`trieste.acquisition.sampler`#

This module is the home of the sampling functionality required by Trieste’s acquisition functions.

Module Contents#

class ThompsonSampler(sample_min_value: bool = False)[source]#

Bases: abc.ABC, Generic[trieste.models.interfaces.ProbabilisticModelType]

A ThompsonSampler samples either the minimum values or minimisers of a function modeled by an underlying ProbabilisticModel across a discrete set of points.

Sample_min_value: If True then sample from the minimum value of the function, else sample the function’s minimiser.

abstract sample(model: trieste.models.interfaces.ProbabilisticModelType, sample_size: int, at: trieste.types.TensorType, select_output: Callable[[trieste.types.TensorType], trieste.types.TensorType] = select_nth_output) → trieste.types.TensorType [source]#

Parameters

model – The model to sample from.
sample_size – The desired number of samples.
at – Input points that define the sampler.
select_output – A method that returns the desired output from the model sampler, with shape [S, N] where S is the number of samples and N is the number of locations. Defaults to the :func:~`trieste.acquisition.utils.select_nth_output` function with output dimension 0.

Returns

Samples.

class ExactThompsonSampler(sample_min_value: bool = False)[source]#

Bases: ThompsonSampler[trieste.models.ProbabilisticModel]

This sampler provides exact Thompson samples of the objective function’s minimiser \(x^*\) over a discrete set of input locations. Although exact Thompson sampling is costly (incuring with an \(O(N^3)\) complexity to sample over a set of N locations), this method can be used for any probabilistic model with a sampling method.

Sample_min_value: If True then sample from the minimum value of the function, else sample the function’s minimiser.

sample(model: trieste.models.ProbabilisticModel, sample_size: int, at: trieste.types.TensorType, select_output: Callable[[trieste.types.TensorType], trieste.types.TensorType] = select_nth_output) → trieste.types.TensorType [source]#

Return exact samples from either the objective function’s minimiser or its minimal value over the candidate set at.

Parameters

model – The model to sample from.
sample_size – The desired number of samples.
at – Where to sample the predictive distribution, with shape [N, D], for points of dimension D.
select_output – A method that returns the desired output from the model sampler, with shape [S, N] where S is the number of samples and N is the number of locations. Defaults to the :func:~`trieste.acquisition.utils.select_nth_output` function with output dimension 0.

Returns

The samples, of shape [S, D] (where S is the sample_size) if sampling the function’s minimiser or shape [S, 1] if sampling the function’s mimimal value.

Raises

ValueError – If at has an invalid shape or if sample_size is not positive.

class GumbelSampler(sample_min_value: bool = False)[source]#

Bases: ThompsonSampler[trieste.models.ProbabilisticModel]

This sampler follows [WJ17] and yields approximate samples of the objective minimum value \(y^*\) via the empirical cdf \(\operatorname{Pr}(y^*<y)\). The cdf is approximated by a Gumbel distribution .. math:: mathcal G(y; a, b) = 1 - e^{-e^frac{y - a}{b}} where \(a, b \in \mathbb R\) are chosen such that the quartiles of the Gumbel and cdf match. Samples are obtained via the Gumbel distribution by sampling \(r\) uniformly from \([0, 1]\) and applying the inverse probability integral transform \(y = \mathcal G^{-1}(r; a, b)\). Note that the GumbelSampler can only sample a function’s minimal value and not its minimiser.

Sample_min_value: If True then sample from the minimum value of the function, else sample the function’s minimiser.

Return approximate samples from of the objective function’s minimum value.

Parameters

model – The model to sample from.
sample_size – The desired number of samples.
at – Points at where to fit the Gumbel distribution, with shape [N, D], for points of dimension D. We recommend scaling N with search space dimension.
select_output – A method that returns the desired output from the model sampler, with shape [S, N] where S is the number of samples and N is the number of locations. Currently unused.

Returns

The samples, of shape [S, 1], where S is the sample_size.

Raises

ValueError – If at has an invalid shape or if sample_size is not positive.

class ThompsonSamplerFromTrajectory(sample_min_value: bool = False)[source]#

Bases: ThompsonSampler[trieste.models.interfaces.HasTrajectorySampler]

This sampler provides approximate Thompson samples of the objective function’s minimiser \(x^*\) by minimizing approximate trajectories sampled from the underlying probabilistic model. This sampling method can be used for any probabilistic model with a trajectory_sampler() method.

Sample_min_value: If True then sample from the minimum value of the function, else sample the function’s minimiser.

Return approximate samples from either the objective function’s minimser or its minimal value over the candidate set at.

Parameters

model – The model to sample from.
sample_size – The desired number of samples.
at – Where to sample the predictive distribution, with shape [N, D], for points of dimension D.
select_output – A method that returns the desired output from the model sampler, with shape [S, N] where S is the number of samples and N is the number of locations. Defaults to the :func:~`trieste.acquisition.utils.select_nth_output` function with output dimension 0.

Returns

The samples, of shape [S, D] (where S is the sample_size) if sampling the function’s minimser or shape [S, 1] if sampling the function’s mimimal value.

Raises

ValueError – If at has an invalid shape or if sample_size is not positive.

trieste.acquisition.sampler#

Module Contents#

`trieste.acquisition.sampler`#