botorch.sampling¶

Engine for qMC sampling from a Multivariate Normal N(0, I_d).

By default, this implementation uses Box-Muller transformed Sobol samples following pg. 123 in [Pages2018numprob]. To use the inverse transform instead, set inv_transform=True.

Example

>>> engine = NormalQMCEngine(3)
>>> samples = engine.draw(10)

Engine for drawing qMC samples from a multivariate normal N(0, I_d).

draw(n=1, out=None, dtype=torch.float32)[source]¶

Draw n qMC samples from the standard Normal.

Parameters:	n (int) – The number of samples to draw. out (Optional[Tensor]) – An option output tensor. If provided, draws are put into this tensor, and the function returns None. dtype (dtype) – The desired torch data type (ignored if out is provided).
Return type:	Optional[Tensor]
Returns:	A n x d tensor of samples if out=None and None otherwise.

Engine for qMC sampling from a multivariate Normal N(mu, Sigma).

By default, this implementation uses Box-Muller transformed Sobol samples following pg. 123 in [Pages2018numprob]. To use the inverse transform instead, set inv_transform=True.

Example

>>> mean = torch.tensor([1.0, 2.0])
>>> cov = torch.tensor([[1.0, 0.25], [0.25, 2.0]])
>>> engine = MultivariateNormalQMCEngine(mean, cov)
>>> samples = engine.draw(10)

Engine for qMC sampling from a multivariate Normal N(mu, Sigma).

draw(n=1, out=None)[source]¶

Draw n qMC samples from the multivariate Normal.

Parameters:	n (int) – The number of samples to draw. out (Optional[Tensor]) – An option output tensor. If provided, draws are put into this tensor, and the function returns None.
Return type:	Optional[Tensor]
Returns:	A n x d tensor of samples if out=None and None otherwise.

Abstract base class for Samplers.

Subclasses must implement the _construct_base_samples method.

sample_shape¶

The shape of each sample.

resample¶

If True, re-draw samples in each forward evaluation - this results in stochastic acquisition functions (and thus should not be used with deterministic optimization algorithms).

collapse_batch_dims¶

If True, collapse the t-batch dimensions of the produced samples to size 1. This is useful for preventing sampling variance across t-batches.

Example

This method is usually not called directly, but via the sampler’s __call__ method: >>> posterior = model.posterior(test_X) >>> samples = sampler(posterior)

forward(posterior)[source]¶

Draws MC samples from the posterior.

Parameters:	posterior (Posterior) – The Posterior to sample from.
Return type:	Tensor
Returns:	The samples drawn from the posterior.

sample_shape

The shape of a single sample

Return type:	Size

Sampler for MC base samples using iid N(0,1) samples.

Example

>>> sampler = IIDNormalSampler(1000, seed=1234)
>>> posterior = model.posterior(test_X)
>>> samples = sampler(posterior)

Sampler for MC base samples using iid N(0,1) samples.

Sampler for quasi-MC base samples using Sobol sequences.

Example

>>> sampler = SobolQMCNormalSampler(1000, seed=1234)
>>> posterior = model.posterior(test_X)
>>> samples = sampler(posterior)

Sampler for quasi-MC base samples using Sobol sequences.