch.float,
).view(2, 2, 2)
batch_shape = torch.Size([2])
for nu in {0.5, 1.5, 2.5}:
for fidelity_dims in ([2], [1, 2]):
kernel = LinearTruncatedFidelityKernel(
fidelity_dims=fidelity_dims,
dimension=3,
nu=nu,
batch_shape=batch_shape,
)
kernel.power = 1
if len(fidelity_dims) > 1:
active_dimsM = [0]
t_2 = torch.tensor(
[
0.0527, 0.167, 0.0383, 0.1159, 0.1159, 0.167,
0.0383, 0.0527
],
dtype=torch.float,
).view(2, 2, 2)
t_3 = torch.tensor(
[
0.1944, 0.3816, 0.1736, 0.3304, 0.36, 0.44, 0.2304,
0.2736
],
dtype=torch.float,
).view(2, 2, 2)
t = 1 + t_1 + t_2 + t_3
else:
active_dimsM = [0, 1]
t = 1 + t_1
matern_ker = MaternKernel(nu=nu,
active_dims=active_dimsM,
batch_shape=batch_shape)
matern_term = matern_ker(x1, x2).evaluate()
actual = t * matern_term
res = kernel(x1, x2).evaluate()
self.assertLess(torch.norm(res - actual), 1e-4)