def __init__(
self,
train_x: torch.Tensor,
train_y: torch.Tensor,
inducing_points: torch.Tensor,
scales: Union[torch.Tensor, float] = 1.0,
mean_module: Optional[Mean] = None,
covar_module: Optional[Kernel] = None,
fixed_prior_mean: Optional[float] = None,
) -> None:
variational_distribution = CholeskyVariationalDistribution(
inducing_points.size(0))
variational_distribution.to(train_x)
variational_strategy = VariationalStrategy(
model=self,
inducing_points=inducing_points,
variational_distribution=variational_distribution,
learn_inducing_locations=False,
)
super(MixedDerivativeVariationalGP,
self).__init__(variational_strategy)
# Set the mean if specified to
if mean_module is None:
self.mean_module = ConstantMeanPartialObsGrad()
else:
self.mean_module = mean_module
if fixed_prior_mean is not None:
self.mean_module.constant.requires_grad_(False)
self.mean_module.constant.copy_(
torch.tensor([fixed_prior_mean], dtype=train_x.dtype))
if covar_module is None:
self.base_kernel = RBFKernelPartialObsGrad(
ard_num_dims=train_x.shape[-1] - 1,
lengthscale_prior=GammaPrior(3.0, 6.0 / scales),
)
self.covar_module = ScaleKernel(self.base_kernel,
outputscale_prior=GammaPrior(
2.0, 0.15))
else:
self.covar_module = covar_module
self._num_outputs = 1
self.train_inputs = (train_x, )
self.train_targets = train_y
self(train_x) # Necessary for CholeskyVariationalDistribution
def __init__(
self,
train_x: torch.Tensor,
train_y: torch.Tensor,
inducing_points: torch.Tensor,
scales: Union[torch.Tensor, float] = 1.0,
mean_module: Optional[Mean] = None,
covar_module: Optional[Kernel] = None,
fixed_prior_mean: Optional[float] = None,
) -> None:
"""Initialize MixedDerivativeVariationalGP
Args:
train_x (torch.Tensor): Training x points. The last column of x is the derivative
indiciator: 0 if it is an observation of f(x), and i if it
is an observation of df/dx_i.
train_y (torch.Tensor): Training y points
inducing_points (torch.Tensor): Inducing points to use
scales (Union[torch.Tensor, float], optional): Typical scale of each dimension
of input space (this is used to set the lengthscale prior).
Defaults to 1.0.
mean_module (Mean, optional): A mean class that supports derivative
indexes as the final dim. Defaults to a constant mean.
covar_module (Kernel, optional): A covariance kernel class that
supports derivative indexes as the final dim. Defaults to RBF kernel.
fixed_prior_mean (float, optional): A prior mean value to use with the
constant mean. Often setting this to the target threshold speeds
up experiments. Defaults to None, in which case the mean will be inferred.
"""
variational_distribution = CholeskyVariationalDistribution(
inducing_points.size(0))
variational_distribution.to(train_x)
variational_strategy = VariationalStrategy(
model=self,
inducing_points=inducing_points,
variational_distribution=variational_distribution,
learn_inducing_locations=False,
)
super(MixedDerivativeVariationalGP,
self).__init__(variational_strategy)
# Set the mean if specified to
if mean_module is None:
self.mean_module = ConstantMeanPartialObsGrad()
else:
self.mean_module = mean_module
if fixed_prior_mean is not None:
self.mean_module.constant.requires_grad_(False)
self.mean_module.constant.copy_(
torch.tensor([fixed_prior_mean], dtype=train_x.dtype))
if covar_module is None:
self.base_kernel = RBFKernelPartialObsGrad(
ard_num_dims=train_x.shape[-1] - 1,
lengthscale_prior=GammaPrior(3.0, 6.0 / scales),
)
self.covar_module = ScaleKernel(self.base_kernel,
outputscale_prior=GammaPrior(
2.0, 0.15))
else:
self.covar_module = covar_module
self._num_outputs = 1
self.train_inputs = (train_x, )
self.train_targets = train_y
self(train_x) # Necessary for CholeskyVariationalDistribution
