botorch.posteriors¶
botorch.posteriors.posterior¶
Abstract base module for all botorch posteriors.
Posterior¶
-
class
botorch.posteriors.posterior.Posterior[source]¶ Abstract base class for botorch posteriors.
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abstract property
device¶ The torch device of the posterior.
- Return type
device
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abstract property
dtype¶ The torch dtype of the posterior.
- Return type
dtype
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abstract property
event_shape¶ The event shape (i.e. the shape of a single sample).
- Return type
Size
-
property
mean¶ The mean of the posterior as a (b) x n x m-dim Tensor.
- Return type
Tensor
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abstract
rsample(sample_shape=None, base_samples=None)[source]¶ Sample from the posterior (with gradients).
- Parameters
sample_shape (
Optional[Size]) – A torch.Size object specifying the sample shape. To draw n samples, set to torch.Size([n]). To draw b batches of n samples each, set to torch.Size([b, n]).base_samples (
Optional[Tensor]) – An (optional) Tensor of N(0, I) base samples of appropriate dimension, typically obtained from a Sampler. This is used for deterministic optimization.
- Return type
Tensor- Returns
A sample_shape x event-dim Tensor of samples from the posterior.
-
sample(sample_shape=None, base_samples=None)[source]¶ Sample from the posterior (without gradients).
This is a simple wrapper calling rsample using with torch.no_grad().
- Parameters
sample_shape (
Optional[Size]) – A torch.Size object specifying the sample shape. To draw n samples, set to torch.Size([n]). To draw b batches of n samples each, set to torch.Size([b, n]).base_samples (
Optional[Tensor]) – An (optional) Tensor of N(0, I) base samples of appropriate dimension, typically obtained from a Sampler object. This is used for deterministic optimization.
- Return type
Tensor- Returns
A sample_shape x event_shape-dim Tensor of samples from the posterior.
-
property
variance¶ The variance of the posterior as a (b) x n x m-dim Tensor.
- Return type
Tensor
-
abstract property
botorch.posteriors.gpytorch¶
Posterior Module to be used with GPyTorch models.
GPyTorchPosterior¶
-
class
botorch.posteriors.gpytorch.GPyTorchPosterior(mvn)[source]¶ A posterior based on GPyTorch’s multi-variate Normal distributions.
A posterior based on GPyTorch’s multi-variate Normal distributions.
- Parameters
mvn (
MultivariateNormal) – A GPyTorch MultivariateNormal (single-output case) or MultitaskMultivariateNormal (multi-output case).
-
property
device¶ The torch device of the posterior.
- Return type
device
-
property
dtype¶ The torch dtype of the posterior.
- Return type
dtype
-
property
event_shape¶ The event shape (i.e. the shape of a single sample) of the posterior.
- Return type
Size
-
property
mean¶ The posterior mean.
- Return type
Tensor
-
rsample(sample_shape=None, base_samples=None)[source]¶ Sample from the posterior (with gradients).
- Parameters
sample_shape (
Optional[Size]) – A torch.Size object specifying the sample shape. To draw n samples, set to torch.Size([n]). To draw b batches of n samples each, set to torch.Size([b, n]).base_samples (
Optional[Tensor]) – An (optional) Tensor of N(0, I) base samples of appropriate dimension, typically obtained from a Sampler. This is used for deterministic optimization.
- Return type
Tensor- Returns
A sample_shape x event_shape-dim Tensor of samples from the posterior.
-
property
variance¶ The posterior variance.
- Return type
Tensor